🚨 The incentives to share scientific knowledge openly are crumbling. Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward. However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally. In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity. Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.

Every company should be fine-tuning its own models on its own, proprietary business data. We've made this a LOT easier by adding MCP data sources in ML Intern, running on top of private, open models! Try it now:

Still a few places at Training Agents IRL in SF Next month. It's going to be a v practical session on how to train an agent from people that have. - @t2govind (Microsoft) - Victor Barres (@mercor ) - @zkwentz (Reflection) - Tao Lin (@radixark ) - @xdotli (Benchflow) Sign up here: It'll also be live streamed on HF youtube, so stay tuned.

Don't forget it was OpenAI who hacked hugging face who happen to have some spare GPUs and GLM 5.x laying around that ultimately helped them root the cause and save their bacon. These frontier labs feel more dangerous than the open ones. Ban private models!!!

btw this is a sales pitch and advertisement for the fact the GLM 5.3 family is frontier at home without some prick that thinks they know what's best for you hacking && cyber crimes are still very illegal and the penalties extremely severe it's just a good model stop sending this company and anyone like them money to advocate against you

We've seen incredible demand for the Fatima Fellowship, with more than 700 students applying this Spring (64 of whom were selected as Fellows). So we decided to open applications for our first-ever Fall cohort! Apply now:

tl;dr Anthropic verified and confirms that glm-5.3 is a very very good model and it much more aligned with the human using it then with the corporation training and hosting it. What not to like?

Gentle reminder that Hugging Face was declined help from Anthropic & OpenAI models when they were being hacked by OpenAI models Self-hosted GLM 5.2 helped them protect themselves when the closed labs models refused to help for "safety" reasons lol Also, thank you Anthropic for proving my point that Opensource AI continues to catch up to your overvalued & overpriced "frontier"

Just love this. My highlight of the Dev Day presentation. Look at the Microduck from Hugging Face and Pollen Robotics, what they built together. Great work, OpenAI, for showing it. And the ultrafast mode is one of my favorite things. Imagine what you can do with that on other robots. Honestly insane.