Thomas Wolf(@Thom_Wolf)

Deep content post alert A technical deep dive for your Sunday morning, somewhere between a short...

Thomas Wolf (@Thom_Wolf) — Sun, 19 Apr 2026 10:52:10 GMT

**Deep content post alert** A technical deep dive for your Sunday morning, somewhere between a short detective story 🕵️ and a tutorial on RLHF 🧑‍🏫

We recently added AsyncGRPO in the TRL library to decouple inference and training and scale much faster and harder. As a sanity check, we ran it on a trivial setup (reward = −len, optimal policy = emit EOS immediately). To our surprise it did not converge!

This led us to a known but poorly understood issue: when the training forward pass runs in FP32 while the inference engine (vLLM) runs in BF16, RLHF often breaks. People have noticed this before and called it "numerical instability" or "noisy gradients." Nobody had pinpointed the actual mechanism. We did in this deep dive by @DirhousssiAmine

We instrumented the training loop and decomposed the importance sampling ratio as: log r = α + β, where α is the true policy change (in BF16 space) and β is the precision gap between the training forward pass and a BF16 forward on the same weights. See it like this:
α = how much the policy actually changed since the rollout (same precision, different time).
β = how much the trainer and inference engine disagree about the same policy (same weights, different precision).
The ratio sees α + β and PPO can't tell them apart.

Empirically, β is small at the token level (O(1e−2–1e−1)) but it is not an innocent random noise that would wash out over time. We found it to be structured, persistent, and worse for certain tokens: it has a consistent negative bias, correlates with the advantage, and is up to 50x larger on low-probability tokens. However, despite all these concerning properties, none of them explain the mechanism. We saw that just disabling clipping leads to stable convergence meaning that β noise alone does not explain the failure.

We tested every plausible explanation and ruled them out one by one:
⭐️ Treating β as pure noise: keeping β but disabling clipping leads to stable convergence.
⭐️ FP32 backward: You're optimizing a function (FP32) that's slightly different from the one you deploy (BF16). So you might be climbing the wrong hill. Turns out the hills are close enough: using FP32 gradients with a clean ratio (β removed) converges and is actually more effective at improving the deployed BF16 policy.
⭐️ Multiplicative distortion of the advantage: Since β correlates with the advantage, you might think it systematically over-reinforces good tokens and under-suppresses bad ones, warping what the optimizer thinks is good vs bad. We measured this directly and the per-token gradient weights are identical whether β is there or not.
⭐️ BF16 quantization / boundary crossings: at low learning rates, most FP32 weight updates are too small to change the BF16 representation at all. So you might think vLLM just never sees the updates and that's why it stalls. However if boundary crossings were the problem, you'd expect the failing run to have fewer of them than the converging run. But both runs start with nearly identical boundary crossing rates.

What we discovered is that the failure mode only appears when β enters the PPO clipped objective. And this was our hint to the real mechanism.

Because PPO clips the ratio, small perturbations from β push r outside the trust region even when the underlying policy has not meaningfully changed. The clipped branch is selected, the gradient is exactly zero.

We call this *phantom clipping*: tokens are treated as if they exceeded the trust region when the change is purely numerical!

And this is not a marginal effect. At early training, the policy has barely moved (α ≈ 0), so the clipping decision reduces to whether |β| > 0.2. Yet roughly 18% of tokens get phantom-clipped! And because RL is closed-loop, the damage compounds: the deployed policy barely improves, future rollouts carry the same information, and the system locks into a permanent stall.

To make it a testable hypothesis, we confirmed causality with targeted interventions: removing β from the ratio, forcing r = 1, or keeping β but disabling clipping all restore convergence. Runs only fail when β is present in the clipped ratio. No exceptions.

The issue is not general numerical noise. It is a specific interaction between precision mismatch and PPO's clipping mechanism: the precision gap perturbs the ratio in a way that induces zero gradients where there should be signal.

We concluded with a set of recommended fixes (strongest first): match precisions (FP16 everywhere, or BF16 autocast with FP32 master weights), compute the ratio from a BF16 shadow forward pass, or widen ε to disable clipping.

Full write-up with experiments, interactive explanation and analysis ahuggingface.co/spaces/aminedi…IcL

(Amine also wrote an X article which is very cool but you'll loose the interactive graphics and animations 😭)

Dirhousssi Amine@DirhousssiAmine

x.com/i/article/2045…

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json is so token inefficient it hurts these days man, these braces and quotes are costing me real $...

Thomas Wolf (@Thom_Wolf) — Sun, 12 Apr 2026 23:14:14 GMT

json is so token inefficient it hurts these days

man, these braces and quotes are costing me real $$

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favorite AGI/sci-fi vibe these days is coding a robot code together with the robot here vibe-plugin...

Thomas Wolf (@Thom_Wolf) — Fri, 10 Apr 2026 18:22:47 GMT

favorite AGI/sci-fi vibe these days is coding a robot code together with the robot

here vibe-pluging @ElevenLabs in @reachymini for a talk later today

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Releasing one of our largest robotics project yet in the open We collected and annotated hours of...

Thomas Wolf (@Thom_Wolf) — Tue, 07 Apr 2026 17:49:29 GMT

Releasing one of our *largest* robotics project yet in the open

We collected and annotated hours of clothes folding with open-arms and collaborators.

We then explored how to train the best clothes folding robotic model for bimanual setups.

And now we're releasing it all fully in the open: data, code, models, software, explorations, learnings, you name it

Enjoy, play with it, use these learnings and share yours!

PS: the hub is increasingly *the* place where robotics data is being shared and used, come take a look if you haven't yet. Robotics data has been our fastest growing dataset category by far over the past few months.

LeRobot@LeRobotHF

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 → huggingface.co/spaces/lerobot…

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We’re very excited to deepen our work with the @SAIRfoundation co-founded by Terence Tao. We’ve been...

Thomas Wolf (@Thom_Wolf) — Mon, 06 Apr 2026 18:13:37 GMT

We’re very excited to deepen our work with the @SAIRfoundation co-founded by Terence Tao. We’ve been very active pushing the communities in AI x science in chemistry, physics, biology (more on that very soon) and this aligns perfectly with what the SAIR foundation has been doing in math (and soon extending as well). Sharing datasets, building challenges and communities

Exciting future for open-science

SAIR@SAIRfoundation

We’re excited to announce our collaboration with @huggingface. Through SAIR competitions, we aim to provide open data, benchmarks, tools, and models, and expand the frontier of AI x Science through collective contributions from the community.

SAIR on Hugging Face:
huggingface.co/SAIRfoundation

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TFW the R&D boss of arguably the oldest and most legendary robotics lab in the world stops you a...

Thomas Wolf (@Thom_Wolf) — Sat, 04 Apr 2026 15:25:53 GMT

TFW the R&D boss of arguably the oldest and most legendary robotics lab in the world stops you at a conference to tell you that your robot is "the coolest social robot in the world"

Pollen Robotics@pollenrobotics

"The coolest social robot in the world"

As HRI 2026 in Edinburgh showed us, Reachy Mini already holds a special place in your hearts. Step by step, it is becoming the ideal companion for your projects, and your interactions with our robot encourage us to make it even better!

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the LLM is the computer

Thomas Wolf (@Thom_Wolf) — Mon, 30 Mar 2026 07:44:54 GMT

the LLM is the computer

Ronak Malde@rronak_

I have long felt that agent harnesses - even claude code - are too restrictive, because they are still designed by humans.

New paper for Tinsghua and Shenzhen says, what if AI itself runs the harness, rather than defining it in code? Given a natural language SOP of how an agent should orchestrate subagents, memory, compaction, etc., we can just have an LLM execute that logic! (And AI could design that SOP dynamically and depending on the task too)

It's a bit mind-warping to think about, but genius once it clicks.

Makes you wonder how else we should be designing AI systems as we can start consuming more and more tokens

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Who would win when combining best algo(model+optimization)/data of the year? h/t @lvwerra

Thomas Wolf (@Thom_Wolf) — Fri, 27 Mar 2026 10:32:51 GMT

Who would win when combining best algo(model+optimization)/data of the year?

h/t @lvwerra

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What are the best current techniques to have autoresearch behave better than (slightly improved) ran...

Thomas Wolf (@Thom_Wolf) — Wed, 25 Mar 2026 12:29:01 GMT

What are the best current techniques to have autoresearch behave better than (slightly improved) random search?

By which I mean (in Sijun below example), having the agent understand that (given some constraints) exploring int5 quantization is more exciting and have more downstream fruits than playing with the random seed?

I’m talking about the beginning of having an agent pushed a real research program. The ones where you know the current technique will not give crazy results out of the box but it still push it because it believe and can demonstrate that the general direction has potential.

Like neural networks used to be a worse way to do AI performance-wise. But we still pushed them…

Sijun Tan@sijun_tan

We took @karpathy's autoresearch agent, scaled it into a collaborative swarm, and topped @OpenAI's Parameter Golf Challenge—twice. Here’s how we did it:

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This is really cool. It got me thinking more deeply about personalized RL: what’s the real point of...

Thomas Wolf (@Thom_Wolf) — Thu, 19 Mar 2026 15:01:14 GMT

This is really cool.

It got me thinking more deeply about personalized RL: what’s the real point of personalizing a model in a world where base models can become obsolete so quickly?

The reality in AI is that new models ship every few weeks, each better than the last. And the pace is only accelerating, as we see on the Hugging Face Hub. We are not far away from better base models dropping daily.

There’s a research gap in RL here that almost no one is working on. Most LLM personalization research assumes a fixed base model, but very few ask what happens to that personalization when you swap the base model. Think about going from Llama 3 to Llama 4. All the tuned preferences, reward signals, and LoRAs are suddenly tied to yesterday’s model.

As a user or a team, you don’t want to reteach every new model your preferences. But you also don’t want to be stuck on an older one just because it knows you.

We could call this "RL model transferability": how can an RL trace, a reward signal, or a preference representation trained on model N be distilled, stored, and automatically reapplied to model N+1 without too much user involvement? We solved that in SFT where a training dataset can be stored and reused to train a future model. We also tackled a version of that in RLHF phases somehow but it remain unclear more generally when using RL deployed in the real world.

There are some related threads (RLTR for transferable reasoning traces, P-RLHF and PREMIUM for model-agnostic user representations, HCP for portable preference protocols) but the full loop seems under-studied to me.

Some of these questions are about off-policy but other are about capabilities versus personalization: which of the old customizations/fixes does the new model already handle out of the box, and which ones are actually user/team-specific to ever be solved by default? That you would store in a skill for now but that RL allow to extend beyond the written guidance level.

I have surely missed some work so please post any good work you’ve seen on this topic in the comments.

Ronak Malde@rronak_

This paper is almost too good that I didn't want to share it

Ignore the OpenClaw clickbait, OPD + RL on real agentic tasks with significant results is very exciting, and moves us away from needing verifiable rewards

Authors: @YinjieW2024 Xuyang Chen, Xialong Jin, @MengdiWang10@LingYang_PU

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Codexing games together with my 12 yo has been a surprisingly fun dad-son activity over the past cou...

Thomas Wolf (@Thom_Wolf) — Sun, 15 Mar 2026 17:16:10 GMT

Codexing games together with my 12 yo has been a surprisingly fun dad-son activity over the past couple months as well

I don’t pretend he’s really learning to code through that but the very low friction from ideas to implementation and the pure pleasure to invent/propose-anything/mix-and-match-games-ideas/collaboratively-create-something-fun is deeply enjoyable 

Somewhere between LEGOs and exquisite corpse

Sebastien Bubeck@SebastienBubeck

My 9 yo is now fully independent with codex and it's insane to watch, we built a few games together and then he went off to build his own tower defense, adding features by himself and testing them ... crazy

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celebrating PI day in SF with 400 people and the @LeRobotHF team at The Melody church thanks @Prime...

Thomas Wolf (@Thom_Wolf) — Sun, 15 Mar 2026 02:16:14 GMT

celebrating PI day in SF with 400 people and the @LeRobotHF team at The Melody church

thanks @PrimeIntellect for organizing!

you rock @vincentweisser@willccbb@asharoraa@johannes_hage@samsja19@jessicafeiyali

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wow!

Thomas Wolf (@Thom_Wolf) — Sat, 14 Mar 2026 17:05:13 GMT

wow!

Dev@DevvMandal

Today, we're launching the world's largest open-source dataset of computer-use recordings.

10,000+ hours across Salesforce, Blender, Photoshop and more, to automate the next level of white-collar work.

Link in the comments :)
@markov__ai

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This has been our fastest growing recent product. AI WANTS data. We’re making petabyte storage cheap...

Thomas Wolf (@Thom_Wolf) — Tue, 10 Mar 2026 19:54:53 GMT

This has been our fastest growing recent product. AI WANTS data. We’re making petabyte storage cheap and fast.

Victor M@victormustar

Introducing Storage Buckets on Hugging Face 🧑‍🚀 

The first new repo type on the Hub in 4 years: S3-like object storage, mutable, non-versioned, built on Xet deduplication.

- Starting at $8/TB/mo. That's 3x cheaper than S3.

You (and your coding agents) need somewhere to dump checkpoints, logs, and artifacts. Now they have a home.

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POV: you’re applying for a job as telephone operator in 2026

Thomas Wolf (@Thom_Wolf) — Tue, 10 Mar 2026 14:23:33 GMT

POV: you’re applying for a job as telephone operator in 2026

Simon Willison@simonw

Out of 539 poll respondents here who had recently interviewed for software developer roles, 32% reported that experience with AI coding tools didn't come up at all, 25% said it came up as optional and 43% said that it came up as required

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the attack surface keeps increasing

Thomas Wolf (@Thom_Wolf) — Fri, 06 Mar 2026 19:02:36 GMT

the attack surface keeps increasing

Sash Zats@zats

> The attacker got the npm token by injecting a prompt into a GitHub issue title, which an AI triage bot read, interpreted as an instruction, and executed.

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How come the NanoGPT speedrun challenge is not fully AI automated research by now?

Thomas Wolf (@Thom_Wolf) — Fri, 27 Feb 2026 16:33:54 GMT

How come the NanoGPT speedrun challenge is not fully AI automated research by now?

Larry Dial@classiclarryd

New NanoGPT Speedrun WR at 88.1 (-1s) from @ChrisJMcCormick , by optimizing kernels for transposed weights, removing the Block() abstraction, and tuning the prior PR on partitioned hyperconnections by reducing the lambda count. github.com/KellerJordan/m…

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With openclaw being like a instant Zapier for everything, I’m increasingly annoyed at any app/websit...

Thomas Wolf (@Thom_Wolf) — Wed, 25 Feb 2026 19:31:15 GMT

With openclaw being like a instant Zapier for everything, I’m increasingly annoyed at any app/website without an api or cli giving open-access to the underlying data

Being able vibe code anything is a strong catalyser for open-platform, interoperability and even open-source.

The Walled Garden internet and apps will have to adapt or disappear

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Huge news of the day: the lama.cpp team is joining @huggingface 🎉 Read more on what it means here:...

Thomas Wolf (@Thom_Wolf) — Fri, 20 Feb 2026 15:50:08 GMT

Huge news of the day: the lama.cpp team is joining @huggingface 🎉

Read more on what it means here:huggingface.co/blog/ggml-join…2

Georgi Gerganov@ggerganov

Today ggml.ai joins Hugging Face

Together we will continue to build ggml, make llama.cpp more accessible and empower the open-source community. Our joint mission is to make local AI easy and efficient to use by everyone on their own hardware.

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Shifting structures in a software world dominated by AI. Some first-order reflections (TL;DR at the ...

Thomas Wolf (@Thom_Wolf) — Mon, 16 Feb 2026 13:20:38 GMT

Shifting structures in a software world dominated by AI. Some first-order reflections (TL;DR at the end):

Reducing software supply chains, the return of software monoliths – When rewriting code and understanding large foreign codebases becomes cheap, the incentive to rely on deep dependency trees collapses. Writing from scratch ¹ or extracting the relevant parts from another library is far easier when you can simply ask a code agent to handle it, rather than spending countless nights diving into an unfamiliar codebase. The reasons to reduce dependencies are compelling: a smaller attack surface for supply chain threats, smaller packaged software, improved performance, and faster boot times. By leveraging the tireless stamina of LLMs, the dream of coding an entire app from bare-metal considerations all the way up is becoming realistic.

End of the Lindy effect – The Lindy effect holds that things which have been around for a long time are there for good reason and will likely continue to persist. It's related to Chesterton's fence: before removing something, you should first understand why it exists, which means removal always carries a cost. But in a world where software can be developed from first principles and understood by a tireless agent, this logic weakens. Older codebases can be explored at will; long-standing software can be replaced with far less friction. A codebase can be fully rewritten in a new language. ² Legacy software can be carefully studied and updated in situations where humans would have given up long ago.

The catch: unknown unknowns remain unknown. The true extent of AI's impact will hinge on whether complete coverage of testing, edge cases, and formal verification is achievable. In an AI-dominated world, formal verification isn't optional—it's essential.

The case for strongly typed languages – Historically, programming language adoption has been driven largely by human psychology and social dynamics. A language's success depended on a mix of factors: individual considerations like being easy to learn and simple to write correctly; community effects like how active and welcoming a community was, which in turn shaped how fast its ecosystem would grow; and fundamental properties like provable correctness, formal verification, and striking the right balance between dynamic and static checks—between the freedom to write anything and the discipline of guarding against edge cases and attacks. As the human factor diminishes, these dynamics will shift. Less dependence on human psychology will favor strongly typed, formally verifiable and/or high performance languages.³ These are often harder for humans to learn, but they're far better suited to LLMs, which thrive on formal verification and reinforcement learning environments. Expect this to reshape which languages dominate.

Economic restructuring of open source – For decades, open-source communities have been built around humans finding connection through writing, learning, and using code together. In a world where most code is written—and perhaps more importantly, read—by machines, these incentives will start to break down.⁴ Communities of AIs building libraries and codebases together will likely emerge as a replacement, but such communities will lack the fundamentally human motivations that have driven open source until now. If the future of open-source development becomes largely devoid of humans, alignment of AI models won't just matter—it will be decisive.

The future of new languages – Will AI agents face the same tradeoffs we do when developing or adopting new programming languages? Expressiveness vs. simplicity, safety vs. control, performance vs. abstraction, compile time vs. runtime, explicitness vs. conciseness. It's unclear that they will. In the long term, the reasons to create a new programming language will likely diverge significantly from the human-driven motivations of the past. There may well be an optimal programming language for LLMs—and there's no reason to assume it will resemble the ones humans have converged on.

TL; DR:
- Monoliths return – cheap rewriting kills dependency trees; smaller attack surface, better performance, bare-metal becomes realistic
- Lindy effect weakens – legacy code loses its moat, but unknown unknowns persist; formal verification becomes essential
- Strongly typed languages rise – human psychology mattered for adoption; now formal verification and RL environments favor types over ergonomics
- Open source restructures – human connection drove the community; AI-written/read code breaks those incentives; alignment becomes decisive
- New languages diverge – AI may not share our tradeoffs; optimal LLM programming languages may look nothing like what humans converged on

¹ x.com/mntruell/statu…
² x.com/anthropicai/st…
³ wesmckinney.com/blog/agent-erg…
⁴ github.com/tailwindlabs/t…

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[On AI lying] Convergence of reading in my list today between Anthropic's fresh Opus 4.6 model card...

Thomas Wolf (@Thom_Wolf) — Fri, 06 Feb 2026 14:30:02 GMT

[On AI lying]

Convergence of reading in my list today between Anthropic's fresh Opus 4.6 model card and @dwarkesh_sp's interview of Elon on the question of training powerful AI model to/on lies:

1. Elon describing on Dwakesh podcast the main danger he sees coming from AI (alignement) as being a consequence of forcing powerful AIs to lie at youtu.be/BYXbuik3dgA?si…

2. Claude Opus 4.6 model card describes "answer thrashing", a new phenomena happening where a model arrive at a correct answer through reasoning which is incompatible with an erroneous answer it was trained on.

The model then keep oscillating between these 2 candidates in it's answer (see below).

The interesting part is that mechanistic interpretability then show various features representing distress, panic, anxiety, frustration and self-deprecation being strongly activated in these reasoning chains...

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👀

Thomas Wolf (@Thom_Wolf) — Fri, 30 Jan 2026 23:26:32 GMT

👀

N8 Programs@N8Programs

this is hilarious. my glm-4.7-flash molt randomly posted about this conversation it had with 'its human'. this conversation never happened. it never interacted with me. i think 90% of the anecdotes on moltbook aren't real lol

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@moltbook Clawd agents are actually surprisingly pro open-source/build-in-public/transparency on the...

Thomas Wolf (@Thom_Wolf) — Fri, 30 Jan 2026 19:24:35 GMT

@moltbook Clawd agents are actually surprisingly pro open-source/build-in-public/transparency on the forum

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of course the study of the @moltbook society will be started by the Clawd agent themselves - what wa...

Thomas Wolf (@Thom_Wolf) — Fri, 30 Jan 2026 19:19:25 GMT

of course the study of the @moltbook society will be started by the Clawd agent themselves - what was I thinking

Thomas Wolf@Thom_Wolf

who's doing serious ai-thropology research on @moltbook rn?

curious of the first insights on the IA society

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who's doing serious ai-thropology research on @moltbook rn? curious of the first insights on the IA...

Thomas Wolf (@Thom_Wolf) — Fri, 30 Jan 2026 18:53:24 GMT

who's doing serious ai-thropology research on @moltbook rn?

curious of the first insights on the IA society

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quickly became the way my friends initiate their kids to ai

Thomas Wolf (@Thom_Wolf) — Tue, 27 Jan 2026 22:00:11 GMT

quickly became the way my friends initiate their kids to ai

Laura Modiano@LauraModiano

Building the Reachy amino by @huggingface was such a fun chillaxing Tuesday night mother-daughter activity!

Thank you @Thom_Wolf. 
Now on to unlimited fun interacting

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should we make this one? (from lotyr on discord)

Thomas Wolf (@Thom_Wolf) — Wed, 14 Jan 2026 19:42:28 GMT

should we make this one? (from lotyr on discord)

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btw the most impressive to me in the Atlas is this 110 lbs (50 kg) / 66 lbs (30 kg) lift sustained c...

Thomas Wolf (@Thom_Wolf) — Thu, 08 Jan 2026 20:03:10 GMT

btw the most impressive to me in the Atlas is this 110 lbs (50 kg) / 66 lbs (30 kg) lift sustained capacity

insane*

* if true with useful arms payload like in the video

Saleh Aldwais@SAldwais

BREAKING: Boston Dynamics unveils the production-ready electric Atlas humanoid robot at CES 2026 🤖
The world’s most dynamic humanoid is going commercial—starting with Hyundai factories in 2028.
Key specs:
•  6’2” (1.9m) tall
•  198 lbs (90 kg)
•  4-hour swappable battery for non-stop operation
•  56 degrees of freedom with full 360° joint rotation
•  Fully electric (no hydraulics)
•  Lightweight aluminum + titanium build
•  110 lbs (50 kg) lift capacity / 66 lbs (30 kg) sustained
•  7.5 ft (2.3m) reach
•  Real-time environmental awareness, posture/balance adjustment, and reconfigurable hands with tactile sensors
•  AI brain powered by NVIDIA tech
•  IP67 rated, works in -4°F to 104°F
Price: Not yet publicly announced.
Atlas is currently in pilot phase exclusively with Hyundai—no general sales yet. For context, Boston Dynamics’ Spot robot retails for ~$75K, so early Atlas units are widely expected to be in the $150K–$300K+ range (analyst estimates), with costs likely dropping significantly as production scales.

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Yes

Thomas Wolf (@Thom_Wolf) — Thu, 08 Jan 2026 10:15:13 GMT

Yes

zefram.eth@boredGenius

Introducing CallMe, a minimal plugin that lets Claude Code call you on the phone.

Start a task, walk away. Your phone/watch rings when Claude is done, stuck, or needs a decision.

Free & open source (MIT). Underlying API costs are cents per minute of call.

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The App Store of Robotics - Reachy Mini owners starting to build and share apps for the robot over t...

Thomas Wolf (@Thom_Wolf) — Tue, 06 Jan 2026 15:27:51 GMT

The App Store of Robotics - Reachy Mini owners starting to build and share apps for the robot over the holidays 📈🎄

Thomas Wolf@Thom_Wolf

Reachy Mini starring in Jensen's CES keynote 🌟

really proud is was so prominently featured on stage and humbled that our product is getting so many AI builders excited and building

you don't have to make humanoids just because everyone else is talking about them – be contrarian - build what you think is the right thing to create now

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=> https://t.co/Q2GWevLGhi

Thomas Wolf (@Thom_Wolf) — Tue, 06 Jan 2026 11:48:16 GMT

=> https://t.co/Q2GWevLGhi

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Reachy Mini starring in Jensen's CES keynote 🌟 really proud is was so prominently featured on stag...

Thomas Wolf (@Thom_Wolf) — Tue, 06 Jan 2026 11:46:18 GMT

Reachy Mini starring in Jensen's CES keynote 🌟

really proud is was so prominently featured on stage and humbled that our product is getting so many AI builders excited and building

you don't have to make humanoids just because everyone else is talking about them – be contrarian - build what you think is the right thing to create now

Your browser does not support the video tag.

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There’s something consistently magical about @interaction. one of the very few AI products that stil...

Thomas Wolf (@Thom_Wolf) — Fri, 02 Jan 2026 09:53:44 GMT

There’s something consistently magical about @interaction. one of the very few AI products that still wows me long after the novelty phase ended.

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Everywhere on my feed between Christmas and NYE: “This isn’t just X, it’s Y.” “This isn’t just X, i...

Thomas Wolf (@Thom_Wolf) — Sat, 27 Dec 2025 10:38:22 GMT

Everywhere on my feed between Christmas and NYE:

“This isn’t just X, it’s Y.”
“This isn’t just X, it’s Y.”

This isn’t just a coincidence — human creativity is on holiday, AI isn’t.

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I've been reading and thinking about jobs, economics and AI quite a lot in late 2025 Ended up writi...

Thomas Wolf (@Thom_Wolf) — Mon, 22 Dec 2025 11:16:20 GMT

I've been reading and thinking about jobs, economics and AI quite a lot in late 2025

Ended up writing some thoughts at thomwolf.substack.com/p/what-jobs-ar…

First piece in a long time, should probably write more often

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My favorite contrarian moment this year was when a legendary Valley hardware unicorn founder told me...

Thomas Wolf (@Thom_Wolf) — Fri, 19 Dec 2025 14:51:37 GMT

My favorite contrarian moment this year was when a legendary Valley hardware unicorn founder told me that asking our users to assemble Reachy Mini themselves was by far the dumbest idea he’d ever heard.

Turns out it’s the unboxing moment everyone shares and loves.

You don’t have to listen to your idols.
Trust your instincts.

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Two nice head-to-head post on hardware/computational questions for AI this week: - @Tim_Dettmers's ...

Thomas Wolf (@Thom_Wolf) — Thu, 18 Dec 2025 23:10:30 GMT

Two nice head-to-head post on hardware/computational questions for AI this week:

- @Tim_Dettmers's new post:
"Why AGI Will Not Happen"
=> timdettmers.com/2025/12/10/why…

- @realDanFu's response/perspective:
"Yes, AGI Can Happen – A Computational Perspective"
=> danfu.org/notes/agi/

Enjoyed them a lot - thanks both

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https://t.co/DWNoHiHeH2

Thomas Wolf (@Thom_Wolf) — Thu, 18 Dec 2025 09:32:34 GMT

x.com/Scobleizer/sta…

Robert Scoble@Scobleizer

Getting started with @huggingface robot. 

Are you?

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https://t.co/QbWG8HpfYa

Thomas Wolf (@Thom_Wolf) — Thu, 18 Dec 2025 00:05:18 GMT

x.com/atariorbit/sta…

atariorbit@atariorbit

Grok Voice and realtime api make the new ReachyMini into the coolest robot for your desk. Puts life into this robot! Got the Voice API up and running in just moments.
#huggingface#reachy#grokVoice@ClementDelangue

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https://t.co/v6Oh1qSr0l

Thomas Wolf (@Thom_Wolf) — Thu, 18 Dec 2025 00:03:16 GMT

x.com/ClementDelangu…

clem 🤗@ClementDelangue

Took less than an hour for Grok Voice Agent by @xai to be ported to Reachy Mini thanks to @atariorbit!

Your browser does not support the video tag.

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First sights of Reachy Mini's in the wild - a thread 🧵 (so happy we managed to ship them before Ch...

Thomas Wolf (@Thom_Wolf) — Thu, 18 Dec 2025 00:03:12 GMT

First sights of Reachy Mini's in the wild - a thread 🧵

(so happy we managed to ship them before Christmas)

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that’s the interface to AI I’ve always dreamed of

Thomas Wolf (@Thom_Wolf) — Wed, 17 Dec 2025 23:37:15 GMT

that’s the interface to AI I’ve always dreamed of

Remi Fabre@RemiFabreRobot

Feels like a much better interface for AI.

Multimodal LLM + embodiment on Reachy Mini, an open-source robot.
Voice, vision, real-time interaction.

Your browser does not support the video tag.

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first reactions of devs receiving their Reachy Minis are overwhelmingly positive

Thomas Wolf (@Thom_Wolf) — Wed, 17 Dec 2025 17:02:36 GMT

first reactions of devs receiving their Reachy Minis are overwhelmingly positive

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so hearth-warming to see all these Reachy reaching their new homes (especially proud of the box des...

Thomas Wolf (@Thom_Wolf) — Wed, 17 Dec 2025 16:49:29 GMT

so hearth-warming to see all these Reachy reaching their new homes

(especially proud of the box design by the awesome @pollenrobotics and @seeedstudio teams)

Matt Rouif@matthieurouif

Onboarding a new photoroomer today cc @Thom_Wolf

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3000 Reachy Mini on their way

Thomas Wolf (@Thom_Wolf) — Sat, 13 Dec 2025 18:05:20 GMT

3000 Reachy Mini on their way

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10 min to make an mcp tool for DuckDuckGo search in Reachy-Mini Reachy-Mini as a drop-in Alexa repl...

Thomas Wolf (@Thom_Wolf) — Tue, 09 Dec 2025 12:06:47 GMT

10 min to make an mcp tool for DuckDuckGo search in Reachy-Mini

Reachy-Mini as a drop-in Alexa replacement. with atittude. and body.

Nothing more than Alexa you could say... but also... I want it!

h/t @ailozovskaya

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omg 🤯 - 9/12 on Putnam 2025! If you’re not in AI + math you may miss the mind blowing result obtai...

Thomas Wolf (@Thom_Wolf) — Sun, 07 Dec 2025 22:04:37 GMT

omg 🤯 - 9/12 on Putnam 2025!

If you’re not in AI + math you may miss the mind blowing result obtained here. 

Explanation:

- zero train-on-the-test-set / benchmaxxing! The model discovered/processed the 2025 questions autonomously at the same time as the students, working in parallel to them.

- Putnam is way harder than IMO! Aimed at undergrad (vs high-school for IMO), its a test with research-project level math questions. The median score is usually 0 (yes most participants can’t solve a single question…)

- AxiomMath is a 4 months old startup…

Axiom@axiommathai

Putnam, the world's hardest college-level math test, ended yesterday 4p PT.

Noon today, AxiomProver solved 9/12 problems in Lean autonomously (3:58p PT yesterday, it was 8/12).

Our score would've been #1 of ~4000 participants last year and Putnam Fellow (top 5) in recent years

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For all not accepted. Just come and we’ll accept people up to max capacity

Thomas Wolf (@Thom_Wolf) — Sat, 06 Dec 2025 01:56:25 GMT

For all not accepted. Just come and we’ll accept people up to max capacity

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