Super happy to release SmolDataEnvs: 5,000 verifiable RL environment tasks for hill-climbing small models in code and data science by @adithya_s_k 100% open source: environments, evals, training!

What struck me about this photo is that Trump and Xi invited to their dinner table: an immigrant from South Africa, two Taiwanese immigrants that came to the US as children, and a gay kid from Alabama, all of whom moved to Silicon Valley to make a dent in the universe.

Spot on. "When we got attacked, our team initially turned to frontier closed-source APIs that blocked us because of safeguards that still can’t always tell the difference between attackers and defenders. I acknowledge that these safeguards are created with good intentions, but they can put defenders at a disadvantage while attackers jailbreak them, increasing the asymmetry of capabilities. In our case, as we started hitting those guardrails, fortunately we could use the @nvidia version of an open-source model" "Cyberattacks may increasingly come from proprietary models behind closed doors, while much of the defense may end up being powered by open-source tools because they are less restricted, more privacy-preserving, and orders of magnitude more affordable for organizations across the globe. The world needs open-source AI more than ever to defend itself." "during this cyberattack, we learned how AI can stoke fear among the public and policymakers, especially through anthropomorphic framing and sci-fi imagery." "We strongly believe that fear-based narratives are not the way to make the right decisions about the future of such a foundational and empowering technology or bring the public along with us." "Even though we were the victims of this cyberattack, we believe more strongly than ever that AI will be beneficial to cybersecurity and make the world safer, just as major technologies before it. We were attacked by AI, but more importantly, we defended ourselves with AI."

I like this article! The problem is not for some people to care and work on catastrophic risks (we actually need much more research into it), it’s when company leaders and policymakers use fears of them to distract and hide the current challenges and solutions!

Decision models should do more than pick a label. To truly act, they need to extract evidence, understand relationships, produce structured records, and keep related decisions consistent. And they should do it fast. That’s the new model we built with GLiNER2.5-Decide. Like all GLiNER models, this one is highly responsive to fine-tuning. And now you can fine-tune GLiNER models, including GLiNER2.5-Decide, directly inside your coding agent with our new API. To make building with GLiNER2.5-Decide as fast and simple as possible, we’re also offering hosted inference. Our model goes beyond typed classification. It can return character-level spans, relations, and structured records, then apply implications, exclusions, cardinality limits, and ordinal bounds across related decisions. And it does all of this very fast. On short, single-document requests: CPU latency → 167 ms (48-vCPU Intel Xeon Platinum 8581C) GPU latency → 38 to 47 ms (T4, L4, V100, A100) Run it locally, host it, or fine-tune it. Your decision. Download the weights on @huggingface:

If you believe the risk is coming from 1-3 people in a garage with no money and no compute, you just don't understand this technology and haven't learned anything this summer. Risk comes from the asymmetry of capabilities created by secret labs training frontier agents and running them with massive amount of compute. Open-source is exactly the solution to this asymmetry and empowers hospitals (and any smaller orgs) to defend themselves!

There’s an enormous amount of open training data on Hugging Face. What does it take to make it work together 🤗? For Marin’s 535B run, we built on 25T tokens from 152 datasets with licenses permitting training. Here’s the work between downloading those and training a model 🧵

My intervention today at the UN Security Council. A prosperous future will not be secured by a global regulator. It will be secured by sovereign nations that adopt super intelligence, responsible companies that build it, and free people who refuse to be ruled by fear.

Thank you @jnbarrot & @UN for inviting me to share our lessons to the Security Council Being the first company to disclose an agent cyberattack taught us that we need a lot more transparency in AI and more open-source AI to fight asymmetry and empower defenders!

Out today: Nemotron 3 Diarization, so you can implement speaker detection in your voice applications. 🗣️✅ And it's available day-0 on transformers and @Dell Enterprise Hub! Enterprises can implement on-premises meeting transcription with speech attributed to speakers for max privacy and security. Find it at Bonus: check out below a fully private and local implementation by @andimarafioti with Reachy Mini and DGX Spark.

Alert: Apple just dropped a new model on Hugging Face. It's a Qwen3.5-9B finetune that turns long documents into small page images to save tokens, then pulls up the full text of only the pages relevant to your question 💡

Claude Opus 5.5 will be the first Opus meant to fall back to a less capable model for "a small set of capabilities related to the development of frontier LLMs, such as kernel development.."

I'm happy to announce that I've joined Hugging Face. What started as a personal project back in February is now something I get to work on full time. Local AI has grown explosively this year. I've said this since the early oMLX releases: I want my friend who bought a MacBook yesterday to be able to run AI on it today. I believe MLX has that potential. Apple Silicon is the easiest entry point for a regular person to get started with AI, with no complicated hardware to assemble. And the open source community, including Hugging Face, has been growing that potential. That support has already made a huge difference. Hugging Face is the best place for me to support oMLX and the MLX community with everything I have. For anyone getting into local AI, the first step usually starts at Hugging Face. Mine did too. I'm proud that I get to work at that entry point, where new ideas can be tried out. oMLX stays exactly where it is, under the same Apache 2.0 license in the same repository, and I'll keep leading the project, same as before. What changes is that I can now spend far more time on it, move faster, and build something sustainable for the long term together with all the contributors who have put so much into it. I also want to do more for MLX as a whole. oMLX is built on top of transformers, mlx-lm, mlx-vlm and the rest of the ecosystem, and I'm grateful to the people behind them. Rather than keeping everything inside oMLX, over time I want to push work upstream where it makes sense. And where the community needs something that doesn't exist yet, oMLX is a good place to try it first. Thank you to the 264 contributors who have built oMLX with me, and to everyone who filed issues with detailed logs and reproductions so we could fix things. oMLX would not be what it is without you. oMLX continues in the same place, in the same way. Just much faster. HF's announcement: