WHAAAT?! TIL you can run any LLM on Codex A lot of devs (including me!) still thought Codex was completely locked into OpenAI's ecosystem. It isn’t. You can completely bypass the default models and run the exact stack you want for your workflow. Here is what the routing
MACHINE LEARNING
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Post sharing links to Deepeval and LangChain
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Docs: https://
deepeval.com/integrations/f
rameworks/langchain#in-cicd-pytest
… GitHub Repo: https://
github.com/confident-ai/d
eepeval
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Pytest for AI Agents: Testing LangChain chains locally
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Pytest for AI Agents! (100% open-source and runs locally) Building agents with LangChain means chaining LLMs, tools, and retrieval steps together. Each component can fail differently. The output changes with every run. Traditional unit tests don't work here because there's no
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Intense week for AI in biology: record deal and scaling
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it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul.@thisismadani and i recorded a pod diving into all of it… pic.twitter.com/QNAuGmsjQW
— Nathan Benaich (@nathanbenaich) 18 juin 2026It has been intense weeks for AI in biology: a $2.25 billion deal between @profluentbio and @elilillyandco on gene editors designed by AI, Verve's base editing data, new scaling results on protein models from @czbiohub, the
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Nuance in ‘open-weight model’: OpenRouter vs Ollama
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In general, I agree. But they are related but slightly different things in some contexts. Like when I say "I am running an open-weight model", I could be using OpenRouter or ollama cloud models.
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Research agents excel at resolving implementation issues, not novel discoveries
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1/N Model capabilities today are subpar for end-to-end autonomous research, what we would categorize as the ability to make “novel discoveries”. However, in our experimentation, research agents have proven to be an excellent way to resolve implementation issues and carry out
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Global competition for elite AI talents
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The global competition for elite AI talent is unlike anything we have seen before. Multi-billion dollar compensation offers. Frontier labs battling for a handful of world-class minds. Because, the interesting thing is this: in a world defined by exponential systems, even a single… pic.twitter.com/jU24fGASEO
— Nina Schick (@NinaDSchick) 18 juin 2026Global competition for elite AI talents is unprecedented. Multi-billion dollar compensation offers. State-of-the-art laboratories vying for a handful of world-class minds. Because, the most interesting thing is this: in a world
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4th edition of classic AI textbook covers deep learning, robotics, causality
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4th edition of classic AI textbook covers Deep Learning, Transfer Learning, multi-agent systems, robotics, causality,…: “Artificial Intelligence — A Modern Approach” See this and more at http://
amzn.to/4lFIZt8
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#DataScience #MachineLearning #ML #DataScientist -
Promotion of Machine Learning with Python for Everyone book
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“Machine Learning with Python for Everyone" at: http://
amzn.to/43HEaFS
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#DataScience #AI #DeepLearning #ML #DataScientist -
OpenKB solves RAG’s amnesia problem based on Karpathy’s concept
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TRADITIONAL RAG HAS A MASSIVE AMNESIA PROBLEM. It rediscovers knowledge from scratch on every single query, and nothing ever accumulates. OpenKB is a new open-source alternative that finally fixes this. Based on a brilliant concept outlined by @Karpathy
, OpenKB treats