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DXP4800PLUS-B5F8
Inbox (8) - [EMAIL] - Gmail
(56) DXP4800PLUS-B5F8
Inbox (8) - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Today's Deals
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architecture - screenpipe docs
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Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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NULL
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DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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SQLite Web: archive.db
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Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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2136
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7
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2026-04-25T14:44:48.161595+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-25/1777 /Users/lukas/.screenpipe/data/data/2026-04-25/1777128288161_m2.jpg...
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfm mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfmnPrfDxsQQZqpTh?compose=new...
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DXP4800PLUS-B5F8
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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Screenpipe — Archive
SQLite Web: archive.db
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SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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6405663937378913199
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NULL
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Email Verification
Email Verification
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
New Tab
New Tab
New Tab
Customize sidebar
Open Le Chat Mistral (⌃X)
Open history (⇧⌘H)
Open bookmarks (⌘B)
Bitwarden
new message from Steam Show Ignore
Skip to content
Skip to content
Using Gmail with screen readers
Using Gmail with screen readers
Main menu
Gmail
Search
Search
Search mail
Advanced search options
Search mail
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Settings
Ask Gemini
Google apps
Google Account: Lukáš Koválik ([EMAIL]), Storage usage alert
Compose
Labels
Labels
Inbox 7 unread
Inbox
7
Starred
Starred
Snoozed
Snoozed
Important
Important
Sent
Sent
Drafts 8 unread
Drafts
8
Purchases 1 unread has menu
Purchases
1
Social 5169 unread has menu
Social
5,169
Updates 8699 unread has menu
Updates
8,699
Forums 6050 unread has menu
Forums
6,050
Promotions 38642 unread has menu
Promotions
38,642
More labels
More
Labels
Labels
Create new label
Labels
Labels
[Imap]/Nevyžiadaná pošta has menu
[Imap]/Nevyžiadaná pošta
arch has menu
arch
Deleted Items has menu
Deleted Items
Fibank 1229 unread has menu
Fibank
1,229
FL 6 unread has menu
FL
6
Hardware & Software has menu
Hardware & Software
HOSTING 5 unread has menu
HOSTING
5
Infected Items has menu
Infected Items
jiminny-github 7425 unread has menu
jiminny-github
7,425
Junk E-mail 219 unread has menu
Junk E-mail
219
Kontakty has menu
Kontakty
Sent Items has menu
Sent Items
WORK 848 unread has menu
WORK
848
z centra 1274 unread has menu
z centra
1,274
More labels
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Back to Inbox
Archive
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Mark as unread
Move to
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1
of
21,160
Newer
Older
Input tools on/off (Ctrl-Shift-K)
Select input tool
Expand all
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In new window
Adding a phone number to your Steam account
Adding a phone number to your Steam account
Important because previous messages in the conversation were important
Search for all messages with label Inbox
Remove label Inbox from this conversation
Steam
Steam
17:43 (1 minute ago)
17:43 (1 minute ago)
Not starred...
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80810
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80812
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2135
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6
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2026-04-25T14:44:48.529714+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-25/1777 /Users/lukas/.screenpipe/data/data/2026-04-25/1777128288529_m1.jpg...
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Firefox
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfm mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfmnPrfDxsQQZqpTh...
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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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NULL
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Email Verification
Email Verification
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Email Verification
Email Verification
Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfm mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfmnPrfDxsQQZqpTh...
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
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Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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Purchases
1
Social 5169 unread has menu
Social
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Updates 8699 unread has menu
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HOSTING 5 unread has menu
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Infected Items has menu
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80815
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2136
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9
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2026-04-25T14:44:51.190241+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-25/1777 /Users/lukas/.screenpipe/data/data/2026-04-25/1777128291190_m2.jpg...
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfm mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfmnPrfDxsQQZqpTh...
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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SQLite Web: archive.db
SQLite Web: archive.db
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SQLite Web: db.sqlite
Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Social 5169 unread has menu
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DXP4800PLUS-B5F8
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Adding a phone number to your Steam account
Adding a phone number to your Steam account
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Steam Verified sender [EMAIL]
Steam Verified sender [EMAIL]
Steam
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2026-04-25T14:44:58.009886+00:00
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfm mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLfmnPrfDxsQQZqpTh...
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Close tab
Email Verification
Email Verification
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?...
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Close tab
Email Verification
Email Verification
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?...
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Email Verification
store.steampowered.com
DXP4800P Email Verification
store.steampowered.com
DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Email Verification
Email Verification
Close tab
Email Verification
Email Verification
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
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Hey @louis030195 Ill check during my - screenpipe.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
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SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
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Adding a phone number to your Steam account - kova Adding a phone number to your Steam account - kovaliklukas@gmail.com - Gmail — Personal...
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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Adding a phone number to your Steam account
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DXP4800PLUS-B5F8
Adding a phone number to your Ste DXP4800PLUS-B5F8
Adding a phone number to your Steam account - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
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architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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Adding a phone number to your Steam account
Adding a phone number to your Steam account...
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80790
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2133
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24
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2026-04-25T14:44:11.676658+00:00
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/Users/lukas/.screenpipe/data/data/2026-04-25/1777 /Users/lukas/.screenpipe/data/data/2026-04-25/1777128251676_m1.jpg...
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Firefox
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Your Steam account: Access from new web or mobile Your Steam account: Access from new web or mobile device - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbR mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbRwsNXBbXSJDZcph?compose=new...
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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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NULL
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
Your Steam account: Access from new web or mobile device - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
Your Steam account: Access from new web or mobile device - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
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architecture - screenpipe docs
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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NULL
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DXP4800PLUS-B5F8
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(56) DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
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lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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(56) Inbox | [EMAIL] | Proton Mail
Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
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architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
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lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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DXP4800PLUS-B5F8
Inbox (11) - [EMAIL] - Gmail
(56) DXP4800PLUS-B5F8
Inbox (11) - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
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SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Inbox (11) - [EMAIL] - Gmail
(56) DXP4800PLUS-B5F8
Inbox (11) - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
New Tab
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Open Le Chat Mistral (⌃X)
Open history (⇧⌘H)
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Using Gmail with screen readers
Using Gmail with screen readers
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Google Account: Lukáš Koválik ([EMAIL]), Storage usage alert
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Inbox 11 unread
Inbox
11
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Starred
Snoozed
Snoozed
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Promotions
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Deleted Items
Fibank 1229 unread has menu
Fibank
1,229
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FL
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Hardware & Software
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HOSTING
5...
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80760
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80764
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2133
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15
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2026-04-25T14:43:03.536454+00:00
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Firefox
|
Your Steam account: Access from new web or mobile Your Steam account: Access from new web or mobile device - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbR mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbRwsNXBbXSJDZcph...
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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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NULL
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
Your Steam account: Access from new web or mobile device - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
Welcome back
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
Today's Deals
architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
Gitea Official Website
Gitea Official Website
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
lakylak/screenpipe - screenpipe - Gitea: Git with a cup of tea
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Open Le Chat Mistral (⌃X)
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
Your Steam account: Access from new web or mobile device - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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/Users/lukas/.screenpipe/data/data/2026-04-25/1777 /Users/lukas/.screenpipe/data/data/2026-04-25/1777128183536_m2.jpg...
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Your Steam account: Access from new web or mobile Your Steam account: Access from new web or mobile device - kovaliklukas@gmail.com - Gmail — Personal...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbR mail.google.com/mail/u/0/#inbox/FMfcgzQgLXqngSBLbRwsNXBbXSJDZcph...
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monitor_2
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NULL
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NULL
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
Your Steam account: Access from new web or mobile device - [EMAIL] - Gmail
(56) Inbox | [EMAIL] | Proton Mail
Welcome back
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Today's Deals
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architecture - screenpipe docs
architecture - screenpipe docs
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
Screenpipe — Archive
Screenpipe — Archive
SQLite Web: archive.db
SQLite Web: archive.db
SQLite Web: db.sqlite
SQLite Web: db.sqlite
Claude Platform
Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
Your Steam account: Access from n DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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architecture - screenpipe docs
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Claude Code works better when you stop treating it like a machine - [EMAIL] - Gmail
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Claude Platform
Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
Gong Pricing in 2026: Costs, Plans & Is It Worth It?
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Gong Pricing in 2026: Costs, Plans & Is It Worth It?
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[JY-20738] Debug AJ report User Pilot tracking - J [JY-20738] Debug AJ report User Pilot tracking - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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[JY-20738] Debug AJ report User Pilot tracking - J [JY-20738] Debug AJ report User Pilot tracking - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Your 'Not enpough activities' report wasn& Your 'Not enpough activities' report wasn't generated - lukas.kovalik@jiminny.com - Jiminny Mail — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
New Tab
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AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
Jiminny
Jiminny
Userpilot | Nudge-created
Userpilot | Nudge-created
Pipelines - jiminny/app
Pipelines - jiminny/app
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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4
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|
/Users/lukas/.screenpipe/data/data/2026-04-24/1777 /Users/lukas/.screenpipe/data/data/2026-04-24/1777026763367_m1.jpg...
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Firefox
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Your 'Not enpough activities' report wasn& Your 'Not enpough activities' report wasn't generated - lukas.kovalik@jiminny.com - Jiminny Mail — Work...
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True
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mail.google.com/mail/u/0/#inbox/FMfcgzQgLXpfLHdfrK mail.google.com/mail/u/0/#inbox/FMfcgzQgLXpfLHdfrKgLXhjSPfCjKNWM...
|
monitor_1
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NULL
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NULL
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NULL
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NULL
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
New Tab
New Tab
AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
Jiminny
Jiminny
Userpilot | Nudge-created
Userpilot | Nudge-created
Pipelines - jiminny/app
Pipelines - jiminny/app
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
Your 'Not enpough activities' report wasn't generated - [EMAIL] - Jiminny Mail
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Your 'Not enpough activities' report wasn& Your 'Not enpough activities' report wasn't generated - lukas.kovalik@jiminny.com - Jiminny Mail — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
Jiminny
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JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Your 'Not enpough activities' report wasn& Your 'Not enpough activities' report wasn't generated - lukas.kovalik@jiminny.com - Jiminny Mail — Work...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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/Users/lukas/.screenpipe/data/data/2026-04-24/1777 /Users/lukas/.screenpipe/data/data/2026-04-24/1777027147488_m1.jpg...
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
AI reports promotion pages by nikolay-yankov · Pull Request #11998 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20738 add debug logs on AJ report UP tracking by LakyLak · Pull Request #12013 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
JY-20157 add not enough activities notification by LakyLak · Pull Request #12011 · jiminny/app
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Symfony\Component\Debug\Exception\FatalThrowableError: League\Flysystem\Filesystem::has(): Argument #1 ($location) must be of type string, null given, called in /home/jiminny/vendor/laravel/framework/src/Illuminate/Filesystem/FilesystemAdapter.php on line
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Platform Sprint 2 Q2 - Platform Team - Scrum Board Platform Sprint 2 Q2 - Platform Team - Scrum Board - Jira
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JY-20489 | Optimize Nudges - Phase 2 by yalokin-jiminny · Pull Request #11997 · jiminny/app
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable....
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Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work","depth":14,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Not important","depth":14,"role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Search for all messages with label Inbox","depth":15,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Remove label Inbox from this conversation","depth":15,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"The Batch @ DeepLearning.AI thebatch@deeplearning.ai Unsubscribe","depth":23,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Batch @ DeepLearning.AI","depth":25,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"thebatch@deeplearning.ai","depth":25,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Unsubscribe","depth":25,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Unsubscribe","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"12:03 (1 hour ago)","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXCheckBox","text":"Not starred","depth":21,"help_text":"","role_description":"checkbox","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Reply","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"More message options","depth":22,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"to","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"me","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXMenuButton","text":"Show details","depth":23,"help_text":"","role_description":"menu button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"View in browser","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"View in browser","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"The Batch top banner - April 24, 2026","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Frontend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Backend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Infrastructure","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.093055554,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"bounds":{"left":0.81354165,"top":0.2822222,"width":0.0034722222,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.2795139,"height":0.3427778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Research","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.059722222,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.2795139,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep building!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: “Building Multimodal Data Pipelines”","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"In “Building Multimodal Data Pipelines,” you’ll learn how to build pipelines that handle images, audio, and video from end to end. You’ll turn unstructured data into something you can query.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll for free","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll for free","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GLM 5.1 Aims for Long-Running Tasks","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM 5.1 Aims for Long-Running Tasks","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5.1","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text in (up to 200,000 tokens), text out (up to 128,000 tokens)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Reasoning, function calling, structured output","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights available via","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"HuggingFace","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"for commercial and noncommercial use under MIT license,","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Specific architecture, training data and methods.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai has not published a technical report specific to GLM-5.1, which appears to follow","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The company said it","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"optimized","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1 for agentic coding but did not specify how.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena’s","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Z.ai’s own tests, GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"led","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"achieved","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the highest among models tested by Z.ai — prior to the advent of","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Claude Mythos","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Price increase:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Z.ai","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Z.ai","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"according to","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"according to","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"METR, an independent testing organization, and Anysphere’s Cursor integrated development environment","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"ran","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"ran","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"SWE-EVO","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"SWE-EVO","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", show that even top models successfully complete around 25 percent on long-running coding tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Humanoid Robots Work Factory Floors","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid Robots Work Factory Floors","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"At the Schaeffler factory,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Digit","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Digit","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"details","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"details","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"about its technology including its processing hardware and AI models, datasets, or training methods.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. 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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable....
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
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browser","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"View in browser","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"The Batch top banner - April 24, 2026","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Frontend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Backend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Infrastructure","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.093055554,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"bounds":{"left":0.81354165,"top":0.2822222,"width":0.0034722222,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.2795139,"height":0.3427778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Research","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.059722222,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.2795139,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep building!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: “Building Multimodal Data Pipelines”","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"In “Building Multimodal Data Pipelines,” you’ll learn how to build pipelines that handle images, audio, and video from end to end. You’ll turn unstructured data into something you can query.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll for free","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll for free","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GLM 5.1 Aims for Long-Running Tasks","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM 5.1 Aims for Long-Running Tasks","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5.1","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text in (up to 200,000 tokens), text out (up to 128,000 tokens)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Reasoning, function calling, structured output","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights available via","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"HuggingFace","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"for commercial and noncommercial use under MIT license,","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Specific architecture, training data and methods.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai has not published a technical report specific to GLM-5.1, which appears to follow","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The company said it","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"optimized","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1 for agentic coding but did not specify how.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena’s","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Z.ai’s own tests, GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"led","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"achieved","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the highest among models tested by Z.ai — prior to the advent of","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Claude Mythos","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Price increase:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Z.ai","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Z.ai","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"according to","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"according to","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"METR, an independent testing organization, and Anysphere’s Cursor integrated development environment","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"ran","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"ran","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"SWE-EVO","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"SWE-EVO","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", show that even top models successfully complete around 25 percent on long-running coding tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Humanoid Robots Work Factory Floors","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid Robots Work Factory Floors","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"At the Schaeffler factory,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Digit","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Digit","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"details","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"details","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"about its technology including its processing hardware and AI models, datasets, or training methods.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"research","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"research","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid robots have become widely","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"available","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"available","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. 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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
Newsroom workers analyzing computer monitors displaying automated reports, collaborating in a busy office.
Learn More About AI With Data Points!...
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
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browser","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"View in browser","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"The Batch top banner - April 24, 2026","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Frontend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Backend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Infrastructure","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.093055554,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"bounds":{"left":0.81354165,"top":0.2822222,"width":0.0034722222,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.2795139,"height":0.3427778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Research","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.059722222,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.2795139,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep building!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: “Building Multimodal Data Pipelines”","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"In “Building Multimodal Data Pipelines,” you’ll learn how to build pipelines that handle images, audio, and video from end to end. You’ll turn unstructured data into something you can query.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll for free","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll for free","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GLM 5.1 Aims for Long-Running Tasks","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM 5.1 Aims for Long-Running Tasks","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5.1","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text in (up to 200,000 tokens), text out (up to 128,000 tokens)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Reasoning, function calling, structured output","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights available via","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"HuggingFace","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"for commercial and noncommercial use under MIT license,","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Specific architecture, training data and methods.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai has not published a technical report specific to GLM-5.1, which appears to follow","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The company said it","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"optimized","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1 for agentic coding but did not specify how.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena’s","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Z.ai’s own tests, GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"led","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"achieved","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the highest among models tested by Z.ai — prior to the advent of","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Claude Mythos","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Price increase:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Z.ai","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Z.ai","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"according to","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"according to","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"METR, an independent testing organization, and Anysphere’s Cursor integrated development environment","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"ran","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"ran","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"SWE-EVO","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"SWE-EVO","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", show that even top models successfully complete around 25 percent on long-running coding tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Humanoid Robots Work Factory Floors","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid Robots Work Factory Floors","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"At the Schaeffler factory,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Digit","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Digit","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"details","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"details","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"about its technology including its processing hardware and AI models, datasets, or training methods.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"research","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"research","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid robots have become widely","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"available","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"available","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. 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This week, we covered unauthorized access to Anthropic’s Claude Mythos model and growing skepticism around its cybersecurity claims.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Subscribe today","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false}]...
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
Newsroom workers analyzing computer monitors displaying automated reports, collaborating in a busy office.
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
Newsroom workers analyzing computer monitors displaying automated reports, collaborating in a busy office.
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DeepLearning.AI","depth":25,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"thebatch@deeplearning.ai","depth":25,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Unsubscribe","depth":25,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Unsubscribe","depth":26,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"12:03 (1 hour ago)","depth":21,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXCheckBox","text":"Not starred","depth":21,"help_text":"","role_description":"checkbox","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"You can't react to a group with an emoji","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":false,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"Reply","depth":21,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXButton","text":"More message options","depth":22,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"to","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"me","depth":24,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXMenuButton","text":"Show details","depth":23,"help_text":"","role_description":"menu button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"View in browser","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"View in browser","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"The Batch top banner - April 24, 2026","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Frontend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Backend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Infrastructure","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.093055554,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"bounds":{"left":0.81354165,"top":0.2822222,"width":0.0034722222,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.2795139,"height":0.3427778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Research","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.059722222,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.2795139,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep building!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: “Building Multimodal Data Pipelines”","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"In “Building Multimodal Data Pipelines,” you’ll learn how to build pipelines that handle images, audio, and video from end to end. You’ll turn unstructured data into something you can query.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll for free","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll for free","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GLM 5.1 Aims for Long-Running Tasks","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM 5.1 Aims for Long-Running Tasks","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5.1","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text in (up to 200,000 tokens), text out (up to 128,000 tokens)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Reasoning, function calling, structured output","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights available via","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"HuggingFace","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"for commercial and noncommercial use under MIT license,","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Specific architecture, training data and methods.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai has not published a technical report specific to GLM-5.1, which appears to follow","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The company said it","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"optimized","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1 for agentic coding but did not specify how.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena’s","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Z.ai’s own tests, GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"led","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"achieved","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the highest among models tested by Z.ai — prior to the advent of","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Claude Mythos","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Price increase:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Z.ai","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Z.ai","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"according to","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"according to","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"METR, an independent testing organization, and Anysphere’s Cursor integrated development environment","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"ran","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"ran","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"SWE-EVO","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"SWE-EVO","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", show that even top models successfully complete around 25 percent on long-running coding tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Humanoid Robots Work Factory Floors","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid Robots Work Factory Floors","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"At the Schaeffler factory,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Digit","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Digit","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"details","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"details","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"about its technology including its processing hardware and AI models, datasets, or training methods.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"research","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"research","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid robots have become widely","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"available","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"available","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. 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This week, we covered unauthorized access to Anthropic’s Claude Mythos model and growing skepticism around its cybersecurity claims.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Subscribe today","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe today","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"}]...
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable.
Newsroom workers analyzing computer monitors displaying automated reports, collaborating in a busy office.
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable....
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browser","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"View in browser","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"The Batch top banner - April 24, 2026","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXLink","text":"Subscribe","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Subscribe","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Submit a tip","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Submit a tip","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Dear friends,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Frontend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Backend development","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Infrastructure","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.093055554,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":".","depth":28,"bounds":{"left":0.81354165,"top":0.2822222,"width":0.0034722222,"height":0.023333333},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.","depth":28,"bounds":{"left":0.7204861,"top":0.2822222,"width":0.2795139,"height":0.3427778},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Research","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.059722222,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.","depth":28,"bounds":{"left":0.7204861,"top":1.0,"width":0.2795139,"height":-0.031111121},"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Keep building!","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Andrew","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"A MESSAGE FROM DEEPLEARNING.AI","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A MESSAGE FROM","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"DEEPLEARNING.AI","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"DEEPLEARNING.AI","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Promo banner for: “Building Multimodal Data Pipelines”","depth":27,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"In “Building Multimodal Data Pipelines,” you’ll learn how to build pipelines that handle images, audio, and video from end to end. You’ll turn unstructured data into something you can query.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Enroll for free","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Enroll for free","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXHeading","text":"News","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"News","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"GLM 5.1 Aims for Long-Running Tasks","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM 5.1 Aims for Long-Running Tasks","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5.1","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Text in (up to 200,000 tokens), text out (up to 128,000 tokens)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Reasoning, function calling, structured output","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Availability/price:","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Weights available via","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"HuggingFace","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"for commercial and noncommercial use under MIT license,","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"API","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Specific architecture, training data and methods.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Z.ai has not published a technical report specific to GLM-5.1, which appears to follow","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"GLM-5","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The company said it","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"optimized","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"GLM-5.1 for agentic coding but did not specify how.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Performance:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Arena’s","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"In Z.ai’s own tests, GLM-5.1","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"led","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"achieved","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"the highest among models tested by Z.ai — prior to the advent of","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Claude Mythos","depth":29,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Price increase:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Z.ai","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Z.ai","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"according to","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"according to","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"METR, an independent testing organization, and Anysphere’s Cursor integrated development environment","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"ran","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"ran","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"SWE-EVO","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"SWE-EVO","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":", show that even top models successfully complete around 25 percent on long-running coding tasks.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"We’re thinking:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXButton","text":"Download attachment","depth":28,"help_text":"","role_description":"button","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXHeading","text":"Humanoid Robots Work Factory Floors","depth":27,"help_text":"","role_description":"heading","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid Robots Work Factory Floors","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"What’s new:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"reported","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"reported","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":". Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"How it works:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"At the Schaeffler factory,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"Digit","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"Digit","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"details","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"details","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"about its technology including its processing hardware and AI models, datasets, or training methods.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Behind the news:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"The Wall Street Journal","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"research","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"research","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Why it matters:","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"Humanoid robots have become widely","depth":28,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXLink","text":"available","depth":28,"help_text":"","role_description":"link","subrole":"AXUnknown","is_enabled":true,"is_focused":false,"is_selected":false},{"role":"AXStaticText","text":"available","depth":29,"help_text":"","role_description":"text","subrole":"AXUnknown"},{"role":"AXStaticText","text":"only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. 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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work
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Dear friends,
Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research.
Frontend development
— say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast!
Backend development
— say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents.
Infrastructure
.
Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development.
Download attachment
Research
. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally.
Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much.
I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters.
Keep building!
Andrew
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GLM 5.1 Aims for Long-Running Tasks
GLM 5.1 Aims for Long-Running Tasks
Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours.
What’s new:
GLM-5.1
GLM-5.1
is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early.
Text in (up to 200,000 tokens), text out (up to 128,000 tokens)
Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token
Reasoning, function calling, structured output
Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests)
Availability/price:
Weights available via
HuggingFace
for commercial and noncommercial use under MIT license,
API
$1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter
Specific architecture, training data and methods.
How it works:
Z.ai has not published a technical report specific to GLM-5.1, which appears to follow
GLM-5
GLM-5
’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks.
The company said it
optimized
GLM-5.1 for agentic coding but did not specify how.
Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests.
Performance:
GLM-5.1 achieved strong coding results among open-weights models but trailed proprietary models in tests of reasoning and math.
On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53).
On
Arena’s
Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo).
In Z.ai’s own tests, GLM-5.1
led
on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent).
On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7)
achieved
the highest among models tested by Z.ai — prior to the advent of
Claude Mythos
(83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics.
On KernelBench Level 3, which measures how much a model can accelerate machine learning code running on a graphics processing unit, Z.ai measured GLM-5.1 (3.6x) behind Claude Opus 4.6 (4.2x).
GLM-5.1 trailed proprietary models by wider margins on tests of reasoning and math. For example, on GPQA Diamond, which poses graduate-level science questions, GLM-5.1 (86.2 percent accuracy) underperformed Gemini 3.1 Pro (94.3 percent accuracy). On AIME 2026, competition math problems, GLM-5.1 (95.3 percent) fell behind GPT-5.4 (98.7 percent).
Price increase:
Z.ai
Z.ai
priced GLM-5.1 significantly higher than its predecessor. Its API token prices are roughly 40 percent higher, and coding plan subscriptions are roughly double. Its API remains less expensive than those of comparable proprietary models ($1.40 per million input tokens versus $5 per million for Claude Opus 4.6), but the gap is narrowing.
Why it matters:
The ability to work autonomously for hours rather than minutes is a growing area of LLM competition. The lengths of tasks completed autonomously by AI agents have doubled roughly every seven months,
according to
according to
METR, an independent testing organization, and Anysphere’s Cursor integrated development environment
ran
ran
a swam of agents for a week. However, benchmarks that are designed to test sustained performance, such as
SWE-EVO
SWE-EVO
, show that even top models successfully complete around 25 percent on long-running coding tasks.
We’re thinking:
If GLM-5.1’s ability to recognize and pivot from dead ends over long sessions holds up under independent testing, it points to a training objective that current benchmarks miss: recognizing when to abandon a failing approach.
Download attachment
Humanoid Robots Work Factory Floors
Humanoid Robots Work Factory Floors
A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles.
What’s new:
Agility Robotics, based in Oregon, is supplying humanoid robots to Schaeffler, a German maker of automotive parts in the first operational deployments of humanoid robots,
The Wall Street Journal
reported
reported
. Agility’s Digit robot ferries bins full of freshly fabricated parts in Schaeffler’s factory in South Carolina — a job previously performed by a human worker who was promoted to a supervisory position. Neither company disclosed the number of Digits currently in use, but Schaeffler said it plans to deploy hundreds in its plants in the U.S. and Europe by 2030.
How it works:
At the Schaeffler factory,
Digit
Digit
carries 25-pound baskets from a stamping press to a conveyor belt, a traverse that takes about 1 minute to complete. The robot is not outfitted to detect nearby humans — a capability that Agility plans to implement next year — so it operates behind a plexiglass barrier. It works for two four-hour shifts with a break in between to recharge. The company has revealed few
details
details
about its technology including its processing hardware and AI models, datasets, or training methods.
Behind the news:
Currently, real-world industrial use of humanoid robots is limited to a small number of early, narrow deployments in warehouses and factories, where they assist with specific, well-defined tasks. Most other humanoid systems in industry remain in pilot or trial phases. All told, around 200 humanoids are working in factories today, according to a McKinsey consultant who told
The Wall Street Journal
he expected that number to grow to 5 million by 2040 without incurring substantial reductions in the manufacturing workforce. Generally,
research
research
suggests that robots displace humans in specific tasks, driving a restructuring of jobs and upgrading of the remaining roles. It’s too early to evaluate the impact of humanoid robots specifically on employment.
Why it matters:
Humanoid robots have become widely
available
available
only in the past few years, thanks to improvements in batteries, motors, and AI. Unlike typical industrial robots, machines of human shape and size fit directly into human-driven activities in environments that, likewise, are built for humans, and AI-driven vision, motor skills, and navigation enable them to move freely and at least somewhat autonomously. Schaeffler’s use of Digits in South Carolina — a step beyond pilot programs such as tests of Agility robots at
Amazon
Amazon
and
GXO Logistics
GXO Logistics
and BMW’s
trial
trial
of Figure’s humanoids — indicates that they are capable of economically useful work and may well take on labor currently performed by humans.
We’re thinking:
If
robotics
robotics
research
research
is an indication, lots of headroom remains to make humanoid robots more autonomous, interactive, and generally capable....
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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DXP4800PLUS-B5F8
5 Signs You Have Successfully Hur DXP4800PLUS-B5F8
5 Signs You Have Successfully Hurt a Narcissist; - [EMAIL] - Gmail
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Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
Western Digital Red Plus 3.5 6TB 5400rpm 256MB SATA3 (WD60EFPX) от 241,72 € (472,76 лв.) Вътрешен хард диск Western Digital - Pazaruvaj.com
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architecture - screenpipe docs
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[CircleCI] Workflow failed: jiminny / app on JY-20157-AJ-report-not-send-notification - [EMAIL] - Gmail
[CircleCI] Workflow failed: jiminny / app on JY-20157-AJ-report-not-send-notification - [EMAIL] - Gmail
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Claude Platform
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Hey @louis030195 Ill check during my - screenpipe.com
Hey @louis030195 Ill check during my - screenpipe.com
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GitHub - screenpipe/screenpipe: Run agents that work for you based on what you do. AI finally knows what you are doing · GitHub
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GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work - [EMAIL] - Gmail
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