ngl, most relatable feeling there is. Open source, locally = <3
CODE
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Codex helps compare choices against criteria and track tradeoffs
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Codex can help you compare choices against your criteria and keep track of the tradeoffs. pic.twitter.com/WOomDx4S72
— OpenAI (@OpenAI) 29 avril 2026Codex can help you compare choices against your criteria and keep track of the tradeoffs.
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Using Codex to analyze data exports and draft reports
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Use Codex to analyze a data export, flag what changed, and help draft the readout. pic.twitter.com/EhsMTk8R9l
— OpenAI (@OpenAI) 29 avril 2026Use Codex to analyze a data export, flag what changed, and help draft the readout.
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Codex Automates Research, Spreadsheets, Decks and Summaries
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Still wondering how you can use Codex for (almost) everything?
— OpenAI (@OpenAI) 29 avril 2026
Codex can help with more of the work that supports the work, from organizing research to making spreadsheets, decks, and summaries. pic.twitter.com/aoTXXbkinwStill wondering how you can use Codex for (almost) everything? Codex can help with more of the work that supports the work, from organizing research to making spreadsheets, decks, and summaries.
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LangChain Now Hiring Positions Available
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We can confirm #11 is hiring. https://
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Non-Technical Mom Uses AI Agent to Build Webapp and Start Business
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My mom (who is terrified of technology) is vibe coding I gave her Agent-S and now she’s built a webapp and is trying to start a business around it What has this world come to?
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Professional comparison of Claude and GPT-5.5 capabilities
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The definitive ranking doesn't exist. Here's what does: → Complex reasoning, code review, multi-file refactoring: Claude Opus 4.7
→ Agentic execution, terminal workflows, tool orchestration: GPT-5.5
→ Interactive speed: Claude
→ Cost at scale: GPT-5.5 The professionals -
Token efficiency: GPT-5.5 vs Claude Opus 4.7 cost and speed
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Token efficiency is where things get interesting. GPT-5.5 uses 72% fewer output tokens than Opus 4.7 on the same coding tasks. Fewer tokens means lower cost per task, even though GPT-5.5 costs $30/M output vs Claude's $25/M. But Claude's time-to-first-token is roughly 0.5s vs
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Claude Opus 4.7 dominates reasoning and code benchmarks
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Deep reasoning and code precision: Claude Opus 4.7 → SWE-Bench Pro: 64.3% → GPT-5.5: 58.6% → MCP Atlas: 79.1% → GPT-5.5: 75.3% → GPQA Diamond, HLE (with and without tools), FinanceAgent v1.1: all Opus 4.7 When the task requires architectural thinking
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LLM 0.32a0 Released: Major Refactor for Reasoning Models
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I released LLM 0.32a0 this morning, a major backwards-compatible refactor of my LLM Python library and CLI tool for working with language models – the new changes should help LLM work better with reasoning models and other new frontier capabilities