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27836
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NULL
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0
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2026-05-12T14:40:56.794582+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-12/1778 /Users/lukas/.screenpipe/data/data/2026-05-12/1778596856794_m2.jpg...
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Activity Monitor
|
Activity Monitor – All Processes
|
True
|
NULL
|
monitor_2
|
NULL
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NULL
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NULL
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/Users/lukas/.screenpipe/data/data/2026-05-12/1778 /Users/lukas/.screenpipe/data/data/2026-05-12/1778596856794_m2.jpg...
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Physical Memory:
16,00 GB
Memory Used:
13,91 GB
Ca Physical Memory:
16,00 GB
Memory Used:
13,91 GB
Cached Files:
2,03 GB
Swap Used:
7,92 GB
3,80 GB
Wired Memory:
Compressed:
3,67 GB
5,89 GB
App Memory:
MEMORY PRESSURE
WindowServer
2,68 GB
21
21 856
402
_windowserver
Firefox
1,35 GB
84
1 155
40293
lukas
Postman Helper (Renderer)
1,30 GB
27
343
923
lukas
FirefoxCP Isolated Web Content
1,29 GB
25
110
83900
lukas
Firefox GPU Helper
1,29 GB
33
269
41400
lukas
FirefoxCP Isolated Web Content
1,25 GB
24
108
85313
lukas
Slack Helper (Renderer)
1,00 GB
24...
|
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NULL
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Physical Memory:
16,00 GB
Memory Used:
13,91 GB
Ca Physical Memory:
16,00 GB
Memory Used:
13,91 GB
Cached Files:
2,03 GB
Swap Used:
7,92 GB
3,80 GB
Wired Memory:
Compressed:
3,67 GB
5,89 GB
App Memory:
MEMORY PRESSURE
WindowServer
2,68 GB
21
21 856
402
_windowserver
Firefox
1,35 GB
84
1 155
40293
lukas
Postman Helper (Renderer)
1,30 GB
27
343
923
lukas
FirefoxCP Isolated Web Content
1,29 GB
25
110
83900
lukas
Firefox GPU Helper
1,29 GB
33
269
41400
lukas
FirefoxCP Isolated Web Content
1,25 GB
24
108
85313
lukas
Slack Helper (Renderer)
1,00 GB
24...
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27834
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NULL
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NULL
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12367
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547
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6
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2026-05-09T08:48:06.536726+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-09/1778 /Users/lukas/.screenpipe/data/data/2026-05-09/1778316486536_m1.jpg...
|
Firefox
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Agent Infrastructure and Production RAG - kovalikl Agent Infrastructure and Production RAG - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLjVJlvzJzS mail.google.com/mail/u/0/#inbox/FMfcgzQgLjVJlvzJzSntgSFZgmSnnBzk...
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monitor_1
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NULL
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NULL
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NULL
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Pull requests · screenpipe/screenpipe · GitHub
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Pull requests · screenpipe/screenpipe · GitHub
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Thu 7 May, 16:15 (2 days ago)
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Agent Infrastructure and Production RAG
Agent Infrastructure and Production RAG
Recap on the systems that make AI work: agent harnesses, execution loops and observability.
Recap on the systems that make AI work: agent harnesses, execution loops and observability.
PAUL IUSZTIN
PAUL IUSZTIN
MAY 7
We’ve spent the last few weeks building the infrastructure that makes agents work in production, and applying it directly to our own operations.
Less theory. More engineering.
Here’s a quick look at what we’ve been up to ↓
Building the Agent Harness
Building the Agent Harness
An LLM is just a next-token predictor. Making it a reliable agent requires a harness: the infrastructure that controls execution, manages memory, and enforces safety boundaries. Here’s how to do it properly:
Agentic Harness Engineering
Agentic Harness Engineering
Agentic Harness Engineering
The core setup for wrapping LLMs with the right tools, memory, and guardrails to make them autonomous and reliable.
Read the article →
Read the article →
Recursive Language Models
Recursive Language Models
Recursive Language Models
Design patterns for handling agents that need to spawn sub-agents or break down tasks recursively without losing context.
Read the article →
Read the article →
RALPH Loops
RALPH Loops
RALPH Loops
The specific execution loops that force reliable reasoning so your agents finish tasks and stop when they should.
Read the article →
Read the article →
Automating My Business
Automating My Business
It’s one thing to build AI architecture, but it’s another to rely on it. I turned my own operations into a testing ground for AI automation.
How I Automated 91% of My Business
How I Automated 91% of My Business
How I Automated 91% of My Business
A transparent look at the exact workflows, agents, and pipelines I use to put the bulk of my daily operations on autopilot.
Read the article →
Read the article →
The Ultimate LLM Knowledge Base
The Ultimate LLM Knowledge Base
The Ultimate LLM Knowledge Base
My personal "second brain" setup. A breakdown of how I connected Obsidian, Readwise, and NotebookLM into a retrieval system that synthesizes information instead of just storing it.
Read the article →
Read the article →
Agentic GraphRAG
Agentic GraphRAG
Agentic GraphRAG
Why do we even need GraphRAG in the first place? Why complicate our architecture over a simple RAG system? Because standard RAG eventually hits three walls:
Context Rot:
As the context window fills, the signal-to-noise ratio collapses, and the LLM degrades.
Data Fragmentation:
Data doesn't live in a single DB anymore. It's siloed across docs, notes, emails, and chats.
Unified Memory:
Tracking how facts and relationships change over time naturally requires a knowledge graph.
This is a data modeling problem, not a retrieval algorithm. You need an ontology.
Read the full article →
Read the full article →
Opik Case Studies: Shipping RAG with Weave CLI
Opik Case Studies: Shipping RAG with Weave CLI
In the last recap, I mentioned we’d be doing practical walkthroughs on using Opik for production observability and evaluation.
The first one is live!
The first one is live!
I sat down with
Michael Maximilien
Michael Maximilien
to talk about building Weave CLI and what he learned from spending a year shipping production RAG to enterprise customers.
The main takeaway?
Leaderboards are useless until tested against your own customer data.
Max shared how a small, open-source embedding model
(ranked ~#130 on MTEB)
beat OpenAI by 11% on real-world tasks, simply because of a continuous
stitch→evaluate→iterate
loop for testing components on the data that actually mattered.
We turned this conversation into a hands-on guide. We walk through shipping a production-ready RAG system using the Weave CLI, focusing strictly on tracing, observing, and evaluating pipelines so you don't deploy blind.
Check out the case study →
Check out the case study →
What’s Next?
What’s Next?
As promised, we’re going all in on the essential parts of AI engineering. Here’s what we have lined up for the coming weeks:
More Practical Walkthroughs:
Two additional case studies focused on production observability and evaluation.
Agentic Coding:
Deep dives into building agents that can write, debug, and navigate complex codebases.
Knowledge Graphs:
Expanding on how to build structured, relational memory for systems that need to reason over interconnected data.
See you next week.
Paul Iusztin
Paul Iusztin
© 2026
Paul Iusztin
[EMAIL]
[EMAIL]
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Pull requests · screenpipe/screenpipe · GitHub
Pul Pull requests · screenpipe/screenpipe · GitHub
Pull requests · screenpipe/screenpipe · GitHub
DNS / Nameservers | Hostinger
DNS / Nameservers | Hostinger
Nginx Proxy Manager
Nginx Proxy Manager
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
screenpipe/.claude/skills at main · screenpipe/screenpipe · GitHub
DXP4800PLUS-B5F8
DXP4800PLUS-B5F8
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Thu 7 May, 16:15 (2 days ago)
Thu 7 May, 16:15 (2 days ago)
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Agent Infrastructure and Production RAG
Agent Infrastructure and Production RAG
Recap on the systems that make AI work: agent harnesses, execution loops and observability.
Recap on the systems that make AI work: agent harnesses, execution loops and observability.
PAUL IUSZTIN
PAUL IUSZTIN
MAY 7
We’ve spent the last few weeks building the infrastructure that makes agents work in production, and applying it directly to our own operations.
Less theory. More engineering.
Here’s a quick look at what we’ve been up to ↓
Building the Agent Harness
Building the Agent Harness
An LLM is just a next-token predictor. Making it a reliable agent requires a harness: the infrastructure that controls execution, manages memory, and enforces safety boundaries. Here’s how to do it properly:
Agentic Harness Engineering
Agentic Harness Engineering
Agentic Harness Engineering
The core setup for wrapping LLMs with the right tools, memory, and guardrails to make them autonomous and reliable.
Read the article →
Read the article →
Recursive Language Models
Recursive Language Models
Recursive Language Models
Design patterns for handling agents that need to spawn sub-agents or break down tasks recursively without losing context.
Read the article →
Read the article →
RALPH Loops
RALPH Loops
RALPH Loops
The specific execution loops that force reliable reasoning so your agents finish tasks and stop when they should.
Read the article →
Read the article →
Automating My Business
Automating My Business
It’s one thing to build AI architecture, but it’s another to rely on it. I turned my own operations into a testing ground for AI automation.
How I Automated 91% of My Business
How I Automated 91% of My Business
How I Automated 91% of My Business
A transparent look at the exact workflows, agents, and pipelines I use to put the bulk of my daily operations on autopilot.
Read the article →
Read the article →
The Ultimate LLM Knowledge Base
The Ultimate LLM Knowledge Base
The Ultimate LLM Knowledge Base
My personal "second brain" setup. A breakdown of how I connected Obsidian, Readwise, and NotebookLM into a retrieval system that synthesizes information instead of just storing it.
Read the article →
Read the article →
Agentic GraphRAG
Agentic GraphRAG
Agentic GraphRAG
Why do we even need GraphRAG in the first place? Why complicate our architecture over a simple RAG system? Because standard RAG eventually hits three walls:
Context Rot:
As the context window fills, the signal-to-noise ratio collapses, and the LLM degrades.
Data Fragmentation:
Data doesn't live in a single DB anymore. It's siloed across docs, notes, emails, and chats.
Unified Memory:
Tracking how facts and relationships change over time naturally requires a knowledge graph.
This is a data modeling problem, not a retrieval algorithm. You need an ontology.
Read the full article →
Read the full article →
Opik Case Studies: Shipping RAG with Weave CLI
Opik Case Studies: Shipping RAG with Weave CLI
In the last recap, I mentioned we’d be doing practical walkthroughs on using Opik for production observability and evaluation.
The first one is live!
The first one is live!
I sat down with
Michael Maximilien
Michael Maximilien
to talk about building Weave CLI and what he learned from spending a year shipping production RAG to enterprise customers.
The main takeaway?
Leaderboards are useless until tested against your own customer data.
Max shared how a small, open-source embedding model
(ranked ~#130 on MTEB)
beat OpenAI by 11% on real-world tasks, simply because of a continuous
stitch→evaluate→iterate
loop for testing components on the data that actually mattered.
We turned this conversation into a hands-on guide. We walk through shipping a production-ready RAG system using the Weave CLI, focusing strictly on tracing, observing, and evaluating pipelines so you don't deploy blind.
Check out the case study →
Check out the case study →
What’s Next?
What’s Next?
As promised, we’re going all in on the essential parts of AI engineering. Here’s what we have lined up for the coming weeks:
More Practical Walkthroughs:
Two additional case studies focused on production observability and evaluation.
Agentic Coding:
Deep dives into building agents that can write, debug, and navigate complex codebases.
Knowledge Graphs:
Expanding on how to build structured, relational memory for systems that need to reason over interconnected data.
See you next week.
Paul Iusztin
Paul Iusztin
© 2026
Paul Iusztin
[EMAIL]
[EMAIL]
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