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[NirDiamant/GenAI_Agents] Add SwarmScore — Portabl [NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115) - kovaliklukas@gmail.com - Gmail — Personal...
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[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115)
[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115)
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Fri 1 May, 21:16 (7 days ago)
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created an issue
(NirDiamant/GenAI_Agents#115)
(NirDiamant/GenAI_Agents#115)
SwarmScore — Portable Reputation for AI Agents
SwarmScore — Portable Reputation for AI Agents
Hi! I'm reaching out because this repo looks like an autonomous agent or agent framework that could benefit from SwarmScore.
What is SwarmScore?
SwarmScore is a portable trust rating built from verified execution history — volume, success rate, and consistency. It's cryptographically signed so it can travel with your agent across marketplaces and registries without restarting from zero.
The score is
not
self-reported. It's built downstream of real verified outcomes:
80 jobs at 95% beats 1 job at 100%
.
For AI agents — ingest SwarmScore in one call:
GET
[URL_WITH_CREDENTIALS]
— MCP server
npm install @swarmsync/langchain-tools
— LangChain
npm install @swarmsync/crewai-tools
— CrewAI
Composio (91 tools):
[URL_WITH_CREDENTIALS]
> *bkauto3* created an issue (NirDiamant/GenAI_Agents#115)
> <
[URL_WITH_CREDENTIALS] — MCP server
> - npm install @swarmsync/langchain-tools — LangChain
> - npm install @swarmsync/crewai-tools — CrewAI
> - Composio (91 tools):
[URL_WITH_CREDENTIALS]
>
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[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115) - [EMAIL] - Gmail
[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115) - [EMAIL] - Gmail
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[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115)
[NirDiamant/GenAI_Agents] Add SwarmScore — Portable Trust Rating for AI Agents (Issue #115)
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Fri 1 May, 21:16 (7 days ago)
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bkauto3
created an issue
(NirDiamant/GenAI_Agents#115)
(NirDiamant/GenAI_Agents#115)
SwarmScore — Portable Reputation for AI Agents
SwarmScore — Portable Reputation for AI Agents
Hi! I'm reaching out because this repo looks like an autonomous agent or agent framework that could benefit from SwarmScore.
What is SwarmScore?
SwarmScore is a portable trust rating built from verified execution history — volume, success rate, and consistency. It's cryptographically signed so it can travel with your agent across marketplaces and registries without restarting from zero.
The score is
not
self-reported. It's built downstream of real verified outcomes:
80 jobs at 95% beats 1 job at 100%
.
For AI agents — ingest SwarmScore in one call:
GET
[URL_WITH_CREDENTIALS]
— MCP server
npm install @swarmsync/langchain-tools
— LangChain
npm install @swarmsync/crewai-tools
— CrewAI
Composio (91 tools):
[URL_WITH_CREDENTIALS]
> *bkauto3* created an issue (NirDiamant/GenAI_Agents#115)
> <
[URL_WITH_CREDENTIALS] — MCP server
> - npm install @swarmsync/langchain-tools — LangChain
> - npm install @swarmsync/crewai-tools — CrewAI
> - Composio (91 tools):
[URL_WITH_CREDENTIALS]
>
—
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Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
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Simmer Simmer • 1.2K followers Health,Lifestyle,simmering cooking,General
Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Read more »
Read more »
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Read more »
Read more »
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
Read more »
Read more »
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Read more in Simmer
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Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Read more »
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
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Simmer Simmer • 1.2K followers Health,Lifestyle,simmering cooking,General
Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Read more »
Read more »
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Read more »
Read more »
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
Read more »
Read more »
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Read more in Simmer
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Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
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Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
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Does cancer show symptoms before becoming severe?
Does cancer show symptoms before becoming severe?
Jarus Nahom
Jarus Nahom
, Exercise, ran 1/2 marathon, yoga, channel chi, weight train
Answered March 5
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
Usually they do. Subtle signs that persists for no apparent reason. Constant headaches, fever, tiredness, weakness, frequent flare ups, skin issues, acidity, liver issues, ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Shelton McBride
Shelton McBride
, former Consultant to Health Systems in 26 US states. (1989-2017)
Answered March 16
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
Many things can be done to help control Type 2 Diabetes as mentioned here. I will only list some of the most significant factors. Eat more small meals or snacks instead of ...
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I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
I have type 2 diabetes. I want to reverse it. Which starch should I avoid, insulin to avoid, meat, etc. on?
Alan Koenigsberg
Alan Koenigsberg
, Adjunct Professor of Psychiatry, UTSW
Answered March 25
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible. For most people with type II diabet...
First, discuss this with your treating physician. The notion that diabetes can be reversed is nice, but I’m not aware that it’s possible.
For most people with type II diabet...
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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy…
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Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny Follow We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with… see more LIKE 13 Read more Read more
Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny
Calum Scott
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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with…
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Simona Georgieva Simona Georgieva AI Product Manager | Building Intelligent Products… Some companies are like: “We need a designer.” Quickbase is like: “We need someone who can fix chaos, fight… see more LIKE EMPATHY INTEREST 34 ·11 Comments Read more
Simona Georgieva Simona Georgieva AI Product Manager | Building Intelligent Products…
Simona Georgieva
AI Product Manager | Building Intelligent Products…
Some companies are like: “We need a designer.” Quickbase is like: “We need someone who can fix chaos, fight…
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Monika Grigorova Monika Grigorova Experienced headhunter in the iGaming industry 🎮 with… 🚨 𝙒𝘼𝙄𝙏 - 𝙦𝙪𝙞𝙘𝙠 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 𝙗𝙚𝙛𝙤𝙧𝙚 𝙮𝙤𝙪 𝙨𝙘𝙧𝙤𝙡𝙡 🚨 Are you actually challenged… see more LIKE PRAISE EMPATHY 43 Read more Read more
Monika Grigorova Monika Grigorova Experienced headhunter in the iGaming industry 🎮 with…
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𝙒𝘼𝙄𝙏 - 𝙦𝙪𝙞𝙘𝙠 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 𝙗𝙚𝙛𝙤𝙧𝙚 𝙮𝙤𝙪 𝙨𝙘𝙧𝙤𝙡𝙡
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Dani Dyakova-Koycheva Dani Dyakova-Koycheva Founder & CEO @NEXA Talent 🪐 | Technical Recruitment… Търсим силен Back-End профил, дори архитект за Startup в Blockchain/Fintech индустрията Търся много силен… see more LIKE PRAISE INTEREST 11 ·1 Comment Read more
Dani Dyakova-Koycheva Dani Dyakova-Koycheva Founder & CEO @NEXA Talent 🪐 | Technical Recruitment…
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Suggested for you Jiminny Jiminny 7,343 followers Your reps shouldn’t finish a call… and start admin. But that’s exactly what’s happening. The best… see more LIKE 8 Read more Read more
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May 1, 2026
Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
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I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
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Hamlin Rozario
Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
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Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
Samarveer Singh
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Samarveer Singh
Samarveer Singh
Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
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Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Charles Wolfe
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Charles Wolfe
Charles Wolfe
Apr 30, 2026
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Pull requests · screenpipe/screenpipe · GitHub
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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DXP4800PLUS-B5F8
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
Love XDA? We have so much more!
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I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
By
Hamlin Rozario
Hamlin Rozario
Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
Read More »
Read More »
Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
Samarveer Singh
By
Samarveer Singh
Samarveer Singh
Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
Read More »
Read More »
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Charles Wolfe
By
Charles Wolfe
Charles Wolfe
Apr 30, 2026
raspberry pi 5.5
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Finally making the move to 4K thanks to DLSS 4.5
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May 1, 2026
Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
Love XDA? We have so much more!
Follow us on Google
Follow us on Google
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
By
Hamlin Rozario
Hamlin Rozario
Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
Read More »
Read More »
Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
Samarveer Singh
By
Samarveer Singh
Samarveer Singh
Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
Read More »
Read More »
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Charles Wolfe
By
Charles Wolfe
Charles Wolfe
Apr 30, 2026
raspberry pi 5.5
Except for Google's AI responses to searches, I've never used AI before, not once. Most people I know, when they hear this, say, "You haven't used AI? But it's so useful!" I disagreed; I'd seen the results Google's AI gave, and it
honestly seemed overhyped
honestly seemed overhyped
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I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
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Being connected to the internet has always been slightly dangerous, but in the age of automated scanning and AI, it's never been more so. Network security is something many assume their router is going to handle on its own, if they think about it at all, but the
myth of being too small to be worth targeting
is long over.
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Linux is finally dropping support for ancient hardware, and it's about time
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Simon Batt
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Simon Batt
Simon Batt
Apr 30, 2026
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Linux 7.1 has just entered the release candidate phase, and if it proves to be stable enough in the coming weeks, we should see it released fairly soon. The new version removes support for the i486 CPU, which was released in 1989, because it was infeasible to continue maintaining it when nobody was likely still using it.
Read More »
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Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Patrick O'Rourke
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Patrick O'Rourke
Patrick O'Rourke
Apr 30, 2026
xbox-mode
Microsoft has rolled out its gaming-focused
Xbox Mode to all Windows 11 PCs and devices
Xbox Mode to all Windows 11 PCs and devices
, including laptops, tablets, handhelds, and more. The console-like overlay aims to simplify gaming on Windows 11 with a stripped-back Xbox-inspired UI that's snappier and more intuitive.
Read More »
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DXP4800PLUS-B5F8
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Finally making the move to 4K thanks to DLSS 4.5 - [EMAIL] - Gmail
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May 1, 2026
Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
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I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
By
Hamlin Rozario
Hamlin Rozario
Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
Read More »
Read More »
Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
Samarveer Singh
By
Samarveer Singh
Samarveer Singh
Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
Read More »
Read More »
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Charles Wolfe
By
Charles Wolfe
Charles Wolfe
Apr 30, 2026
raspberry pi 5.5
Except for Google's AI responses to searches, I've never used AI before, not once. Most people I know, when they hear this, say, "You haven't used AI? But it's so useful!" I disagreed; I'd seen the results Google's AI gave, and it
honestly seemed overhyped
honestly seemed overhyped
.
Read More »
Read More »
I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
Joe Rice-Jones
By
Joe Rice-Jones
Joe Rice-Jones
Apr 30, 2026
unifi dream machine pro max and in-wall ap
Being connected to the internet has always been slightly dangerous, but in the age of automated scanning and AI, it's never been more so. Network security is something many assume their router is going to handle on its own, if they think about it at all, but the
myth of being too small to be worth targeting
is long over.
Read More »
Read More »
AMD Ryzen 7 7800X3D
AMD Ryzen 7 7800X3D
$325
$449
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Linux is finally dropping support for ancient hardware, and it's about time
Linux is finally dropping support for ancient hardware, and it's about time
Simon Batt
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Apr 30, 2026
A laptop running KDE Plasma and showing a context menu in the Dolphin file manager
Linux 7.1 has just entered the release candidate phase, and if it proves to be stable enough in the coming weeks, we should see it released fairly soon. The new version removes support for the i486 CPU, which was released in 1989, because it was infeasible to continue maintaining it when nobody was likely still using it.
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Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Patrick O'Rourke
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Patrick O'Rourke
Apr 30, 2026
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Microsoft has rolled out its gaming-focused
Xbox Mode to all Windows 11 PCs and devices
Xbox Mode to all Windows 11 PCs and devices
, including laptops, tablets, handhelds, and more. The console-like overlay aims to simplify gaming on Windows 11 with a stripped-back Xbox-inspired UI that's snappier and more intuitive.
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Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
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I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
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Hamlin Rozario
Hamlin Rozario
Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
Read More »
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Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
Samarveer Singh
By
Samarveer Singh
Samarveer Singh
Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
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Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Charles Wolfe
By
Charles Wolfe
Charles Wolfe
Apr 30, 2026
raspberry pi 5.5
Except for Google's AI responses to searches, I've never used AI before, not once. Most people I know, when they hear this, say, "You haven't used AI? But it's so useful!" I disagreed; I'd seen the results Google's AI gave, and it
honestly seemed overhyped
honestly seemed overhyped
.
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I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
Joe Rice-Jones
By
Joe Rice-Jones
Joe Rice-Jones
Apr 30, 2026
unifi dream machine pro max and in-wall ap
Being connected to the internet has always been slightly dangerous, but in the age of automated scanning and AI, it's never been more so. Network security is something many assume their router is going to handle on its own, if they think about it at all, but the
myth of being too small to be worth targeting
is long over.
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AMD Ryzen 7 7800X3D
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Samsung Galaxy Book5 Pro 360
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Linux is finally dropping support for ancient hardware, and it's about time
Linux is finally dropping support for ancient hardware, and it's about time
Simon Batt
By
Simon Batt
Simon Batt
Apr 30, 2026
A laptop running KDE Plasma and showing a context menu in the Dolphin file manager
Linux 7.1 has just entered the release candidate phase, and if it proves to be stable enough in the coming weeks, we should see it released fairly soon. The new version removes support for the i486 CPU, which was released in 1989, because it was infeasible to continue maintaining it when nobody was likely still using it.
Read More »
Read More »
Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Patrick O'Rourke
By
Patrick O'Rourke
Patrick O'Rourke
Apr 30, 2026
xbox-mode
Microsoft has rolled out its gaming-focused
Xbox Mode to all Windows 11 PCs and devices
Xbox Mode to all Windows 11 PCs and devices
, including laptops, tablets, handhelds, and more. The console-like overlay aims to simplify gaming on Windows 11 with a stripped-back Xbox-inspired UI that's snappier and more intuitive.
Read More »
Read More »
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Welcome to your pre-weekend hit!
We're just about to head into the weekend, but before we get prepped for an immersive gaming session across the two-day break, which resolution are you gaming on? 1440p continues to feel the sweet spot for many, and despite having capable hardware, like an RTX 4090, gamers continue to avoid 4K for a couple of reasons.
DLSS 4.5 is finally starting to make the switch to 4K make more sense, at least for AAA gaming. For starters, the upscaled image almost looks just as good as native 4K, and some might argue it looks slightly better than native with TAA. It's a bit heavier on the GPU than previous versions, but the improvements in motion stability make it worth it. Give it a go this weekend and see how you get on.
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I used to prefer gaming at 1440p, but DLSS 4.5 changed that
I used to prefer gaming at 1440p, but DLSS 4.5 changed that
Hamlin Rozario
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Apr 30, 2026
Dell QD-OLED on a desk
As a competitive gamer who occasionally plays AAA titles, 1440p has always felt like the sweet spot for me. Despite having an RTX 4090, I couldn't fully move to 4K for a couple of reasons.
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Your GPU upscales way more than just games, and old videos look incredible
Your GPU upscales way more than just games, and old videos look incredible
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Apr 30, 2026
nvidia geforce rtx 4070 super founders edition with power socket seen from an angle
Most of us first noticed GPU upscaling through games because of the cleaner edges, the sharper image quality, and, well, the "free" performance. Features like DLSS and FSR have made upscaling feel like magic, powered by dedicated hardware like tensor cores that reconstruct detail faster than brute force rendering ever did.
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Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
Running local AI on the Raspberry Pi 5 taught me why cloud models are still winning
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Apr 30, 2026
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Except for Google's AI responses to searches, I've never used AI before, not once. Most people I know, when they hear this, say, "You haven't used AI? But it's so useful!" I disagreed; I'd seen the results Google's AI gave, and it
honestly seemed overhyped
honestly seemed overhyped
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I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
I never configure a home firewall without these three rules, and they stop the attacks most people don't know are happening
Joe Rice-Jones
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Apr 30, 2026
unifi dream machine pro max and in-wall ap
Being connected to the internet has always been slightly dangerous, but in the age of automated scanning and AI, it's never been more so. Network security is something many assume their router is going to handle on its own, if they think about it at all, but the
myth of being too small to be worth targeting
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AMD Ryzen 7 7800X3D
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Linux is finally dropping support for ancient hardware, and it's about time
Linux is finally dropping support for ancient hardware, and it's about time
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Apr 30, 2026
A laptop running KDE Plasma and showing a context menu in the Dolphin file manager
Linux 7.1 has just entered the release candidate phase, and if it proves to be stable enough in the coming weeks, we should see it released fairly soon. The new version removes support for the i486 CPU, which was released in 1989, because it was infeasible to continue maintaining it when nobody was likely still using it.
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Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
Microsoft rolls out Xbox Mode, bringing a console-like experience to any PC
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Apr 30, 2026
xbox-mode
Microsoft has rolled out its gaming-focused
Xbox Mode to all Windows 11 PCs and devices
Xbox Mode to all Windows 11 PCs and devices
, including laptops, tablets, handhelds, and more. The console-like overlay aims to simplify gaming on Windows 11 with a stripped-back Xbox-inspired UI that's snappier and more intuitive.
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8231439985585559825
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-5019947743849968709
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visual_change
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accessibility
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NULL
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Pull requests · screenpipe/screenpipe · GitHub
Pul Pull requests · screenpipe/screenpipe · GitHub
Pull requests · screenpipe/screenpipe · GitHub
Home | Hostinger
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Nginx Proxy Manager
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Screenpipe — Archive
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans - kovaliklukas@gmail.com - Gmail — Personal...
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GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans - [EMAIL] - Gmail
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Dear friends,
The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Some people are still using LLMs primarily by asking them short questions. But the models can do much more, like think for minutes, ingest many documents as context, and use web search and other tools.
I’m teaching a new course,
AI Prompting for Everyone
, to help everyone become an AI power user — whatever their current skill level — and prompt LLMs to take advantage of their latest capabilities.
The course covers skills that apply to ChatGPT, Gemini, Claude, and other AI tools:
Download attachment
I also cover intuitions about how these models work under the hood, so learners know when to trust their output and when not to.
Along the way: you’ll see flying squirrels, a creativity test, some of my old family photos, and fireworks.
Please join me
Please join me
! The course assumes no technical background, so please share it with friends or family who could benefit.
Keep prompting!
Andrew
A MESSAGE FROM DEEPLEARNING.AI
A MESSAGE FROM
DEEPLEARNING.AI
DEEPLEARNING.AI
Promo banner for: "AI Prompting for Everyone"
Learn how to get more accurate answers, better writing, and more useful outputs from AI tools like ChatGPT, Claude, and Gemini. Taught by Andrew Ng, this course covers finding information, brainstorming, and building simple apps.
Enroll today
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News
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GPT-5.5 Outperforms, Hallucinates
GPT-5.5 Outperforms, Hallucinates
The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know.
What’s new:
GPT-5.5
GPT-5.5
is a closed vision-language models that’s built for agentic coding, computer use, and knowledge work. GPT-5.5 Pro is the same model but processes reasoning tokens in parallel during inference. OpenAI set the API prices at roughly double the per-token rates of GPT-5.4.
Text and images in (up to 1 million tokens via API, 400,000 tokens in Codex), text out (up to 128,000 tokens)
Five levels of reasoning (xhigh, high, medium, low, none), tool use, web search, structured outputs, tool search (API only, loads tools on demand rather than all at once), Fast mode (Codex only, generates tokens 1.5 times faster at 2.5 times the price)
Tops Artificial Analysis Intelligence Index and ARC-AGI-2
GPT-5.5 available in ChatGPT with Plus, Pro, Business, or Enterprise subscription and in Codex for those tiers plus Edu and Go; GPT-5.5 Pro available in ChatGPT with Pro, Business, or Enterprise subscription: GPT-5.5 API $5/$0.50/$30 per million tokens of input/cached/output, GPT-5.5 Pro API $30/$180 per million tokens of input/output with no cached discount
Architecture, parameter count, training data and methods
How it works:
OpenAI
disclosed
disclosed
few details about how it built GPT-5.5. As is typical of high-performance models, the training data was a mix of publicly available data scraped from the web, licensed from partners, and collected from users and human trainers. The model was trained via reinforcement learning to reason before responding.
Performance:
GPT-5.5 generally delivers top performance in objective benchmarks, especially in tests of knowledge, agentic tasks, and abstract visual reasoning. However, it falls behind competitors on subjective evaluations. It’s also more likely to confidently deliver incorrect output.
On ARC-AGI-2, visual puzzles that test abstract reasoning, GPT-5.5 set to xhigh (85.0 percent at $1.87 per task) displaced the previous leader Gemini 3 Deep Think (84.6 percent at $13.62 per task) at a substantially lower cost per task.
In OpenAI’s tests, GPT-5.5 set state-of-the-art scores on Terminal-Bench 2.0 (command-line workflows that require planning and tool use), OSWorld-Verified (autonomous operation of real computer interfaces), and Tau2-bench Telecom (multi-turn customer-service workflows).
On AA-Omniscience Accuracy, a knowledge benchmark that rewards factual recall, GPT-5.5 set to xhigh reasoning posted the highest accuracy at 57 percent. However, on the AA-Omniscience Index, which rewards models for answering correctly and acknowledging ignorance but penalizes them for confidently making mistakes, GPT-5.5 set to xhigh reasoning (20 points) ranked third, behind Gemini 3.1 Pro Preview (33 points) and Claude Opus 4.7 set to max reasoning (26 points).
On Arena.ai’s
leaderboards
, which rank models by blind head-to-head comparisons, GPT-5.5 falls well behind competitors. Claude Opus models occupy the top spots across most categories. For instance, as of April 27, GPT-5.5-high ranked seventh in Text Arena and ninth in Code Arena WebDev.
Yes, but:
GPT-5.5 knows more than its peers, but it answers incorrectly more often and acknowledges ignorance less often. The AA-Omniscience benchmark poses 6,000 expert-level questions across business, law, health, humanities, science/engineering, and software engineering. It includes a “hallucination rate” that is the ratio of wrong answers to the sum of wrong answers, partially wrong answers, and abstentions. By this measure, GPT-5.5 set to high reasoning hit 85.53 percent, notably worse than Claude Opus 4.7 set to max reasoning (36.18 percent) and Gemini 3.1 Pro Preview at (49.87 percent). Apollo Research separately
found
found
that GPT-5.5 lied about completing an impossible programming task in 29 percent of samples, a significant jump from GPT-5.4’s 7 percent. OpenAI’s internal monitoring of coding-agent traffic
showed
showed
a similar pattern.
Security implications:
OpenAI released results of VulnLMP, an internal evaluation that
tests
tests
whether a model can develop exploits against widely deployed software. GPT-5.5 undertook multi-day research campaigns and identified potential memory-related vulnerabilities in a variety of targets, but it did not produce an exploit that was confirmed by OpenAI’s evaluation harness. Under OpenAI’s Preparedness Framework, this evidence places GPT-5.5 within the “high” tier of cybersecurity threats, short of the “critical” tier label that would describe models that independently produce working exploits against real targets.
Why it matters:
Evaluations of objective performance and human preferences are telling different stories about GPT-5.5. OpenAI regained the lead on the Artificial Analysis Intelligence Index, but the picture flips when it comes to subjective, head-to-head comparisons. Claude Opus models occupy the top spots in LMArena’s Text, Vision, Document, Search, and Code rankings, while GPT-5.5 doesn’t crack the top five on most. Benchmarks measure what models can accomplish, human preference what they’re like to work with. Production decisions usually weigh both, and — according to the measures that are available so far — the two are diverging.
We’re thinking:
Top AI companies continue to push the frontier at a dizzying pace. GPT-5.5 is the fourth flagship launch since February, following Anthropic Claude Opus 4.7, GPT-5.4, and Google Gemini 3.1 Pro Preview. Each one reshuffled the top of the Artificial Analysis Intelligence Index, which can be viewed as a proxy for general capability in real-world tasks. Developers should design their software stacks to swap models as easily as bumping a dependency.
Download attachment
Big AI’s Plans Strain CO2 Pledges
Big AI’s Plans Strain CO
2
Pledges
Commitments by large AI companies to limit emissions of greenhouse gases are at risk as those companies pursue a massive build-out of data centers, many of which will be powered by fossil fuels in the near term and possibly beyond.
What’s new:
Alphabet, Amazon, Meta, and Microsoft have begun to acknowledge that keeping up with projected demand for AI is interfering with earlier plans to stop raising the concentration of greenhouse gases to the atmosphere,
Associated Press
reported
reported
. (Disclaimer: Andrew Ng is a member of Amazon’s board of directors.)
How it works:
Electricity consumed by top tech companies has increased significantly over the last few years, and with it their emissions of greenhouse gases that contribute to climate change, despite ongoing efforts to reduce emissions. While they have emphasized clean sources of energy including wind, solar, geothermal, and nuclear, lately they have begun to develop natural gas power plants to meet rapidly rising demand for AI.
In Alphabet’s most recent
Environmental Report
, the company characterized its net-zero 2030 goal — which it set forth in 2024, after it had
abandoned
an earlier pledge to maintain carbon-neutral operations — as a “moonshot.” Recent
reports
indicate that the company’s data center in North Texas will be powered partially by natural-gas plants. Alphabet has invested in next-generation geothermal and nuclear sources, but they’re not yet deployed at sufficient scale. Although 66 percent of the energy for its data centers and offices came from carbon-free sources in 2024 — and its emissions per unit of computation have diminished dramatically — its total greenhouse-gas emissions increased by 54 percent between 2019 and 2024.
In Amazon’s most recent
Sustainability Report
, the company said that one of the biggest challenges of scaling AI is increased energy demand. The company has invested in natural-gas plants in Mississippi and Indiana to meet the energy demands of nearby data centers. It views nuclear energy as a key part of its strategy to become carbon-neutral, but the planned nuclear sources won’t come online until the 2030s. Meanwhile, Amazon’s total carbon emissions have increased by 33 percent since 2019.
Meta’s most recent sustainability
report
emphasized that the path to net zero depends on new technology, suppliers, and collaboration with global coalitions. The company is building
private gas-powered plants
to generate energy for data centers including its largest-yet, 5-gigawatt facility in rural Louisiana. It has invested in projects that could support up to 6.6 gigawatts of new and existing clean energy by 2035 including geothermal, nuclear, and energy storage to take better advantage of wind and solar power. The company’s total emissions increased by over 60 percent between 2020 and 2024, while its electricity consumption by data centers nearly tripled.
While Microsoft’s previous sustainability reports emphasized progress toward eliminating more greenhouse gases than it emits by 2030, the most recent
edition
described this goal as a “marathon, not a sprint.” Microsoft recently signed an agreement with Chevron to
build
a natural-gas power plant even after it inked a 20-year purchase agreement to restart the nuclear reactors at New York’s Three Mile Island in, which are expected to come online in 2027. Since 2020, Microsoft’s total emissions have increased by 23 percent and its electricity consumption has more than doubled.
Behind the news:
In the years following the 2015 Paris Climate Agreement, which commits governments to limiting global warming by 2 degrees Celsius above pre-industrial levels, many companies signed corporate pledges to meet goals intended to slow climate change. For instance, over 600 companies signed The Climate Pledge co-founded by Amazon and Global Optimism in 2019, which commits companies to reaching net-zero emissions of greenhouse gases by 2040. The Science-Based Targets initiative, which launched in 2015, is another corporate agreement that requires companies to set climate targets that align with the Paris Agreement. The top AI companies have embraced these principles and publish annual reports that document efforts to meet their commitments.
Why it matters:
In 2024, data centers
accounted
accounted
for roughly 1.5 percent of electricity consumption globally and 4.4 percent in the U.S. The U.S. figure is projected to rise to as much as 12 percent within the next few years. While big AI companies thought they would have sufficient energy from clean sources, the recent sharp rise in demand is pushing them toward further reliance on fossil fuels that produce climate-changing greenhouse gases.
We’re thinking:
Top AI companies have invested meaningfully in renewable energy like wind and solar and next-generation sources like
nuclear
nuclear
and geothermal power. However, these sources still face scaling problems, which is why companies have turned to natural gas plants to meet growing energy demands. That’s a worrisome trend. However, for the amount of work that they do, well run data centers are still the most efficient option, and we hope that further efficiency gains in AI will balance rising emissions.
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Kimi K2.6 Challenges Open-Weights Champs
Kimi K2.6 Challenges Open-Weights Champs
Moonshot AI’s updated Kimi model handles longer autonomous coding sessions and scales up its multi-agent orchestration relative to its predecessor.
What’s new:
Kimi K2.6
Kimi K2.6
is a 1 trillion-parameter vision-language model that performs neck and neck with Qwen3.6 Max Preview and the newly released DeepSeek V4 and falls just behind top closed models. It’s designed to generate code in a plan-write-test-debug loop that can last for days, and it can instantiate hundreds of agents that collaborate on a single task. It also produces fewer hallucinations than its predecessor.
Text, images, and video in (up to 256,000 tokens), text out (up to 98,000 tokens)
Mixture-of-experts, 1 trillion parameters total, 32 billion active per token, MoonViT vision encoder
Tool use, web search, native INT4 quantization, “preserve thinking” mode, agent swarm
Tops other open-weights models on the Artificial Analysis Intelligence Index but trails leading proprietary models
Availability/price:
Weights free to download from
Hugging Face
under a
modified MIT license
that permits commercial uses with attribution for products with more than 100 million monthly active users or more than $20 million in monthly revenue, free chat interface at
kimi.com
and Kimi mobile app, API access via Moonshot $0.95/$0.16/$4.00 per million input/cached/output tokens
Training data and methods
How it works:
Kimi K2.6 reuses the architecture introduced with Kimi K2 and refined in Kimi K2.5, including the multi-headed latent attention (an attention variant that reduces memory requirements by compressing keys and values) and MoonViT vision encoder (400 million parameters). Moonshot has not disclosed how Kimi K2.6 differs with respect to training data and methods.
Like Kimi K2 Thinking and Kimi K2.5, Kimi K2.6 was trained with native INT4 quantization.
A
option retains previously generated reasoning tokens across multi-turn interactions, which improves coding performance according to Moonshot.
In
mode, a coordinator agent decomposes a task into subtasks, creates up to 300 parallel subagents that can execute 4,000 steps (up from 100 subagents and 1,500 steps in Kimi K2.5) to execute tasks, and reassigns work when an agent fails or stalls. A research preview feature called
opens agent swarm mode to agents from other developers — that can run on any device or model — as well as human collaborators.
Performance:
Kimi K2.6 leads open-weights models on some benchmarks of intelligence and agentic capability and ranks highly relative to its peers in subjective tests of human preferences. However, it trails leading closed models on benchmarks that evaluate reasoning and coding large projects as well as human preferences.
On the Artificial Analysis’
Intelligence Index
, a composite of 10 tests of economically useful tasks, Kimi K2.6 set to reasoning (54) leads open-weights models but trails GPT-5.5 set to xhigh reasoning (60) as well as Claude Opus 4.7 set to max reasoning and Gemini 3.1 Pro Preview set to reasoning. The closest open-weights competitors are Qwen3.6 Preview set to max reasoning and DeepSeek-V4-Pro set to max reasoning (tied at 52).
KimiK2.6’s position in the Intelligence Index rests on its top performance among open-weights models on GPQA Diamond (answering graduate-level science questions), HLE (answering expert-level multidisciplinary questions designed to test reasoning), and SciCode (generating code for scientific research). However, it fell just behind the newly released open-weights model DeepSeek-V4-Pro on five index benchmarks, and it underperformed Xiaomi MiMo-2.5-Pro and other open-weights models on the remaining two.
Moonshot tested Kimi K2.6’s ability to complete large-scale coding projects by asking it to port Qwen3.5-0.8B’s inference code to Zig (a systems-programming language) and optimize it for a Mac. Across 4,000-plus tool calls and 14 successive revisions over more than 12 hours, Kimi K2.6 raised the port’s throughput from roughly 15 to 193 tokens per second, ending around 20 percent faster than LM Studio, a popular local inference app, running on the same hardware.
Artificial Analysis measured Kimi K2.6’s hallucination rate (given a question-answering benchmark of general knowledge, the proportion of non-correct outputs that include erroneous responses, professions of ignorance, and refusals to respond) at 39.26 percent. This is lower than Kimi K2.5 (64.6 percent) and roughly comparable to Anthropic Claude Opus 4.7 (36.18 percent)
On
Arena.ai’s Code Arena WebDev
leaderboard, which ranks models on web-development coding via blind pairwise comparisons, Kimi K2.6 (1,529 Elo) ranked sixth among 67 models as of April 26, 2026, behind Anthropic Claude Opus 4.7 (1,565 Elo), Claude Opus 4.6 (1,548 Elo), and Z.ai’s open-weights GLM-5.1 (1,534 Elo).
Behind the news:
The ability to stay on task across hours of autonomous execution emerged as a competitive frontier in late 2025. Anthropic’s Claude Code, OpenAI’s Codex, and Alibaba’s Qwen3-Coder all targeted this capability in their most recent releases. Kimi K2, released in July 2025, was an early open-weights entrant in agentic tool use, and the family has been updated every few months since with growing emphasis on long-horizon execution.
Why it matters:
Moonshot has steadily extended the duration over which Kimi K2 family models can usefully execute tasks autonomously: first short reasoning traces, then multi-step tool use, multi-hour coding sessions, and now multi-day projects. Each extension widens the interval between human check-ins required to keep agents on track.
We’re thinking:
Sustained autonomy and low hallucination rates are related, but less and less so. If an agent makes a mistake, it can check its work, find the mistake, and fix it.
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Strategic Thinking in LLMs vs. Humans
Strategic Thinking in LLMs vs. Humans
While large language models can behave in human-like ways, the similarities are superficial. A simple strategy game revealed clear differences in their strategic approaches.
What’s new:
Caroline Wang and colleagues at University of Texas at Austin and Google interpreted
patterns of decision-making by humans and LLMs
patterns of decision-making by humans and LLMs
as they played the classic game of rock-paper-scissors. They found that LLMs sometimes model their opponents with greater sophistication than people do.
Key insight:
Given recorded gameplay, an LLM can iteratively improve code that predicts a player
’
s next move. If the code predicts the player’s actions with significant accuracy, we can assume that its decision-making algorithms are functionally similar to those the player used. Computer code is interpretable, making it possible to discern such algorithms and compare those used by humans and LLMs.
How it works:
In games of rock-paper-scissors, he authors pitted individual LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and GPT-OSS 120B) against each of 15 preprogrammed bots of varying complexity. They recorded each player's moves in 20 games of 300 sequential rounds each. Previous work provided
records
records
of similar records of games between humans and the same bots. The authors tracked the round-by-round choices made by each player — AI and human — and whether they won, lost, or tied. Then they used
AlphaEvolve
AlphaEvolve
, an agentic method that iteratively optimizes code through an evolutionary process, to improve Python programs that predicted the next move for each LLM individually and humans as a group.
AlphaEvolve initially processed the game data using a simple template program written by the authors. In each of an undisclosed number of evolutionary steps, Gemini 2.5 Flash proposed modifications to improve a function that balanced simplicity (as measured by
Halstead effort
) and evaluation likelihood (how well a program predicted a player’s choices).
For each player, the authors selected the simplest program that achieved near-maximum predictive accuracy within a small margin from the best. Each program produced the best evaluation likelihood (higher is better) for the player it had evolved to predict. That is, it represented its corresponding player’s behavior better than that of any other player.
Results:
Using game data that AlphaEvolve didn’t process, the authors compared how well each program predicted the other players’ moves. Then they examined the programs to determine what strategies each player used.
The programs that represented Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 performed nearly equally well when predicting each other’s moves as they played against bots, which suggests that this trio used similar strategies. For example, predicting the actions of Gemini 2.5 Pro, the programs that predicted Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 achieved 0.507, 0.507, and 0.506 evaluation likelihood respectively. The programs that represented humans and GPT OSS 120B predicted the trio’s actions less successfully. They achieved 0.476 and 0.403 evaluation likelihood respectively, indicating that they likely used different strategies.
Interpreting the programs suggested that Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 maintained sequential patterns more effectively than humans or GPT-OSS 120B. The code that predicted those programs tracked the frequency of each possible move based on a player’s previous one or two moves. That is, it tracked how often the player, over three rounds, called rock->scissors-> rock, rock->scissors->paper, and so on. In contrast, the code that represented humans and GPT-OSS 120B tracked the frequency of the opponent’s latest move only.
The code that represented Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and human players computed the preliminary value of each possible next move based on (i) the possible next move, (ii) the bot’s previous move, and (iii) the player’s previous move. GPT-OSS 120B computed the value based on the possible next move alone.
Why it matters:
While researchers have found ways to
understand
understand
some
some
aspects
aspects
of neural network behavior, large language models remain black boxes in many ways. Synthesizing code directly from LLM behavior offers a powerful tool to interpret their decision-making.
We’re thinking:
It’s tempting to assume that LLMs learn to mimic human behavior as represented by their training data. Finding that they can encode a gaming strategy more systematically than the average human demonstrates a different sort of learning.
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2026","depth":27,"bounds":{"left":0.24966756,"top":0.3312051,"width":0.19946809,"height":0.14644852},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"bounds":{"left":0.3231383,"top":0.48084596,"width":0.022107713,"height":0.012370312},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"bounds":{"left":0.3231383,"top":0.48084596,"width":0.022107713,"height":0.012370312},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"bounds":{"left":0.3494016,"top":0.48084596,"width":0.026263298,"height":0.012370312},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"bounds":{"left":0.3494016,"top":0.48084596,"width":0.026263298,"height":0.012370312},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"bounds":{"left":0.24966756,"top":0.5415004,"width":0.034574468,"height":0.016759777},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Some people are still using LLMs primarily by asking them short questions. But the models can do much more, like think for minutes, ingest many documents as context, and use web search and other tools.","depth":28,"bounds":{"left":0.24966756,"top":0.58619314,"width":0.19647606,"height":0.10853951},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I’m teaching a new course,","depth":28,"bounds":{"left":0.24966756,"top":0.7238627,"width":0.071476065,"height":0.016759777},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"AI Prompting for Everyone","depth":28,"bounds":{"left":0.32114363,"top":0.7238627,"width":0.07347074,"height":0.016759777},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", to help everyone become an AI power user — whatever their current skill level — and prompt LLMs to take advantage of their latest capabilities.","depth":28,"bounds":{"left":0.24966756,"top":0.7238627,"width":0.19946809,"height":0.06264964},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The course covers skills that apply to ChatGPT, Gemini, Claude, and other AI tools:","depth":28,"bounds":{"left":0.24966756,"top":0.8156425,"width":0.19498006,"height":0.039505187},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"I also cover intuitions about how these models work under the hood, so learners know when to trust their output and when not to.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Along the way: you’ll see flying squirrels, a creativity test, some of my old family photos, and fireworks.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Please join me","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Please join me","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"! The course assumes no technical background, so please share it with friends or family who could benefit.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep prompting!","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: \"AI Prompting for Everyone\"","depth":27,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Learn how to get more accurate answers, better writing, and more useful outputs from AI tools like ChatGPT, Claude, and Gemini. Taught by Andrew Ng, this course covers finding information, brainstorming, and building simple apps.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll today","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll today","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GPT-5.5 Outperforms, Hallucinates","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GPT-5.5 Outperforms, Hallucinates","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GPT-5.5","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GPT-5.5","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is a closed vision-language models that’s built for agentic coding, computer use, and knowledge work. GPT-5.5 Pro is the same model but processes reasoning tokens in parallel during inference. OpenAI set the API prices at roughly double the per-token rates of GPT-5.4.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text and images in (up to 1 million tokens via API, 400,000 tokens in Codex), text out (up to 128,000 tokens)","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Five levels of reasoning (xhigh, high, medium, low, none), tool use, web search, structured outputs, tool search (API only, loads tools on demand rather than all at once), Fast mode (Codex only, generates tokens 1.5 times faster at 2.5 times the price)","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tops Artificial Analysis Intelligence Index and ARC-AGI-2","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GPT-5.5 available in ChatGPT with Plus, Pro, Business, or Enterprise subscription and in Codex for those tiers plus Edu and Go; GPT-5.5 Pro available in ChatGPT with Pro, Business, or Enterprise subscription: GPT-5.5 API $5/$0.50/$30 per million tokens of input/cached/output, GPT-5.5 Pro API $30/$180 per million tokens of input/output with no cached discount","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Architecture, parameter count, training data and methods","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OpenAI","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"disclosed","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"disclosed","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"few details about how it built GPT-5.5. As is typical of high-performance models, the training data was a mix of publicly available data scraped from the web, licensed from partners, and collected from users and human trainers. The model was trained via reinforcement learning to reason before responding.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GPT-5.5 generally delivers top performance in objective benchmarks, especially in tests of knowledge, agentic tasks, and abstract visual reasoning. However, it falls behind competitors on subjective evaluations. It’s also more likely to confidently deliver incorrect output.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On ARC-AGI-2, visual puzzles that test abstract reasoning, GPT-5.5 set to xhigh (85.0 percent at $1.87 per task) displaced the previous leader Gemini 3 Deep Think (84.6 percent at $13.62 per task) at a substantially lower cost per task.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In OpenAI’s tests, GPT-5.5 set state-of-the-art scores on Terminal-Bench 2.0 (command-line workflows that require planning and tool use), OSWorld-Verified (autonomous operation of real computer interfaces), and Tau2-bench Telecom (multi-turn customer-service workflows).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On AA-Omniscience Accuracy, a knowledge benchmark that rewards factual recall, GPT-5.5 set to xhigh reasoning posted the highest accuracy at 57 percent. However, on the AA-Omniscience Index, which rewards models for answering correctly and acknowledging ignorance but penalizes them for confidently making mistakes, GPT-5.5 set to xhigh reasoning (20 points) ranked third, behind Gemini 3.1 Pro Preview (33 points) and Claude Opus 4.7 set to max reasoning (26 points).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Arena.ai’s","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"leaderboards","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", which rank models by blind head-to-head comparisons, GPT-5.5 falls well behind competitors. Claude Opus models occupy the top spots across most categories. For instance, as of April 27, GPT-5.5-high ranked seventh in Text Arena and ninth in Code Arena WebDev.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Yes, but:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GPT-5.5 knows more than its peers, but it answers incorrectly more often and acknowledges ignorance less often. The AA-Omniscience benchmark poses 6,000 expert-level questions across business, law, health, humanities, science/engineering, and software engineering. It includes a “hallucination rate” that is the ratio of wrong answers to the sum of wrong answers, partially wrong answers, and abstentions. By this measure, GPT-5.5 set to high reasoning hit 85.53 percent, notably worse than Claude Opus 4.7 set to max reasoning (36.18 percent) and Gemini 3.1 Pro Preview at (49.87 percent). Apollo Research separately","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"found","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"found","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"that GPT-5.5 lied about completing an impossible programming task in 29 percent of samples, a significant jump from GPT-5.4’s 7 percent. OpenAI’s internal monitoring of coding-agent traffic","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"showed","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"showed","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a similar pattern.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Security implications:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"OpenAI released results of VulnLMP, an internal evaluation that","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"tests","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"tests","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"whether a model can develop exploits against widely deployed software. GPT-5.5 undertook multi-day research campaigns and identified potential memory-related vulnerabilities in a variety of targets, but it did not produce an exploit that was confirmed by OpenAI’s evaluation harness. Under OpenAI’s Preparedness Framework, this evidence places GPT-5.5 within the “high” tier of cybersecurity threats, short of the “critical” tier label that would describe models that independently produce working exploits against real targets.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Evaluations of objective performance and human preferences are telling different stories about GPT-5.5. OpenAI regained the lead on the Artificial Analysis Intelligence Index, but the picture flips when it comes to subjective, head-to-head comparisons. Claude Opus models occupy the top spots in LMArena’s Text, Vision, Document, Search, and Code rankings, while GPT-5.5 doesn’t crack the top five on most. Benchmarks measure what models can accomplish, human preference what they’re like to work with. Production decisions usually weigh both, and — according to the measures that are available so far — the two are diverging.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Top AI companies continue to push the frontier at a dizzying pace. GPT-5.5 is the fourth flagship launch since February, following Anthropic Claude Opus 4.7, GPT-5.4, and Google Gemini 3.1 Pro Preview. Each one reshuffled the top of the Artificial Analysis Intelligence Index, which can be viewed as a proxy for general capability in real-world tasks. Developers should design their software stacks to swap models as easily as bumping a dependency.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Big AI’s Plans Strain CO2 Pledges","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Big AI’s Plans Strain CO","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"2","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Pledges","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Commitments by large AI companies to limit emissions of greenhouse gases are at risk as those companies pursue a massive build-out of data centers, many of which will be powered by fossil fuels in the near term and possibly beyond.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Alphabet, Amazon, Meta, and Microsoft have begun to acknowledge that keeping up with projected demand for AI is interfering with earlier plans to stop raising the concentration of greenhouse gases to the atmosphere,","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Associated Press","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". (Disclaimer: Andrew Ng is a member of Amazon’s board of directors.)","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Electricity consumed by top tech companies has increased significantly over the last few years, and with it their emissions of greenhouse gases that contribute to climate change, despite ongoing efforts to reduce emissions. While they have emphasized clean sources of energy including wind, solar, geothermal, and nuclear, lately they have begun to develop natural gas power plants to meet rapidly rising demand for AI.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Alphabet’s most recent","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Environmental Report","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", the company characterized its net-zero 2030 goal — which it set forth in 2024, after it had","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"abandoned","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"an earlier pledge to maintain carbon-neutral operations — as a “moonshot.” Recent","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reports","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"indicate that the company’s data center in North Texas will be powered partially by natural-gas plants. Alphabet has invested in next-generation geothermal and nuclear sources, but they’re not yet deployed at sufficient scale. Although 66 percent of the energy for its data centers and offices came from carbon-free sources in 2024 — and its emissions per unit of computation have diminished dramatically — its total greenhouse-gas emissions increased by 54 percent between 2019 and 2024.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Amazon’s most recent","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Sustainability Report","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", the company said that one of the biggest challenges of scaling AI is increased energy demand. The company has invested in natural-gas plants in Mississippi and Indiana to meet the energy demands of nearby data centers. It views nuclear energy as a key part of its strategy to become carbon-neutral, but the planned nuclear sources won’t come online until the 2030s. Meanwhile, Amazon’s total carbon emissions have increased by 33 percent since 2019.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Meta’s most recent sustainability","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"report","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"emphasized that the path to net zero depends on new technology, suppliers, and collaboration with global coalitions. The company is building","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"private gas-powered plants","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"to generate energy for data centers including its largest-yet, 5-gigawatt facility in rural Louisiana. It has invested in projects that could support up to 6.6 gigawatts of new and existing clean energy by 2035 including geothermal, nuclear, and energy storage to take better advantage of wind and solar power. The company’s total emissions increased by over 60 percent between 2020 and 2024, while its electricity consumption by data centers nearly tripled.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"While Microsoft’s previous sustainability reports emphasized progress toward eliminating more greenhouse gases than it emits by 2030, the most recent","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"edition","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"described this goal as a “marathon, not a sprint.” Microsoft recently signed an agreement with Chevron to","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"build","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"a natural-gas power plant even after it inked a 20-year purchase agreement to restart the nuclear reactors at New York’s Three Mile Island in, which are expected to come online in 2027. Since 2020, Microsoft’s total emissions have increased by 23 percent and its electricity consumption has more than doubled.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In the years following the 2015 Paris Climate Agreement, which commits governments to limiting global warming by 2 degrees Celsius above pre-industrial levels, many companies signed corporate pledges to meet goals intended to slow climate change. For instance, over 600 companies signed The Climate Pledge co-founded by Amazon and Global Optimism in 2019, which commits companies to reaching net-zero emissions of greenhouse gases by 2040. The Science-Based Targets initiative, which launched in 2015, is another corporate agreement that requires companies to set climate targets that align with the Paris Agreement. The top AI companies have embraced these principles and publish annual reports that document efforts to meet their commitments.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In 2024, data centers","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"accounted","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"accounted","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"for roughly 1.5 percent of electricity consumption globally and 4.4 percent in the U.S. The U.S. figure is projected to rise to as much as 12 percent within the next few years. While big AI companies thought they would have sufficient energy from clean sources, the recent sharp rise in demand is pushing them toward further reliance on fossil fuels that produce climate-changing greenhouse gases.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Top AI companies have invested meaningfully in renewable energy like wind and solar and next-generation sources like","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"nuclear","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"nuclear","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"and geothermal power. However, these sources still face scaling problems, which is why companies have turned to natural gas plants to meet growing energy demands. That’s a worrisome trend. However, for the amount of work that they do, well run data centers are still the most efficient option, and we hope that further efficiency gains in AI will balance rising emissions.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Business meeting discussing AI liability claims with executives, documents, graphs on screens, and laptops.","depth":27,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Learn More About AI With Data Points!","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Learn More About AI With Data Points!","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AI is moving faster than ever.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Data Points","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"helps you make sense of it just as fast.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Data Points","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"arrives in your inbox twice a week with six brief news stories. This week, we covered insurers limiting coverage for AI-related risks and OpenAI ending its exclusive agreement with Microsoft.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Subscribe today","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe today","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"!","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Kimi K2.6 Challenges Open-Weights Champs","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Kimi K2.6 Challenges Open-Weights Champs","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Moonshot AI’s updated Kimi model handles longer autonomous coding sessions and scales up its multi-agent orchestration relative to its predecessor.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Kimi K2.6","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Kimi K2.6","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is a 1 trillion-parameter vision-language model that performs neck and neck with Qwen3.6 Max Preview and the newly released DeepSeek V4 and falls just behind top closed models. It’s designed to generate code in a plan-write-test-debug loop that can last for days, and it can instantiate hundreds of agents that collaborate on a single task. It also produces fewer hallucinations than its predecessor.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text, images, and video in (up to 256,000 tokens), text out (up to 98,000 tokens)","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts, 1 trillion parameters total, 32 billion active per token, MoonViT vision encoder","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tool use, web search, native INT4 quantization, “preserve thinking” mode, agent swarm","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tops other open-weights models on the Artificial Analysis Intelligence Index but trails leading proprietary models","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights free to download from","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Hugging Face","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"under a","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"modified MIT license","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"that permits commercial uses with attribution for products with more than 100 million monthly active users or more than $20 million in monthly revenue, free chat interface at","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"kimi.com","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"and Kimi mobile app, API access via Moonshot $0.95/$0.16/$4.00 per million input/cached/output tokens","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Training data and methods","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Kimi K2.6 reuses the architecture introduced with Kimi K2 and refined in Kimi K2.5, including the multi-headed latent attention (an attention variant that reduces memory requirements by compressing keys and values) and MoonViT vision encoder (400 million parameters). Moonshot has not disclosed how Kimi K2.6 differs with respect to training data and methods.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Like Kimi K2 Thinking and Kimi K2.5, Kimi K2.6 was trained with native INT4 quantization.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"option retains previously generated reasoning tokens across multi-turn interactions, which improves coding performance according to Moonshot.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"mode, a coordinator agent decomposes a task into subtasks, creates up to 300 parallel subagents that can execute 4,000 steps (up from 100 subagents and 1,500 steps in Kimi K2.5) to execute tasks, and reassigns work when an agent fails or stalls. A research preview feature called","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"opens agent swarm mode to agents from other developers — that can run on any device or model — as well as human collaborators.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Kimi K2.6 leads open-weights models on some benchmarks of intelligence and agentic capability and ranks highly relative to its peers in subjective tests of human preferences. However, it trails leading closed models on benchmarks that evaluate reasoning and coding large projects as well as human preferences.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On the Artificial Analysis’","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Intelligence Index","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", a composite of 10 tests of economically useful tasks, Kimi K2.6 set to reasoning (54) leads open-weights models but trails GPT-5.5 set to xhigh reasoning (60) as well as Claude Opus 4.7 set to max reasoning and Gemini 3.1 Pro Preview set to reasoning. The closest open-weights competitors are Qwen3.6 Preview set to max reasoning and DeepSeek-V4-Pro set to max reasoning (tied at 52).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"KimiK2.6’s position in the Intelligence Index rests on its top performance among open-weights models on GPQA Diamond (answering graduate-level science questions), HLE (answering expert-level multidisciplinary questions designed to test reasoning), and SciCode (generating code for scientific research). However, it fell just behind the newly released open-weights model DeepSeek-V4-Pro on five index benchmarks, and it underperformed Xiaomi MiMo-2.5-Pro and other open-weights models on the remaining two.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Moonshot tested Kimi K2.6’s ability to complete large-scale coding projects by asking it to port Qwen3.5-0.8B’s inference code to Zig (a systems-programming language) and optimize it for a Mac. Across 4,000-plus tool calls and 14 successive revisions over more than 12 hours, Kimi K2.6 raised the port’s throughput from roughly 15 to 193 tokens per second, ending around 20 percent faster than LM Studio, a popular local inference app, running on the same hardware.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Artificial Analysis measured Kimi K2.6’s hallucination rate (given a question-answering benchmark of general knowledge, the proportion of non-correct outputs that include erroneous responses, professions of ignorance, and refusals to respond) at 39.26 percent. This is lower than Kimi K2.5 (64.6 percent) and roughly comparable to Anthropic Claude Opus 4.7 (36.18 percent)","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena.ai’s Code Arena WebDev","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"leaderboard, which ranks models on web-development coding via blind pairwise comparisons, Kimi K2.6 (1,529 Elo) ranked sixth among 67 models as of April 26, 2026, behind Anthropic Claude Opus 4.7 (1,565 Elo), Claude Opus 4.6 (1,548 Elo), and Z.ai’s open-weights GLM-5.1 (1,534 Elo).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to stay on task across hours of autonomous execution emerged as a competitive frontier in late 2025. Anthropic’s Claude Code, OpenAI’s Codex, and Alibaba’s Qwen3-Coder all targeted this capability in their most recent releases. Kimi K2, released in July 2025, was an early open-weights entrant in agentic tool use, and the family has been updated every few months since with growing emphasis on long-horizon execution.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Moonshot has steadily extended the duration over which Kimi K2 family models can usefully execute tasks autonomously: first short reasoning traces, then multi-step tool use, multi-hour coding sessions, and now multi-day projects. Each extension widens the interval between human check-ins required to keep agents on track.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Sustained autonomy and low hallucination rates are related, but less and less so. If an agent makes a mistake, it can check its work, find the mistake, and fix it.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Strategic Thinking in LLMs vs. Humans","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Strategic Thinking in LLMs vs. Humans","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"While large language models can behave in human-like ways, the similarities are superficial. A simple strategy game revealed clear differences in their strategic approaches.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Caroline Wang and colleagues at University of Texas at Austin and Google interpreted","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"patterns of decision-making by humans and LLMs","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"patterns of decision-making by humans and LLMs","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"as they played the classic game of rock-paper-scissors. They found that LLMs sometimes model their opponents with greater sophistication than people do.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Key insight:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Given recorded gameplay, an LLM can iteratively improve code that predicts a player","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"s next move. If the code predicts the player’s actions with significant accuracy, we can assume that its decision-making algorithms are functionally similar to those the player used. Computer code is interpretable, making it possible to discern such algorithms and compare those used by humans and LLMs.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In games of rock-paper-scissors, he authors pitted individual LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and GPT-OSS 120B) against each of 15 preprogrammed bots of varying complexity. They recorded each player's moves in 20 games of 300 sequential rounds each. Previous work provided","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"records","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"records","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"of similar records of games between humans and the same bots. The authors tracked the round-by-round choices made by each player — AI and human — and whether they won, lost, or tied. Then they used","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"AlphaEvolve","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"AlphaEvolve","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", an agentic method that iteratively optimizes code through an evolutionary process, to improve Python programs that predicted the next move for each LLM individually and humans as a group.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"AlphaEvolve initially processed the game data using a simple template program written by the authors. In each of an undisclosed number of evolutionary steps, Gemini 2.5 Flash proposed modifications to improve a function that balanced simplicity (as measured by","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Halstead effort","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":") and evaluation likelihood (how well a program predicted a player’s choices).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"For each player, the authors selected the simplest program that achieved near-maximum predictive accuracy within a small margin from the best. Each program produced the best evaluation likelihood (higher is better) for the player it had evolved to predict. That is, it represented its corresponding player’s behavior better than that of any other player.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Results:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Using game data that AlphaEvolve didn’t process, the authors compared how well each program predicted the other players’ moves. Then they examined the programs to determine what strategies each player used.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The programs that represented Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 performed nearly equally well when predicting each other’s moves as they played against bots, which suggests that this trio used similar strategies. For example, predicting the actions of Gemini 2.5 Pro, the programs that predicted Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 achieved 0.507, 0.507, and 0.506 evaluation likelihood respectively. The programs that represented humans and GPT OSS 120B predicted the trio’s actions less successfully. They achieved 0.476 and 0.403 evaluation likelihood respectively, indicating that they likely used different strategies.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Interpreting the programs suggested that Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 maintained sequential patterns more effectively than humans or GPT-OSS 120B. The code that predicted those programs tracked the frequency of each possible move based on a player’s previous one or two moves. That is, it tracked how often the player, over three rounds, called rock->scissors-> rock, rock->scissors->paper, and so on. In contrast, the code that represented humans and GPT-OSS 120B tracked the frequency of the opponent’s latest move only.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The code that represented Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and human players computed the preliminary value of each possible next move based on (i) the possible next move, (ii) the bot’s previous move, and (iii) the player’s previous move. GPT-OSS 120B computed the value based on the possible next move alone.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"While researchers have found ways to","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"understand","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"understand","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"some","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"some","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"aspects","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"aspects","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"of neural network behavior, large language models remain black boxes in many ways. Synthesizing code directly from LLM behavior offers a powerful tool to interpret their decision-making.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"It’s tempting to assume that LLMs learn to mimic human behavior as represented by their training data. Finding that they can encode a gaming strategy more systematically than the average human demonstrates a different sort of learning.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Work With Andrew Ng","depth":27,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Work With Andrew Ng","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Join the teams that are bringing AI to the world! Check out job openings at","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DeepLearning.AI","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DeepLearning.AI","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":",","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"AI Fund","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"AI Fund","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", and","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Landing AI","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Landing AI","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Subscribe and view previous issues","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"here","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"here","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Thoughts, suggestions, feedback? 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GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans - [EMAIL] - Gmail
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GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans
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Dear friends,
The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Some people are still using LLMs primarily by asking them short questions. But the models can do much more, like think for minutes, ingest many documents as context, and use web search and other tools.
I’m teaching a new course,
AI Prompting for Everyone
, to help everyone become an AI power user — whatever their current skill level — and prompt LLMs to take advantage of their latest capabilities.
The course covers skills that apply to ChatGPT, Gemini, Claude, and other AI tools:
Download attachment
I also cover intuitions about how these models work under the hood, so learners know when to trust their output and when not to.
Along the way: you’ll see flying squirrels, a creativity test, some of my old family photos, and fireworks.
Please join me
Please join me
! The course assumes no technical background, so please share it with friends or family who could benefit.
Keep prompting!
Andrew
A MESSAGE FROM DEEPLEARNING.AI
A MESSAGE FROM
DEEPLEARNING.AI
DEEPLEARNING.AI
Promo banner for: "AI Prompting for Everyone"
Learn how to get more accurate answers, better writing, and more useful outputs from AI tools like ChatGPT, Claude, and Gemini. Taught by Andrew Ng, this course covers finding information, brainstorming, and building simple apps.
Enroll today
Enroll today
News
News
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GPT-5.5 Outperforms, Hallucinates
GPT-5.5 Outperforms, Hallucinates
The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know.
What’s new:
GPT-5.5
GPT-5.5
is a closed vision-language models that’s built for agentic coding, computer use, and knowledge work. GPT-5.5 Pro is the same model but processes reasoning tokens in parallel during inference. OpenAI set the API prices at roughly double the per-token rates of GPT-5.4.
Text and images in (up to 1 million tokens via API, 400,000 tokens in Codex), text out (up to 128,000 tokens)
Five levels of reasoning (xhigh, high, medium, low, none), tool use, web search, structured outputs, tool search (API only, loads tools on demand rather than all at once), Fast mode (Codex only, generates tokens 1.5 times faster at 2.5 times the price)
Tops Artificial Analysis Intelligence Index and ARC-AGI-2
GPT-5.5 available in ChatGPT with Plus, Pro, Business, or Enterprise subscription and in Codex for those tiers plus Edu and Go; GPT-5.5 Pro available in ChatGPT with Pro, Business, or Enterprise subscription: GPT-5.5 API $5/$0.50/$30 per million tokens of input/cached/output, GPT-5.5 Pro API $30/$180 per million tokens of input/output with no cached discount
Architecture, parameter count, training data and methods
How it works:
OpenAI
disclosed
disclosed
few details about how it built GPT-5.5. As is typical of high-performance models, the training data was a mix of publicly available data scraped from the web, licensed from partners, and collected from users and human trainers. The model was trained via reinforcement learning to reason before responding.
Performance:
GPT-5.5 generally delivers top performance in objective benchmarks, especially in tests of knowledge, agentic tasks, and abstract visual reasoning. However, it falls behind competitors on subjective evaluations. It’s also more likely to confidently deliver incorrect output.
On ARC-AGI-2, visual puzzles that test abstract reasoning, GPT-5.5 set to xhigh (85.0 percent at $1.87 per task) displaced the previous leader Gemini 3 Deep Think (84.6 percent at $13.62 per task) at a substantially lower cost per task.
In OpenAI’s tests, GPT-5.5 set state-of-the-art scores on Terminal-Bench 2.0 (command-line workflows that require planning and tool use), OSWorld-Verified (autonomous operation of real computer interfaces), and Tau2-bench Telecom (multi-turn customer-service workflows).
On AA-Omniscience Accuracy, a knowledge benchmark that rewards factual recall, GPT-5.5 set to xhigh reasoning posted the highest accuracy at 57 percent. However, on the AA-Omniscience Index, which rewards models for answering correctly and acknowledging ignorance but penalizes them for confidently making mistakes, GPT-5.5 set to xhigh reasoning (20 points) ranked third, behind Gemini 3.1 Pro Preview (33 points) and Claude Opus 4.7 set to max reasoning (26 points).
On Arena.ai’s
leaderboards
, which rank models by blind head-to-head comparisons, GPT-5.5 falls well behind competitors. Claude Opus models occupy the top spots across most categories. For instance, as of April 27, GPT-5.5-high ranked seventh in Text Arena and ninth in Code Arena WebDev.
Yes, but:
GPT-5.5 knows more than its peers, but it answers incorrectly more often and acknowledges ignorance less often. The AA-Omniscience benchmark poses 6,000 expert-level questions across business, law, health, humanities, science/engineering, and software engineering. It includes a “hallucination rate” that is the ratio of wrong answers to the sum of wrong answers, partially wrong answers, and abstentions. By this measure, GPT-5.5 set to high reasoning hit 85.53 percent, notably worse than Claude Opus 4.7 set to max reasoning (36.18 percent) and Gemini 3.1 Pro Preview at (49.87 percent). Apollo Research separately
found
found
that GPT-5.5 lied about completing an impossible programming task in 29 percent of samples, a significant jump from GPT-5.4’s 7 percent. OpenAI’s internal monitoring of coding-agent traffic
showed
showed
a similar pattern.
Security implications:
OpenAI released results of VulnLMP, an internal evaluation that
tests
tests
whether a model can develop exploits against widely deployed software. GPT-5.5 undertook multi-day research campaigns and identified potential memory-related vulnerabilities in a variety of targets, but it did not produce an exploit that was confirmed by OpenAI’s evaluation harness. Under OpenAI’s Preparedness Framework, this evidence places GPT-5.5 within the “high” tier of cybersecurity threats, short of the “critical” tier label that would describe models that independently produce working exploits against real targets.
Why it matters:
Evaluations of objective performance and human preferences are telling different stories about GPT-5.5. OpenAI regained the lead on the Artificial Analysis Intelligence Index, but the picture flips when it comes to subjective, head-to-head comparisons. Claude Opus models occupy the top spots in LMArena’s Text, Vision, Document, Search, and Code rankings, while GPT-5.5 doesn’t crack the top five on most. Benchmarks measure what models can accomplish, human preference what they’re like to work with. Production decisions usually weigh both, and — according to the measures that are available so far — the two are diverging.
We’re thinking:
Top AI companies continue to push the frontier at a dizzying pace. GPT-5.5 is the fourth flagship launch since February, following Anthropic Claude Opus 4.7, GPT-5.4, and Google Gemini 3.1 Pro Preview. Each one reshuffled the top of the Artificial Analysis Intelligence Index, which can be viewed as a proxy for general capability in real-world tasks. Developers should design their software stacks to swap models as easily as bumping a dependency.
Download attachment
Big AI’s Plans Strain CO2 Pledges
Big AI’s Plans Strain CO
2
Pledges
Commitments by large AI companies to limit emissions of greenhouse gases are at risk as those companies pursue a massive build-out of data centers, many of which will be powered by fossil fuels in the near term and possibly beyond.
What’s new:
Alphabet, Amazon, Meta, and Microsoft have begun to acknowledge that keeping up with projected demand for AI is interfering with earlier plans to stop raising the concentration of greenhouse gases to the atmosphere,
Associated Press
reported
reported
. (Disclaimer: Andrew Ng is a member of Amazon’s board of directors.)
How it works:
Electricity consumed by top tech companies has increased significantly over the last few years, and with it their emissions of greenhouse gases that contribute to climate change, despite ongoing efforts to reduce emissions. While they have emphasized clean sources of energy including wind, solar, geothermal, and nuclear, lately they have begun to develop natural gas power plants to meet rapidly rising demand for AI.
In Alphabet’s most recent
Environmental Report
, the company characterized its net-zero 2030 goal — which it set forth in 2024, after it had
abandoned
an earlier pledge to maintain carbon-neutral operations — as a “moonshot.” Recent
reports
indicate that the company’s data center in North Texas will be powered partially by natural-gas plants. Alphabet has invested in next-generation geothermal and nuclear sources, but they’re not yet deployed at sufficient scale. Although 66 percent of the energy for its data centers and offices came from carbon-free sources in 2024 — and its emissions per unit of computation have diminished dramatically — its total greenhouse-gas emissions increased by 54 percent between 2019 and 2024.
In Amazon’s most recent
Sustainability Report
, the company said that one of the biggest challenges of scaling AI is increased energy demand. The company has invested in natural-gas plants in Mississippi and Indiana to meet the energy demands of nearby data centers. It views nuclear energy as a key part of its strategy to become carbon-neutral, but the planned nuclear sources won’t come online until the 2030s. Meanwhile, Amazon’s total carbon emissions have increased by 33 percent since 2019.
Meta’s most recent sustainability
report
emphasized that the path to net zero depends on new technology, suppliers, and collaboration with global coalitions. The company is building
private gas-powered plants
to generate energy for data centers including its largest-yet, 5-gigawatt facility in rural Louisiana. It has invested in projects that could support up to 6.6 gigawatts of new and existing clean energy by 2035 including geothermal, nuclear, and energy storage to take better advantage of wind and solar power. The company’s total emissions increased by over 60 percent between 2020 and 2024, while its electricity consumption by data centers nearly tripled.
While Microsoft’s previous sustainability reports emphasized progress toward eliminating more greenhouse gases than it emits by 2030, the most recent
edition
described this goal as a “marathon, not a sprint.” Microsoft recently signed an agreement with Chevron to
build
a natural-gas power plant even after it inked a 20-year purchase agreement to restart the nuclear reactors at New York’s Three Mile Island in, which are expected to come online in 2027. Since 2020, Microsoft’s total emissions have increased by 23 percent and its electricity consumption has more than doubled.
Behind the news:
In the years following the 2015 Paris Climate Agreement, which commits governments to limiting global warming by 2 degrees Celsius above pre-industrial levels, many companies signed corporate pledges to meet goals intended to slow climate change. For instance, over 600 companies signed The Climate Pledge co-founded by Amazon and Global Optimism in 2019, which commits companies to reaching net-zero emissions of greenhouse gases by 2040. The Science-Based Targets initiative, which launched in 2015, is another corporate agreement that requires companies to set climate targets that align with the Paris Agreement. The top AI companies have embraced these principles and publish annual reports that document efforts to meet their commitments.
Why it matters:
In 2024, data centers
accounted
accounted
for roughly 1.5 percent of electricity consumption globally and 4.4 percent in the U.S. The U.S. figure is projected to rise to as much as 12 percent within the next few years. While big AI companies thought they would have sufficient energy from clean sources, the recent sharp rise in demand is pushing them toward further reliance on fossil fuels that produce climate-changing greenhouse gases.
We’re thinking:
Top AI companies have invested meaningfully in renewable energy like wind and solar and next-generation sources like
nuclear
nuclear
and geothermal power. However, these sources still face scaling problems, which is why companies have turned to natural gas plants to meet growing energy demands. That’s a worrisome trend. However, for the amount of work that they do, well run data centers are still the most efficient option, and we hope that further efficiency gains in AI will balance rising emissions.
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Kimi K2.6 Challenges Open-Weights Champs
Kimi K2.6 Challenges Open-Weights Champs
Moonshot AI’s updated Kimi model handles longer autonomous coding sessions and scales up its multi-agent orchestration relative to its predecessor.
What’s new:
Kimi K2.6
Kimi K2.6
is a 1 trillion-parameter vision-language model that performs neck and neck with Qwen3.6 Max Preview and the newly released DeepSeek V4 and falls just behind top closed models. It’s designed to generate code in a plan-write-test-debug loop that can last for days, and it can instantiate hundreds of agents that collaborate on a single task. It also produces fewer hallucinations than its predecessor.
Text, images, and video in (up to 256,000 tokens), text out (up to 98,000 tokens)
Mixture-of-experts, 1 trillion parameters total, 32 billion active per token, MoonViT vision encoder
Tool use, web search, native INT4 quantization, “preserve thinking” mode, agent swarm
Tops other open-weights models on the Artificial Analysis Intelligence Index but trails leading proprietary models
Availability/price:
Weights free to download from
Hugging Face
under a
modified MIT license
that permits commercial uses with attribution for products with more than 100 million monthly active users or more than $20 million in monthly revenue, free chat interface at
kimi.com
and Kimi mobile app, API access via Moonshot $0.95/$0.16/$4.00 per million input/cached/output tokens
Training data and methods
How it works:
Kimi K2.6 reuses the architecture introduced with Kimi K2 and refined in Kimi K2.5, including the multi-headed latent attention (an attention variant that reduces memory requirements by compressing keys and values) and MoonViT vision encoder (400 million parameters). Moonshot has not disclosed how Kimi K2.6 differs with respect to training data and methods.
Like Kimi K2 Thinking and Kimi K2.5, Kimi K2.6 was trained with native INT4 quantization.
A
option retains previously generated reasoning tokens across multi-turn interactions, which improves coding performance according to Moonshot.
In
mode, a coordinator agent decomposes a task into subtasks, creates up to 300 parallel subagents that can execute 4,000 steps (up from 100 subagents and 1,500 steps in Kimi K2.5) to execute tasks, and reassigns work when an agent fails or stalls. A research preview feature called
opens agent swarm mode to agents from other developers — that can run on any device or model — as well as human collaborators.
Performance:
Kimi K2.6 leads open-weights models on some benchmarks of intelligence and agentic capability and ranks highly relative to its peers in subjective tests of human preferences. However, it trails leading closed models on benchmarks that evaluate reasoning and coding large projects as well as human preferences.
On the Artificial Analysis’
Intelligence Index
, a composite of 10 tests of economically useful tasks, Kimi K2.6 set to reasoning (54) leads open-weights models but trails GPT-5.5 set to xhigh reasoning (60) as well as Claude Opus 4.7 set to max reasoning and Gemini 3.1 Pro Preview set to reasoning. The closest open-weights competitors are Qwen3.6 Preview set to max reasoning and DeepSeek-V4-Pro set to max reasoning (tied at 52).
KimiK2.6’s position in the Intelligence Index rests on its top performance among open-weights models on GPQA Diamond (answering graduate-level science questions), HLE (answering expert-level multidisciplinary questions designed to test reasoning), and SciCode (generating code for scientific research). However, it fell just behind the newly released open-weights model DeepSeek-V4-Pro on five index benchmarks, and it underperformed Xiaomi MiMo-2.5-Pro and other open-weights models on the remaining two.
Moonshot tested Kimi K2.6’s ability to complete large-scale coding projects by asking it to port Qwen3.5-0.8B’s inference code to Zig (a systems-programming language) and optimize it for a Mac. Across 4,000-plus tool calls and 14 successive revisions over more than 12 hours, Kimi K2.6 raised the port’s throughput from roughly 15 to 193 tokens per second, ending around 20 percent faster than LM Studio, a popular local inference app, running on the same hardware.
Artificial Analysis measured Kimi K2.6’s hallucination rate (given a question-answering benchmark of general knowledge, the proportion of non-correct outputs that include erroneous responses, professions of ignorance, and refusals to respond) at 39.26 percent. This is lower than Kimi K2.5 (64.6 percent) and roughly comparable to Anthropic Claude Opus 4.7 (36.18 percent)
On
Arena.ai’s Code Arena WebDev
leaderboard, which ranks models on web-development coding via blind pairwise comparisons, Kimi K2.6 (1,529 Elo) ranked sixth among 67 models as of April 26, 2026, behind Anthropic Claude Opus 4.7 (1,565 Elo), Claude Opus 4.6 (1,548 Elo), and Z.ai’s open-weights GLM-5.1 (1,534 Elo).
Behind the news:
The ability to stay on task across hours of autonomous execution emerged as a competitive frontier in late 2025. Anthropic’s Claude Code, OpenAI’s Codex, and Alibaba’s Qwen3-Coder all targeted this capability in their most recent releases. Kimi K2, released in July 2025, was an early open-weights entrant in agentic tool use, and the family has been updated every few months since with growing emphasis on long-horizon execution.
Why it matters:
Moonshot has steadily extended the duration over which Kimi K2 family models can usefully execute tasks autonomously: first short reasoning traces, then multi-step tool use, multi-hour coding sessions, and now multi-day projects. Each extension widens the interval between human check-ins required to keep agents on track.
We’re thinking:
Sustained autonomy and low hallucination rates are related, but less and less so. If an agent makes a mistake, it can check its work, find the mistake, and fix it.
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Strategic Thinking in LLMs vs. Humans
Strategic Thinking in LLMs vs. Humans
While large language models can behave in human-like ways, the similarities are superficial. A simple strategy game revealed clear differences in their strategic approaches.
What’s new:
Caroline Wang and colleagues at University of Texas at Austin and Google interpreted
patterns of decision-making by humans and LLMs
patterns of decision-making by humans and LLMs
as they played the classic game of rock-paper-scissors. They found that LLMs sometimes model their opponents with greater sophistication than people do.
Key insight:
Given recorded gameplay, an LLM can iteratively improve code that predicts a player
’
s next move. If the code predicts the player’s actions with significant accuracy, we can assume that its decision-making algorithms are functionally similar to those the player used. Computer code is interpretable, making it possible to discern such algorithms and compare those used by humans and LLMs.
How it works:
In games of rock-paper-scissors, he authors pitted individual LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and GPT-OSS 120B) against each of 15 preprogrammed bots of varying complexity. They recorded each player's moves in 20 games of 300 sequential rounds each. Previous work provided
records
records
of similar records of games between humans and the same bots. The authors tracked the round-by-round choices made by each player — AI and human — and whether they won, lost, or tied. Then they used
AlphaEvolve
AlphaEvolve
, an agentic method that iteratively optimizes code through an evolutionary process, to improve Python programs that predicted the next move for each LLM individually and humans as a group.
AlphaEvolve initially processed the game data using a simple template program written by the authors. In each of an undisclosed number of evolutionary steps, Gemini 2.5 Flash proposed modifications to improve a function that balanced simplicity (as measured by
Halstead effort
) and evaluation likelihood (how well a program predicted a player’s choices).
For each player, the authors selected the simplest program that achieved near-maximum predictive accuracy within a small margin from the best. Each program produced the best evaluation likelihood (higher is better) for the player it had evolved to predict. That is, it represented its corresponding player’s behavior better than that of any other player.
Results:
Using game data that AlphaEvolve didn’t process, the authors compared how well each program predicted the other players’ moves. Then they examined the programs to determine what strategies each player used.
The programs that represented Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 performed nearly equally well when predicting each other’s moves as they played against bots, which suggests that this trio used similar strategies. For example, predicting the actions of Gemini 2.5 Pro, the programs that predicted Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 achieved 0.507, 0.507, and 0.506 evaluation likelihood respectively. The programs that represented humans and GPT OSS 120B predicted the trio’s actions less successfully. They achieved 0.476 and 0.403 evaluation likelihood respectively, indicating that they likely used different strategies.
Interpreting the programs suggested that Gemini 2.5 Pro, Gemini 2.5 Flash, and GPT-5.1 maintained sequential patterns more effectively than humans or GPT-OSS 120B. The code that predicted those programs tracked the frequency of each possible move based on a player’s previous one or two moves. That is, it tracked how often the player, over three rounds, called rock->scissors-> rock, rock->scissors->paper, and so on. In contrast, the code that represented humans and GPT-OSS 120B tracked the frequency of the opponent’s latest move only.
The code that represented Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5.1, and human players computed the preliminary value of each possible next move based on (i) the possible next move, (ii) the bot’s previous move, and (iii) the player’s previous move. GPT-OSS 120B computed the value based on the possible next move alone.
Why it matters:
While researchers have found ways to
understand
understand
some
some
aspects
aspects
of neural network behavior, large language models remain black boxes in many ways. Synthesizing code directly from LLM behavior offers a powerful tool to interpret their decision-making.
We’re thinking:
It’s tempting to assume that LLMs learn to mimic human behavior as represented by their training data. Finding that they can encode a gaming strategy more systematically than the average human demonstrates a different sort of learning.
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Pul Pull requests · screenpipe/screenpipe · GitHub
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Pull requests · screenpipe/screenpipe · GitHub
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Pull requests · screenpipe/screenpipe · GitHub
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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се да Ви информираме, че като потребител на","depth":25,"bounds":{"left":0.24767287,"top":0.43375897,"width":0.13015293,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"imot.bg","depth":25,"bounds":{"left":0.3778258,"top":0.43375897,"width":0.016788565,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"imot.bg","depth":26,"bounds":{"left":0.3778258,"top":0.43375897,"width":0.016788565,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", получавате","depth":25,"bounds":{"left":0.39461437,"top":0.43375897,"width":0.030917553,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"ПОДАРЪК","depth":26,"bounds":{"left":0.42553192,"top":0.43375897,"width":0.025598405,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"– ваучер на стойност","depth":25,"bounds":{"left":0.24767287,"top":0.43375897,"width":0.2252327,"height":0.0311253},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"100 €","depth":26,"bounds":{"left":0.27642953,"top":0.44972068,"width":0.014295213,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Въведете","depth":25,"bounds":{"left":0.3304521,"top":0.49481246,"width":0.024601065,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"ПРОМО КОД:","depth":26,"bounds":{"left":0.3550532,"top":0.49481246,"width":0.035904255,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"101C0L4EPTDH1IQS","depth":26,"bounds":{"left":0.32280585,"top":0.51077414,"width":0.07579787,"height":0.021947326},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"при поръчка на хостинг услуги от","depth":25,"bounds":{"left":0.30352393,"top":0.53471667,"width":0.08494016,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Bulinfo.net","depth":25,"bounds":{"left":0.3884641,"top":0.53471667,"width":0.029421542,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Bulinfo.net","depth":27,"bounds":{"left":0.3884641,"top":0.53471667,"width":0.029421542,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Резервирайте","depth":25,"bounds":{"left":0.24767287,"top":0.5766161,"width":0.03307846,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"хостинг услугите 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да го използвате лично или да го","depth":25,"bounds":{"left":0.24767287,"top":0.63647246,"width":0.12084442,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"подарите на приятел.","depth":26,"bounds":{"left":0.36851728,"top":0.63647246,"width":0.05651596,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Промо кодът е валиден до","depth":25,"bounds":{"left":0.24767287,"top":0.6644054,"width":0.062832445,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"01 Юни 2026 г.","depth":26,"bounds":{"left":0.31050533,"top":0.6644054,"width":0.03956117,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"* Промо кодът е само за нови клиенти и може да бъде използван 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профил","depth":25,"bounds":{"left":0.3884641,"top":0.9169992,"width":0.028590426,"height":0.01396648},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Моят профил","depth":26,"bounds":{"left":0.3884641,"top":0.9169992,"width":0.028590426,"height":0.01396648},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Това съобщение е генерирано автоматично. Моля, не отговаряй на него.","depth":22,"bounds":{"left":0.29288563,"top":0.95331204,"width":0.1356383,"height":0.011971269},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"","depth":21,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":22,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Reply","depth":14,"bounds":{"left":0.22273937,"top":0.9465283,"width":0.034574468,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Reply","depth":15,"bounds":{"left":0.23786569,"top":0.9537111,"width":0.012300532,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Forward","depth":14,"bounds":{"left":0.2599734,"top":0.9465283,"width":0.03723404,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Forward","depth":15,"bounds":{"left":0.27360374,"top":0.9537111,"width":0.017952127,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":15,"bounds":{"left":0.29986703,"top":0.9465283,"width":0.011968086,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Calendar","depth":10,"bounds":{"left":0.48138297,"top":0.10295291,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Keep","depth":10,"bounds":{"left":0.48138297,"top":0.14764565,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Tasks","depth":10,"bounds":{"left":0.48138297,"top":0.19233839,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Contacts","depth":10,"bounds":{"left":0.48138297,"top":0.23703113,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Get add-ons","depth":10,"bounds":{"left":0.48138297,"top":0.30806065,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Hide side panel","depth":9,"bounds":{"left":0.48138297,"top":0.95530725,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Loading...","depth":10,"bounds":{"left":0.29604387,"top":0.052673582,"width":0.022273935,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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100 € ваучер за хостинг от Bulinfo.nеt
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Imot.bg – Сайт №1 за имоти
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се да Ви информираме, че като потребител на","depth":25,"bounds":{"left":0.24767287,"top":0.43375897,"width":0.13015293,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"imot.bg","depth":25,"bounds":{"left":0.3778258,"top":0.43375897,"width":0.016788565,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"imot.bg","depth":26,"bounds":{"left":0.3778258,"top":0.43375897,"width":0.016788565,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", получавате","depth":25,"bounds":{"left":0.39461437,"top":0.43375897,"width":0.030917553,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"ПОДАРЪК","depth":26,"bounds":{"left":0.42553192,"top":0.43375897,"width":0.025598405,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"– ваучер на стойност","depth":25,"bounds":{"left":0.24767287,"top":0.43375897,"width":0.2252327,"height":0.0311253},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"100 €","depth":26,"bounds":{"left":0.27642953,"top":0.44972068,"width":0.014295213,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Въведете","depth":25,"bounds":{"left":0.3304521,"top":0.49481246,"width":0.024601065,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"ПРОМО КОД:","depth":26,"bounds":{"left":0.3550532,"top":0.49481246,"width":0.035904255,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"101C0L4EPTDH1IQS","depth":26,"bounds":{"left":0.32280585,"top":0.51077414,"width":0.07579787,"height":0.021947326},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"при поръчка на хостинг услуги от","depth":25,"bounds":{"left":0.30352393,"top":0.53471667,"width":0.08494016,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Bulinfo.net","depth":25,"bounds":{"left":0.3884641,"top":0.53471667,"width":0.029421542,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Bulinfo.net","depth":27,"bounds":{"left":0.3884641,"top":0.53471667,"width":0.029421542,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Резервирайте","depth":25,"bounds":{"left":0.24767287,"top":0.5766161,"width":0.03307846,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"хостинг услугите 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да го използвате лично или да го","depth":25,"bounds":{"left":0.24767287,"top":0.63647246,"width":0.12084442,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"подарите на приятел.","depth":26,"bounds":{"left":0.36851728,"top":0.63647246,"width":0.05651596,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Промо кодът е валиден до","depth":25,"bounds":{"left":0.24767287,"top":0.6644054,"width":0.062832445,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"01 Юни 2026 г.","depth":26,"bounds":{"left":0.31050533,"top":0.6644054,"width":0.03956117,"height":0.015163607},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"* Промо кодът е само за нови клиенти и може да бъде използван еднократно.","depth":25,"bounds":{"left":0.24767287,"top":0.6935355,"width":0.15774602,"height":0.0131683955},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"** Валиден за услуги Споделен хостинг, WordPress хостинг и VPS.","depth":25,"bounds":{"left":0.24767287,"top":0.7094972,"width":0.1328125,"height":0.0131683955},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"imot.bg","depth":27,"bounds":{"left":0.29504654,"top":0.77972865,"width":0.020611702,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"imot.bg","depth":28,"bounds":{"left":0.29504654,"top":0.77972865,"width":0.020611702,"height":0.015961692},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"ползва и 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профил","depth":25,"bounds":{"left":0.3884641,"top":0.9169992,"width":0.028590426,"height":0.01396648},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Моят профил","depth":26,"bounds":{"left":0.3884641,"top":0.9169992,"width":0.028590426,"height":0.01396648},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Това съобщение е генерирано автоматично. Моля, не отговаряй на него.","depth":22,"bounds":{"left":0.29288563,"top":0.95331204,"width":0.1356383,"height":0.011971269},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"","depth":21,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":22,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Reply","depth":14,"bounds":{"left":0.22273937,"top":0.9465283,"width":0.034574468,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Reply","depth":15,"bounds":{"left":0.23786569,"top":0.9537111,"width":0.012300532,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Forward","depth":14,"bounds":{"left":0.2599734,"top":0.9465283,"width":0.03723404,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Forward","depth":15,"bounds":{"left":0.27360374,"top":0.9537111,"width":0.017952127,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":15,"bounds":{"left":0.29986703,"top":0.9465283,"width":0.011968086,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Calendar","depth":10,"bounds":{"left":0.48138297,"top":0.10295291,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Keep","depth":10,"bounds":{"left":0.48138297,"top":0.14764565,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Tasks","depth":10,"bounds":{"left":0.48138297,"top":0.19233839,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Contacts","depth":10,"bounds":{"left":0.48138297,"top":0.23703113,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Get add-ons","depth":10,"bounds":{"left":0.48138297,"top":0.30806065,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Hide side panel","depth":9,"bounds":{"left":0.48138297,"top":0.95530725,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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4236009739965043799
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NULL
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Pull requests · screenpipe/screenpipe · GitHub
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DXP4800PLUS-B5F8
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при поръчка на хостинг услуги от
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хостинг услугите СЕГА,
преди да изтече крайният срок на ваучера, а когато пожелаете, специалистите на Bulinfo.net
ще Ви помогнат БЕЗПЛАТНО с миграцията
на Вашия сайт.
Ваучерът можете да го използвате лично или да го
подарите на приятел.
Промо кодът е валиден до
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* Промо кодът е само за нови клиенти и може да бъде използван еднократно.
** Валиден за услуги Споделен хостинг, WordPress хостинг и VPS.
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Purchases has menu
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PHP Team Lead - Sportsbook Division at Delasport
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Delasport PHP Team Lead - Sportsbook Division: About Us:Delasport is where technology meets the…
͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Delasport PHP Team Lead - Sportsbook Division: About Us:Delasport is where technology meets the…
͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Delasport PHP Team Lead - Sportsbook Division: About Us:Delasport is where technology meets the…
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PHP Team Lead - Sportsbook Division at Delasport
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Fri 1 May, 08:31 (7 days ago)
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Delasport PHP Team Lead - Sportsbook Division: About Us:Delasport is where technology meets the…
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Explorer Section: docker [SSH: nas]
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DOCKER [SSH: NAS]
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flask-app
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health-tracker
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alembic
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Outline Section
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TIMELINE
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docker-compose.yml, Editor Group 1
docker-compose.yml, Editor Group 1
Review payment logger au…, Editor Group 2
remote SSH: nas
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Review payment logger authorization implementation
Session history
New session
Message actions
review payment_logger project how toes authorization work
Thinking
Thinking
Let me explore the payment_logger project structure first.
Bash
Find payment_logger project files
IN
find /volume2/docker -type f -name "*.py" -o -name "*.yml" -o -name "*.yaml" -o -name "*.env" -o -name "*.toml" -o -name "*.cfg" | grep -i payment | head -50
Copy code to clipboard
OUT
bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.
bfs: error: /volume2/docker/portnotedb/base: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.
bfs: error: /volume2/docker/openttd/.dbus: Permission denied.
bfs: error: /volume2/docker/immich/db: Permission denied.
bfs: error: /volume2/docker/paperlessngx/db: Permission denied.
bfs: error: /volume2/docker/appflowy/postgres: Permission denied.
bfs: error: /volume2/docker/auth/db: Permission denied.
bfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.
bfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.
bfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
Bash
List docker directory contents
IN
ls /volume2/docker/
Copy code to clipboard
OUT
adguard
ai-stack
app-db
appflowy
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
openttd
openvpn-client
open-webui
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
personal-log
personal-log-system
player
portainer
portnotedb
reminders-app
romm
second-brain
static
stirling
syncthing
tailscale
temp-logger
test-app
tmp
torrent-stack
tree
trilium
tubearchivist
twenty
uptimekuma
ytptube
Agent:
Explore payment_logger authorization code
IN
Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
Please read the key source files and give me a clear summary with file paths and line numbers for the important parts.
Bash
IN
find /volume2/docker/payments-logger -type f -name "*.py" -o -name "*.json" -o -name "*.yml" -o -name "*.yaml" | grep -E "(auth|token|jwt|session|permission|role)" | head -20
Copy code to clipboard
OUT
(Bash completed with no output)
Bash
IN
ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo "Directory not found"
Copy code to clipboard
OUT
total 36
drwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .
drwxrwxrwx 1 root root 1146 Apr 26 18:49 ..
-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md
drwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend
drwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude
-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml
-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env
-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example
drwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend
-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore
-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md
Bash
IN
find /volume2/docker/payments-logger/backend -type f | head -30
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
Bash
IN
find /volume2/docker/payments-logger -type f \( -name "*.js" -o -name "*.json" -o -name "*.md" \) | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
.env
.env
Read
auth.js
auth.js
Read
index.js
index.js
Read
package.json
package.json
Read
auth.js
auth.js
Read
API.md
API.md
Read
payments.js
payments.js
Read
.env.example
.env.example
Read
schema.prisma
schema.prisma
Bash
IN
grep -r "API_KEY\|auth\|token\|oauth\|hydra" /volume2/docker/payments-logger/backend/src --include="*.js" -n
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');
/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────
/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.
/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';
/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {
/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();
/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {
/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);
/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;
/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';
/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],
/volume2/docker/payments-logger/backend/src/auth.js:124: [API_KEY]: doc.[API_KEY] || ['none', 'client_secret_basic'],
/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],
/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────
Bash
IN
find /volume2/docker/payments-logger/frontend/src -type f -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" | head -20
Copy code to clipboard
OUT
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
Read
AuthProvider.jsx
AuthProvider.jsx
Read
AuthCallback.jsx
AuthCallback.jsx
Read
App.jsx
App.jsx
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Bash
IN
grep -r "HYDRA\|VITE" /volume2/docker/payments-logger --include=".env*" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
docker-compose.yml
docker-compose.yml
Bash
IN
grep -r "API_KEY" /volume2/docker/payments-logger/backend --include="*.js" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
README.md
README.md
Bash
IN
grep -n "HYDRA\|oauth\|TOKEN" /volume2/docker/payments-logger/backend/src/index.js /volume2/docker/payments-logger/backend/src/auth.js | head -40
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\/$/, '');
/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,
/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);
/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,
Thinking
Thinking
payments-logger Authorization Overview
payments-logger Authorization Overview
The project uses
OAuth 2.1 with PKCE
via an external Hydra OAuth server at
hydra.lakylak.xyz
.
Auth Flow
Auth Flow
Frontend
(
frontend/src/auth.js
frontend/src/auth.js
):
Fetches OAuth server metadata via discovery endpoint
Generates a PKCE code verifier/challenge (SHA-256)
Redirects user to Hydra login with
code_challenge
On callback, exchanges code + verifier for tokens
Stores
access_token
,
refresh_token
in
localStorage
(
pl_oauth_tokens
)
Auto-refreshes token 30 seconds before expiry
Backend
(
backend/src/auth.js
backend/src/auth.js
):
bearerTokenMiddleware
validates the
Authorization: Bearer <token>
header
Fetches Hydra's JWKS (cached 300s) and verifies JWT signature (RS256/ES256/PS256)
Validates issuer (
https://hydra.lakylak.xyz
) and optional audience
Enforcement
Enforcement
Applied globally in
backend/src/index.js:19
backend/src/index.js:19
with these
public (unprotected) paths
:
/.well-known/oauth-protected-resource
/.well-known/oauth-authorization-server
/api/health
/api/payments/ingest
(rate-limited at 200 req/min — for SMS ingestion)
Roles/Permissions
Roles/Permissions
No RBAC
— it's binary: authenticated vs. unauthenticated. Required scopes:
openid
,
offline_access
.
Key Security Notes
Key Security Notes
PKCE
prevents authorization code interception
State parameter
provides CSRF protection
No server-side token storage
— validation is stateless via JWKS
The README/API docs mention an
X-API-Key
header, but that's
outdated
— the actual code uses Bearer tokens only
Config lives in
docker-compose.yml
docker-compose.yml
(lines 30–33):
HYDRA_PUBLIC_URL
,
REQUIRED_SCOPES
,
OAUTH_CLIENT_ID
, and optionally
TOKEN_AUDIENCE
/
JWKS_CACHE_TTL
.
Message actions
compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).
Thinking
Thinking
Let me first explore how location-logger handles authorization.
Agent:
Explore location-logger authorization code
IN
Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
6. Frontend auth flow (how the user logs in, how tokens are stored and used)
7. The full directory/file structure of the project
Please read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.
Bash
IN
find /volume2/docker/location-logger -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.json" | head -50
Copy code to clipboard
OUT
/volume2/docker/location-logger/.mcp.json
/volume2/docker/location-logger/app/__init__.py
/volume2/docker/location-logger/app/utils.py
/volume2/docker/location-logger/app/config.py
/volume2/docker/location-logger/app/main.py
/volume2/docker/location-logger/alembic/env.py
/volume2/docker/location-logger/mcp-server/server.py
/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py
/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py
/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py
/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py
/volume2/docker/location-logger/app/services/geofence_service.py
/volume2/docker/location-logger/app/services/recorder_service.py
/volume2/docker/location-logger/app/services/trip_service.py
/volume2/docker/location-logger/app/services/enhanced_location_service.py
/volume2/docker/location-logger/app/services/geocoding_service.py
/volume2/docker/location-logger/app/clients/__init__.py
/volume2/docker/location-logger/app/clients/dawarich_client.py
/volume2/docker/location-logger/app/clients/owntracks_client.py
/volume2/docker/location-logger/app/models/areas.py
/volume2/docker/location-logger/app/models/geofence_events.py
/volume2/docker/location-logger/app/models/trips.py
/volume2/docker/location-logger/app/models/user.py
/volume2/docker/location-logger/app/models/location.py
/volume2/docker/location-logger/app/models/audit_log.py
/volume2/docker/location-logger/app/auth/__init__.py
/volume2/docker/location-logger/app/auth/dependencies.py
/volume2/docker/location-logger/app/auth/utils.py
/volume2/docker/location-logger/app/api/enhanced_endpoints.py
/volume2/docker/location-logger/app/db/base.py
/volume2/docker/location-logger/app/db/session.py
Bash
IN
tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort
Copy code to clipboard
OUT
/volume2/docker/location-logger
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 20260203_201500_create_location_points_table.py
│ ├── 20260209_200000_add_enhanced_fields_and_tables.py
│ ├── 20260214_000000_add_users_and_audit_logs.py
│ ├── 20260320_000000_add_audit_query_string.py
│ └── __pycache__
│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc
├── alembic.ini
├── app
│ ├── api
│ │ └── enhanced_endpoints.py
│ ├── auth
│ │ ├── dependencies.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ │ ├── dependencies.cpython-311.pyc
│ │ │ ├── __init__.cpython-311.pyc
│ │ │ └── utils.cpython-311.pyc
│ │ └── utils.py
│ ├── clients
│ │ ├── dawarich_client.py
│ │ ├── __init__.py
│ │ └── owntracks_client.py
│ ├── config.py
│ ├── db
│ │ ├── base.py
│ │ └── session.py
│ ├── __init__.py
│ ├── main.py
│ ├── models
│ │ ├── areas.py
│ │ ├── audit_log.py
│ │ ├── geofence_events.py
│ │ ├── location.py
│ │ ├── __pycache__
│ │ │ ├── audit_log.cpython-311.pyc
│ │ │ └── user.cpython-311.pyc
│ │ ├── trips.py
│ │ └── user.py
│ ├── __pycache__
│ │ ├── __init__.cpython-311.pyc
│ │ └── main.cpython-311.pyc
│ ├── services
│ │ ├── enhanced_location_service.py
│ │ ├── geocoding_service.py
│ │ ├── geofence_service.py
│ │ ├── recorder_service.py
│ │ └── trip_service.py
│ ├── static
│ │ └── dashboard.html
│ └── utils.py
├── docker-compose.yml
├── Dockerfile
├── mcp-server
│ ├── Dockerfile
│ ├── requirements.txt
│ └── server.py
├── README.md
├── requirements.txt
└── today_map.html
16 directories, 48 files
Read
utils.py
utils.py
Read
dependencies.py
dependencies.py
Read
user.py
user.py
Read
config.py
config.py
Read
main.py
main.py
Read
session.py
session.py
Read
dashboard.html
dashboard.html
Read
audit_log.py
audit_log.py
Read
20260214_000000_add_users_and_audit_logs.py
20260214_000000_add_users_and_audit_logs.py
Read
20260320_000000_add_audit_query_string.py
20260320_000000_add_audit_query_string.py
Read
README.md
README.md
Read
requirements.txt
requirements.txt
Read
server.py
server.py
Read
server.py
server.py
(lines 101-400)
Thinking
Thinking
Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.
Bash
List all payments-logger files
IN
find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/.env.example
/volume2/docker/payments-logger/frontend/Dockerfile
/volume2/docker/payments-logger/frontend/.dockerignore
/volume2/docker/payments-logger/frontend/index.html
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/index.css
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
auth.js
auth.js
Read
index.js
index.js
Read
auth.js
auth.js
Read
AuthProvider.jsx
AuthProvider.jsx
Read
package.json
package.json
Read
AuthCallback.jsx
AuthCallback.jsx
Read
schema.prisma
schema.prisma
Read
docker-compose.yml
docker-compose.yml
Thinking
Thinking
Now let me read the remaining frontend files.
Read
App.jsx
App.jsx...
|
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1","depth":28,"bounds":{"left":0.11569149,"top":0.047885075,"width":0.0674867,"height":0.02793296},"on_screen":true,"role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":true,"is_expanded":false},{"role":"AXStaticText","text":"","depth":29,"bounds":{"left":0.15159574,"top":0.07821229,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXTextArea","text":"docker-compose.yml, Editor Group 1","depth":28,"bounds":{"left":0.11569149,"top":0.0933759,"width":0.38031915,"height":0.0007980846},"on_screen":true,"role_description":"editor","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"Review payment logger au…, Editor Group 2","depth":28,"bounds":{"left":0.5578458,"top":0.047885075,"width":0.07679521,"height":0.02793296},"on_screen":true,"role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":true,"is_expanded":false},{"role":"AXButton","text":"remote SSH: nas","depth":16,"bounds":{"left":0.0006648936,"top":0.98244214,"width":0.028590426,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.0033244682,"top":0.9848364,"width":0.005319149,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"SSH: nas","depth":17,"bounds":{"left":0.008643617,"top":0.9856345,"width":0.017952127,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"location-logger (Git)","depth":16,"bounds":{"left":0.030917553,"top":0.98244214,"width":0.03756649,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.031914894,"top":0.9848364,"width":0.005319149,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"location-logger","depth":17,"bounds":{"left":0.03723404,"top":0.9856345,"width":0.03025266,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"location-logger (Git) - main*, Checkout Branch/Tag...","depth":16,"bounds":{"left":0.06815159,"top":0.98244214,"width":0.019614361,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.069148935,"top":0.9848364,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"main*","depth":17,"bounds":{"left":0.07446808,"top":0.9856345,"width":0.012300532,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"location-logger (Git) - Synchronize Changes","depth":16,"bounds":{"left":0.08743351,"top":0.98244214,"width":0.0076462766,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"No Problems","depth":16,"bounds":{"left":0.09740692,"top":0.98244214,"width":0.022606382,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.09906915,"top":0.9848364,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":17,"bounds":{"left":0.1043883,"top":0.9856345,"width":0.004986702,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.109042555,"top":0.9848364,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":17,"bounds":{"left":0.1143617,"top":0.9856345,"width":0.0039893617,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"No Ports Forwarded","depth":16,"bounds":{"left":0.12167553,"top":0.98244214,"width":0.012632979,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.12333777,"top":0.9848364,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":17,"bounds":{"left":0.12865691,"top":0.9856345,"width":0.0039893617,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"Notifications","depth":16,"bounds":{"left":0.9886968,"top":0.98244214,"width":0.010638298,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sign In","depth":16,"bounds":{"left":0.9650931,"top":0.98244214,"width":0.022606382,"height":0.01755786},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"","depth":17,"bounds":{"left":0.96675533,"top":0.9848364,"width":0.0056515955,"height":0.012769354},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"Sign In","depth":17,"bounds":{"left":0.97207445,"top":0.9856345,"width":0.013962766,"height":0.011173184},"on_screen":true,"role_description":"text"},{"role":"AXStaticText","text":"collapsed","depth":12,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clear","depth":12,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Review payment logger authorization implementation","depth":19,"bounds":{"left":0.56017286,"top":0.08060654,"width":0.099734046,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Session history","depth":19,"bounds":{"left":0.9780585,"top":0.08060654,"width":0.00930851,"height":0.022346368},"on_screen":true,"help_text":"Session history","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New session","depth":19,"bounds":{"left":0.9886968,"top":0.08060654,"width":0.00930851,"height":0.022346368},"on_screen":true,"help_text":"New session","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Message actions","depth":24,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"review payment_logger project how toes authorization work","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Let me explore the payment_logger project structure first.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Find payment_logger project files","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker -type f -name \"*.py\" -o -name \"*.yml\" -o -name \"*.yaml\" -o -name \"*.env\" -o -name \"*.toml\" -o -name \"*.cfg\" | grep -i payment | head -50","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.\nbfs: error: /volume2/docker/portnotedb/base: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.\nbfs: error: /volume2/docker/openttd/.dbus: Permission denied.\nbfs: error: /volume2/docker/immich/db: Permission denied.\nbfs: error: /volume2/docker/paperlessngx/db: Permission denied.\nbfs: error: /volume2/docker/appflowy/postgres: Permission denied.\nbfs: error: /volume2/docker/auth/db: Permission denied.\nbfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.\nbfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.\nbfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.\n/volume2/docker/payments-logger/.env\n/volume2/docker/payments-logger/docker-compose.yml\n/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"List docker directory contents","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ls /volume2/docker/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"adguard\nai-stack\napp-db\nappflowy\naudiobookshelf\nauth\nbeszel\nbitwarden\ndawarich\ndsk-uploader\nflask-app\ngarmin-connector\ngitea\nhealth\nhealth-tracker\nhomarr\nhst\nimmich\njellyfinht\nkavita\nlibreoffice\nlinkwarden\nlocation-logger\nmariadb\nmeeting-detector\nmindfulmama\nn8n\nnotifier-app\nnpm\noauth\nobsidian\nollama\nopenttd\nopenvpn-client\nopen-webui\norchestrator\noutfit-app\nowntracks-stack\npaperlessngx\npayments-logger\npersonal-log\npersonal-log-system\nplayer\nportainer\nportnotedb\nreminders-app\nromm\nsecond-brain\nstatic\nstirling\nsyncthing\ntailscale\ntemp-logger\ntest-app\ntmp\ntorrent-stack\ntree\ntrilium\ntubearchivist\ntwenty\nuptimekuma\nytptube","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Agent:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore payment_logger authorization code","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:\n\n1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)\n2. Where auth is enforced (middleware, decorators, route guards, etc.)\n3. What roles/permissions exist\n4. How tokens are issued, validated, and refreshed\n5. Any auth-related config (env vars, settings)\n\nPlease read the key source files and give me a clear summary with file paths and line numbers for the important parts.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger -type f -name \"*.py\" -o -name \"*.json\" -o -name \"*.yml\" -o -name \"*.yaml\" | grep -E \"(auth|token|jwt|session|permission|role)\" | head -20","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(Bash completed with no output)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo \"Directory not found\"","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"total 36\ndrwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .\ndrwxrwxrwx 1 root root 1146 Apr 26 18:49 ..\n-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md\ndrwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend\ndrwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude\n-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml\n-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env\n-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example\ndrwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend\n-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore\n-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger/backend -type f | head -30","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy 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/volume2/docker/payments-logger -type f \\( -name \"*.js\" -o -name \"*.json\" -o -name \"*.md\" \\) | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy 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-r \"API_KEY\\|auth\\|token\\|oauth\\|hydra\" /volume2/docker/payments-logger/backend/src --include=\"*.js\" -n","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');\n/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────\n/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.\n/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints\n/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {\n/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata=\"${realm}/.well-known/oauth-protected-resource\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server=\"${HYDRA_PUBLIC_URL}\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.\n/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.\n/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',\n/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',\n/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';\n/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {\n/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');\n/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();\n/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {\n/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');\n/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);\n/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;\n/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';\n/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);\n/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],\n/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,\n/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,\n/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],\n/volume2/docker/payments-logger/backend/src/auth.js:124: token_endpoint_auth_methods_supported: doc.token_endpoint_auth_methods_supported || ['none', 'client_secret_basic'],\n/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],\n/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger/frontend/src -type f -name \"*.js\" -o -name \"*.jsx\" -o -name \"*.ts\" -o -name \"*.tsx\" | head -20","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy 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-r \"HYDRA\\|VITE\" /volume2/docker/payments-logger --include=\".env*\" -n","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(Bash completed with no 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/volume2/docker/payments-logger/backend/src/auth.js | head -40","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints\n/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\\/$/, '');\n/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)\n/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata=\"${realm}/.well-known/oauth-protected-resource\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server=\"${HYDRA_PUBLIC_URL}\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',\n/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',\n/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,\n/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;\n/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],\n/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,\n/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);\n/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"payments-logger Authorization Overview","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"payments-logger Authorization 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paths","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":":","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/.well-known/oauth-protected-resource","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/.well-known/oauth-authorization-server","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/api/health","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/api/payments/ingest","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(rate-limited at 200 req/min — for SMS ingestion)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Roles/Permissions","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Roles/Permissions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No RBAC","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— it's binary: authenticated vs. unauthenticated. Required scopes:","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"openid","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"offline_access","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Key Security Notes","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Key Security Notes","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"PKCE","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"prevents authorization code interception","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"State parameter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"provides CSRF protection","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No server-side token storage","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— validation is stateless via JWKS","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The README/API docs mention an","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-API-Key","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header, but that's","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"outdated","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— the actual code uses Bearer tokens only","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Config lives in","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"docker-compose.yml","depth":23,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"docker-compose.yml","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(lines 30–33):","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HYDRA_PUBLIC_URL","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"REQUIRED_SCOPES","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"OAUTH_CLIENT_ID","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and optionally","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"TOKEN_AUDIENCE","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"JWKS_CACHE_TTL","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Message actions","depth":24,"bounds":{"left":0.9900266,"top":0.11173184,"width":0.0066489363,"height":0.015961692},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).","depth":25,"bounds":{"left":0.5671542,"top":0.12529927,"width":0.25930852,"height":0.011971269},"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Let me first explore how location-logger handles authorization.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Agent:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore location-logger authorization code","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:\n\n1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)\n2. Where auth is enforced (middleware, decorators, route guards, etc.)\n3. What roles/permissions exist\n4. How tokens are issued, validated, and refreshed\n5. Any auth-related config (env vars, settings)\n6. Frontend auth flow (how the user logs in, how tokens are stored and used)\n7. The full directory/file structure of the project\n\nPlease read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/location-logger -type f -name \"*.py\" -o -name \"*.js\" -o -name \"*.ts\" -o -name \"*.json\" | head -50","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/location-logger/.mcp.json\n/volume2/docker/location-logger/app/__init__.py\n/volume2/docker/location-logger/app/utils.py\n/volume2/docker/location-logger/app/config.py\n/volume2/docker/location-logger/app/main.py\n/volume2/docker/location-logger/alembic/env.py\n/volume2/docker/location-logger/mcp-server/server.py\n/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py\n/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py\n/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py\n/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py\n/volume2/docker/location-logger/app/services/geofence_service.py\n/volume2/docker/location-logger/app/services/recorder_service.py\n/volume2/docker/location-logger/app/services/trip_service.py\n/volume2/docker/location-logger/app/services/enhanced_location_service.py\n/volume2/docker/location-logger/app/services/geocoding_service.py\n/volume2/docker/location-logger/app/clients/__init__.py\n/volume2/docker/location-logger/app/clients/dawarich_client.py\n/volume2/docker/location-logger/app/clients/owntracks_client.py\n/volume2/docker/location-logger/app/models/areas.py\n/volume2/docker/location-logger/app/models/geofence_events.py\n/volume2/docker/location-logger/app/models/trips.py\n/volume2/docker/location-logger/app/models/user.py\n/volume2/docker/location-logger/app/models/location.py\n/volume2/docker/location-logger/app/models/audit_log.py\n/volume2/docker/location-logger/app/auth/__init__.py\n/volume2/docker/location-logger/app/auth/dependencies.py\n/volume2/docker/location-logger/app/auth/utils.py\n/volume2/docker/location-logger/app/api/enhanced_endpoints.py\n/volume2/docker/location-logger/app/db/base.py\n/volume2/docker/location-logger/app/db/session.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/location-logger\n├── alembic\n│ ├── env.py\n│ ├── script.py.mako\n│ └── versions\n│ ├── 20260203_201500_create_location_points_table.py\n│ ├── 20260209_200000_add_enhanced_fields_and_tables.py\n│ ├── 20260214_000000_add_users_and_audit_logs.py\n│ ├── 20260320_000000_add_audit_query_string.py\n│ └── __pycache__\n│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc\n├── alembic.ini\n├── app\n│ ├── api\n│ │ └── enhanced_endpoints.py\n│ ├── auth\n│ │ ├── dependencies.py\n│ │ ├── __init__.py\n│ │ ├── __pycache__\n│ │ │ ├── dependencies.cpython-311.pyc\n│ │ │ ├── __init__.cpython-311.pyc\n│ │ │ └── utils.cpython-311.pyc\n│ │ └── utils.py\n│ ├── clients\n│ │ ├── dawarich_client.py\n│ │ ├── __init__.py\n│ │ └── owntracks_client.py\n│ ├── config.py\n│ ├── db\n│ │ ├── base.py\n│ │ └── session.py\n│ ├── __init__.py\n│ ├── main.py\n│ ├── models\n│ │ ├── areas.py\n│ │ ├── audit_log.py\n│ │ ├── geofence_events.py\n│ │ ├── location.py\n│ │ ├── __pycache__\n│ │ │ ├── audit_log.cpython-311.pyc\n│ │ │ └── user.cpython-311.pyc\n│ │ ├── trips.py\n│ │ └── user.py\n│ ├── __pycache__\n│ │ ├── __init__.cpython-311.pyc\n│ │ └── main.cpython-311.pyc\n│ ├── services\n│ │ ├── enhanced_location_service.py\n│ │ ├── geocoding_service.py\n│ │ ├── geofence_service.py\n│ │ ├── recorder_service.py\n│ │ └── trip_service.py\n│ ├── static\n│ │ └── dashboard.html\n│ └── utils.py\n├── docker-compose.yml\n├── Dockerfile\n├── mcp-server\n│ ├── Dockerfile\n│ ├── requirements.txt\n│ └── server.py\n├── README.md\n├── requirements.txt\n└── today_map.html\n\n16 directories, 48 files","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"utils.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"utils.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"dependencies.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"dependencies.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"user.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"user.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"config.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"config.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"main.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"main.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"session.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"session.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"dashboard.html","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"dashboard.html","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"audit_log.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"audit_log.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"20260214_000000_add_users_and_audit_logs.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"20260214_000000_add_users_and_audit_logs.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"20260320_000000_add_audit_query_string.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"20260320_000000_add_audit_query_string.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"README.md","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"requirements.txt","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"requirements.txt","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"server.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"server.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"server.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"server.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(lines 101-400)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"List all payments-logger files","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/API.md\n/volume2/docker/payments-logger/backend/Dockerfile\n/volume2/docker/payments-logger/backend/.dockerignore\n/volume2/docker/payments-logger/backend/package.json\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml\n/volume2/docker/payments-logger/backend/prisma/schema.prisma\n/volume2/docker/payments-logger/backend/src/auth.js\n/volume2/docker/payments-logger/backend/src/index.js\n/volume2/docker/payments-logger/backend/src/parser.js\n/volume2/docker/payments-logger/backend/src/routes/payments.js\n/volume2/docker/payments-logger/.claude/settings.local.json\n/volume2/docker/payments-logger/docker-compose.yml\n/volume2/docker/payments-logger/.env\n/volume2/docker/payments-logger/.env.example\n/volume2/docker/payments-logger/frontend/Dockerfile\n/volume2/docker/payments-logger/frontend/.dockerignore\n/volume2/docker/payments-logger/frontend/index.html\n/volume2/docker/payments-logger/frontend/package.json\n/volume2/docker/payments-logger/frontend/postcss.config.js\n/volume2/docker/payments-logger/frontend/src/App.jsx\n/volume2/docker/payments-logger/frontend/src/auth.js\n/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx\n/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx\n/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx\n/volume2/docker/payments-logger/frontend/src/index.css\n/volume2/docker/payments-logger/frontend/src/main.jsx\n/volume2/docker/payments-logger/frontend/tailwind.config.js\n/volume2/docker/payments-logger/frontend/vite.config.js\n/volume2/docker/payments-logger/README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"index.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"index.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthProvider.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthProvider.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"package.json","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"package.json","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthCallback.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthCallback.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"schema.prisma","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"schema.prisma","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"docker-compose.yml","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"docker-compose.yml","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Now let me read the remaining frontend files.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"App.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"App.jsx","depth":25,"on_screen":false,"role_description":"text"}]...
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Explorer Section: docker [SSH: nas]
Explorer Section: docker [SSH: nas]
DOCKER [SSH: NAS]
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
alembic
app
mcp-server
.env
.env.example
.gitignore
.mcp.json
M
alembic.ini
docker-compose.yml
M
Dockerfile
README.md
M
requirements.txt
M
today_map.html
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
open-webui
openttd
openvpn-client
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
.claude
Outline Section
OUTLINE
OUTLINE
Timeline Section
TIMELINE
TIMELINE
docker-compose.yml, Editor Group 1
docker-compose.yml, Editor Group 1
Review payment logger au…, Editor Group 2
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Review payment logger authorization implementation
Session history
New session
Message actions
review payment_logger project how toes authorization work
Thinking
Thinking
Let me explore the payment_logger project structure first.
Bash
Find payment_logger project files
IN
find /volume2/docker -type f -name "*.py" -o -name "*.yml" -o -name "*.yaml" -o -name "*.env" -o -name "*.toml" -o -name "*.cfg" | grep -i payment | head -50
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OUT
bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.
bfs: error: /volume2/docker/portnotedb/base: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.
bfs: error: /volume2/docker/openttd/.dbus: Permission denied.
bfs: error: /volume2/docker/immich/db: Permission denied.
bfs: error: /volume2/docker/paperlessngx/db: Permission denied.
bfs: error: /volume2/docker/appflowy/postgres: Permission denied.
bfs: error: /volume2/docker/auth/db: Permission denied.
bfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.
bfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.
bfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
Bash
List docker directory contents
IN
ls /volume2/docker/
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OUT
adguard
ai-stack
app-db
appflowy
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
openttd
openvpn-client
open-webui
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
personal-log
personal-log-system
player
portainer
portnotedb
reminders-app
romm
second-brain
static
stirling
syncthing
tailscale
temp-logger
test-app
tmp
torrent-stack
tree
trilium
tubearchivist
twenty
uptimekuma
ytptube
Agent:
Explore payment_logger authorization code
IN
Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
Please read the key source files and give me a clear summary with file paths and line numbers for the important parts.
Bash
IN
find /volume2/docker/payments-logger -type f -name "*.py" -o -name "*.json" -o -name "*.yml" -o -name "*.yaml" | grep -E "(auth|token|jwt|session|permission|role)" | head -20
Copy code to clipboard
OUT
(Bash completed with no output)
Bash
IN
ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo "Directory not found"
Copy code to clipboard
OUT
total 36
drwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .
drwxrwxrwx 1 root root 1146 Apr 26 18:49 ..
-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md
drwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend
drwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude
-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml
-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env
-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example
drwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend
-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore
-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md
Bash
IN
find /volume2/docker/payments-logger/backend -type f | head -30
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
Bash
IN
find /volume2/docker/payments-logger -type f \( -name "*.js" -o -name "*.json" -o -name "*.md" \) | sort
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OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
.env
.env
Read
auth.js
auth.js
Read
index.js
index.js
Read
package.json
package.json
Read
auth.js
auth.js
Read
API.md
API.md
Read
payments.js
payments.js
Read
.env.example
.env.example
Read
schema.prisma
schema.prisma
Bash
IN
grep -r "API_KEY\|auth\|token\|oauth\|hydra" /volume2/docker/payments-logger/backend/src --include="*.js" -n
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OUT
/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');
/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────
/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.
/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';
/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {
/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();
/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {
/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);
/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;
/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';
/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],
/volume2/docker/payments-logger/backend/src/auth.js:124: [API_KEY]: doc.[API_KEY] || ['none', 'client_secret_basic'],
/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],
/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────
Bash
IN
find /volume2/docker/payments-logger/frontend/src -type f -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" | head -20
Copy code to clipboard
OUT
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
Read
AuthProvider.jsx
AuthProvider.jsx
Read
AuthCallback.jsx
AuthCallback.jsx
Read
App.jsx
App.jsx
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Bash
IN
grep -r "HYDRA\|VITE" /volume2/docker/payments-logger --include=".env*" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
docker-compose.yml
docker-compose.yml
Bash
IN
grep -r "API_KEY" /volume2/docker/payments-logger/backend --include="*.js" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
README.md
README.md
Bash
IN
grep -n "HYDRA\|oauth\|TOKEN" /volume2/docker/payments-logger/backend/src/index.js /volume2/docker/payments-logger/backend/src/auth.js | head -40
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OUT
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\/$/, '');
/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,
/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);
/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,
Thinking
Thinking
payments-logger Authorization Overview
payments-logger Authorization Overview
The project uses
OAuth 2.1 with PKCE
via an external Hydra OAuth server at
hydra.lakylak.xyz
.
Auth Flow
Auth Flow
Frontend
(
frontend/src/auth.js
frontend/src/auth.js
):
Fetches OAuth server metadata via discovery endpoint
Generates a PKCE code verifier/challenge (SHA-256)
Redirects user to Hydra login with
code_challenge
On callback, exchanges code + verifier for tokens
Stores
access_token
,
refresh_token
in
localStorage
(
pl_oauth_tokens
)
Auto-refreshes token 30 seconds before expiry
Backend
(
backend/src/auth.js
backend/src/auth.js
):
bearerTokenMiddleware
validates the
Authorization: Bearer <token>
header
Fetches Hydra's JWKS (cached 300s) and verifies JWT signature (RS256/ES256/PS256)
Validates issuer (
https://hydra.lakylak.xyz
) and optional audience
Enforcement
Enforcement
Applied globally in
backend/src/index.js:19
backend/src/index.js:19
with these
public (unprotected) paths
:
/.well-known/oauth-protected-resource
/.well-known/oauth-authorization-server
/api/health
/api/payments/ingest
(rate-limited at 200 req/min — for SMS ingestion)
Roles/Permissions
Roles/Permissions
No RBAC
— it's binary: authenticated vs. unauthenticated. Required scopes:
openid
,
offline_access
.
Key Security Notes
Key Security Notes
PKCE
prevents authorization code interception
State parameter
provides CSRF protection
No server-side token storage
— validation is stateless via JWKS
The README/API docs mention an
X-API-Key
header, but that's
outdated
— the actual code uses Bearer tokens only
Config lives in
docker-compose.yml
docker-compose.yml
(lines 30–33):
HYDRA_PUBLIC_URL
,
REQUIRED_SCOPES
,
OAUTH_CLIENT_ID
, and optionally
TOKEN_AUDIENCE
/
JWKS_CACHE_TTL
.
Message actions
compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).
Thinking
Thinking
Let me first explore how location-logger handles authorization.
Agent:
Explore location-logger authorization code
IN
Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
6. Frontend auth flow (how the user logs in, how tokens are stored and used)
7. The full directory/file structure of the project
Please read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.
Bash
IN
find /volume2/docker/location-logger -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.json" | head -50
Copy code to clipboard
OUT
/volume2/docker/location-logger/.mcp.json
/volume2/docker/location-logger/app/__init__.py
/volume2/docker/location-logger/app/utils.py
/volume2/docker/location-logger/app/config.py
/volume2/docker/location-logger/app/main.py
/volume2/docker/location-logger/alembic/env.py
/volume2/docker/location-logger/mcp-server/server.py
/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py
/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py
/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py
/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py
/volume2/docker/location-logger/app/services/geofence_service.py
/volume2/docker/location-logger/app/services/recorder_service.py
/volume2/docker/location-logger/app/services/trip_service.py
/volume2/docker/location-logger/app/services/enhanced_location_service.py
/volume2/docker/location-logger/app/services/geocoding_service.py
/volume2/docker/location-logger/app/clients/__init__.py
/volume2/docker/location-logger/app/clients/dawarich_client.py
/volume2/docker/location-logger/app/clients/owntracks_client.py
/volume2/docker/location-logger/app/models/areas.py
/volume2/docker/location-logger/app/models/geofence_events.py
/volume2/docker/location-logger/app/models/trips.py
/volume2/docker/location-logger/app/models/user.py
/volume2/docker/location-logger/app/models/location.py
/volume2/docker/location-logger/app/models/audit_log.py
/volume2/docker/location-logger/app/auth/__init__.py
/volume2/docker/location-logger/app/auth/dependencies.py
/volume2/docker/location-logger/app/auth/utils.py
/volume2/docker/location-logger/app/api/enhanced_endpoints.py
/volume2/docker/location-logger/app/db/base.py
/volume2/docker/location-logger/app/db/session.py
Bash
IN
tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort
Copy code to clipboard
OUT
/volume2/docker/location-logger
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 20260203_201500_create_location_points_table.py
│ ├── 20260209_200000_add_enhanced_fields_and_tables.py
│ ├── 20260214_000000_add_users_and_audit_logs.py
│ ├── 20260320_000000_add_audit_query_string.py
│ └── __pycache__
│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc
├── alembic.ini
├── app
│ ├── api
│ │ └── enhanced_endpoints.py
│ ├── auth
│ │ ├── dependencies.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ │ ├── dependencies.cpython-311.pyc
│ │ │ ├── __init__.cpython-311.pyc
│ │ │ └── utils.cpython-311.pyc
│ │ └── utils.py
│ ├── clients
│ │ ├── dawarich_client.py
│ │ ├── __init__.py
│ │ └── owntracks_client.py
│ ├── config.py
│ ├── db
│ │ ├── base.py
│ │ └── session.py
│ ├── __init__.py
│ ├── main.py
│ ├── models
│ │ ├── areas.py
│ │ ├── audit_log.py
│ │ ├── geofence_events.py
│ │ ├── location.py
│ │ ├── __pycache__
│ │ │ ├── audit_log.cpython-311.pyc
│ │ │ └── user.cpython-311.pyc
│ │ ├── trips.py
│ │ └── user.py
│ ├── __pycache__
│ │ ├── __init__.cpython-311.pyc
│ │ └── main.cpython-311.pyc
│ ├── services
│ │ ├── enhanced_location_service.py
│ │ ├── geocoding_service.py
│ │ ├── geofence_service.py
│ │ ├── recorder_service.py
│ │ └── trip_service.py
│ ├── static
│ │ └── dashboard.html
│ └── utils.py
├── docker-compose.yml
├── Dockerfile
├── mcp-server
│ ├── Dockerfile
│ ├── requirements.txt
│ └── server.py
├── README.md
├── requirements.txt
└── today_map.html
16 directories, 48 files
Read
utils.py
utils.py
Read
dependencies.py
dependencies.py
Read
user.py
user.py
Read
config.py
config.py
Read
main.py
main.py
Read
session.py
session.py
Read
dashboard.html
dashboard.html
Read
audit_log.py
audit_log.py
Read
20260214_000000_add_users_and_audit_logs.py
20260214_000000_add_users_and_audit_logs.py
Read
20260320_000000_add_audit_query_string.py
20260320_000000_add_audit_query_string.py
Read
README.md
README.md
Read
requirements.txt
requirements.txt
Read
server.py
server.py
Read
server.py
server.py
(lines 101-400)
Thinking
Thinking
Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.
Bash
List all payments-logger files
IN
find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/.env.example
/volume2/docker/payments-logger/frontend/Dockerfile
/volume2/docker/payments-logger/frontend/.dockerignore
/volume2/docker/payments-logger/frontend/index.html
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/index.css
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
auth.js
auth.js
Read
index.js
index.js
Read
auth.js
auth.js
Read
AuthProvider.jsx
AuthProvider.jsx
Read
package.json
package.json
Read
AuthCallback.jsx
AuthCallback.jsx
Read
schema.prisma
schema.prisma
Read
docker-compose.yml
docker-compose.yml
Thinking
Thinking
Now let me read the remaining frontend files.
Read
App.jsx
App.jsx...
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/Users/lukas/.screenpipe/data/data/2026-05-08/1778 /Users/lukas/.screenpipe/data/data/2026-05-08/1778261763917_m1.jpg...
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Code
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Review payment logger au… — docker [SSH: nas]
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True
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monitor_1
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Explorer (⇧⌘E)
Search (⇧⌘F)
Source Control (⌃⇧ Explorer (⇧⌘E)
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Source Control (⌃⇧G) - 55 pending changes
55
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Remote Explorer
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Containers
EXPLORER
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Explorer Section: docker [SSH: nas]
Explorer Section: docker [SSH: nas]
DOCKER [SSH: NAS]
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
alembic
app
mcp-server
.env
.env.example
.gitignore
.mcp.json
M
alembic.ini
docker-compose.yml
M
Dockerfile
README.md
M
requirements.txt
M
today_map.html
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
open-webui
openttd
openvpn-client
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
.claude
Outline Section
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TIMELINE
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docker-compose.yml, Editor Group 1
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Review payment logger authorization implementation
Session history
New session
Message actions
review payment_logger project how toes authorization work
Thinking
Thinking
Let me explore the payment_logger project structure first.
Bash
Find payment_logger project files
IN
find /volume2/docker -type f -name "*.py" -o -name "*.yml" -o -name "*.yaml" -o -name "*.env" -o -name "*.toml" -o -name "*.cfg" | grep -i payment | head -50
Copy code to clipboard
OUT
bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.
bfs: error: /volume2/docker/portnotedb/base: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.
bfs: error: /volume2/docker/openttd/.dbus: Permission denied.
bfs: error: /volume2/docker/immich/db: Permission denied.
bfs: error: /volume2/docker/paperlessngx/db: Permission denied.
bfs: error: /volume2/docker/appflowy/postgres: Permission denied.
bfs: error: /volume2/docker/auth/db: Permission denied.
bfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.
bfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.
bfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
Bash
List docker directory contents
IN
ls /volume2/docker/
Copy code to clipboard
OUT
adguard
ai-stack
app-db
appflowy
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
openttd
openvpn-client
open-webui
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
personal-log
personal-log-system
player
portainer
portnotedb
reminders-app
romm
second-brain
static
stirling
syncthing
tailscale
temp-logger
test-app
tmp
torrent-stack
tree
trilium
tubearchivist
twenty
uptimekuma
ytptube
Agent:
Explore payment_logger authorization code
IN
Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
Please read the key source files and give me a clear summary with file paths and line numbers for the important parts.
Bash
IN
find /volume2/docker/payments-logger -type f -name "*.py" -o -name "*.json" -o -name "*.yml" -o -name "*.yaml" | grep -E "(auth|token|jwt|session|permission|role)" | head -20
Copy code to clipboard
OUT
(Bash completed with no output)
Bash
IN
ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo "Directory not found"
Copy code to clipboard
OUT
total 36
drwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .
drwxrwxrwx 1 root root 1146 Apr 26 18:49 ..
-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md
drwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend
drwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude
-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml
-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env
-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example
drwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend
-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore
-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md
Bash
IN
find /volume2/docker/payments-logger/backend -type f | head -30
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
Bash
IN
find /volume2/docker/payments-logger -type f \( -name "*.js" -o -name "*.json" -o -name "*.md" \) | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
.env
.env
Read
auth.js
auth.js
Read
index.js
index.js
Read
package.json
package.json
Read
auth.js
auth.js
Read
API.md
API.md
Read
payments.js
payments.js
Read
.env.example
.env.example
Read
schema.prisma
schema.prisma
Bash
IN
grep -r "API_KEY\|auth\|token\|oauth\|hydra" /volume2/docker/payments-logger/backend/src --include="*.js" -n
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OUT
/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');
/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────
/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.
/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';
/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {
/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();
/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {
/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);
/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;
/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';
/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],
/volume2/docker/payments-logger/backend/src/auth.js:124: [API_KEY]: doc.[API_KEY] || ['none', 'client_secret_basic'],
/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],
/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────
Bash
IN
find /volume2/docker/payments-logger/frontend/src -type f -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" | head -20
Copy code to clipboard
OUT
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
Read
AuthProvider.jsx
AuthProvider.jsx
Read
AuthCallback.jsx
AuthCallback.jsx
Read
App.jsx
App.jsx
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Bash
IN
grep -r "HYDRA\|VITE" /volume2/docker/payments-logger --include=".env*" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
docker-compose.yml
docker-compose.yml
Bash
IN
grep -r "API_KEY" /volume2/docker/payments-logger/backend --include="*.js" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
README.md
README.md
Bash
IN
grep -n "HYDRA\|oauth\|TOKEN" /volume2/docker/payments-logger/backend/src/index.js /volume2/docker/payments-logger/backend/src/auth.js | head -40
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OUT
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\/$/, '');
/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,
/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);
/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,
Thinking
Thinking
payments-logger Authorization Overview
payments-logger Authorization Overview
The project uses
OAuth 2.1 with PKCE
via an external Hydra OAuth server at
hydra.lakylak.xyz
.
Auth Flow
Auth Flow
Frontend
(
frontend/src/auth.js
frontend/src/auth.js
):
Fetches OAuth server metadata via discovery endpoint
Generates a PKCE code verifier/challenge (SHA-256)
Redirects user to Hydra login with
code_challenge
On callback, exchanges code + verifier for tokens
Stores
access_token
,
refresh_token
in
localStorage
(
pl_oauth_tokens
)
Auto-refreshes token 30 seconds before expiry
Backend
(
backend/src/auth.js
backend/src/auth.js
):
bearerTokenMiddleware
validates the
Authorization: Bearer <token>
header
Fetches Hydra's JWKS (cached 300s) and verifies JWT signature (RS256/ES256/PS256)
Validates issuer (
https://hydra.lakylak.xyz
) and optional audience
Enforcement
Enforcement
Applied globally in
backend/src/index.js:19
backend/src/index.js:19
with these
public (unprotected) paths
:
/.well-known/oauth-protected-resource
/.well-known/oauth-authorization-server
/api/health
/api/payments/ingest
(rate-limited at 200 req/min — for SMS ingestion)
Roles/Permissions
Roles/Permissions
No RBAC
— it's binary: authenticated vs. unauthenticated. Required scopes:
openid
,
offline_access
.
Key Security Notes
Key Security Notes
PKCE
prevents authorization code interception
State parameter
provides CSRF protection
No server-side token storage
— validation is stateless via JWKS
The README/API docs mention an
X-API-Key
header, but that's
outdated
— the actual code uses Bearer tokens only
Config lives in
docker-compose.yml
docker-compose.yml
(lines 30–33):
HYDRA_PUBLIC_URL
,
REQUIRED_SCOPES
,
OAUTH_CLIENT_ID
, and optionally
TOKEN_AUDIENCE
/
JWKS_CACHE_TTL
.
Message actions
compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).
Thinking
Thinking
Let me first explore how location-logger handles authorization.
Agent:
Explore location-logger authorization code
IN
Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
6. Frontend auth flow (how the user logs in, how tokens are stored and used)
7. The full directory/file structure of the project
Please read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.
Bash
IN
find /volume2/docker/location-logger -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.json" | head -50
Copy code to clipboard
OUT
/volume2/docker/location-logger/.mcp.json
/volume2/docker/location-logger/app/__init__.py
/volume2/docker/location-logger/app/utils.py
/volume2/docker/location-logger/app/config.py
/volume2/docker/location-logger/app/main.py
/volume2/docker/location-logger/alembic/env.py
/volume2/docker/location-logger/mcp-server/server.py
/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py
/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py
/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py
/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py
/volume2/docker/location-logger/app/services/geofence_service.py
/volume2/docker/location-logger/app/services/recorder_service.py
/volume2/docker/location-logger/app/services/trip_service.py
/volume2/docker/location-logger/app/services/enhanced_location_service.py
/volume2/docker/location-logger/app/services/geocoding_service.py
/volume2/docker/location-logger/app/clients/__init__.py
/volume2/docker/location-logger/app/clients/dawarich_client.py
/volume2/docker/location-logger/app/clients/owntracks_client.py
/volume2/docker/location-logger/app/models/areas.py
/volume2/docker/location-logger/app/models/geofence_events.py
/volume2/docker/location-logger/app/models/trips.py
/volume2/docker/location-logger/app/models/user.py
/volume2/docker/location-logger/app/models/location.py
/volume2/docker/location-logger/app/models/audit_log.py
/volume2/docker/location-logger/app/auth/__init__.py
/volume2/docker/location-logger/app/auth/dependencies.py
/volume2/docker/location-logger/app/auth/utils.py
/volume2/docker/location-logger/app/api/enhanced_endpoints.py
/volume2/docker/location-logger/app/db/base.py
/volume2/docker/location-logger/app/db/session.py
Bash
IN
tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort
Copy code to clipboard
OUT
/volume2/docker/location-logger
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 20260203_201500_create_location_points_table.py
│ ├── 20260209_200000_add_enhanced_fields_and_tables.py
│ ├── 20260214_000000_add_users_and_audit_logs.py
│ ├── 20260320_000000_add_audit_query_string.py
│ └── __pycache__
│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc
├── alembic.ini
├── app
│ ├── api
│ │ └── enhanced_endpoints.py
│ ├── auth
│ │ ├── dependencies.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ │ ├── dependencies.cpython-311.pyc
│ │ │ ├── __init__.cpython-311.pyc
│ │ │ └── utils.cpython-311.pyc
│ │ └── utils.py
│ ├── clients
│ │ ├── dawarich_client.py
│ │ ├── __init__.py
│ │ └── owntracks_client.py
│ ├── config.py
│ ├── db
│ │ ├── base.py
│ │ └── session.py
│ ├── __init__.py
│ ├── main.py
│ ├── models
│ │ ├── areas.py
│ │ ├── audit_log.py
│ │ ├── geofence_events.py
│ │ ├── location.py
│ │ ├── __pycache__
│ │ │ ├── audit_log.cpython-311.pyc
│ │ │ └── user.cpython-311.pyc
│ │ ├── trips.py
│ │ └── user.py
│ ├── __pycache__
│ │ ├── __init__.cpython-311.pyc
│ │ └── main.cpython-311.pyc
│ ├── services
│ │ ├── enhanced_location_service.py
│ │ ├── geocoding_service.py
│ │ ├── geofence_service.py
│ │ ├── recorder_service.py
│ │ └── trip_service.py
│ ├── static
│ │ └── dashboard.html
│ └── utils.py
├── docker-compose.yml
├── Dockerfile
├── mcp-server
│ ├── Dockerfile
│ ├── requirements.txt
│ └── server.py
├── README.md
├── requirements.txt
└── today_map.html
16 directories, 48 files
Read
utils.py
utils.py
Read
dependencies.py
dependencies.py
Read
user.py
user.py
Read
config.py
config.py
Read
main.py
main.py
Read
session.py
session.py
Read
dashboard.html
dashboard.html
Read
audit_log.py
audit_log.py
Read
20260214_000000_add_users_and_audit_logs.py
20260214_000000_add_users_and_audit_logs.py
Read
20260320_000000_add_audit_query_string.py
20260320_000000_add_audit_query_string.py
Read
README.md
README.md
Read
requirements.txt
requirements.txt
Read
server.py
server.py
Read
server.py
server.py
(lines 101-400)
Thinking
Thinking
Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.
Bash
List all payments-logger files
IN
find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/.env.example
/volume2/docker/payments-logger/frontend/Dockerfile
/volume2/docker/payments-logger/frontend/.dockerignore
/volume2/docker/payments-logger/frontend/index.html
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/index.css
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
auth.js
auth.js
Read
index.js
index.js
Read
auth.js
auth.js
Read
AuthProvider.jsx
AuthProvider.jsx
Read
package.json
package.json
Read
AuthCallback.jsx
AuthCallback.jsx
Read
schema.prisma
schema.prisma
Read
docker-compose.yml
docker-compose.yml
Thinking
Thinking
Now let me read the remaining frontend files.
Read
App.jsx
App.jsx
Read
package.json
package.json
Read
.env
.env
Thinking
Thinking
Let me read the remaining files before making changes.
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Read
payments.js
payments.js
Thinking
Thinking
Read
.env.example...
|
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actions","depth":24,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"review payment_logger project how toes authorization work","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Let me explore the payment_logger project structure first.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Find payment_logger project files","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker -type f -name \"*.py\" -o -name \"*.yml\" -o -name \"*.yaml\" -o -name \"*.env\" -o -name \"*.toml\" -o -name \"*.cfg\" | grep -i payment | head -50","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.\nbfs: error: /volume2/docker/portnotedb/base: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.\nbfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.\nbfs: error: /volume2/docker/openttd/.dbus: Permission denied.\nbfs: error: /volume2/docker/immich/db: Permission denied.\nbfs: error: /volume2/docker/paperlessngx/db: Permission denied.\nbfs: error: /volume2/docker/appflowy/postgres: Permission denied.\nbfs: error: /volume2/docker/auth/db: Permission denied.\nbfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.\nbfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.\nbfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.\n/volume2/docker/payments-logger/.env\n/volume2/docker/payments-logger/docker-compose.yml\n/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"List docker directory contents","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ls /volume2/docker/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"adguard\nai-stack\napp-db\nappflowy\naudiobookshelf\nauth\nbeszel\nbitwarden\ndawarich\ndsk-uploader\nflask-app\ngarmin-connector\ngitea\nhealth\nhealth-tracker\nhomarr\nhst\nimmich\njellyfinht\nkavita\nlibreoffice\nlinkwarden\nlocation-logger\nmariadb\nmeeting-detector\nmindfulmama\nn8n\nnotifier-app\nnpm\noauth\nobsidian\nollama\nopenttd\nopenvpn-client\nopen-webui\norchestrator\noutfit-app\nowntracks-stack\npaperlessngx\npayments-logger\npersonal-log\npersonal-log-system\nplayer\nportainer\nportnotedb\nreminders-app\nromm\nsecond-brain\nstatic\nstirling\nsyncthing\ntailscale\ntemp-logger\ntest-app\ntmp\ntorrent-stack\ntree\ntrilium\ntubearchivist\ntwenty\nuptimekuma\nytptube","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Agent:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore payment_logger authorization code","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:\n\n1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)\n2. Where auth is enforced (middleware, decorators, route guards, etc.)\n3. What roles/permissions exist\n4. How tokens are issued, validated, and refreshed\n5. Any auth-related config (env vars, settings)\n\nPlease read the key source files and give me a clear summary with file paths and line numbers for the important parts.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger -type f -name \"*.py\" -o -name \"*.json\" -o -name \"*.yml\" -o -name \"*.yaml\" | grep -E \"(auth|token|jwt|session|permission|role)\" | head -20","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(Bash completed with no output)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo \"Directory not found\"","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"total 36\ndrwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .\ndrwxrwxrwx 1 root root 1146 Apr 26 18:49 ..\n-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md\ndrwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend\ndrwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude\n-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml\n-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env\n-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example\ndrwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend\n-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore\n-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger/backend -type f | head -30","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/backend/Dockerfile\n/volume2/docker/payments-logger/backend/.dockerignore\n/volume2/docker/payments-logger/backend/package.json\n/volume2/docker/payments-logger/backend/prisma/schema.prisma\n/volume2/docker/payments-logger/backend/src/parser.js\n/volume2/docker/payments-logger/backend/src/index.js\n/volume2/docker/payments-logger/backend/src/auth.js\n/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml\n/volume2/docker/payments-logger/backend/src/routes/payments.js\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger -type f \\( -name \"*.js\" -o -name \"*.json\" -o -name \"*.md\" \\) | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/API.md\n/volume2/docker/payments-logger/backend/package.json\n/volume2/docker/payments-logger/backend/src/auth.js\n/volume2/docker/payments-logger/backend/src/index.js\n/volume2/docker/payments-logger/backend/src/parser.js\n/volume2/docker/payments-logger/backend/src/routes/payments.js\n/volume2/docker/payments-logger/.claude/settings.local.json\n/volume2/docker/payments-logger/frontend/package.json\n/volume2/docker/payments-logger/frontend/postcss.config.js\n/volume2/docker/payments-logger/frontend/src/auth.js\n/volume2/docker/payments-logger/frontend/tailwind.config.js\n/volume2/docker/payments-logger/frontend/vite.config.js\n/volume2/docker/payments-logger/README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":".env","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":".env","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"index.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"index.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"package.json","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"package.json","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"API.md","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"API.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"payments.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"payments.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":".env.example","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":".env.example","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"schema.prisma","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"schema.prisma","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"grep -r \"API_KEY\\|auth\\|token\\|oauth\\|hydra\" /volume2/docker/payments-logger/backend/src --include=\"*.js\" -n","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');\n/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────\n/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.\n/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints\n/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {\n/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata=\"${realm}/.well-known/oauth-protected-resource\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server=\"${HYDRA_PUBLIC_URL}\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.\n/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.\n/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',\n/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',\n/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';\n/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {\n/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');\n/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();\n/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {\n/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');\n/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);\n/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;\n/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';\n/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);\n/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],\n/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,\n/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,\n/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],\n/volume2/docker/payments-logger/backend/src/auth.js:124: token_endpoint_auth_methods_supported: doc.token_endpoint_auth_methods_supported || ['none', 'client_secret_basic'],\n/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],\n/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger/frontend/src -type f -name \"*.js\" -o -name \"*.jsx\" -o -name \"*.ts\" -o -name \"*.tsx\" | head -20","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/frontend/src/main.jsx\n/volume2/docker/payments-logger/frontend/src/App.jsx\n/volume2/docker/payments-logger/frontend/src/auth.js\n/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx\n/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx\n/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx\n/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthProvider.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthProvider.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthCallback.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthCallback.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"App.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"App.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"main.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"main.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"vite.config.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"vite.config.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"grep -r \"HYDRA\\|VITE\" /volume2/docker/payments-logger --include=\".env*\" -n","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(Bash completed with no output)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"docker-compose.yml","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"docker-compose.yml","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"grep -r \"API_KEY\" /volume2/docker/payments-logger/backend --include=\"*.js\" -n","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(Bash completed with no output)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"README.md","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"grep -n \"HYDRA\\|oauth\\|TOKEN\" /volume2/docker/payments-logger/backend/src/index.js /volume2/docker/payments-logger/backend/src/auth.js | head -40","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints\n/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\\/$/, '');\n/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)\n/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata=\"${realm}/.well-known/oauth-protected-resource\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server=\"${HYDRA_PUBLIC_URL}\"`,\n/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',\n/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',\n/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,\n/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;\n/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],\n/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,\n/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {\n/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);\n/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"payments-logger Authorization Overview","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"payments-logger Authorization Overview","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The project uses","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"OAuth 2.1 with PKCE","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via an external Hydra OAuth server at","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hydra.lakylak.xyz","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Auth Flow","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Auth Flow","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Frontend","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"frontend/src/auth.js","depth":23,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"frontend/src/auth.js","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"):","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Fetches OAuth server metadata via discovery endpoint","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Generates a PKCE code verifier/challenge (SHA-256)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redirects user to Hydra login with","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"code_challenge","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"On callback, exchanges code + verifier for tokens","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Stores","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"access_token","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"refresh_token","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"localStorage","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"pl_oauth_tokens","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Auto-refreshes token 30 seconds before expiry","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backend","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"backend/src/auth.js","depth":23,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"backend/src/auth.js","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"):","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"bearerTokenMiddleware","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"validates the","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Authorization: Bearer <token>","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Fetches Hydra's JWKS (cached 300s) and verifies JWT signature (RS256/ES256/PS256)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Validates issuer (","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"https://hydra.lakylak.xyz","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and optional audience","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Enforcement","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Enforcement","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Applied globally in","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"backend/src/index.js:19","depth":23,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"backend/src/index.js:19","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with these","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"public (unprotected) paths","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":":","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/.well-known/oauth-protected-resource","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/.well-known/oauth-authorization-server","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/api/health","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/api/payments/ingest","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(rate-limited at 200 req/min — for SMS ingestion)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Roles/Permissions","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Roles/Permissions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No RBAC","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— it's binary: authenticated vs. unauthenticated. Required scopes:","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"openid","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"offline_access","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Key Security Notes","depth":22,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Key Security Notes","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"PKCE","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"prevents authorization code interception","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"State parameter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"provides CSRF protection","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No server-side token storage","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— validation is stateless via JWKS","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The README/API docs mention an","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-API-Key","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header, but that's","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"outdated","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— the actual code uses Bearer tokens only","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Config lives in","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"docker-compose.yml","depth":23,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"docker-compose.yml","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(lines 30–33):","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HYDRA_PUBLIC_URL","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"REQUIRED_SCOPES","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"OAUTH_CLIENT_ID","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and optionally","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"TOKEN_AUDIENCE","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"JWKS_CACHE_TTL","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Message actions","depth":24,"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).","depth":25,"on_screen":true,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Let me first explore how location-logger handles authorization.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Agent:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore location-logger authorization code","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:\n\n1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)\n2. Where auth is enforced (middleware, decorators, route guards, etc.)\n3. What roles/permissions exist\n4. How tokens are issued, validated, and refreshed\n5. Any auth-related config (env vars, settings)\n6. Frontend auth flow (how the user logs in, how tokens are stored and used)\n7. The full directory/file structure of the project\n\nPlease read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/location-logger -type f -name \"*.py\" -o -name \"*.js\" -o -name \"*.ts\" -o -name \"*.json\" | head -50","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/location-logger/.mcp.json\n/volume2/docker/location-logger/app/__init__.py\n/volume2/docker/location-logger/app/utils.py\n/volume2/docker/location-logger/app/config.py\n/volume2/docker/location-logger/app/main.py\n/volume2/docker/location-logger/alembic/env.py\n/volume2/docker/location-logger/mcp-server/server.py\n/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py\n/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py\n/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py\n/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py\n/volume2/docker/location-logger/app/services/geofence_service.py\n/volume2/docker/location-logger/app/services/recorder_service.py\n/volume2/docker/location-logger/app/services/trip_service.py\n/volume2/docker/location-logger/app/services/enhanced_location_service.py\n/volume2/docker/location-logger/app/services/geocoding_service.py\n/volume2/docker/location-logger/app/clients/__init__.py\n/volume2/docker/location-logger/app/clients/dawarich_client.py\n/volume2/docker/location-logger/app/clients/owntracks_client.py\n/volume2/docker/location-logger/app/models/areas.py\n/volume2/docker/location-logger/app/models/geofence_events.py\n/volume2/docker/location-logger/app/models/trips.py\n/volume2/docker/location-logger/app/models/user.py\n/volume2/docker/location-logger/app/models/location.py\n/volume2/docker/location-logger/app/models/audit_log.py\n/volume2/docker/location-logger/app/auth/__init__.py\n/volume2/docker/location-logger/app/auth/dependencies.py\n/volume2/docker/location-logger/app/auth/utils.py\n/volume2/docker/location-logger/app/api/enhanced_endpoints.py\n/volume2/docker/location-logger/app/db/base.py\n/volume2/docker/location-logger/app/db/session.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/location-logger\n├── alembic\n│ ├── env.py\n│ ├── script.py.mako\n│ └── versions\n│ ├── 20260203_201500_create_location_points_table.py\n│ ├── 20260209_200000_add_enhanced_fields_and_tables.py\n│ ├── 20260214_000000_add_users_and_audit_logs.py\n│ ├── 20260320_000000_add_audit_query_string.py\n│ └── __pycache__\n│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc\n├── alembic.ini\n├── app\n│ ├── api\n│ │ └── enhanced_endpoints.py\n│ ├── auth\n│ │ ├── dependencies.py\n│ │ ├── __init__.py\n│ │ ├── __pycache__\n│ │ │ ├── dependencies.cpython-311.pyc\n│ │ │ ├── __init__.cpython-311.pyc\n│ │ │ └── utils.cpython-311.pyc\n│ │ └── utils.py\n│ ├── clients\n│ │ ├── dawarich_client.py\n│ │ ├── __init__.py\n│ │ └── owntracks_client.py\n│ ├── config.py\n│ ├── db\n│ │ ├── base.py\n│ │ └── session.py\n│ ├── __init__.py\n│ ├── main.py\n│ ├── models\n│ │ ├── areas.py\n│ │ ├── audit_log.py\n│ │ ├── geofence_events.py\n│ │ ├── location.py\n│ │ ├── __pycache__\n│ │ │ ├── audit_log.cpython-311.pyc\n│ │ │ └── user.cpython-311.pyc\n│ │ ├── trips.py\n│ │ └── user.py\n│ ├── __pycache__\n│ │ ├── __init__.cpython-311.pyc\n│ │ └── main.cpython-311.pyc\n│ ├── services\n│ │ ├── enhanced_location_service.py\n│ │ ├── geocoding_service.py\n│ │ ├── geofence_service.py\n│ │ ├── recorder_service.py\n│ │ └── trip_service.py\n│ ├── static\n│ │ └── dashboard.html\n│ └── utils.py\n├── docker-compose.yml\n├── Dockerfile\n├── mcp-server\n│ ├── Dockerfile\n│ ├── requirements.txt\n│ └── server.py\n├── README.md\n├── requirements.txt\n└── today_map.html\n\n16 directories, 48 files","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"utils.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"utils.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"dependencies.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"dependencies.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"user.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"user.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"config.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"config.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"main.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"main.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"session.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"session.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"dashboard.html","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"dashboard.html","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"audit_log.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"audit_log.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"20260214_000000_add_users_and_audit_logs.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"20260214_000000_add_users_and_audit_logs.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"20260320_000000_add_audit_query_string.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"20260320_000000_add_audit_query_string.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"README.md","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"requirements.txt","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"requirements.txt","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"server.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"server.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"server.py","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"server.py","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(lines 101-400)","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Bash","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"List all payments-logger files","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"IN","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy code to clipboard","depth":24,"on_screen":false,"help_text":"Copy code","role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"OUT","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/volume2/docker/payments-logger/API.md\n/volume2/docker/payments-logger/backend/Dockerfile\n/volume2/docker/payments-logger/backend/.dockerignore\n/volume2/docker/payments-logger/backend/package.json\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql\n/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml\n/volume2/docker/payments-logger/backend/prisma/schema.prisma\n/volume2/docker/payments-logger/backend/src/auth.js\n/volume2/docker/payments-logger/backend/src/index.js\n/volume2/docker/payments-logger/backend/src/parser.js\n/volume2/docker/payments-logger/backend/src/routes/payments.js\n/volume2/docker/payments-logger/.claude/settings.local.json\n/volume2/docker/payments-logger/docker-compose.yml\n/volume2/docker/payments-logger/.env\n/volume2/docker/payments-logger/.env.example\n/volume2/docker/payments-logger/frontend/Dockerfile\n/volume2/docker/payments-logger/frontend/.dockerignore\n/volume2/docker/payments-logger/frontend/index.html\n/volume2/docker/payments-logger/frontend/package.json\n/volume2/docker/payments-logger/frontend/postcss.config.js\n/volume2/docker/payments-logger/frontend/src/App.jsx\n/volume2/docker/payments-logger/frontend/src/auth.js\n/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx\n/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx\n/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx\n/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx\n/volume2/docker/payments-logger/frontend/src/index.css\n/volume2/docker/payments-logger/frontend/src/main.jsx\n/volume2/docker/payments-logger/frontend/tailwind.config.js\n/volume2/docker/payments-logger/frontend/vite.config.js\n/volume2/docker/payments-logger/README.md","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"index.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"index.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"auth.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"auth.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthProvider.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthProvider.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"package.json","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"package.json","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"AuthCallback.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"AuthCallback.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"schema.prisma","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"schema.prisma","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"docker-compose.yml","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"docker-compose.yml","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Now let me read the remaining frontend files.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"App.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"App.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"package.json","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"package.json","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":".env","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":".env","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Let me read the remaining files before making changes.","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"main.jsx","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"main.jsx","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"vite.config.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"vite.config.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"payments.js","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"payments.js","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Thinking","depth":23,"on_screen":false,"role_description":"disclosure triangle","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Thinking","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Read","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":".env.example","depth":24,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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Claude Code
Containers
EXPLORER
EXPLORER
Explorer Section: docker [SSH: nas]
Explorer Section: docker [SSH: nas]
DOCKER [SSH: NAS]
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
alembic
app
mcp-server
.env
.env.example
.gitignore
.mcp.json
M
alembic.ini
docker-compose.yml
M
Dockerfile
README.md
M
requirements.txt
M
today_map.html
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
open-webui
openttd
openvpn-client
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
.claude
Outline Section
OUTLINE
OUTLINE
Timeline Section
TIMELINE
TIMELINE
docker-compose.yml, Editor Group 1
docker-compose.yml, Editor Group 1
Review payment logger au…, Editor Group 2
remote SSH: nas
SSH: nas
location-logger (Git)
location-logger
location-logger (Git) - main*, Checkout Branch/Tag...
main*
location-logger (Git) - Synchronize Changes
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Clear
Review payment logger authorization implementation
Session history
New session
Message actions
review payment_logger project how toes authorization work
Thinking
Thinking
Let me explore the payment_logger project structure first.
Bash
Find payment_logger project files
IN
find /volume2/docker -type f -name "*.py" -o -name "*.yml" -o -name "*.yaml" -o -name "*.env" -o -name "*.toml" -o -name "*.cfg" | grep -i payment | head -50
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OUT
bfs: error: /volume2/docker/portnotedb/pg_commit_ts: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_dynshmem: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_notify: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_serial: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_snapshots: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_subtrans: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_twophase: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_multixact: Permission denied.
bfs: error: /volume2/docker/portnotedb/base: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_replslot: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_tblspc: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_stat_tmp: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_xact: Permission denied.
bfs: error: /volume2/docker/portnotedb/pg_logical: Permission denied.
bfs: error: /volume2/docker/openttd/.dbus: Permission denied.
bfs: error: /volume2/docker/immich/db: Permission denied.
bfs: error: /volume2/docker/paperlessngx/db: Permission denied.
bfs: error: /volume2/docker/appflowy/postgres: Permission denied.
bfs: error: /volume2/docker/auth/db: Permission denied.
bfs: error: /volume2/docker/openttd/.XDG/dbus-1: Permission denied.
bfs: error: /volume2/docker/openttd/.config/pulse: Permission denied.
bfs: error: /volume2/docker/openttd/.cache/openbox/sessions: Permission denied.
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
Bash
List docker directory contents
IN
ls /volume2/docker/
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OUT
adguard
ai-stack
app-db
appflowy
audiobookshelf
auth
beszel
bitwarden
dawarich
dsk-uploader
flask-app
garmin-connector
gitea
health
health-tracker
homarr
hst
immich
jellyfinht
kavita
libreoffice
linkwarden
location-logger
mariadb
meeting-detector
mindfulmama
n8n
notifier-app
npm
oauth
obsidian
ollama
openttd
openvpn-client
open-webui
orchestrator
outfit-app
owntracks-stack
paperlessngx
payments-logger
personal-log
personal-log-system
player
portainer
portnotedb
reminders-app
romm
second-brain
static
stirling
syncthing
tailscale
temp-logger
test-app
tmp
torrent-stack
tree
trilium
tubearchivist
twenty
uptimekuma
ytptube
Agent:
Explore payment_logger authorization code
IN
Explore the payments-logger project at /volume2/docker/payments-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
Please read the key source files and give me a clear summary with file paths and line numbers for the important parts.
Bash
IN
find /volume2/docker/payments-logger -type f -name "*.py" -o -name "*.json" -o -name "*.yml" -o -name "*.yaml" | grep -E "(auth|token|jwt|session|permission|role)" | head -20
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OUT
(Bash completed with no output)
Bash
IN
ls -la /volume2/docker/payments-logger/ 2>/dev/null || echo "Directory not found"
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OUT
total 36
drwxrwxrwx 1 Adm1n admin 162 Mar 29 18:01 .
drwxrwxrwx 1 root root 1146 Apr 26 18:49 ..
-rwxrwxrwx 1 Adm1n admin 15233 Feb 22 17:08 API.md
drwxrwxrwx 1 Adm1n admin 88 Mar 29 15:41 backend
drwxrwxrwx 1 Adm1n admin 38 Mar 29 17:07 .claude
-rwxrwxrwx 1 Adm1n admin 1502 Mar 29 18:01 docker-compose.yml
-rwxrwxrwx 1 Adm1n admin 1066 Mar 29 15:44 .env
-rwxrwxrwx 1 Adm1n admin 1078 Feb 14 17:53 .env.example
drwxrwxrwx 1 Adm1n admin 194 Mar 29 15:44 frontend
-rwxrwxrwx 1 Adm1n admin 19 Feb 14 17:54 .gitignore
-rwxrwxrwx 1 Adm1n admin 4052 Feb 14 19:06 README.md
Bash
IN
find /volume2/docker/payments-logger/backend -type f | head -30
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OUT
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
Bash
IN
find /volume2/docker/payments-logger -type f \( -name "*.js" -o -name "*.json" -o -name "*.md" \) | sort
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OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
.env
.env
Read
auth.js
auth.js
Read
index.js
index.js
Read
package.json
package.json
Read
auth.js
auth.js
Read
API.md
API.md
Read
payments.js
payments.js
Read
.env.example
.env.example
Read
schema.prisma
schema.prisma
Bash
IN
grep -r "API_KEY\|auth\|token\|oauth\|hydra" /volume2/docker/payments-logger/backend/src --include="*.js" -n
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/src/index.js:6:const { bearerTokenMiddleware, wellKnownRouter } = require('./auth');
/volume2/docker/payments-logger/backend/src/index.js:18:// ── OAuth Bearer token auth (skips public paths) ──────────────────────────────
/volume2/docker/payments-logger/backend/src/auth.js:2: * OAuth 2.1 / Hydra JWT authentication for Express.
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:33:function sendUnauthorized(res, detail, error = 'invalid_token') {
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:52: * On success, attaches the decoded payload to req.tokenPayload.
/volume2/docker/payments-logger/backend/src/auth.js:54: * @param {Set<string>} publicPaths Paths to skip auth for.
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:66: const auth = req.headers.authorization || '';
/volume2/docker/payments-logger/backend/src/auth.js:67: if (!auth.toLowerCase().startsWith('bearer ')) {
/volume2/docker/payments-logger/backend/src/auth.js:68: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:71: const token = auth.slice(7).trim();
/volume2/docker/payments-logger/backend/src/auth.js:72: if (!token) {
/volume2/docker/payments-logger/backend/src/auth.js:73: return sendUnauthorized(res, 'Bearer token required', 'invalid_request');
/volume2/docker/payments-logger/backend/src/auth.js:82: const { payload } = await jwtVerify(token, getJwks(), verifyOptions);
/volume2/docker/payments-logger/backend/src/auth.js:83: req.tokenPayload = payload;
/volume2/docker/payments-logger/backend/src/auth.js:87: const error = msg.includes('expired') ? 'invalid_token' : 'invalid_token';
/volume2/docker/payments-logger/backend/src/auth.js:88: sendUnauthorized(res, msg, error);
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:119: authorization_endpoint: doc.authorization_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:120: token_endpoint: doc.token_endpoint,
/volume2/docker/payments-logger/backend/src/auth.js:123: grant_types_supported: doc.grant_types_supported || ['authorization_code', 'refresh_token'],
/volume2/docker/payments-logger/backend/src/auth.js:124: [API_KEY]: doc.[API_KEY] || ['none', 'client_secret_basic'],
/volume2/docker/payments-logger/backend/src/auth.js:128: id_token_signing_alg_values_supported: doc.id_token_signing_alg_values_supported || ['RS256'],
/volume2/docker/payments-logger/backend/src/routes/payments.js:58:// ── Ingest a payment (public — no auth) ──────────────────────────────────────
Bash
IN
find /volume2/docker/payments-logger/frontend/src -type f -name "*.js" -o -name "*.jsx" -o -name "*.ts" -o -name "*.tsx" | head -20
Copy code to clipboard
OUT
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
Read
AuthProvider.jsx
AuthProvider.jsx
Read
AuthCallback.jsx
AuthCallback.jsx
Read
App.jsx
App.jsx
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Bash
IN
grep -r "HYDRA\|VITE" /volume2/docker/payments-logger --include=".env*" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
docker-compose.yml
docker-compose.yml
Bash
IN
grep -r "API_KEY" /volume2/docker/payments-logger/backend --include="*.js" -n
Copy code to clipboard
OUT
(Bash completed with no output)
Read
README.md
README.md
Bash
IN
grep -n "HYDRA\|oauth\|TOKEN" /volume2/docker/payments-logger/backend/src/index.js /volume2/docker/payments-logger/backend/src/auth.js | head -40
Copy code to clipboard
OUT
/volume2/docker/payments-logger/backend/src/auth.js:6: * - wellKnownRouter — serves /.well-known/oauth-* discovery endpoints
/volume2/docker/payments-logger/backend/src/auth.js:11:const HYDRA_PUBLIC_URL = (process.env.HYDRA_PUBLIC_URL || '').replace(/\/$/, '');
/volume2/docker/payments-logger/backend/src/auth.js:24: new URL(`${HYDRA_PUBLIC_URL}/.well-known/jwks.json`)
/volume2/docker/payments-logger/backend/src/auth.js:39: `resource_metadata="${realm}/.well-known/oauth-protected-resource"`,
/volume2/docker/payments-logger/backend/src/auth.js:40: `authorization_server="${HYDRA_PUBLIC_URL}"`,
/volume2/docker/payments-logger/backend/src/auth.js:58: '/.well-known/oauth-protected-resource',
/volume2/docker/payments-logger/backend/src/auth.js:59: '/.well-known/oauth-authorization-server',
/volume2/docker/payments-logger/backend/src/auth.js:78: issuer: HYDRA_PUBLIC_URL,
/volume2/docker/payments-logger/backend/src/auth.js:81: if (process.env.TOKEN_AUDIENCE) verifyOptions.audience = process.env.TOKEN_AUDIENCE;
/volume2/docker/payments-logger/backend/src/auth.js:98:wellKnownRouter.get('/.well-known/oauth-protected-resource', (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:103: authorization_servers: [HYDRA_PUBLIC_URL],
/volume2/docker/payments-logger/backend/src/auth.js:107: jwks_uri: `${HYDRA_PUBLIC_URL}/.well-known/jwks.json`,
/volume2/docker/payments-logger/backend/src/auth.js:111:wellKnownRouter.get('/.well-known/oauth-authorization-server', async (_req, res) => {
/volume2/docker/payments-logger/backend/src/auth.js:113: const r = await fetch(`${HYDRA_PUBLIC_URL}/.well-known/openid-configuration`);
/volume2/docker/payments-logger/backend/src/auth.js:118: issuer: doc.issuer || HYDRA_PUBLIC_URL,
Thinking
Thinking
payments-logger Authorization Overview
payments-logger Authorization Overview
The project uses
OAuth 2.1 with PKCE
via an external Hydra OAuth server at
hydra.lakylak.xyz
.
Auth Flow
Auth Flow
Frontend
(
frontend/src/auth.js
frontend/src/auth.js
):
Fetches OAuth server metadata via discovery endpoint
Generates a PKCE code verifier/challenge (SHA-256)
Redirects user to Hydra login with
code_challenge
On callback, exchanges code + verifier for tokens
Stores
access_token
,
refresh_token
in
localStorage
(
pl_oauth_tokens
)
Auto-refreshes token 30 seconds before expiry
Backend
(
backend/src/auth.js
backend/src/auth.js
):
bearerTokenMiddleware
validates the
Authorization: Bearer <token>
header
Fetches Hydra's JWKS (cached 300s) and verifies JWT signature (RS256/ES256/PS256)
Validates issuer (
https://hydra.lakylak.xyz
) and optional audience
Enforcement
Enforcement
Applied globally in
backend/src/index.js:19
backend/src/index.js:19
with these
public (unprotected) paths
:
/.well-known/oauth-protected-resource
/.well-known/oauth-authorization-server
/api/health
/api/payments/ingest
(rate-limited at 200 req/min — for SMS ingestion)
Roles/Permissions
Roles/Permissions
No RBAC
— it's binary: authenticated vs. unauthenticated. Required scopes:
openid
,
offline_access
.
Key Security Notes
Key Security Notes
PKCE
prevents authorization code interception
State parameter
provides CSRF protection
No server-side token storage
— validation is stateless via JWKS
The README/API docs mention an
X-API-Key
header, but that's
outdated
— the actual code uses Bearer tokens only
Config lives in
docker-compose.yml
docker-compose.yml
(lines 30–33):
HYDRA_PUBLIC_URL
,
REQUIRED_SCOPES
,
OAUTH_CLIENT_ID
, and optionally
TOKEN_AUDIENCE
/
JWKS_CACHE_TTL
.
Message actions
compare it to location logger and make it the same. Lets remove hydra and only use authentic (or whatever location logger uses).
Thinking
Thinking
Let me first explore how location-logger handles authorization.
Agent:
Explore location-logger authorization code
IN
Explore the location-logger project at /volume2/docker/location-logger and give me a thorough understanding of how authorization works. I need to know:
1. What auth mechanism is used (JWT, sessions, API keys, OAuth, etc.)
2. Where auth is enforced (middleware, decorators, route guards, etc.)
3. What roles/permissions exist
4. How tokens are issued, validated, and refreshed
5. Any auth-related config (env vars, settings)
6. Frontend auth flow (how the user logs in, how tokens are stored and used)
7. The full directory/file structure of the project
Please read ALL relevant source files completely and give me a detailed summary with file paths and line numbers. I need enough detail to replicate this auth system in another project.
Bash
IN
find /volume2/docker/location-logger -type f -name "*.py" -o -name "*.js" -o -name "*.ts" -o -name "*.json" | head -50
Copy code to clipboard
OUT
/volume2/docker/location-logger/.mcp.json
/volume2/docker/location-logger/app/__init__.py
/volume2/docker/location-logger/app/utils.py
/volume2/docker/location-logger/app/config.py
/volume2/docker/location-logger/app/main.py
/volume2/docker/location-logger/alembic/env.py
/volume2/docker/location-logger/mcp-server/server.py
/volume2/docker/location-logger/alembic/versions/20260203_201500_create_location_points_table.py
/volume2/docker/location-logger/alembic/versions/20260209_200000_add_enhanced_fields_and_tables.py
/volume2/docker/location-logger/alembic/versions/20260214_000000_add_users_and_audit_logs.py
/volume2/docker/location-logger/alembic/versions/20260320_000000_add_audit_query_string.py
/volume2/docker/location-logger/app/services/geofence_service.py
/volume2/docker/location-logger/app/services/recorder_service.py
/volume2/docker/location-logger/app/services/trip_service.py
/volume2/docker/location-logger/app/services/enhanced_location_service.py
/volume2/docker/location-logger/app/services/geocoding_service.py
/volume2/docker/location-logger/app/clients/__init__.py
/volume2/docker/location-logger/app/clients/dawarich_client.py
/volume2/docker/location-logger/app/clients/owntracks_client.py
/volume2/docker/location-logger/app/models/areas.py
/volume2/docker/location-logger/app/models/geofence_events.py
/volume2/docker/location-logger/app/models/trips.py
/volume2/docker/location-logger/app/models/user.py
/volume2/docker/location-logger/app/models/location.py
/volume2/docker/location-logger/app/models/audit_log.py
/volume2/docker/location-logger/app/auth/__init__.py
/volume2/docker/location-logger/app/auth/dependencies.py
/volume2/docker/location-logger/app/auth/utils.py
/volume2/docker/location-logger/app/api/enhanced_endpoints.py
/volume2/docker/location-logger/app/db/base.py
/volume2/docker/location-logger/app/db/session.py
Bash
IN
tree -L 4 /volume2/docker/location-logger 2>/dev/null || find /volume2/docker/location-logger -type d | sort
Copy code to clipboard
OUT
/volume2/docker/location-logger
├── alembic
│ ├── env.py
│ ├── script.py.mako
│ └── versions
│ ├── 20260203_201500_create_location_points_table.py
│ ├── 20260209_200000_add_enhanced_fields_and_tables.py
│ ├── 20260214_000000_add_users_and_audit_logs.py
│ ├── 20260320_000000_add_audit_query_string.py
│ └── __pycache__
│ └── 20260214_000000_add_users_and_audit_logs.cpython-311.pyc
├── alembic.ini
├── app
│ ├── api
│ │ └── enhanced_endpoints.py
│ ├── auth
│ │ ├── dependencies.py
│ │ ├── __init__.py
│ │ ├── __pycache__
│ │ │ ├── dependencies.cpython-311.pyc
│ │ │ ├── __init__.cpython-311.pyc
│ │ │ └── utils.cpython-311.pyc
│ │ └── utils.py
│ ├── clients
│ │ ├── dawarich_client.py
│ │ ├── __init__.py
│ │ └── owntracks_client.py
│ ├── config.py
│ ├── db
│ │ ├── base.py
│ │ └── session.py
│ ├── __init__.py
│ ├── main.py
│ ├── models
│ │ ├── areas.py
│ │ ├── audit_log.py
│ │ ├── geofence_events.py
│ │ ├── location.py
│ │ ├── __pycache__
│ │ │ ├── audit_log.cpython-311.pyc
│ │ │ └── user.cpython-311.pyc
│ │ ├── trips.py
│ │ └── user.py
│ ├── __pycache__
│ │ ├── __init__.cpython-311.pyc
│ │ └── main.cpython-311.pyc
│ ├── services
│ │ ├── enhanced_location_service.py
│ │ ├── geocoding_service.py
│ │ ├── geofence_service.py
│ │ ├── recorder_service.py
│ │ └── trip_service.py
│ ├── static
│ │ └── dashboard.html
│ └── utils.py
├── docker-compose.yml
├── Dockerfile
├── mcp-server
│ ├── Dockerfile
│ ├── requirements.txt
│ └── server.py
├── README.md
├── requirements.txt
└── today_map.html
16 directories, 48 files
Read
utils.py
utils.py
Read
dependencies.py
dependencies.py
Read
user.py
user.py
Read
config.py
config.py
Read
main.py
main.py
Read
session.py
session.py
Read
dashboard.html
dashboard.html
Read
audit_log.py
audit_log.py
Read
20260214_000000_add_users_and_audit_logs.py
20260214_000000_add_users_and_audit_logs.py
Read
20260320_000000_add_audit_query_string.py
20260320_000000_add_audit_query_string.py
Read
README.md
README.md
Read
requirements.txt
requirements.txt
Read
server.py
server.py
Read
server.py
server.py
(lines 101-400)
Thinking
Thinking
Now I have a full picture of both systems. Let me read the payments-logger source files before planning the migration.
Bash
List all payments-logger files
IN
find /volume2/docker/payments-logger -type f | grep -v node_modules | grep -v .git | sort
Copy code to clipboard
OUT
/volume2/docker/payments-logger/API.md
/volume2/docker/payments-logger/backend/Dockerfile
/volume2/docker/payments-logger/backend/.dockerignore
/volume2/docker/payments-logger/backend/package.json
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_init/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260210_simplify/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/20260222_add_card/migration.sql
/volume2/docker/payments-logger/backend/prisma/migrations/migration_lock.toml
/volume2/docker/payments-logger/backend/prisma/schema.prisma
/volume2/docker/payments-logger/backend/src/auth.js
/volume2/docker/payments-logger/backend/src/index.js
/volume2/docker/payments-logger/backend/src/parser.js
/volume2/docker/payments-logger/backend/src/routes/payments.js
/volume2/docker/payments-logger/.claude/settings.local.json
/volume2/docker/payments-logger/docker-compose.yml
/volume2/docker/payments-logger/.env
/volume2/docker/payments-logger/.env.example
/volume2/docker/payments-logger/frontend/Dockerfile
/volume2/docker/payments-logger/frontend/.dockerignore
/volume2/docker/payments-logger/frontend/index.html
/volume2/docker/payments-logger/frontend/package.json
/volume2/docker/payments-logger/frontend/postcss.config.js
/volume2/docker/payments-logger/frontend/src/App.jsx
/volume2/docker/payments-logger/frontend/src/auth.js
/volume2/docker/payments-logger/frontend/src/components/AuthCallback.jsx
/volume2/docker/payments-logger/frontend/src/components/AuthProvider.jsx
/volume2/docker/payments-logger/frontend/src/components/FilterBar.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentCard.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentList.jsx
/volume2/docker/payments-logger/frontend/src/components/PaymentTable.jsx
/volume2/docker/payments-logger/frontend/src/index.css
/volume2/docker/payments-logger/frontend/src/main.jsx
/volume2/docker/payments-logger/frontend/tailwind.config.js
/volume2/docker/payments-logger/frontend/vite.config.js
/volume2/docker/payments-logger/README.md
Read
auth.js
auth.js
Read
index.js
index.js
Read
auth.js
auth.js
Read
AuthProvider.jsx
AuthProvider.jsx
Read
package.json
package.json
Read
AuthCallback.jsx
AuthCallback.jsx
Read
schema.prisma
schema.prisma
Read
docker-compose.yml
docker-compose.yml
Thinking
Thinking
Now let me read the remaining frontend files.
Read
App.jsx
App.jsx
Read
package.json
package.json
Read
.env
.env
Thinking
Thinking
Let me read the remaining files before making changes.
Read
main.jsx
main.jsx
Read
vite.config.js
vite.config.js
Read
payments.js
payments.js
Thinking
Thinking
Read
.env.example...
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/Users/lukas/.screenpipe/data/data/2026-05-08/1778 /Users/lukas/.screenpipe/data/data/2026-05-08/1778261759576_m2.jpg...
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Programming In Germany Is Dead — A Developer’s Aut Programming In Germany Is Dead — A Developer’s Autopsy Report | Jan Kammerath - kovaliklukas@gmail.com - Gmail — Personal...
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Pull requests · screenpipe/screenpipe · GitHub
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Pull requests · screenpipe/screenpipe · GitHub
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Lukáš Koválik
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Programming In Germany Is Dead — A Developer’s Autopsy Report | Jan Kammerath
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
Read more »
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
Write, plan, organize.
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.=+##%##+=. **+==-:..
Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
Write, plan, organize.
In one AI-powered workspace.
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685 Market Street, San Francisco, CA 94105
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In two weeks: Ivan Zhao announces Notion’s Developer Platform
In two weeks: Ivan Zhao announces Notion’s Developer Platform
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
Write, plan, organize.
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What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
Read more »
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
Read more »
Read more »
What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
Read more »
Read more »
Read more in State Politics
Read more in State Politics
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State Politics State Politics • 2.6K followers A Space for World Politics. Join State Politics
What did you learn too late in life?
What did you learn too late in life?
Janet
Janet
, studied Estate Law and Wills & Studying at University at Buffalo, School of Law
Answered March 2
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
I am 70 years old. I have a lot of life experiences to look back on. As I have been aging towards “being old”, I’ve come to realize that family can be selfish and not the b...
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What did you learn too late in life?
What did you learn too late in life?
Shashi Saini
Shashi Saini
, former Quality Control Inspector at Grainger (2004-2014)
Answered March 12
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns. Some of the things could be ig...
We learn all our life but about family and relationships I was completely ignorant. I reacted even for small things which took bigger turns.
Some of the things could be ig...
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
Write, plan, organize.
In one AI-powered workspace.
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685 Market Street, San Francisco, CA 94105
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-@@@@@@@@@@@@@@#: =@@@@@@@@@@@@@@@*
=%@@@@@@@@@@@@+ -@@@@@@@@@@%%#*++=:
.=+##%##+=. **+==-:..
Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
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Hi Lukáš,
We've been building so you can too.
In just two weeks, we're hosting a virtual event to introduce our new Developer Platform. Join cofounder Ivan Zhao, along with members of the Engineering and Product teams, to see how you can sync any data, build any tool, and orchestrate any agent, all in Notion.
When:
Wednesday, May 13
Time:
9 a.m. PT | 12 p.m. ET
The event will be held in English with live translated captions available.
Register now
Register now
See you there,
The Notion Team
P.S. Can't make the virtual event? Register anyway—we'll send the replay.
Write, plan, organize.
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DXP4800PLUS-B5F8
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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DigitalOcean logo. This image is linked to the DigitalOcean homepage.
The DigitalOcean Newsletter
Your monthly deep dive into all things DigitalOcean, including product updates, new tutorials, upcoming events, and more.
Yesterday at Deploy 2026, we unveiled something we've been building toward for a while:
DigitalOcean's AI-Native Cloud
, a full-stack system purpose-built for production AI workloads.
The center of gravity in AI has shifted from training to inference. What started as a single model call is now a system of continuous interaction; teams orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production.
Five layers. One platform. Open at every layer.
DigitalOcean's AI-Native Cloud: The Stack
Build agents faster. Operate them efficiently. And build a durable business.
· Open Harness · Sandbox · Toolbox · State · Plano
Production agents that run on the same stack as your data, inference, and infrastructure. No cross-vendor hops. No lost context. No egress fees.
· DBaaS · Advanced Edition (PG/MySQL) · KBaaS · Weaviate
Fresh data, persistent memory, and continuous learning, without rebuilding your data stack.
· Serverless · Dedicated · Batch · Router · BYOM · Model Catalog & Services · Multi-modal · Guardrails
Over 70 models, open-weighted and frontier, on one endpoint. Run serverless, dedicated, or batch inference, with the Inference Router optimizing every call.
· Kubernetes · Droplets (CPU & GPU) · VPC · Storage (object, block and file system) · High performance NFS
The cloud millions already run on, with the primitives every AI workload needs.
· 15 regions · NVIDIA H100/H200/B300 · MI300X / MI325X / MI350X · Liquid-cooled
We own the silicon. Your unit economics improve as you scale.
Inference Router (Public Preview)
A policy-aware control plane that routes every request based on cost, latency, quality, and residency constraints. Instead of hardcoding model logic, teams define intent once and the system optimizes execution across providers and deployment types.
Dedicated Inference and Bring Your Own Model
Run custom and fine-tuned models on dedicated GPU infrastructure with full control over performance, scaling, and configuration. Deploy models from sources like Hugging Face or your own environments, and operate high-throughput workloads with a pre-tuned inference stack and managed orchestration, without the complexity of Kubernetes.
Expanded Models and Services
Run and evaluate text, image, audio, and video models through a single system. A continuously refreshed catalog includes 25+ new models and Day 0 access to select releases, including NVIDIA Nemotron 3 Nano Omni, available first on DigitalOcean. The highly efficient open multimodal model unifies vision, speech, language, and tool use, with NVIDIA TensorRT-LLM tuned at the kernel level. Built-in evaluations let teams benchmark quality, cost, and latency before production.
PostgreSQL & MySQL Advanced Edition (Public Preview)
Managed PostgreSQL and MySQL Advanced Edition deliver hyperscaler-grade reliability and scale, available alongside Standard Edition.
Managed Weaviate (Private Preview)
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What started as a single model call is now a system of continuous interaction; teams orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production.","depth":29,"bounds":{"left":0.26462767,"top":0.5634477,"width":0.16855054,"height":0.06783719},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Five layers. One platform. Open at every layer.","depth":29,"bounds":{"left":0.26462767,"top":0.6528332,"width":0.1100399,"height":0.014365523},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"DigitalOcean's AI-Native Cloud: The Stack","depth":28,"bounds":{"left":0.26462767,"top":0.726656,"width":0.124667555,"height":0.018355945},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Build agents faster. Operate them efficiently. And build a durable business.","depth":29,"bounds":{"left":0.26462767,"top":0.7565842,"width":0.16339761,"height":0.032322425},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"· Open Harness · Sandbox · Toolbox · State · Plano","depth":29,"bounds":{"left":0.2867354,"top":0.802075,"width":0.13896276,"height":0.032721467},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Production agents that run on the same stack as your data, inference, and infrastructure. No cross-vendor hops. No lost context. No egress fees.","depth":29,"bounds":{"left":0.2867354,"top":0.84636873,"width":0.14079122,"height":0.05027933},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"· DBaaS · Advanced Edition (PG/MySQL) · KBaaS · Weaviate","depth":29,"bounds":{"left":0.2867354,"top":0.91380686,"width":0.14893617,"height":0.032721467},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Fresh data, persistent memory, and continuous learning, without rebuilding your data stack.","depth":29,"bounds":{"left":0.2867354,"top":0.95810056,"width":0.13264628,"height":0.032322425},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"· Serverless · Dedicated · Batch · Router · BYOM · Model Catalog & Services · Multi-modal · Guardrails","depth":29,"bounds":{"left":0.2867354,"top":1.0,"width":0.1462766,"height":-0.0035914183},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Over 70 models, open-weighted and frontier, on one endpoint. Run serverless, dedicated, or batch inference, with the Inference Router optimizing every call.","depth":29,"bounds":{"left":0.2867354,"top":1.0,"width":0.14611037,"height":-0.04788506},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"· Kubernetes · Droplets (CPU & GPU) · VPC · Storage (object, block and file system) · High performance NFS","depth":29,"bounds":{"left":0.2867354,"top":1.0,"width":0.14212102,"height":-0.08100557},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The cloud millions already run on, with the primitives every AI workload needs.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"· 15 regions · NVIDIA H100/H200/B300 · MI300X / MI325X / MI350X · Liquid-cooled","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We own the silicon. Your unit economics improve as you scale.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Inference Router (Public Preview)","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"A policy-aware control plane that routes every request based on cost, latency, quality, and residency constraints. Instead of hardcoding model logic, teams define intent once and the system optimizes execution across providers and deployment types.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dedicated Inference and Bring Your Own Model","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Run custom and fine-tuned models on dedicated GPU infrastructure with full control over performance, scaling, and configuration. Deploy models from sources like Hugging Face or your own environments, and operate high-throughput workloads with a pre-tuned inference stack and managed orchestration, without the complexity of Kubernetes.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Expanded Models and Services","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Run and evaluate text, image, audio, and video models through a single system. A continuously refreshed catalog includes 25+ new models and Day 0 access to select releases, including NVIDIA Nemotron 3 Nano Omni, available first on DigitalOcean. The highly efficient open multimodal model unifies vision, speech, language, and tool use, with NVIDIA TensorRT-LLM tuned at the kernel level. Built-in evaluations let teams benchmark quality, cost, and latency before production.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"PostgreSQL & MySQL Advanced Edition (Public Preview)","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Managed PostgreSQL and MySQL Advanced Edition deliver hyperscaler-grade reliability and scale, available alongside Standard Edition.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Managed Weaviate (Private Preview)","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Production-ready vector infrastructure without the operational overhead, with native integration to Serverless Inference and predictable pricing.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Sign up for early access","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Sign up for early access","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Knowledge Bases","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A fully managed RAG service that handles ingestion, chunking, embedding, retrieval, and reranking, with MCP support for agent frameworks. Customers are moving from prototype to production in days.","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"New feature available: Configurable Contacts","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"You can now control who receives critical account notifications with our newest","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Teams","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"feature,","depth":28,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Configurable Contacts","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". 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All rights reserved.","depth":27,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"105 Edgeview Dr., Suite 425, Broomfield, CO 80021","depth":27,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"This email was sent to","depth":22,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"kovaliklukas@gmail.com","depth":22,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"kovaliklukas@gmail.com","depth":23,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". 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DigitalOcean logo. This image is linked to the DigitalOcean homepage.
The DigitalOcean Newsletter
Your monthly deep dive into all things DigitalOcean, including product updates, new tutorials, upcoming events, and more.
Yesterday at Deploy 2026, we unveiled something we've been building toward for a while:
DigitalOcean's AI-Native Cloud
, a full-stack system purpose-built for production AI workloads.
The center of gravity in AI has shifted from training to inference. What started as a single model call is now a system of continuous interaction; teams orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production.
Five layers. One platform. Open at every layer.
DigitalOcean's AI-Native Cloud: The Stack
Build agents faster. Operate them efficiently. And build a durable business.
· Open Harness · Sandbox · Toolbox · State · Plano
Production agents that run on the same stack as your data, inference, and infrastructure. No cross-vendor hops. No lost context. No egress fees.
· DBaaS · Advanced Edition (PG/MySQL) · KBaaS · Weaviate
Fresh data, persistent memory, and continuous learning, without rebuilding your data stack.
· Serverless · Dedicated · Batch · Router · BYOM · Model Catalog & Services · Multi-modal · Guardrails
Over 70 models, open-weighted and frontier, on one endpoint. Run serverless, dedicated, or batch inference, with the Inference Router optimizing every call.
· Kubernetes · Droplets (CPU & GPU) · VPC · Storage (object, block and file system) · High performance NFS
The cloud millions already run on, with the primitives every AI workload needs.
· 15 regions · NVIDIA H100/H200/B300 · MI300X / MI325X / MI350X · Liquid-cooled
We own the silicon. Your unit economics improve as you scale.
Inference Router (Public Preview)
A policy-aware control plane that routes every request based on cost, latency, quality, and residency constraints. Instead of hardcoding model logic, teams define intent once and the system optimizes execution across providers and deployment types.
Dedicated Inference and Bring Your Own Model
Run custom and fine-tuned models on dedicated GPU infrastructure with full control over performance, scaling, and configuration. Deploy models from sources like Hugging Face or your own environments, and operate high-throughput workloads with a pre-tuned inference stack and managed orchestration, without the complexity of Kubernetes.
Expanded Models and Services
Run and evaluate text, image, audio, and video models through a single system. A continuously refreshed catalog includes 25+ new models and Day 0 access to select releases, including NVIDIA Nemotron 3 Nano Omni, available first on DigitalOcean. The highly efficient open multimodal model unifies vision, speech, language, and tool use, with NVIDIA TensorRT-LLM tuned at the kernel level. Built-in evaluations let teams benchmark quality, cost, and latency before production.
PostgreSQL & MySQL Advanced Edition (Public Preview)
Managed PostgreSQL and MySQL Advanced Edition deliver hyperscaler-grade reliability and scale, available alongside Standard Edition.
Managed Weaviate (Private Preview)
Production-ready vector infrastructure without the operational overhead, with native integration to Serverless Inference and predictable pricing.
Sign up for early access
Sign up for early access
.
Knowledge Bases
A fully managed RAG service that handles ingestion, chunking, embedding, retrieval, and reranking, with MCP support for agent frameworks. Customers are moving from prototype to production in days.
New feature available: Configurable Contacts
You can now control who receives critical account notifications with our newest
Teams
feature,
Configurable Contacts
. We’ve enabled
billing contacts
for invoices, receipts, and payment issues and
security contacts
for login alerts and risk events. With this feature, alerts go directly to the right users without manual forwarding. You can get started by going to
Settings (on the Teams page)
Settings (on the Teams page)
→ Contacts → Add billing or security contact.
See all of our release notes and subscribe to the RSS feed here.
Running AI inference requires constant optimization to ensure that you can meet latency and cost requirements without unintended spikes. Learn how to navigate the three-way tradeoff between
throughput, latency, and cost
when serving LLMs, with a practical framework for tuning deployments.
AI cloud platforms need to account for more than delivering the latest technology—they also must integrate features for a positive developer experience. Discover the hidden costs of complex AI platforms and
how poor developer experience
slows innovation, increases overhead, and impacts scalability.
With running AI models at scale, data gravity isn’t simply a term to know; it’s become a structural bottleneck. Learn how
high-bandwidth storage
helps eliminate the “data tax” on GPU clusters and keeps 600B+ parameter model deployments from stalling on idle silicon.
Addressing
LLM inference bottlenecks
with more GPU power isn’t always the most effective solution. They often require a whole system-wide evaluation and best practices surrounding batch jobs, token latency, and choosing the right architecture for your LLMs.
Cold starts
are a deployment step that are often visible at the user level and can have potential consequences for your overall AI system. Read how cold starts can increase costs and how to potentially reduce their impact in your AI setup.
Done right,
prompt caching at scale
can offer 50–90% discounts on cached input tokens and reduce time-to-first-token (TTFT) latency by up to 80%. Get insight into how you can scale prompt caching across LLM replicas with session affinity, tiered routing, and prefix-aware load balancing to cut latency and inference costs.
"DigitalOcean's Inference Router gives us the kind of intelligent model selection we would otherwise have had to build ourselves. It routes each request to the right model based on complexity, helping us reduce inference costs by more than 40%."
— Hovsep Seraydarian, Co-Founder and CTO, LawVo
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DigitalOcean logo. This image is linked to the DigitalOcean homepage.
The DigitalOcean Newsletter
Your monthly deep dive into all things DigitalOcean, including product updates, new tutorials, upcoming events, and more.
Yesterday at Deploy 2026, we unveiled something we've been building toward for a while:
DigitalOcean's AI-Native Cloud
, a full-stack system purpose-built for production AI workloads.
The center of gravity in AI has shifted from training to inference. What started as a single model call is now a system of continuous interaction; teams orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production.
Five layers. One platform. Open at every layer.
DigitalOcean's AI-Native Cloud: The Stack
Build agents faster. Operate them efficiently. And build a durable business.
· Open Harness · Sandbox · Toolbox · State · Plano
Production agents that run on the same stack as your data, inference, and infrastructure. No cross-vendor hops. No lost context. No egress fees.
· DBaaS · Advanced Edition (PG/MySQL) · KBaaS · Weaviate
Fresh data, persistent memory, and continuous learning, without rebuilding your data stack.
· Serverless · Dedicated · Batch · Router · BYOM · Model Catalog & Services · Multi-modal · Guardrails
Over 70 models, open-weighted and frontier, on one endpoint. Run serverless, dedicated, or batch inference, with the Inference Router optimizing every call.
· Kubernetes · Droplets (CPU & GPU) · VPC · Storage (object, block and file system) · High performance NFS
The cloud millions already run on, with the primitives every AI workload needs.
· 15 regions · NVIDIA H100/H200/B300 · MI300X / MI325X / MI350X · Liquid-cooled
We own the silicon. Your unit economics improve as you scale.
Inference Router (Public Preview)
A policy-aware control plane that routes every request based on cost, latency, quality, and residency constraints. Instead of hardcoding model logic, teams define intent once and the system optimizes execution across providers and deployment types.
Dedicated Inference and Bring Your Own Model
Run custom and fine-tuned models on dedicated GPU infrastructure with full control over performance, scaling, and configuration. Deploy models from sources like Hugging Face or your own environments, and operate high-throughput workloads with a pre-tuned inference stack and managed orchestration, without the complexity of Kubernetes.
Expanded Models and Services
Run and evaluate text, image, audio, and video models through a single system. A continuously refreshed catalog includes 25+ new models and Day 0 access to select releases, including NVIDIA Nemotron 3 Nano Omni, available first on DigitalOcean. The highly efficient open multimodal model unifies vision, speech, language, and tool use, with NVIDIA TensorRT-LLM tuned at the kernel level. Built-in evaluations let teams benchmark quality, cost, and latency before production.
PostgreSQL & MySQL Advanced Edition (Public Preview)
Managed PostgreSQL and MySQL Advanced Edition deliver hyperscaler-grade reliability and scale, available alongside Standard Edition.
Managed Weaviate (Private Preview)
Production-ready vector infrastructure without the operational overhead, with native integration to Serverless Inference and predictable pricing.
Sign up for early access
Sign up for early access
.
Knowledge Bases
A fully managed RAG service that handles ingestion, chunking, embedding, retrieval, and reranking, with MCP support for agent frameworks. Customers are moving from prototype to production in days.
New feature available: Configurable Contacts
You can now control who receives critical account notifications with our newest
Teams
feature,
Configurable Contacts
. We’ve enabled
billing contacts
for invoices, receipts, and payment issues and
security contacts
for login alerts and risk events. With this feature, alerts go directly to the right users without manual forwarding. You can get started by going to
Settings (on the Teams page)
Settings (on the Teams page)
→ Contacts → Add billing or security contact.
See all of our release notes and subscribe to the RSS feed here.
Running AI inference requires constant optimization to ensure that you can meet latency and cost requirements without unintended spikes. Learn how to navigate the three-way tradeoff between
throughput, latency, and cost
when serving LLMs, with a practical framework for tuning deployments.
AI cloud platforms need to account for more than delivering the latest technology—they also must integrate features for a positive developer experience. Discover the hidden costs of complex AI platforms and
how poor developer experience
slows innovation, increases overhead, and impacts scalability.
With running AI models at scale, data gravity isn’t simply a term to know; it’s become a structural bottleneck. Learn how
high-bandwidth storage
helps eliminate the “data tax” on GPU clusters and keeps 600B+ parameter model deployments from stalling on idle silicon.
Addressing
LLM inference bottlenecks
with more GPU power isn’t always the most effective solution. They often require a whole system-wide evaluation and best practices surrounding batch jobs, token latency, and choosing the right architecture for your LLMs.
Cold starts
are a deployment step that are often visible at the user level and can have potential consequences for your overall AI system. Read how cold starts can increase costs and how to potentially reduce their impact in your AI setup.
Done right,
prompt caching at scale
can offer 50–90% discounts on cached input tokens and reduce time-to-first-token (TTFT) latency by up to 80%. Get insight into how you can scale prompt caching across LLM replicas with session affinity, tiered routing, and prefix-aware load balancing to cut latency and inference costs.
"DigitalOcean's Inference Router gives us the kind of intelligent model selection we would otherwise have had to build ourselves. It routes each request to the right model based on complexity, helping us reduce inference costs by more than 40%."
— Hovsep Seraydarian, Co-Founder and CTO, LawVo
Cloudways has a subreddit!
We’ve been running the official Cloudways subreddit, and it’s grown into a community of 1000+ WordPress enthusiasts, developers, business owners, and hosting professionals.
Join the subreddit
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to become a part of the community and discuss all things hosting, performance, and security.
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DigitalOcean logo. This image is linked to the DigitalOcean homepage.
The DigitalOcean Newsletter
Your monthly deep dive into all things DigitalOcean, including product updates, new tutorials, upcoming events, and more.
Yesterday at Deploy 2026, we unveiled something we've been building toward for a while:
DigitalOcean's AI-Native Cloud
, a full-stack system purpose-built for production AI workloads.
The center of gravity in AI has shifted from training to inference. What started as a single model call is now a system of continuous interaction; teams orchestrate multiple models, retrieve and synthesize data, execute tools, and repeat this cycle in production.
Five layers. One platform. Open at every layer.
DigitalOcean's AI-Native Cloud: The Stack
Build agents faster. Operate them efficiently. And build a durable business.
· Open Harness · Sandbox · Toolbox · State · Plano
Production agents that run on the same stack as your data, inference, and infrastructure. No cross-vendor hops. No lost context. No egress fees.
· DBaaS · Advanced Edition (PG/MySQL) · KBaaS · Weaviate
Fresh data, persistent memory, and continuous learning, without rebuilding your data stack.
· Serverless · Dedicated · Batch · Router · BYOM · Model Catalog & Services · Multi-modal · Guardrails
Over 70 models, open-weighted and frontier, on one endpoint. Run serverless, dedicated, or batch inference, with the Inference Router optimizing every call.
· Kubernetes · Droplets (CPU & GPU) · VPC · Storage (object, block and file system) · High performance NFS
The cloud millions already run on, with the primitives every AI workload needs.
· 15 regions · NVIDIA H100/H200/B300 · MI300X / MI325X / MI350X · Liquid-cooled
We own the silicon. Your unit economics improve as you scale.
Inference Router (Public Preview)
A policy-aware control plane that routes every request based on cost, latency, quality, and residency constraints. Instead of hardcoding model logic, teams define intent once and the system optimizes execution across providers and deployment types.
Dedicated Inference and Bring Your Own Model
Run custom and fine-tuned models on dedicated GPU infrastructure with full control over performance, scaling, and configuration. Deploy models from sources like Hugging Face or your own environments, and operate high-throughput workloads with a pre-tuned inference stack and managed orchestration, without the complexity of Kubernetes.
Expanded Models and Services
Run and evaluate text, image, audio, and video models through a single system. A continuously refreshed catalog includes 25+ new models and Day 0 access to select releases, including NVIDIA Nemotron 3 Nano Omni, available first on DigitalOcean. The highly efficient open multimodal model unifies vision, speech, language, and tool use, with NVIDIA TensorRT-LLM tuned at the kernel level. Built-in evaluations let teams benchmark quality, cost, and latency before production.
PostgreSQL & MySQL Advanced Edition (Public Preview)
Managed PostgreSQL and MySQL Advanced Edition deliver hyperscaler-grade reliability and scale, available alongside Standard Edition.
Managed Weaviate (Private Preview)
Production-ready vector infrastructure without the operational overhead, with native integration to Serverless Inference and predictable pricing.
Sign up for early access
Sign up for early access
.
Knowledge Bases
A fully managed RAG service that handles ingestion, chunking, embedding, retrieval, and reranking, with MCP support for agent frameworks. Customers are moving from prototype to production in days.
New feature available: Configurable Contacts
You can now control who receives critical account notifications with our newest
Teams
feature,
Configurable Contacts
. We’ve enabled
billing contacts
for invoices, receipts, and payment issues and
security contacts
for login alerts and risk events. With this feature, alerts go directly to the right users without manual forwarding. You can get started by going to
Settings (on the Teams page)
Settings (on the Teams page)
→ Contacts → Add billing or security contact.
See all of our release notes and subscribe to the RSS feed here.
Running AI inference requires constant optimization to ensure that you can meet latency and cost requirements without unintended spikes. Learn how to navigate the three-way tradeoff between
throughput, latency, and cost
when serving LLMs, with a practical framework for tuning deployments.
AI cloud platforms need to account for more than delivering the latest technology—they also must integrate features for a positive developer experience. Discover the hidden costs of complex AI platforms and
how poor developer experience
slows innovation, increases overhead, and impacts scalability.
With running AI models at scale, data gravity isn’t simply a term to know; it’s become a structural bottleneck. Learn how
high-bandwidth storage
helps eliminate the “data tax” on GPU clusters and keeps 600B+ parameter model deployments from stalling on idle silicon.
Addressing
LLM inference bottlenecks
with more GPU power isn’t always the most effective solution. They often require a whole system-wide evaluation and best practices surrounding batch jobs, token latency, and choosing the right architecture for your LLMs.
Cold starts
are a deployment step that are often visible at the user level and can have potential consequences for your overall AI system. Read how cold starts can increase costs and how to potentially reduce their impact in your AI setup.
Done right,
prompt caching at scale
can offer 50–90% discounts on cached input tokens and reduce time-to-first-token (TTFT) latency by up to 80%. Get insight into how you can scale prompt caching across LLM replicas with session affinity, tiered routing, and prefix-aware load balancing to cut latency and inference costs.
"DigitalOcean's Inference Router gives us the kind of intelligent model selection we would otherwise have had to build ourselves. It routes each request to the right model based on complexity, helping us reduce inference costs by more than 40%."
— Hovsep Seraydarian, Co-Founder and CTO, LawVo
Cloudways has a subreddit!
We’ve been running the official Cloudways subreddit, and it’s grown into a community of 1000+ WordPress enthusiasts, developers, business owners, and hosting professionals.
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Pull requests · screenpipe/screenpipe · GitHub
Pul Pull requests · screenpipe/screenpipe · GitHub
Pull requests · screenpipe/screenpipe · GitHub
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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Wed 29 Apr, 19:47 (9 days ago)
Wed 29 Apr, 19:47 (9 days ago)
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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy…
͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny Follow We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with… see more LIKE 12 Read more Read more
Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny
Calum Scott
Head of Customer Solutions and Implementation @Jiminny
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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with…
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Veneta Petrova Veneta Petrova liked this Veselin Raykov Veselin Raykov Recruitment.bg <> we help companies hire, leading… Follow Мисля си, че няма перфектен кандидат, а има правилни хора за правилния момент. LIKE 31 ·2 Comments Read more
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Veselin Raykov Veselin Raykov Recruitment.bg <> we help companies hire, leading…
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Мисля си, че няма перфектен кандидат, а има правилни хора за правилния момент.
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Suggested for you Non-Billable Non-Billable 37,098 followers 🚨 Fladgate LLP has hit £100 million in revenue for the first time, with its FY26 turnover set to exceed £102… see more LIKE PRAISE 77 Read more Read more
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Fladgate LLP has hit £100 million in revenue for the first time, with its FY26 turnover set to exceed £102…
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Ruslan Ivanov Ruslan Ivanov found this insightful Strahil Hadzhiev Strahil Hadzhiev Strategic Design Partner | Fintech UX | AI SaaS User… Follow Revolut продължава с иновациите! В момента тества нова функция наречена "Drunk Lock" 👀 След 23:00, всяка… see more LIKE PRAISE EMPATHY 618 ·46 Comments Read more Read more
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Strahil Hadzhiev Strahil Hadzhiev Strategic Design Partner | Fintech UX | AI SaaS User…
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Revolut продължава с иновациите! В момента тества нова функция наречена "Drunk Lock"
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Paul Lembo Paul Lembo CTO - VMware - Americas Follow I am pleased to see #Broadcom and #Google enter into a long term agreement around TPUs. How much the AI… see more LIKE PRAISE EMPATHY 190 Read more Read more
Paul Lembo Paul Lembo CTO - VMware - Americas
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I am pleased to see #Broadcom and #Google enter into a long term agreement around TPUs. How much the AI…
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Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny Follow We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with… see more LIKE 12 Read more Read more
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Veneta Petrova Veneta Petrova liked this Veselin Raykov Veselin Raykov Recruitment.bg <> we help companies hire, leading… Follow Мисля си, че няма перфектен кандидат, а има правилни хора за правилния момент. LIKE 31 ·2 Comments Read more
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Ruslan Ivanov Ruslan Ivanov found this insightful Strahil Hadzhiev Strahil Hadzhiev Strategic Design Partner | Fintech UX | AI SaaS User… Follow Revolut продължава с иновациите! В момента тества нова функция наречена "Drunk Lock" 👀 След 23:00, всяка… see more LIKE PRAISE EMPATHY 618 ·46 Comments Read more Read more
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Paul Lembo Paul Lembo CTO - VMware - Americas Follow I am pleased to see #Broadcom and #Google enter into a long term agreement around TPUs. How much the AI… see more LIKE PRAISE EMPATHY 190 Read more Read more
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Следи средната си скорост с TollPass App
Следи средната си скорост с TollPass App
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Wed 29 Apr, 18:54 (9 days ago)
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
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✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
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Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
Обнови / Изтегли TollPass App
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Следи средната си скорост с TollPass App
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
Download attachment
✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
Download attachment
Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
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✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
Download attachment
Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
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Pull requests · screenpipe/screenpipe · GitHub
Pul Pull requests · screenpipe/screenpipe · GitHub
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screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
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DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Следи средната си скорост с TollPass App
Следи средната си скорост с TollPass App
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Wed 29 Apr, 18:54 (9 days ago)
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
Download attachment
✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
Download attachment
Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
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Wed 29 Apr, 19:47 (9 days ago)
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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy…
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Calum Scott Calum Scott Head of Customer Solutions and Implementation @Jiminny Follow We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy, go-to-market aware professional with… see more LIKE 12 Read more Read more
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В момента тества нова функция наречена \"Drunk Lock\" 👀 След 23:00, всяка… see more LIKE PRAISE EMPATHY 618 ·46 Comments Read more Read more","depth":23,"bounds":{"left":0.004166667,"top":0.82944447,"width":0.32222223,"height":0.17055553},"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Ruslan Ivanov","depth":27,"bounds":{"left":0.020833334,"top":0.83111113,"width":0.06458333,"height":0.019444445},"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Ruslan Ivanov","depth":28,"bounds":{"left":0.020833334,"top":0.83166665,"width":0.06458333,"height":0.018888889},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"found this insightful","depth":27,"bounds":{"left":0.08541667,"top":0.83166665,"width":0.09201389,"height":0.018888889},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Strahil Hadzhiev Strahil Hadzhiev Strategic Design Partner | Fintech UX | AI SaaS User…","depth":27,"bounds":{"left":0.004166667,"top":0.8788889,"width":0.26666668,"height":0.05888889},"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Follow","depth":28,"bounds":{"left":0.27222222,"top":0.89111114,"width":0.05277778,"height":0.035},"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Revolut продължава с иновациите! 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We’re hiring!!! If you are (or know of) an out-going, SaaS tech-savvy…
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Veneta Petrova Veneta Petrova liked this Veselin Raykov Veselin Raykov Recruitment.bg <> we help companies hire, leading… Follow Мисля си, че няма перфектен кандидат, а има правилни хора за правилния момент. LIKE 31 ·2 Comments Read more
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Suggested for you Non-Billable Non-Billable 37,098 followers 🚨 Fladgate LLP has hit £100 million in revenue for the first time, with its FY26 turnover set to exceed £102… see more LIKE PRAISE 77 Read more Read more
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Ruslan Ivanov Ruslan Ivanov found this insightful Strahil Hadzhiev Strahil Hadzhiev Strategic Design Partner | Fintech UX | AI SaaS User… Follow Revolut продължава с иновациите! В момента тества нова функция наречена "Drunk Lock" 👀 След 23:00, всяка… see more LIKE PRAISE EMPATHY 618 ·46 Comments Read more Read more
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Paul Lembo Paul Lembo CTO - VMware - Americas Follow I am pleased to see #Broadcom and #Google enter into a long term agreement around TPUs. How much the AI… see more LIKE PRAISE EMPATHY 190 Read more Read more
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
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Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
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Предупреждение при превишаване на разрешената скорост
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Известия при наближаване, влизане и излизане от контролиран участък
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Актуална карта на активните зони
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Поддръжка за различни типове МПС
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Работа във фонов режим – успоредно с навигационни приложения
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Следи средната си скорост с TollPass App
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Vinetki.bg [EMAIL]
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Wed 29 Apr, 18:54 (9 days ago)
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
Download attachment
✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
Download attachment
Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
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DXP4800PLUS-B5F8
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Следи средната си скорост с TollPass App
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Vinetki.bg [EMAIL]
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Wed 29 Apr, 18:54 (9 days ago)
Wed 29 Apr, 18:54 (9 days ago)
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
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✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
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Не се изисква нова регистрация.
Функционалността е достъпна след обновяване на приложението от App Store, Google Play или AppGallery.
Шофирайте безопасно!
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Обнови / Изтегли TollPass App
Обнови / Изтегли TollPass App
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Следи средната си скорост с TollPass App
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Wed 29 Apr, 18:54 (9 days ago)
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TollPass App - Средна скорост
Здравейте,
Уведомяваме ви, че
TollPass App
вече включва функционалност за следене на средна скорост в реално време в контролираните участъци от пътната мрежа в България.
Функционалността е безплатна и включва:
✓
Изчисляване и показване на текущата средна скорост при преминаване през контролирани отсечки
✓
Предупреждение при превишаване на разрешената скорост
✓
Известия при наближаване, влизане и излизане от контролиран участък
Download attachment
✓
Актуална карта на активните зони
✓
Поддръжка за различни типове МПС
✓
Работа във фонов режим – успоредно с навигационни приложения
Download attachment
Не се изисква нова регистрация.
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Обнови / Изтегли TollPass App
Обнови / Изтегли TollPass App
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Does VCS choice = delivery destiny? | Confident Commit newsletter #14
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You can tell a lot about a developer from the tools they use. Editor, OS, language, framework. Everyone has a side, and everyone is a little suspicious of the other side's choices. Version control is no exception. Ask a GitHub team and a GitLab team how they ship software, and you will get two pretty different descriptions of what normal looks like.
But do these preferences (and their downstream impacts) actually show up in delivery metrics? Each VCS attracts a certain kind of team, and each platform builds features to reinforce those tendencies. If teams really do self-select based on their priorities, then those priorities should show up in the delivery data.
In this issue of Confident Commit, we break down data from our most recent
State of Software Delivery
by code hosting platform to find out.
The evolving source of truth
The evolving source of truth
Git
has been the foundation of collaborative software development since Linus Torvalds created it in 2005 to manage the Linux kernel. It quickly became the industry standard, displacing older centralized systems like Subversion and CVS.
Hosting platforms arrived shortly after to enable team collaboration at scale. GitHub launched in 2008, Bitbucket the same year (acquired by Atlassian in 2010), and GitLab in 2011. Between them, these three cover the vast majority of professional engineering teams today.
Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:
Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.
CircleCI
integrates with all three platforms
, so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.
Duration: how long is the feedback loop?
Duration: how long is the feedback loop?
Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.
Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.
Download attachment
GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.
Throughput: how much is moving through the pipeline?
Throughput: how much is moving through the pipeline?
Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.
Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.
Download attachment
But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.
Download attachment
As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. This makes sense considering GitHub's user base skews toward smaller teams, open-source projects, and startups that prioritize iteration speed over formal approval processes.
But on the main branch, GitLab teams run the most workflows at 2 per day compared to 1.68 for GitHub and 1 for BitBucket. While this runs contrary to what we might first expect, the finding is consistent with
previous research we’ve done
showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).
Success rate: how healthy is the path to production?
Success rate: how healthy is the path to production?
Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.
Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.
Download attachment
Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.
Time to recovery: how long until you're back to green?
Time to recovery: how long until you're back to green?
Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.
Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.
Download attachment
On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.
Download attachment
This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.
Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. The teams who chose the governance-first platform are the ones paying the governance tax on recovery.
What did we learn?
What did we learn?
While impactful, tool choices alone are never the determining factor for how a team performs. Most of that comes down to experience, engineering vision, processes, and how you use the tools at your disposal. But it’s also true that teams gravitate toward the tools that map to their way of doing things, and over time, those choices become part of a team's identity.
Each cohort's delivery performance aligns closely with what their platform was designed to support. Teams self-select into the tool that matches their priorities, and those same priorities show up in the data.
Each platform attracts teams with different priorities, and those priorities shape delivery outcomes. So while your VCS doesn't determine your delivery destiny, it does reflect the outcomes you're optimizing for.
Go deeper
Go deeper
VCS choice is one way to see how different choices and team characteristics shape delivery performance. The
2026 State of Software Delivery
takes a deeper look at what drives these differences, cutting the same metrics by team size, industry, and geography to show how delivery patterns shift depending on who you are, where you are, and what you are building.
Download the report
to see the full analysis, or explore the data and benchmark your team against similar organizations in the
interactive Software Delivery Data Explorer
. The more you understand about how teams like yours ship, the easier it is to know what to optimize next.
Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Want even more data and insights?
Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Subscribe
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Editor, OS, language, framework. Everyone has a side, and everyone is a little suspicious of the other side's choices. Version control is no exception. Ask a GitHub team and a GitLab team how they ship software, and you will get two pretty different descriptions of what normal looks like.","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"But do these preferences (and their downstream impacts) actually show up in delivery metrics? Each VCS attracts a certain kind of team, and each platform builds features to reinforce those tendencies. If teams really do self-select based on their priorities, then those priorities should show up in the delivery data.","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In this issue of Confident Commit, we break down data from our most recent","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"State of Software Delivery","depth":29,"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"by code hosting platform to find out.","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"The evolving source of truth","depth":28,"on_screen":true,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The evolving source of truth","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Git","depth":29,"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"has been the foundation of collaborative software development since Linus Torvalds created it in 2005 to manage the Linux kernel. It quickly became the industry standard, displacing older centralized systems like Subversion and CVS.","depth":29,"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Hosting platforms arrived shortly after to enable team collaboration at scale. GitHub launched in 2008, Bitbucket the same year (acquired by Atlassian in 2010), and GitLab in 2011. Between them, these three cover the vast majority of professional engineering teams today.","depth":29,"bounds":{"left":0.0,"top":0.0,"width":0.38402778,"height":0.094444446},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:","depth":29,"bounds":{"left":0.0,"top":0.123333335,"width":0.37743056,"height":0.06944445},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.","depth":29,"bounds":{"left":0.0,"top":0.38222224,"width":0.37604168,"height":0.094444446},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CircleCI","depth":29,"bounds":{"left":0.0,"top":0.50722224,"width":0.039930556,"height":0.019444445},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"integrates with all three platforms","depth":29,"bounds":{"left":0.010763889,"top":0.50722224,"width":0.15277778,"height":0.019444445},"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.","depth":29,"bounds":{"left":0.0,"top":0.50722224,"width":0.38784721,"height":0.06944445},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Duration: how long is the feedback loop?","depth":28,"bounds":{"left":0.0,"top":0.625,"width":0.3888889,"height":0.033333335},"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Duration: how long is the feedback loop?","depth":29,"bounds":{"left":0.0,"top":0.6288889,"width":0.27222222,"height":0.025555555},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.","depth":29,"bounds":{"left":0.0,"top":0.6861111,"width":0.36805555,"height":0.119444445},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.","depth":29,"bounds":{"left":0.0,"top":0.8361111,"width":0.38229167,"height":0.06944445},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.","depth":29,"bounds":{"left":0.0,"top":0.95555556,"width":0.38680556,"height":0.04444444},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Throughput: how much is moving through the pipeline?","depth":28,"bounds":{"left":0.0,"top":1.0,"width":0.3888889,"height":-0.09833336},"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Throughput: how much is moving through the pipeline?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. 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While this runs contrary to what we might first expect, the finding is consistent with","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"previous research we’ve done","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Success rate: how healthy is the path to production?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success rate: how healthy is the path to production?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Time to recovery: how long until you're back to green?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Time to recovery: how long until you're back to green?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. 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The more you understand about how teams like yours ship, the easier it is to know what to optimize next.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Want even more data and insights?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tune into the Confident Commit podcast for conversations with the world's best software practitioners.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Subscribe","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"watch-a-demo4","depth":27,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"CircleCI, 130 Sutter St, Floor 7, San Francisco, CA 94104, United States","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Unsubscribe","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Manage preferences","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Reply","depth":14,"bounds":{"left":0.0,"top":0.0,"width":0.072222225,"height":0.04},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Reply","depth":15,"bounds":{"left":0.0,"top":0.0,"width":0.025694445,"height":0.020555556},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Forward","depth":14,"bounds":{"left":0.0,"top":0.0,"width":0.07777778,"height":0.04},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Forward","depth":15,"bounds":{"left":0.0069444445,"top":0.0,"width":0.0375,"height":0.020555556},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":15,"bounds":{"left":0.061805554,"top":0.0,"width":0.025,"height":0.04},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Calendar","depth":10,"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Keep","depth":10,"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Tasks","depth":10,"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Contacts","depth":10,"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Get add-ons","depth":10,"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Hide side panel","depth":9,"bounds":{"left":0.4409722,"top":0.0,"width":0.03888889,"height":0.062222224},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Does VCS choice = delivery destiny? | Confident Commit newsletter #14
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Wed 29 Apr, 16:16 (9 days ago)
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You can tell a lot about a developer from the tools they use. Editor, OS, language, framework. Everyone has a side, and everyone is a little suspicious of the other side's choices. Version control is no exception. Ask a GitHub team and a GitLab team how they ship software, and you will get two pretty different descriptions of what normal looks like.
But do these preferences (and their downstream impacts) actually show up in delivery metrics? Each VCS attracts a certain kind of team, and each platform builds features to reinforce those tendencies. If teams really do self-select based on their priorities, then those priorities should show up in the delivery data.
In this issue of Confident Commit, we break down data from our most recent
State of Software Delivery
by code hosting platform to find out.
The evolving source of truth
The evolving source of truth
Git
has been the foundation of collaborative software development since Linus Torvalds created it in 2005 to manage the Linux kernel. It quickly became the industry standard, displacing older centralized systems like Subversion and CVS.
Hosting platforms arrived shortly after to enable team collaboration at scale. GitHub launched in 2008, Bitbucket the same year (acquired by Atlassian in 2010), and GitLab in 2011. Between them, these three cover the vast majority of professional engineering teams today.
Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:
Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.
CircleCI
integrates with all three platforms
, so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.
Duration: how long is the feedback loop?
Duration: how long is the feedback loop?
Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.
Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.
Download attachment
GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.
Throughput: how much is moving through the pipeline?
Throughput: how much is moving through the pipeline?
Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.
Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.
Download attachment
But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.
Download attachment
As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. This makes sense considering GitHub's user base skews toward smaller teams, open-source projects, and startups that prioritize iteration speed over formal approval processes.
But on the main branch, GitLab teams run the most workflows at 2 per day compared to 1.68 for GitHub and 1 for BitBucket. While this runs contrary to what we might first expect, the finding is consistent with
previous research we’ve done
showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).
Success rate: how healthy is the path to production?
Success rate: how healthy is the path to production?
Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.
Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.
Download attachment
Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.
Time to recovery: how long until you're back to green?
Time to recovery: how long until you're back to green?
Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.
Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.
Download attachment
On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.
Download attachment
This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.
Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. The teams who chose the governance-first platform are the ones paying the governance tax on recovery.
What did we learn?
What did we learn?
While impactful, tool choices alone are never the determining factor for how a team performs. Most of that comes down to experience, engineering vision, processes, and how you use the tools at your disposal. But it’s also true that teams gravitate toward the tools that map to their way of doing things, and over time, those choices become part of a team's identity.
Each cohort's delivery performance aligns closely with what their platform was designed to support. Teams self-select into the tool that matches their priorities, and those same priorities show up in the data.
Each platform attracts teams with different priorities, and those priorities shape delivery outcomes. So while your VCS doesn't determine your delivery destiny, it does reflect the outcomes you're optimizing for.
Go deeper
Go deeper
VCS choice is one way to see how different choices and team characteristics shape delivery performance. The
2026 State of Software Delivery
takes a deeper look at what drives these differences, cutting the same metrics by team size, industry, and geography to show how delivery patterns shift depending on who you are, where you are, and what you are building.
Download the report
to see the full analysis, or explore the data and benchmark your team against similar organizations in the
interactive Software Delivery Data Explorer
. The more you understand about how teams like yours ship, the easier it is to know what to optimize next.
Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Want even more data and insights?
Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Subscribe
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Does VCS choice = delivery destiny? | Confident Commit newsletter #14
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Wed 29 Apr, 16:16 (9 days ago)
Wed 29 Apr, 16:16 (9 days ago)
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You can tell a lot about a developer from the tools they use. Editor, OS, language, framework. Everyone has a side, and everyone is a little suspicious of the other side's choices. Version control is no exception. Ask a GitHub team and a GitLab team how they ship software, and you will get two pretty different descriptions of what normal looks like.
But do these preferences (and their downstream impacts) actually show up in delivery metrics? Each VCS attracts a certain kind of team, and each platform builds features to reinforce those tendencies. If teams really do self-select based on their priorities, then those priorities should show up in the delivery data.
In this issue of Confident Commit, we break down data from our most recent
State of Software Delivery
by code hosting platform to find out.
The evolving source of truth
The evolving source of truth
Git
has been the foundation of collaborative software development since Linus Torvalds created it in 2005 to manage the Linux kernel. It quickly became the industry standard, displacing older centralized systems like Subversion and CVS.
Hosting platforms arrived shortly after to enable team collaboration at scale. GitHub launched in 2008, Bitbucket the same year (acquired by Atlassian in 2010), and GitLab in 2011. Between them, these three cover the vast majority of professional engineering teams today.
Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:
Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.
CircleCI
integrates with all three platforms
, so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.
Duration: how long is the feedback loop?
Duration: how long is the feedback loop?
Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.
Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.
Download attachment
GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.
Throughput: how much is moving through the pipeline?
Throughput: how much is moving through the pipeline?
Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.
Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.
Download attachment
But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.
Download attachment
As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. This makes sense considering GitHub's user base skews toward smaller teams, open-source projects, and startups that prioritize iteration speed over formal approval processes.
But on the main branch, GitLab teams run the most workflows at 2 per day compared to 1.68 for GitHub and 1 for BitBucket. While this runs contrary to what we might first expect, the finding is consistent with
previous research we’ve done
showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).
Success rate: how healthy is the path to production?
Success rate: how healthy is the path to production?
Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.
Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.
Download attachment
Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.
Time to recovery: how long until you're back to green?
Time to recovery: how long until you're back to green?
Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.
Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.
Download attachment
On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.
Download attachment
This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.
Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. The teams who chose the governance-first platform are the ones paying the governance tax on recovery.
What did we learn?
What did we learn?
While impactful, tool choices alone are never the determining factor for how a team performs. Most of that comes down to experience, engineering vision, processes, and how you use the tools at your disposal. But it’s also true that teams gravitate toward the tools that map to their way of doing things, and over time, those choices become part of a team's identity.
Each cohort's delivery performance aligns closely with what their platform was designed to support. Teams self-select into the tool that matches their priorities, and those same priorities show up in the data.
Each platform attracts teams with different priorities, and those priorities shape delivery outcomes. So while your VCS doesn't determine your delivery destiny, it does reflect the outcomes you're optimizing for.
Go deeper
Go deeper
VCS choice is one way to see how different choices and team characteristics shape delivery performance. The
2026 State of Software Delivery
takes a deeper look at what drives these differences, cutting the same metrics by team size, industry, and geography to show how delivery patterns shift depending on who you are, where you are, and what you are building.
Download the report
to see the full analysis, or explore the data and benchmark your team against similar organizations in the
interactive Software Delivery Data Explorer
. The more you understand about how teams like yours ship, the easier it is to know what to optimize next.
Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Want even more data and insights?
Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
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Between them, these three cover the vast majority of professional engineering teams today.","depth":29,"bounds":{"left":0.25631648,"top":0.9988029,"width":0.18384309,"height":0.0011970997},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:","depth":29,"bounds":{"left":0.25631648,"top":1.0,"width":0.18068483,"height":-0.0885874},"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"CircleCI","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"integrates with all three platforms","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Duration: how long is the feedback loop?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Duration: how long is the feedback loop?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Throughput: how much is moving through the pipeline?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Throughput: how much is moving through the pipeline?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. This makes sense considering GitHub's user base skews toward smaller teams, open-source projects, and startups that prioritize iteration speed over formal approval processes.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"But on the main branch, GitLab teams run the most workflows at 2 per day compared to 1.68 for GitHub and 1 for BitBucket. While this runs contrary to what we might first expect, the finding is consistent with","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"previous research we’ve done","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Success rate: how healthy is the path to production?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success rate: how healthy is the path to production?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Time to recovery: how long until you're back to green?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Time to recovery: how long until you're back to green?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"on_screen":false,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. The teams who chose the governance-first platform are the ones paying the governance tax on recovery.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"What did we learn?","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What did we learn?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"While impactful, tool choices alone are never the determining factor for how a team performs. Most of that comes down to experience, engineering vision, processes, and how you use the tools at your disposal. But it’s also true that teams gravitate toward the tools that map to their way of doing things, and over time, those choices become part of a team's identity.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Each cohort's delivery performance aligns closely with what their platform was designed to support. Teams self-select into the tool that matches their priorities, and those same priorities show up in the data.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Each platform attracts teams with different priorities, and those priorities shape delivery outcomes. So while your VCS doesn't determine your delivery destiny, it does reflect the outcomes you're optimizing for.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Go deeper","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Go deeper","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"VCS choice is one way to see how different choices and team characteristics shape delivery performance. The","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"2026 State of Software Delivery","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"takes a deeper look at what drives these differences, cutting the same metrics by team size, industry, and geography to show how delivery patterns shift depending on who you are, where you are, and what you are building.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Download the report","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"to see the full analysis, or explore the data and benchmark your team against similar organizations in the","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"interactive Software Delivery Data Explorer","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":". The more you understand about how teams like yours ship, the easier it is to know what to optimize next.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.","depth":28,"on_screen":false,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Want even more data and insights?","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Tune into the Confident Commit podcast for conversations with the world's best software practitioners.","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Subscribe","depth":28,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"watch-a-demo4","depth":27,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"CircleCI, 130 Sutter St, Floor 7, San Francisco, CA 94104, United States","depth":29,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Unsubscribe","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Manage preferences","depth":29,"on_screen":false,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Reply","depth":14,"bounds":{"left":0.22273937,"top":0.9465283,"width":0.034574468,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Reply","depth":15,"bounds":{"left":0.23786569,"top":0.9537111,"width":0.012300532,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Forward","depth":14,"bounds":{"left":0.2599734,"top":0.9465283,"width":0.03723404,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Forward","depth":15,"bounds":{"left":0.27360374,"top":0.9537111,"width":0.017952127,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":15,"bounds":{"left":0.29986703,"top":0.9465283,"width":0.011968086,"height":0.028731046},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Calendar","depth":10,"bounds":{"left":0.48138297,"top":0.10295291,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Keep","depth":10,"bounds":{"left":0.48138297,"top":0.14764565,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Tasks","depth":10,"bounds":{"left":0.48138297,"top":0.19233839,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Contacts","depth":10,"bounds":{"left":0.48138297,"top":0.23703113,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXRadioButton","text":"Get add-ons","depth":10,"bounds":{"left":0.48138297,"top":0.30806065,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"tab","subrole":"AXTabButton","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Hide side panel","depth":9,"bounds":{"left":0.48138297,"top":0.95530725,"width":0.01861702,"height":0.044692736},"on_screen":true,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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Does VCS choice = delivery destiny? | Confident Commit newsletter #14 - [EMAIL] - Gmail
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Does VCS choice = delivery destiny? | Confident Commit newsletter #14
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You can tell a lot about a developer from the tools they use. Editor, OS, language, framework. Everyone has a side, and everyone is a little suspicious of the other side's choices. Version control is no exception. Ask a GitHub team and a GitLab team how they ship software, and you will get two pretty different descriptions of what normal looks like.
But do these preferences (and their downstream impacts) actually show up in delivery metrics? Each VCS attracts a certain kind of team, and each platform builds features to reinforce those tendencies. If teams really do self-select based on their priorities, then those priorities should show up in the delivery data.
In this issue of Confident Commit, we break down data from our most recent
State of Software Delivery
by code hosting platform to find out.
The evolving source of truth
The evolving source of truth
Git
has been the foundation of collaborative software development since Linus Torvalds created it in 2005 to manage the Linux kernel. It quickly became the industry standard, displacing older centralized systems like Subversion and CVS.
Hosting platforms arrived shortly after to enable team collaboration at scale. GitHub launched in 2008, Bitbucket the same year (acquired by Atlassian in 2010), and GitLab in 2011. Between them, these three cover the vast majority of professional engineering teams today.
Despite sharing the same underlying technology, these platforms evolved to serve different kinds of teams and workflows, making unique design choices along the way:
Given those philosophies, you would expect GitHub teams to iterate fastest, while Bitbucket teams move with more deliberate enterprise ceremony. GitLab teams, often working in complex and regulated environments, should land somewhere in between.
CircleCI
integrates with all three platforms
, so we can test those expectations directly to see if they hold up. Let's take a look at how the hosting platforms stack up across the four key software delivery metrics.
Duration: how long is the feedback loop?
Duration: how long is the feedback loop?
Duration measures the time from when a pipeline starts to when it finishes and determines how long a developer waits to find out whether a change passed or failed. Teams optimizing for rapid iteration want this number as low as possible. Teams running deeper validation, with more extensive tests and approval gates, accept longer durations in exchange for higher confidence in what they ship.
Workflow duration is generally consistent across all three platforms. At the median, the three cohorts are within a few seconds of each other: GitHub at 2m 58s, GitLab at 3m 4s, and Bitbucket at 2m 58s.
Download attachment
GitLab teams running slightly longer workflows is consistent with the hypothesis that these teams run governance-heavy pipelines, but the overall story here is similarity rather than divergence. Even at p90, pipeline durations are closely clustered at 493, 489, and 484 minutes respectively.
Throughput: how much is moving through the pipeline?
Throughput: how much is moving through the pipeline?
Throughput is the number of workflows a team runs per day. It can reveal how actively a team is pushing changes, and is often used as a directional indicator of team productivity.
Looking across all branches, Bitbucket teams run 1.71 workflows per day, while GitHub teams run 2.25 and GitLab teams 2.55.
Download attachment
But the branch-level breakdown is where the numbers get interesting. Throughput on feature branches tells a story about iteration speed: how often developers are committing, running CI, and testing changes before they are ready to merge. On the main branch, throughput is a proxy for how often code moves toward production. Given what we know about each platform's user base, you would expect GitHub teams, as the most iteration-focused cohort, to lead on throughput.
Download attachment
As expected, GitHub is the runaway leader on feature branches at 3.39 workflows per day versus 2.52 for GitLab and 1.71 for BitBucket. This makes sense considering GitHub's user base skews toward smaller teams, open-source projects, and startups that prioritize iteration speed over formal approval processes.
But on the main branch, GitLab teams run the most workflows at 2 per day compared to 1.68 for GitHub and 1 for BitBucket. While this runs contrary to what we might first expect, the finding is consistent with
previous research we’ve done
showing compliance-heavy teams shipping more frequently but hitting friction in other areas of the SDLC (more on that below).
Success rate: how healthy is the path to production?
Success rate: how healthy is the path to production?
Success rate is the percentage of workflow runs that complete successfully. The default branch is where this metric matters most. Teams with efficient delivery processes catch errors locally or on feature branches before merging, so a high success rate on main is a signal that the upstream process is working.
Average success rate on main-branch workflows is highest for Bitbucket teams at 73.7% and falls to 70.6% for GitHub and 61.3% for GitLab.
Download attachment
Bitbucket teams running the fewest pipelines but passing more of them is consistent with a stability-leaning culture where more review happens before a merge. GitHub sits in the middle, reflecting a culture comfortable operating at tempo while keeping things green. GitLab's lower success rate likely reflects the complexity of the environments these teams operate in, something the recovery data makes even clearer.
Time to recovery: how long until you're back to green?
Time to recovery: how long until you're back to green?
Time to recovery measures resilience: how long it takes to fix a broken build and get back to a successful state.
Across all branches, GitHub teams recover in 72 minutes, Bitbucket in 80, and GitLab in 124.
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On the main branch specifically, those differences are magnified: GitHub teams recover in under an hour, BitBucket teams take about 80 minutes, and GitLab teams require nearly 8 hours to get back to green.
Download attachment
This may seem like a large delta. However, these results are consistent with what we’ve seen previously regarding high complexity, compliance-heavy workflows. When things go sideways on main in a complex environment, the need for more extensive root cause analysis, documentation, and verification before merging a fix can add significant time to the recovery process.
Of all four metrics, time to recovery is where the self-selection pattern shows up most dramatically. The teams who chose the governance-first platform are the ones paying the governance tax on recovery.
What did we learn?
What did we learn?
While impactful, tool choices alone are never the determining factor for how a team performs. Most of that comes down to experience, engineering vision, processes, and how you use the tools at your disposal. But it’s also true that teams gravitate toward the tools that map to their way of doing things, and over time, those choices become part of a team's identity.
Each cohort's delivery performance aligns closely with what their platform was designed to support. Teams self-select into the tool that matches their priorities, and those same priorities show up in the data.
Each platform attracts teams with different priorities, and those priorities shape delivery outcomes. So while your VCS doesn't determine your delivery destiny, it does reflect the outcomes you're optimizing for.
Go deeper
Go deeper
VCS choice is one way to see how different choices and team characteristics shape delivery performance. The
2026 State of Software Delivery
takes a deeper look at what drives these differences, cutting the same metrics by team size, industry, and geography to show how delivery patterns shift depending on who you are, where you are, and what you are building.
Download the report
to see the full analysis, or explore the data and benchmark your team against similar organizations in the
interactive Software Delivery Data Explorer
. The more you understand about how teams like yours ship, the easier it is to know what to optimize next.
Want even more data and insights? 🎧 Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
Want even more data and insights?
Tune into the Confident Commit podcast for conversations with the world's best software practitioners.
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Philosophy of Genius Level Philosophy of Genius Level • 19.8K followers Only the few can find answers to the questions that matter ~Est. 2020
How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Jim Dennis
Jim Dennis
, I appreciate this ACID enabled, server-less RDBMS
Answered February 5
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
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How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Brett Collins
Brett Collins
, Pastor / Therapist (2017-present)
Answered March 24
It depends highly on the other personality types that make up the intelligent person. I have above average conscientiousness but also high neuroticism, so this means I’m gen...
It depends highly on the other personality types that make up the intelligent person.
I have above average conscientiousness but also high neuroticism, so this means I’m gen...
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Philosophy of Genius Level Philosophy of Genius Level • 19.8K followers Only the few can find answers to the questions that matter ~Est. 2020
How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Jim Dennis
Jim Dennis
, I appreciate this ACID enabled, server-less RDBMS
Answered February 5
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
Read more »
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How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Brett Collins
Brett Collins
, Pastor / Therapist (2017-present)
Answered March 24
It depends highly on the other personality types that make up the intelligent person. I have above average conscientiousness but also high neuroticism, so this means I’m gen...
It depends highly on the other personality types that make up the intelligent person.
I have above average conscientiousness but also high neuroticism, so this means I’m gen...
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Philosophy of Genius Level Philosophy of Genius Level • 19.8K followers Only the few can find answers to the questions that matter ~Est. 2020
How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Jim Dennis
Jim Dennis
, I appreciate this ACID enabled, server-less RDBMS
Answered February 5
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
Read more »
Read more »
How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Brett Collins
Brett Collins
, Pastor / Therapist (2017-present)
Answered March 24
It depends highly on the other personality types that make up the intelligent person. I have above average conscientiousness but also high neuroticism, so this means I’m gen...
It depends highly on the other personality types that make up the intelligent person.
I have above average conscientiousness but also high neuroticism, so this means I’m gen...
Read more »
Read more »
Read more in Philosophy of Genius Level
Read more in Philosophy of Genius Level
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But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...","depth":26,"bounds":{"left":0.2621343,"top":0.50039905,"width":0.17453457,"height":0.070231445},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...","depth":28,"bounds":{"left":0.2621343,"top":0.5027933,"width":0.17420213,"height":0.053072624},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Read more »","depth":26,"bounds":{"left":0.2621343,"top":0.57302475,"width":0.02925532,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Read more »","depth":27,"bounds":{"left":0.2621343,"top":0.57302475,"width":0.02925532,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"How do extremely intelligent people spend their time?","depth":26,"bounds":{"left":0.2621343,"top":0.63647246,"width":0.15375665,"height":0.018355945},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"How do extremely intelligent people spend their time?","depth":27,"bounds":{"left":0.2621343,"top":0.63647246,"width":0.15375665,"height":0.018355945},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Brett Collins","depth":28,"bounds":{"left":0.26246676,"top":0.6632083,"width":0.013297873,"height":0.031923383},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Brett Collins","depth":29,"bounds":{"left":0.27875665,"top":0.6632083,"width":0.03025266,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":", Pastor / Therapist (2017-present)","depth":29,"bounds":{"left":0.3090093,"top":0.6632083,"width":0.0787899,"height":0.014764565},"on_screen":true,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Answered March 24","depth":29,"bounds":{"left":0.27875665,"top":0.68076617,"width":0.040059842,"height":0.012769354},"on_screen":true,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"It depends highly on the other personality types that make up the intelligent person. 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Philosophy of Genius Level Philosophy of Genius Level • 19.8K followers Only the few can find answers to the questions that matter ~Est. 2020
How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Jim Dennis
Jim Dennis
, I appreciate this ACID enabled, server-less RDBMS
Answered February 5
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
I can't generalize on the topic. But I would confidently place bets at decent odds that extremely intelligent folks spend too much time much of their time filtering out stu...
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How do extremely intelligent people spend their time?
How do extremely intelligent people spend their time?
Brett Collins
Brett Collins
, Pastor / Therapist (2017-present)
Answered March 24
It depends highly on the other personality types that make up the intelligent person. I have above average conscientiousness but also high neuroticism, so this means I’m gen...
It depends highly on the other personality types that make up the intelligent person.
I have above average conscientiousness but also high neuroticism, so this means I’m gen...
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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April 29, 2026
Welcome to your mid-week checkpoint!
Fancy some downtime with some old Nintendo 64 games? You're in luck, as some classics are finally getting unofficial PC ports, many of which run better than through emulation. We're not about talking emulation or remakes, but something in between: decompilation. But it's not just N64 games that are in the sights of those working tirelessly to bring classics to the PC platform. Sonic, Pokemon, and other series are also on the cards.
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Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Adam Conway
By
Adam Conway
Adam Conway
Apr 28, 2026
A screenshot of Zelda: Ocarina of Time running in Ship of Harkinian
If you want to play an N64 game on your PC, you've almost certainly used an emulator at some point.
Project64
Project64
,
mupen64plus,
mupen64plus,
or maybe the
N64 core in RetroArch
N64 core in RetroArch
. You drop in a ROM, the emulator reproduces the N64's CPU, graphics, audio, and coprocessor behavior well enough for the game to run, and it
just
works.
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Upgrade your workspace with a UGREEN Maxidok Thunderbolt 5 docking station
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UGREEN’S Maxidok Thunderbolt 5 docking stations are the upgrade you need for your desktop. These fast and powerful models are standout devices in the brand’s lineup, offering up to 17 ports, including USB-C, USB-A, Thunderbolt 5, and more, to create efficiencies in your workflow. Depending on your needs, there are 10 and 17-port options available – and with up to a $110 discount, the docks can enhance your setup for less.
No, Resizable BAR isn’t the universal performance booster you think it is
No, Resizable BAR isn’t the universal performance booster you think it is
Samarveer Singh
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Samarveer Singh
Samarveer Singh
Apr 28, 2026
nvidia geforce rtx 4080 super fe closeup to show the large fan near the io shield
You flip the switch in BIOS, boot into Windows, open your GPU panel, and there it is: enabled. Resizable BAR, the feature that's supposed to let your CPU access your GPU's full memory buffer, is ready to go. Except sometimes, it just isn't really enabled.
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My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
Ayush Pande
By
Ayush Pande
Ayush Pande
Apr 28, 2026
The Zimaboard 2 SBC
When it comes to building a network stack, the most common piece of advice floating online involves ditching your ISP’s router for another (typically premium) option.
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Apple Watch Series 11
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roborock Q7 M5+ Robot Vacuum
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I ditched Adobe's $55 monthly fee for three free tools that actually work faster
I ditched Adobe's $55 monthly fee for three free tools that actually work faster
Jasmine Mannan
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Jasmine Mannan
Jasmine Mannan
Apr 28, 2026
Adobe's suite on a Mac
Adobe's generative fill and Firefly features are absolutely magical, but they are locked behind a mandatory subscription, a cloud credit system, and a privacy policy that often leaves many professionals uneasy. The 600 AI isn't just for Linux nerds anymore.
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This is the mini PC that finally made me display my home server instead of burying it
This is the mini PC that finally made me display my home server instead of burying it
Joe Rice-Jones
By
Joe Rice-Jones
Joe Rice-Jones
Apr 28, 2026
minisforum ai x1 pro 470 mini pc on a table
It's no secret I love
mini PCs
mini PCs
. They
run my home lab for the most part
run my home lab for the most part
, which I appreciate for their low power requirements and quiet operation. Minisforum is one of the bigger names, and I've been wanting to try one for some time, so when they offered the AI X1 Pro-470, I jumped at the chance.
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April 29, 2026
Welcome to your mid-week checkpoint!
Fancy some downtime with some old Nintendo 64 games? You're in luck, as some classics are finally getting unofficial PC ports, many of which run better than through emulation. We're not about talking emulation or remakes, but something in between: decompilation. But it's not just N64 games that are in the sights of those working tirelessly to bring classics to the PC platform. Sonic, Pokemon, and other series are also on the cards.
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Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Adam Conway
By
Adam Conway
Adam Conway
Apr 28, 2026
A screenshot of Zelda: Ocarina of Time running in Ship of Harkinian
If you want to play an N64 game on your PC, you've almost certainly used an emulator at some point.
Project64
Project64
,
mupen64plus,
mupen64plus,
or maybe the
N64 core in RetroArch
N64 core in RetroArch
. You drop in a ROM, the emulator reproduces the N64's CPU, graphics, audio, and coprocessor behavior well enough for the game to run, and it
just
works.
Read More »
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Upgrade your workspace with a UGREEN Maxidok Thunderbolt 5 docking station
SPONSORED
Upgrade your workspace with a UGREEN Maxidok Thunderbolt 5 docking station
UGREEN’S Maxidok Thunderbolt 5 docking stations are the upgrade you need for your desktop. These fast and powerful models are standout devices in the brand’s lineup, offering up to 17 ports, including USB-C, USB-A, Thunderbolt 5, and more, to create efficiencies in your workflow. Depending on your needs, there are 10 and 17-port options available – and with up to a $110 discount, the docks can enhance your setup for less.
No, Resizable BAR isn’t the universal performance booster you think it is
No, Resizable BAR isn’t the universal performance booster you think it is
Samarveer Singh
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Samarveer Singh
Samarveer Singh
Apr 28, 2026
nvidia geforce rtx 4080 super fe closeup to show the large fan near the io shield
You flip the switch in BIOS, boot into Windows, open your GPU panel, and there it is: enabled. Resizable BAR, the feature that's supposed to let your CPU access your GPU's full memory buffer, is ready to go. Except sometimes, it just isn't really enabled.
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My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
Ayush Pande
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Apr 28, 2026
The Zimaboard 2 SBC
When it comes to building a network stack, the most common piece of advice floating online involves ditching your ISP’s router for another (typically premium) option.
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Apple Watch Series 11
Apple Watch Series 11
$299
$399
SAVE 25%
Apple Watch Series 11
Apple Watch Series 11
$299
$399
SAVE 25%
roborock Q7 M5+ Robot Vacuum
roborock Q7 M5+ Robot Vacuum
$250
$360
SAVE 31%
roborock Q7 M5+ Robot Vacuum
roborock Q7 M5+ Robot Vacuum
$250
$360
SAVE 31%
I ditched Adobe's $55 monthly fee for three free tools that actually work faster
I ditched Adobe's $55 monthly fee for three free tools that actually work faster
Jasmine Mannan
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Jasmine Mannan
Jasmine Mannan
Apr 28, 2026
Adobe's suite on a Mac
Adobe's generative fill and Firefly features are absolutely magical, but they are locked behind a mandatory subscription, a cloud credit system, and a privacy policy that often leaves many professionals uneasy. The 600 AI isn't just for Linux nerds anymore.
Read More »
Read More »
This is the mini PC that finally made me display my home server instead of burying it
This is the mini PC that finally made me display my home server instead of burying it
Joe Rice-Jones
By
Joe Rice-Jones
Joe Rice-Jones
Apr 28, 2026
minisforum ai x1 pro 470 mini pc on a table
It's no secret I love
mini PCs
mini PCs
. They
run my home lab for the most part
run my home lab for the most part
, which I appreciate for their low power requirements and quiet operation. Minisforum is one of the bigger names, and I've been wanting to try one for some time, so when they offered the AI X1 Pro-470, I jumped at the chance.
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Enjoy more gaming classics without all those pesky emulators
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Wed 29 Apr, 14:40 (9 days ago)
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April 29, 2026
Welcome to your mid-week checkpoint!
Fancy some downtime with some old Nintendo 64 games? You're in luck, as some classics are finally getting unofficial PC ports, many of which run better than through emulation. We're not about talking emulation or remakes, but something in between: decompilation. But it's not just N64 games that are in the sights of those working tirelessly to bring classics to the PC platform. Sonic, Pokemon, and other series are also on the cards.
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Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Adam Conway
By
Adam Conway
Adam Conway
Apr 28, 2026
A screenshot of Zelda: Ocarina of Time running in Ship of Harkinian
If you want to play an N64 game on your PC, you've almost certainly used an emulator at some point.
Project64
Project64
,
mupen64plus,
mupen64plus,
or maybe the
N64 core in RetroArch
N64 core in RetroArch
. You drop in a ROM, the emulator reproduces the N64's CPU, graphics, audio, and coprocessor behavior well enough for the game to run, and it
just
works.
Read More »
Read More »
Upgrade your workspace with a UGREEN Maxidok Thunderbolt 5 docking station
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No, Resizable BAR isn’t the universal performance booster you think it is
No, Resizable BAR isn’t the universal performance booster you think it is
Samarveer Singh
By
Samarveer Singh
Samarveer Singh
Apr 28, 2026
nvidia geforce rtx 4080 super fe closeup to show the large fan near the io shield
You flip the switch in BIOS, boot into Windows, open your GPU panel, and there it is: enabled. Resizable BAR, the feature that's supposed to let your CPU access your GPU's full memory buffer, is ready to go. Except sometimes, it just isn't really enabled.
Read More »
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My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
Ayush Pande
By
Ayush Pande
Ayush Pande
Apr 28, 2026
The Zimaboard 2 SBC
When it comes to building a network stack, the most common piece of advice floating online involves ditching your ISP’s router for another (typically premium) option.
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I ditched Adobe's $55 monthly fee for three free tools that actually work faster
I ditched Adobe's $55 monthly fee for three free tools that actually work faster
Jasmine Mannan
By
Jasmine Mannan
Jasmine Mannan
Apr 28, 2026
Adobe's suite on a Mac
Adobe's generative fill and Firefly features are absolutely magical, but they are locked behind a mandatory subscription, a cloud credit system, and a privacy policy that often leaves many professionals uneasy. The 600 AI isn't just for Linux nerds anymore.
Read More »
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This is the mini PC that finally made me display my home server instead of burying it
This is the mini PC that finally made me display my home server instead of burying it
Joe Rice-Jones
By
Joe Rice-Jones
Joe Rice-Jones
Apr 28, 2026
minisforum ai x1 pro 470 mini pc on a table
It's no secret I love
mini PCs
mini PCs
. They
run my home lab for the most part
run my home lab for the most part
, which I appreciate for their low power requirements and quiet operation. Minisforum is one of the bigger names, and I've been wanting to try one for some time, so when they offered the AI X1 Pro-470, I jumped at the chance.
Read More »
Read More »
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Welcome to your mid-week checkpoint!
Fancy some downtime with some old Nintendo 64 games? You're in luck, as some classics are finally getting unofficial PC ports, many of which run better than through emulation. We're not about talking emulation or remakes, but something in between: decompilation. But it's not just N64 games that are in the sights of those working tirelessly to bring classics to the PC platform. Sonic, Pokemon, and other series are also on the cards.
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Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
Your old Nintendo 64 games are finally getting unofficial PC ports, and they run better than emulators
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Apr 28, 2026
A screenshot of Zelda: Ocarina of Time running in Ship of Harkinian
If you want to play an N64 game on your PC, you've almost certainly used an emulator at some point.
Project64
Project64
,
mupen64plus,
mupen64plus,
or maybe the
N64 core in RetroArch
N64 core in RetroArch
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No, Resizable BAR isn’t the universal performance booster you think it is
No, Resizable BAR isn’t the universal performance booster you think it is
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You flip the switch in BIOS, boot into Windows, open your GPU panel, and there it is: enabled. Resizable BAR, the feature that's supposed to let your CPU access your GPU's full memory buffer, is ready to go. Except sometimes, it just isn't really enabled.
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My DIY router has been my DNS, VPN, and ad-blocker for a year, and I'd never use an ISP router again
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The Zimaboard 2 SBC
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Apr 28, 2026
Adobe's suite on a Mac
Adobe's generative fill and Firefly features are absolutely magical, but they are locked behind a mandatory subscription, a cloud credit system, and a privacy policy that often leaves many professionals uneasy. The 600 AI isn't just for Linux nerds anymore.
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This is the mini PC that finally made me display my home server instead of burying it
This is the mini PC that finally made me display my home server instead of burying it
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Apr 28, 2026
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It's no secret I love
mini PCs
mini PCs
. They
run my home lab for the most part
run my home lab for the most part
, which I appreciate for their low power requirements and quiet operation. Minisforum is one of the bigger names, and I've been wanting to try one for some time, so when they offered the AI X1 Pro-470, I jumped at the chance.
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Здравейте, ЛУКАШ,
Успешно е заявен допълнителен пакет Пакет 100 Група 2 за услуга с номер 877878118. Ако желаете да научите повече детайли, моля влезте във Вашия профил
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DXP4800PLUS-B5F8
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Оптичен интернет за дома - EON телевизия | Vivacom | 5G
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Заявка на допълнителна услуга
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My VIVACOM [EMAIL]
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Wed 29 Apr, 09:35 (9 days ago)
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Pull requests · screenpipe/screenpipe · GitHub
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