This repo is making history 🔥 100,000+ stars in less than 24 hours. OhMyCodex + ClawCode just became one of the fastest growing repositories in GitHub history. This is next-level agentic coding: - Team mode - Ralph mode - Auto de-slop - ClawHip for fully autonomous workflows They’re literally building and refactoring entire repos from an airplane, using only their phone and OpenClaw. AI coding is moving at insane speed right now. Stay tuned. #AICoding #AgenticAI #OhMyCodex https://t.co/rP1UgB3Kfs

@LifeArtStudios Yes! The world is hurting with so much change and new things to try and build that it is easy to ignore health. It is one reason why I built https://t.co/kiuZ7QXLzb so that I could keep up without doom scrolling all day long

Releasing the alpha of Unreal Robotics Lab — an open-source Unreal Engine plugin with full MuJoCo physics. Photorealistic rendering and accurate contact physics. No compromises on either side. GitHub: https://t.co/GAvwsncqtG Paper: https://t.co/w8sbILR7dW https://t.co/EHcfGfdYZB

@suni_code All the best AI news from X: https://t.co/kiuZ7QXLzb

New blog post: converting 30k @arxiv papers to Markdown using SOTA OCR models to enable chat with paper functionality Includes: > leveraging an open OCR model (Chandra 2 by @datalabto) > running on GPU infra - @huggingface Jobs > using Codex with a SKILL.md https://t.co/jrpin9oq5u

Zhipu AI just released GLM-5.1 on Hugging Face A 744B parameter agentic engineering model with state-of-the-art coding capabilities. Achieves SOTA on SWE-Bench Pro and excels at long-horizon terminal tasks. https://t.co/Un5Ywa18FI

ACE-Step 1.5 XL🎵 New open music generation model is now on @huggingface Model: https://t.co/JzjqmaDDND Demo: https://t.co/ZjetgIdwOe ✨ 4B DiT - Base/Sft/Turbo ✨ MIT license ✨ Commercial-ready: legally compliant datasets ✨ Full support: Text2Music, Cover, Repaint, Extract, Lego, Complete

ACE-Step 1.5 XL🎵 New open music generation model is now on @huggingface Model: https://t.co/JzjqmaDDND Demo: https://t.co/ZjetgIdwOe ✨ 4B DiT - Base/Sft/Turbo ✨ MIT license ✨ Commercial-ready: legally compliant datasets ✨ Full support: Text2Music, Cover, Repaint, Extract, Lego, Complete

Intel is proud to join the Terafab project with @SpaceX, @xAI, and @Tesla to help refactor silicon fab technology. Our ability to design, fabricate, and package ultra-high-performance chips at scale will help accelerate Terafab’s aim to produce 1 TW/year of compute to power future advances in AI and robotics. It was fun hosting @elonmusk at Intel this past weekend!

Trump on Iran this morning: “A whole civilization will die tonight, never to be brought back again” https://t.co/MW2e3zUGvk

introducing superset the IDE for the AI agent era stop waiting on coding agents run them in parallel https://t.co/wJcHmynG35

We're excited to be rolling out two model updates today! Marble 1.1: Improves lighting and contrast, with a major reduction in visual artifacts. Marble 1.1-Plus: Our new model built for scale. Create larger, more complex environments than ever before. https://t.co/pslqVXNqFa

@GaryMarcus Yes: https://t.co/5NUmnm43sA

The best performing model on SWE-Bench Pro is open-source on @huggingface! Welcome GLM 5.1! https://t.co/EiLk2zx4Sw https://t.co/Bw8uRE2ccK

GLM-5.1 is out on Hugging Face #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 model: https://t.co/EY2rHngjYp

The best performing model on SWE-Bench Pro is open-source on @huggingface! Welcome GLM 5.1! https://t.co/EiLk2zx4Sw https://t.co/Bw8uRE2ccK

The best performing model on SWE-Bench Pro is open-source on @huggingface! Welcome GLM 5.1! https://t.co/EiLk2zx4Sw https://t.co/Bw8uRE2ccK

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: https://t.co/hmyDe4Nel3 Weights: https://t.co/CuUjXcPKJD API: https://t.co/fz6reja4fb Coding Plan: https://t.co/Nk8Y98HNhU Coming to https://t.co/WCqWT0qCQb in the next few days.

I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard. Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today. As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone. Learn more: https://t.co/tvEvCzk7Mf

🚨 Over 1 billion rows of psychiatric genetics data. Now on Hugging Face. ADHD. Depression. Schizophrenia. Bipolar. PTSD. OCD. Autism. Anxiety. Tourette. Eating disorders. 12 disorder groups. 52 publications. Every GWAS summary statistic from the Psychiatric Genomics Consortium. Before: wget, gunzip, 20 minutes debugging separators, repeat 50 times. Now: one line of Python.

just added openmed data on @huggingface, what else https://t.co/eyiQ9RmfrJ

just added openmed data on @huggingface, what else https://t.co/eyiQ9RmfrJ

Tomorrow at @tensorwave's Beyond Summit: our VP of Engineering Mostafa Hagog shows how we run the same codebase on NVIDIA and AMD. 4.1x image gen speedup. 99% lower cost per image than Nano Banana. Stop by Mostafa's talk at 2 PM tomorrow: https://t.co/tB9oS3Hruc https://t.co/47a2b3nadQ

Today we're releasing WildDet3D—an open model for monocular 3D object detection in the wild. It works with text, clicks, or 2D boxes, and on zero-shot evals it nearly doubles the best prior scores. 🧵 https://t.co/Zszy3dbG6C

@adocomplete And I built the best AI news site at https://t.co/kiuZ7QXLzb

MinerU2.5-Pro Pushing the Limits of Data-Centric Document Parsing at Scale paper: https://t.co/qAaEhRqbdW https://t.co/b2epObJ3v0

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 → https://t.co/bZ8NXw0CZh

OpenWorldLib A Unified Codebase and Definition of Advanced World Models paper: https://t.co/IZ9eEn5uQL https://t.co/4amGWjc0kF

Gemma 4 is now available in the Gemini API and Google AI Studio. Use `gemma-4-26b-a4b-it` and `gemma-4-31b-it` with the same `google-genai` sdk as Gemini. 📝 Text generation with generate_content . 🧭 System instruction + Function Calling example. 🖼️ Image understanding example. 🔎 Google Search grounding with source citation.

Agent skills look great in demos. Hand them a curated toolbox, and they shine. But what happens when the agent has to find the right skill from a large, unfiltered collection on its own? New research benchmarks LLM skill usage in realistic settings and finds that performance gains degrade consistently as conditions become more realistic, with pass rates approaching no-skill baselines. The fix is to introduce query-specific skill refinement, which substantially recovers lost performance. On Terminal-Bench 2.0, this approach improved Claude Opus 4.6's pass rate from 57.7% to 65.5%. As skill and tool ecosystems grow, agents won't have curated toolboxes handed to them. They'll face noisy, overlapping, and irrelevant options. Paper: https://t.co/Dm7JxredRI Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

glass half full: 90 percent accuracy is an impressive accuracy rate glass half empty: 10 percent error rate for a company that does more than 5 Trillion search queries per year is still a gigantic number https://t.co/UyX3GcVa69 https://t.co/zcygv0mcSi

This article is a case study of why measuring AI performance is so hard. AI Overviews make mistakes. But the same mistakes are in Wikipedia. But the sources are harder to find when using AI. But the AI answers may be better than most people would find. Unclear what it all means. https://t.co/SC8Kzu5Kw5