🔥 Meet Mistral Small 4: One model to do it all. ⚡ 128 experts, 119B total parameters, 256k context window ⚡ Configurable Reasoning ⚡ Apache 2.0 ⚡ 40% faster, 3x more throughput Our first model to unify the capabilities of our flagship models into a single, versatile model. https://t.co/2M1VNaDkRz

Wifeguy Lenin https://t.co/OPdSG8B5j8

Wifeguy Lenin https://t.co/OPdSG8B5j8

Different AI models find different bugs. So why not use all of them? Try this out in Copilot CLI: 1. Run /review 2. Ask it to use multiple model providers at once for a multi-agent code review 3. Get the highest possible signal and catch bugs before anyone else @_Evan_Boyle shows how it's done. ▶️ https://t.co/TWNzPBFcmC

San Francisco is a beach town https://t.co/xEUwKZte3n

Thank you pydantic https://t.co/KTkty0tUKx

💚🤗💚 Jensen showing @huggingface during GTC keynote, where @NVIDIAAI dropped amazing new open models, datasets and blogs! Some of my favorites, links in comments: 🧠 Nemotron 3 Super 120A12B - Reasoning LLM 🏥 Open-H-Embodiment - Healthcare Robotics Dataset 🩻 Cosmos-H-Surgical-Simulator - World Foundation Model 🚕 Alpamayo 1.5 - Autonomous Vehicle Model and Datasets 🕺 Kimodo v1 - Generate body movement from prompts! And SO MUCH MORE to explore at https://t.co/PFeA2y9bi6

In Marin, we are trying to get really good at scaling laws. We have trained models up to 1e22 FLOPs and have made a prediction of the loss at 1e23 FLOPs, which @WilliamBarrHeld is running. This prediction is preregistered on GitHub, so we'll see in a few days how accurate our prediction was. What we want is not just a single model but a training recipe that scales reliably.

Here's the GitHub issue with all the details: https://t.co/fYre7BLjv6 This is part of our Delphi suite, a "modernized" version of Pythia: https://t.co/9G6NiADnMo

JUST IN: Meta announces they'll be shutting down the Metaverse, after pouring $80,000,000,000.00 into the project. https://t.co/32VHBmfRQ2

Introducing the Paper Pages skill! Simply paste this SKILL.md, so your coding agent knows how to work with @huggingface papers Ask it to summarize papers, search papers, or list linked models or datasets https://t.co/Cf8iaFngN5

“The past was erased, the erasure was forgotten, the lie became the truth” — George Orwell https://t.co/ZLwE1V73Wh

Streaming was supposed to democratize music. But the data tells a different story. In Australia, the share of local artists in top streaming rankings has dropped sharply, as algorithms optimize for engagement over geography. If discovery is driven by global data, what happens to local culture? https://t.co/HH4ESlDyeY @ConversationEDU @ConversationUS

I can’t stop laughing. https://t.co/uwepIoObeA

Interesting that it was @grok that did the final science heavy lifting in the man cures dog's cancer with AI story. Grok corrected errors in the vaccine construct missed by Gemini Full pod down in the replies https://t.co/V5WjaQ9m7q

@_akhaliq wow https://t.co/k8TDHTMA1w

@_akhaliq wow https://t.co/k8TDHTMA1w

? https://t.co/PeY8kBMIby

Context engineering is the new prompt engineering — and if you're building AI agents, you need to understand the difference and why parsing your data correctly sits at the heart of it Andrej Karpathy put it well: context engineering is "the delicate art and science of filling the context window with just the right information for the next step." It's not just about the instructions you give an LLM. It's about what you put IN front of it. That context can come from a lot of places: — System prompts — Chat history & long-term memory — Knowledge base retrieval — Tool definitions & responses — Structured outputs One of the most underrated levers? Structured information. This is exactly what LlamaParse + LlamaExtract are built for. Parse your complex documents properly → extract structured, relevant fields → pass clean, dense context to your agent. Better parsing = better context = better agents. It really is that simple. Take a look back on a piece by @tuanacelik and @LoganMarkewich about the full breakdown: what context engineering is, what makes up context, and the key techniques to consider — from memory blocks to workflow engineering. Read it here 👇 https://t.co/fE6cuzDJMj

I had the pleasure of being there for this moment. The conversations between minds of this caliber during one of the most revolutionary times was a moment i will never forget. both @karpathy and jensen have the highest information density in their words of anyone i’ve met. https://t.co/taW0JXVAvk

OpenClaw is live on Noah 🦞 Launching your first @solana Agent is simple. Here's how you can deploy your wallet-scoped, persistent agent in under 60 seconds 👇 https://t.co/DlN1NvwVA3

Are you up for a challenge? https://t.co/GNryIDhnut https://t.co/ZX7ZiuhGgu

Grounding lets vision-language models do more than describe—they can point to where a robot should grasp, which button to click, or which object to track across video frames. Today we're releasing MolmoPoint, a better way for models to point. 🧵 https://t.co/g7fYEOjOpQ

RARÍSSIMO! Fotógrafo registra beija-flor com “orelhas”. A espécie Colibri coruscans vive em altas altitudes e possui penas laterais que criam esse efeito. https://t.co/FDfuabF4Dz

I've been busy at NVIDIA's GTC working for CNQQ, the @ChinaAMC_HQ and @rayliant Tech ETF (https://t.co/2BjIVhqvR4). This ETF focuses on China's quickly growing tech companies, which is why it asked me to cover GTC for it. I've been taking the QQbubu (its mascot) around to see the most interesting things and meet many of the leaders at GTC. They sponsored my visit to GTC and did this awesome edit of my first day to show its sizeable Chinese audience. I have a ton of video, so over the weekend will run a bunch of them. Just interviewed NVIDIA's head of robotics, which was an awesome conversation about the state of the robotics industry (Amit Goel has the best view of anyone).

OK it's training!!! (A100 80GB, there was no H100 available on Colab Pro) Yay @UnslothAI Studio https://t.co/AMpUHT6JKP

OK it's training!!! (A100 80GB, there was no H100 available on Colab Pro) Yay @UnslothAI Studio https://t.co/AMpUHT6JKP

V-Co A Closer Look at Visual Representation Alignment via Co-Denoising paper: https://t.co/yFmatjr2xS https://t.co/e9XqEsUmi5

Hugging Face Papers for AI Agents When AI agents such as Cursor or Claude Code fetch a Hugging Face Papers page, Markdown versions are served automatically, saving tokens and improving efficiency A new hugging-face-paper-pages skill for AI agents lets agents search papers by title, author, or semantic similarity, read their content, and discover linked models, datasets, and Spaces on the Hub https://t.co/4QhUTLkep2

Today, we’re evolving @StitchbyGoogle from @GoogleLabs into an AI design canvas transforms natural language prompts into production-ready front-end code. Some highlights from what’s new: 1. A complete redesign of the Stitch UI, which can now ingest multimodal references (text prompts, images, or code) as creative seeds for your design ideas 2. A brand new, context-aware design agent that can share feedback on builds, generate PRDs, and ask questions to better understand your vision. You can even talk to the agent if you prefer a verbal sounding board 3. A new agent-friendly markdown file, DESIGN.md, which you can use to export or import your design rules to or from other design and coding tools Whether you’ve been designing for decades or you’re whiteboarding your first software idea, Stitch can help you turn concepts into prototypes in minutes rather than days ➡️ https://t.co/efI6cbjAhm

Stitch is accessible by users 18+ who are located in regions where Gemini is available. Learn more about the platform and the updates we’re making today in our blog: https://t.co/TipILeDV6n