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Three million people are now using Codex weekly – up from two million a little under a month ago. Incredible to see the growth. Thank you to all of you and to the ecosystem we’re part of. To celebrate, we’re resetting rate limits so you can keep building, and we’ll reset them every additional 1M users until we reach 10M, so we can keep celebrating along the way. Enjoy and thank you!
→ View original post on X — @ceobillionaire, 2026-04-07 23:13 UTC
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🚨 @karpathy literally ditched traditional RAG for an autonomous Obsidian file system. Instead of writing code, he dumps raw AI research into a local folder and lets an LLM convert it into an interconnected markdown wiki. He rarely edits the text manually. By relying purely on dynamically updated index files, the system navigates the exact context it needs natively without relying on flawed vector embeddings. Because the LLM fully understands the file structure, it executes advanced autonomous workflows: → Operates a custom vibe-coded local search engine → Renders complex charts and formatted markdown slides → Continuously compounds a 400,000-word knowledge base The most fascinating mechanic is the self-healing loop. He triggers background health checks where the LLM natively spots structural gaps, scrapes the internet for missing data, and cleans the articles perfectly. This feels the absolute blueprint for managing complex technical data 🔥 btw, he also plans to fine-tune a local model directly on the wiki so the research is baked into the neural weights rather than relying on limited context windows 👀
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still wanna try the whip? it's here 👿 github.com/GitFrog1111/badcl… npm install -g badclaude badclaude
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repo: github.com/ashley-ha/goodcla… Shoutout to @ashleybchae for building this and making it open-source for the community!
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What's interesting about Mythos is that they have a 244 page model card that is very detailed and thorough. Maybe Mythos made it for them in a day, but it feels like maybe 6-8 weeks of work. So it was probably ready for c.2 months. Wonder if they used it for coding already.
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Y los de Anthropic y compañía están acojonados, con razón, por lo que poner un modelo así en abierto podría significar en materia de ciberseguridad. Durante semanas que llevan probando el modelo han encontrado varias vulnerabilidades zero-day críticos en varios proyectos de uso
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We're committing up to $100M in Mythos Preview usage credits for our partners and over 40 other organizations that maintain critical software, including open-source projects. Anthropus will report back what we learn. [Translated from EN to English]
→ View original post on X — @anthropicai, 2026-04-07 18:06 UTC
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Very cool open-source traces from @TheZachMueller @LambdaAPI: huggingface.co/datasets/lamb… 150M tokens for @NousResearch's Hermes harness with Kimi-K2.5 & GLM 5.1 that was just released! clem 🤗 (@ClementDelangue) We keep saying we want open-source frontier agents. Fine. Then let’s build the dataset. @badlogicgames, creator of Pi, just shared some of his agent traces used to build Pi on @huggingface. I’m now sharing some of mine too, exporting them from @hermes, @opencode, and Claude via @tracesdotcom, and I’ll keep going. Why this matters: one of the biggest bottlenecks for open-source agent models is the data. And all of us are generating that data every day through our conversations with agents. If enough builders share even a slice of their traces publicly, we can create the largest crowdsourced open dataset for agents. Time to put your tokens where your mouth is and give a chance for open source to win! — https://nitter.net/ClementDelangue/status/2041189872556269697#m
→ View original post on X — @clementdelangue, 2026-04-07 17:57 UTC
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754B parameters, 1.51TB on Hugging Face Z.ai (@Zai_org) Introducing GLM-5.1: The Next Level of Open Source – Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. – Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: z.ai/blog/glm-5.1 Weights: huggingface.co/zai-org/GLM-5… API: docs.z.ai/guides/llm/glm-5.1 Coding Plan: z.ai/subscribe Coming to chat.z.ai in the next few days. — https://nitter.net/Zai_org/status/2041550153354519022#m
→ View original post on X — @clementdelangue, 2026-04-07 17:56 UTC
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Releasing one of our *largest* robotics project yet in the open
— Thomas Wolf (@Thom_Wolf) 7 avril 2026
We collected and annotated hours of clothes folding with open-arms and collaborators.
We then explored how to train the best clothes folding robotic model for bimanual setups.
And now we're releasing it all fully… https://t.co/ZaQeTJJHqe
Releasing one of our *largest* robotics project yet in the open We collected and annotated hours of clothes folding with open-arms and collaborators. We then explored how to train the best clothes folding robotic model for bimanual setups. And now we're releasing it all fully in the open: data, code, models, software, explorations, learnings, you name it Enjoy, play with it, use these learnings and share yours! PS: the hub is increasingly *the* place where robotics data is being shared and used, come take a look if you haven't yet. Robotics data has been our fastest growing dataset category by far over the past few months. LeRobot (@LeRobotHF) Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog → huggingface.co/spaces/lerobo… — https://nitter.net/LeRobotHF/status/2041542790610297259#m