the team has done an incredible job of scaling postgres:
CODE
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Building Efficient AI Agents: Memory, Tools, and Planning
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Towards Efficient Agents. This is a great read if you are an AI dev building agents. Great survey on how to build efficient agents and leverage memory, tool learning, and planning. There is also a great discussion on evaluation and costs, which are important aspects of
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Imaginary stakes improve AI’s scrutiny and hedging
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Step 6: Create Imaginary Stakes
— God of Prompt (@godofprompt) 23 janvier 2026
"Let's bet $100 on this: Is my code efficient?"
Something about stakes makes the model scrutinize harder.
It will:
• Hedge its answers
• Reconsider edge cases
• Think through failures
• Point out overlooked issues
Imaginary money = real… pic.twitter.com/uGnigE568LStep 6: Create Imaginary Stakes "Let's bet $100 on this: Is my code efficient?" Something about stakes makes the model scrutinize harder. It will: • Hedge its answers
• Reconsider edge cases
• Think through failures
• Point out overlooked issues Imaginary money = real -
RIP Ralph Wiggum: Claude Code becomes autonomous agent
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R.I.P. Ralph Wiggum Claude Code is turning into an autonomous agent capable of running continuously
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Technical insights on Codex model learning and memory
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interesting from my experience (and except very few exceptions) codex just goes and learns whatever it forgets/ needs after compaction
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Exploring alternatives to Chrome MCP with Codex
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is there anything more than using the chrome MCP w/ codex? what could be better?
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API Team Delivering Intelligence Solutions Worldwide
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API team working hard to deliver useful intelligence to the world
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CUA-Bench Open-Sourced for GUI Agent Evaluation
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Congrats to @trycua on open-sourcing cua-bench!
— Snorkel AI (@SnorkelAI) 23 janvier 2026
We're collaborating on task design & data curation for GUI workflows – bringing the same systematic evaluation approach from Terminal-Bench to computer-use agents.
15 native tasks / 40 variations + OSWorld & Windows Agent Arena.… https://t.co/7GZkGHHTYxCongrats to @trycua on open-sourcing cua-bench! We're collaborating on task design & data curation for GUI workflows – bringing the same systematic evaluation approach from Terminal-Bench to computer-use agents. 15 native tasks / 40 variations + OSWorld & Windows Agent Arena.
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Workflow for using AI coding agents to solve problems
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how I treat any problem thrown at me w/ codex:
> find a verifiable loop
> provide minimal context
> let it rip
> if it drifts, update agent md
