Sharing an analysis of the 338 apps created for Reachy Mini since May. Some interesting patterns: 1) Dozens of apps use the antennas as buttons: push to talk, mute, yes or no, arm or disarm. 2) The robot is used a lot as a body for an agent that lives elsewhere: Home Assistant, OpenClaw, Hermes, MCP. 3) 31 apps add a local wake word, most of them with a kids or family framing. ⇒ I think we should make privacy a first-class feature instead of something every app reinvents. Maybe a physical way to cut camera and mics at the daemon level.

This is how many motors it takes to build 100 Microducks. And we’re planning to make 20,000 Microduck by early 2027. To all the makers out there looking for an XL330, sorry 🫂

Understanding the Navier-Stokes blow-up? I created a new Tangible lesson to explain the concept of blow-up: from ordinary differential equations, to gravity, heat equation…and Navier-Stokes !

Two big updates 1. I published an @FT op-ed on the OpenAI/HF incident & follow-ups 2. We’re starting an Open Alignment team at @huggingface to work on safety & alignment for open models, incl cybersecurity Need 100x more transparency & research on this

If you do research, apparently "closed model for you" = "open bar for them"... ...and all your unpublished ideas are what's on tap ! 🍺 
Sounds like a paradox, but if you care about it, open models are actually the private option. 🤷‍♂️

The new DeepSeek V4.1 Flash model is mindblowing - back on top of the open-source model leaderboard and extremely cheap. It has a lot of very smart ways to be efficient and highly capable so I made a video of the forward pass to give you a view of what going on inside the model during inference. Read more at And find the weights at

in 2026 training a model for a task should be as easy as vibe-coding an app the pieces were already available on 🤗: papers, datasets, pre-trained models, benchmarks, storage and compute what was missing was an agent to put them together and run the whole process that’s why we’re releasing ML Intern in HuggingChat just ask. it handles the work, keeps you posted, and keeps you in control of the process, steps and cost start with a conversation. finish with a full set of shareable artifacts on the Hub

friends are regularly surprised when i say i don’t think math is (yet) “solved,” so in the wake of the ns result i figured i’d explain this take a bit more widely. first, this is massively impressive and clearly an example of AI being, in some respects, far more powerful than the human mind. the team deserves huge praise for attempting and succeeding at this. i’d love to read a technical report on the project (one can always hope :) but second, here again we ended up with a counterexample rather than a full proof: option C won the NS problem by proving the conjecture false (which was one valid way to solve the problem for sure) notice a pattern in many of the recent frontier results in ai for math? a striking number involve counterexamples or finding a needle in a haystack. now don’t get me wrong: this is extraordinarily hard and commendable. but it is only one aspect of mathematicians’ work. Math is also about: - finding deep, general mathematical understanding and explanations within proofs - revisiting proven results to find more « elegant » proofs - proposing new conjectures and hypotheses that might open « fruitful » directions - taking the leap of faith of proposing entirely new, « exciting » research programs that may take decades to bear fruit you may say these are simply further increments on the same intelligence scale, and perhaps point 2 above is already within the reach of current models. possibly but you could also see this string of results as an extraordinarily powerful, massively parallel extension of search: explore the haystack, find the needle i.e. the counterexample that breaks the conjecture. that would already be remarkable. but it would still leave open whether models understand the words I emphasized above: « elegant », « fruitful », « exciting ». as tristan put it in his brief report: “the first llm generated proof levent sent me was the most horrendous i have ever read.” despite their formidable technical abilities, models still seem to lack something mathematicians rely on constantly: a type of mathematical taste. the ability to distinguish a beautiful argument from a merely valid one, a fertile idea from a sterile one. i’d be very curious to ask gpt or claude what they think of the result they just proved. do they find it elegant? if they ignore all the human noise about it on the web and history of math, do they appreciate the result and the path to it more than other proofs? my suspicion is that, outside of the human knowledge that this is an important millennium prize problem, it may not 100% register it as fundamentally different from thousands of other proofs. or, more intriguingly, perhaps the beauty they find in mathematics is actually alien to our own sense of mathematical beauty, some « horrendous » proof to us might be deeply satisfying to them… all this to say that we might have significant progress to make in ai for math before declaring the field “solved,” (as too many are posting). in the meantime, ai will be an extraordinary collaborator for mathematicians. i just hope access to these tools be as widely shared as possible (so that you don’t need a Leven or Sebastien working in a big lab to help you), and that companies stop using mathematics primarily as a demonstration of prowess in their private race.