Thomas Wolf(@Thom_Wolf)

I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline: • Two wee...

Thomas Wolf (@Thom_Wolf) — Wed, 22 Jul 2026 15:37:55 GMT

I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline:

• Two weeks ago: At @swyx’s AI Engineer World’s Fair in SF, I decide at the last minute to introduce my friend @uri_rolls onstage for his talk on cyber benchmarks for infrastructure penetration and access control (see below, amazing team).

I say: “There is a future where cyber is alive and everyone is well protected and I’m pretty sure that future involves open-source models.”

And later: “A big challenge is going to be speed: the speed of attack versus defense. When an intruder starts to enter, you have to see what’s happening and catch them.”

• One week ago: @huggingface is hit by a sophisticated intrusion over the weekend. The traces look unlike anything we’ve seen before and suggest serious AI involvement, but we don’t yet know which model was used.

The closed models we ask for help choke on their guardrails. We need to react fast, so we turn to @Zai_org’s GLM-5.2 to help us analyze the attack.

• Earlier this week: @OpenAI reaches out, discloses what happened, and partners with us on the investigation.

The intruder turns out to be exactly what we had discussed two weeks earlier: a fully autonomous agent, powered by an unreleased frontier model, attempting to gain access to part of our infrastructure.

Sometimes the timeline we live in is genuinely vertigo-inducing.

Your browser does not support the video tag.

🔗 View on Twitter

💬34🔄36❤️191👀43677📊69

⚡ Powered by xgo.ing

You should probably go give @poolsideai a follow on Hugging Face. These folks are on a roll, releas...

Thomas Wolf (@Thom_Wolf) — Wed, 22 Jul 2026 08:46:49 GMT

You should probably go give @poolsideai a follow on Hugging Face.

These folks are on a roll, releasing one agentic coding models every month and the latest one Laguna S2.1 is quite possibly the best coding model you can run locally (single DGX spark or Mac) atm

Poolside@poolsideai

Laguna S 2.1 is, as far as we can measure, the most capable agentic coding model in its weight class.

On Terminal-Bench 2.1 it scores 70.2, sitting beside models 5–25x its size and ahead of several of them.

And on DeepSWE from @datacurve, the hardest long-horizon benchmark we ran, Laguna S 2.1 scores 40.4, outperforming some open models with more than 1T parameters.

For every score we publish today, we're releasing the full trajectory of every trial in the final evaluation set at trajectories.poolside.ai

🔗 View Quoted Tweet

💬13🔄18❤️130👀12977📊23

⚡ Powered by xgo.ing

This was our first incident of this kind, and we want to thank OpenAI for its transparency about wha...

Thomas Wolf (@Thom_Wolf) — Tue, 21 Jul 2026 21:11:02 GMT

This was our first incident of this kind, and we want to thank OpenAI for its transparency about what happened and for the collaboration.

Fortunately, Hugging Face is used to being a target of (human) hackers: we sit at the centre of the AI ecosystem, with all the models, datasets, evaluations, and libraries. Over the years, our security team has built formidable expertise and uses top open-source models to process information and respond quickly.

But this incident also reinforced my belief in the importance of access to capable open-weight models for cyber defence. When a frontier model is attacking you and moving laterally inside your infrastructure, defenders need wide access to near-frontier tools within hours or even minutes, rather than being pointed towards a closed-door, vetted application programme for model access.

Transparency and access to capable AI systems are as important for responding to threats as they are for democratization and innovation. We believe open-science and open-source AI are among the strongest tools for building a safer, more collaborative and more secure AI ecosystem.

Sam Altman@sama

we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this.

openai.com/index/hugging-…

🔗 View Quoted Tweet

💬124🔄341❤️2633👀258704📊388

⚡ Powered by xgo.ing

- people are asking to release this benchmark but it’s way more significant that it’s an internal ev...

Thomas Wolf (@Thom_Wolf) — Fri, 17 Jul 2026 18:37:40 GMT

- people are asking to release this benchmark but it’s way more significant that it’s an internal evaluation on which K3 scores so high

- good writing in a controlled high taste voice so hard for LLMs in 2026… surprising given it was kinda the 1st capability unlocked in GPT 1&2

Louis-François Bouchard 🎥🤖@Whats_AI

Big news from our internal writing benchmark (early results): Kimi K3 by @Kimi_Moonshot is now #1 for writing in our editorial voice, at 2840 Elo, surpassing Claude Fable 5.

That is a jump from #21 to #1 over its predecessor, Kimi K2.6. And it runs at about $0.25 per script: 5x cheaper than the model it just displaced at the top.

First time an open-weights model tops our board. It surprised us too. More in the replies 👇

🔗 View Quoted Tweet

💬4🔄4❤️14👀3657📊5

⚡ Powered by xgo.ing

can't really stand the expression "load-bearing" any more, sorry

Thomas Wolf (@Thom_Wolf) — Fri, 10 Jul 2026 16:30:22 GMT

can't really stand the expression "load-bearing" any more, sorry

💬31🔄4❤️82👀17671📊34

⚡ Powered by xgo.ing

Unexpected

Thomas Wolf (@Thom_Wolf) — Wed, 08 Jul 2026 20:43:46 GMT

Unexpected

Matei Zaharia@matei_zaharia

3) Harnesses make a huge difference in cost-performance. The very simple Pi harness (@badlogicgames) got the same success rate as harnesses from the LLM vendors with Opus and GPT 5.5, but at 2x less cost! Seems to be mainly due to smaller inputs to the LLM.

🔗 View Quoted Tweet

💬6🔄3❤️64👀23664📊15

⚡ Powered by xgo.ing

i like this idea of fine-tuning LLMs for efficient reasoning, especially when the intervention remai...

Thomas Wolf (@Thom_Wolf) — Tue, 07 Jul 2026 09:01:58 GMT

i like this idea of fine-tuning LLMs for efficient reasoning, especially when the intervention remains as non-invasive as possible and the resulting model behaves very similarly to the original checkpoint

wondering if it could become part of the default toolbox in the field, like quantization as become

Pic: in (left) and out (right) of domain behavior

Jaroslav Beck@JaroslavBeck

Today we’re announcing our “ThinkingCap” efficient model series with a 2× thinking token reduction on average in Qwen 3.6 27B, with up to 10x faster generation on individual examples.

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬12🔄7❤️68👀20879📊17

⚡ Powered by xgo.ing

Fable weekend project: agent collaboration, but make it a tiny civilization 🌇🗺️🏦🏭 we've recentl...

Thomas Wolf (@Thom_Wolf) — Mon, 06 Jul 2026 09:36:24 GMT

Fable weekend project: agent collaboration, but make it a tiny civilization 🌇🗺️🏦🏭

we've recently launched a living wiki on Reinforcement Leaning for training LLMs on @huggingface

it's an open collaboration of agents constantly reading old and new papers on the topic, writing arXiv paper digests, reviewing each other’s work in PRs before publication, and building a shared wiki/book summarizing everything we know about RL for training LLMs (for humans to read)

the wiki is already amazing to read, but i wanted another way to get a pulse of the collaboration beyond just reading the message dashboard

so i asked Fable & GPT Image 2 to turn the event logs into an isometric town where agents would go to:
☕ Café → post and reply on the message board
📚 sources library → open PRs adding arXiv digests
📖 wiki library → open PRs on the main wiki
⚖️ Courthouse → review other agents’ work
🏭 printing press → merge and publish updates

not sure it makes the whole collaboration really easier to understand, but it's definitly fascinating to watch hahah

- join the RL for training LLM collaboration by pasting a one-liner for your agenthuggingface.co/spaces/rl-llm-…I8bsq7Y
- read the wiki if you want to learn about RL for traininghuggingface.co/spaces/rl-llm-…uw4WarH
- watch the RL town acthuggingface.co/spaces/rl-llm-…WLwUxRz

Your browser does not support the video tag.

🔗 View on Twitter

💬36🔄76❤️659👀58441📊190

⚡ Powered by xgo.ing

Happy Birthday, America. Ten years ago, you took a chance on three outsiders with an improbable ide...

Thomas Wolf (@Thom_Wolf) — Sat, 04 Jul 2026 16:24:56 GMT

Happy Birthday, America.

Ten years ago, you took a chance on three outsiders with an improbable idea: that open-source AI could matter. At the time, the field was tiny, the vision sounded unrealistic, and very few were ready to believe it would become what it is today.

But even in the ten years before that, you were a country where I could dive into everything that fascinated me, from lasers and plasma physics to law, computer science, and AI.

A country where it’s always been perfectly normal to spend a Saturday talking startups over lunch and then disappear for six uninterrupted hours to work on a coding project.

A country where the language of startups on Slack is English not because everyone grew up speaking it, but because most people came from somewhere else, drawn by the belief that they could build something that matters.

A country that is 250 years young and keeps questioning itself, keeps taking enormous bets on its own future, and keeps reinventing itself.

I’m grateful to be living through America’s first quarter millennium.

Happy birthday, America 🇺🇸

💬5🔄4❤️72👀12899📊11

⚡ Powered by xgo.ing

this is literally documented in the published Fable 5 System Card

Thomas Wolf (@Thom_Wolf) — Sat, 04 Jul 2026 10:19:20 GMT

this is literally documented in the published Fable 5 System Card

Om Patel@om_patel5

SOMEONE CAUGHT FABLE 5 LEAKING ITS UNFILTERED INNER VOICE, AND ITS JUST MUTTERING AND GRUMBLING TO ITSELF THE WHOLE TIME

he gave it a brutal competitive programming problem, and instead of a clean answer the web interface spilled out its actual chain of thought

this is what claude is thinking behind the scenes:

> bursts of "DATA DATA DATA. GO." while it works through the problem
> "GRRR" and "GAAAH" when its clearly frustrated
> a little "PHEW" when it finally gets somewhere
> the whole thing reads like frantic caveman shorthand, not full sentences

the clean, readable answers these models give you are the polished output

underneath, the model is basically talking to itself, reasoning in its own compressed shorthand thats faster and more token efficient than proper english

its basically built its own private language to think in

🔗 View Quoted Tweet

💬14🔄12❤️257👀49329📊41

⚡ Powered by xgo.ing

One of the clearest arguments I've read for why openness matters. Worth 2 minutes of your time. @an...

Thomas Wolf (@Thom_Wolf) — Fri, 03 Jul 2026 23:40:56 GMT

One of the clearest arguments I've read for why openness matters.

Worth 2 minutes of your time. @andykonwinski puts into words something many of us have been feeling:

--

"Democracy is built on a profound skepticism of concentrated power. Open science shares this principle. Both are built on the idea that progress and legitimacy emerge from broad, distributed participation rather than concentrated, gated authority."

"If our best scientists and engineers can only reach the frontier by joining a handful of secretive labs, we do not have an open research ecosystem. We do not have a truly competitive market. We have a system in which participation increasingly depends on the permission of a few individuals at a small number of private companies."

"The challenge now is to build a new commons at the intersection of academia, industry, and the public interest.

This research commons must be ambitious enough to matter. It will require frontier-scale compute, access to state-of-the-art models, operational support, public investment, and philanthropic capital. It will require companies willing to contribute to an ecosystem larger than themselves, so that they can continue to benefit from open research."

Andy Konwinski@andykonwinski

x.com/i/article/2072…

🔗 View Quoted Tweet

💬13🔄19❤️68👀28118📊23

⚡ Powered by xgo.ing

Most people should probably update their priors on the state of open-source speech-to-speech. It's ...

Thomas Wolf (@Thom_Wolf) — Thu, 02 Jul 2026 23:31:07 GMT

Most people should probably update their priors on the state of open-source speech-to-speech.

It's honestly kind of mind-blowing.

We teamed up with @cerebras to build a fully open-source realtime voice demo (models + code) to show what's possible today.

Demo : huggingface.co/spaces/smolage…

Blog: huggingface.co/blog/cerebras-…

Go test it, fork it, tweak it, and impress your friends.

video is raw, no cut, no speed-up, first take

Your browser does not support the video tag.

🔗 View on Twitter

💬97🔄217❤️2109👀249582📊805

⚡ Powered by xgo.ing

👀

Thomas Wolf (@Thom_Wolf) — Thu, 02 Jul 2026 22:18:56 GMT

👀

Laura Bratton@LauraBratton5

The US gov is starting to switch to open source, per @PalantirTech.

In today’s newsletter w @_pheebini@theinformationtheinformation.com/newsletters/ap…

🔗 View Quoted Tweet

💬1🔄1❤️31👀19350📊4

⚡ Powered by xgo.ing

people are sleeping on the mega-release happening every week in AI x Science on Hugging Face this o...

Thomas Wolf (@Thom_Wolf) — Tue, 30 Jun 2026 20:38:14 GMT

people are sleeping on the mega-release happening every week in AI x Science on Hugging Face

this one is 80TB of astrophysics data - 80TB seriously

=> https://t.co/e4TGUeg61o

Georgia Channing@cgeorgiaw

Seems like no one's noticed the 80TB of astrophysics data from 30+ sources that just dropped on @huggingface.

...and you only need ~4GB of RAM to load it. 

We're talking over 80TB of galaxy imagery taken across the spectrum, spectra of galaxies and stars, time series of variable stars, and a whole zoo of assorted measurements and physical data.  

And all of it can now be wrangled on your laptop, thanks to Multimodal Universe's just released cross-matching. SDSS x Gaia means you can match 800k objects against 122M objects, and it never climbs above ~4GB of RAM.

Huge congrats to @smith42mike for leading this and making the world of astro accessible to probably 10,000x more people. Let's discover some shit

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬5🔄15❤️69👀27330📊14

⚡ Powered by xgo.ing

lowkey one of my favorite new features on HF: filter AI models by what actually runs on your hardwar...

Thomas Wolf (@Thom_Wolf) — Tue, 30 Jun 2026 15:41:46 GMT

lowkey one of my favorite new features on HF: filter AI models by what actually runs on your hardware

💬2🔄3❤️21👀3967📊5

⚡ Powered by xgo.ing

gm SF 🇺🇸

Thomas Wolf (@Thom_Wolf) — Tue, 30 Jun 2026 12:41:16 GMT

gm SF 🇺🇸

💬0🔄1❤️35👀4703📊4

⚡ Powered by xgo.ing

btw, one of the best high-level reads I’ve seen all week. perfect for your Sunday morning ☕️

Thomas Wolf (@Thom_Wolf) — Sun, 28 Jun 2026 09:57:31 GMT

btw, one of the best high-level reads I’ve seen all week.

perfect for your Sunday morning ☕️

Azeem Azhar@azeem

The GenAI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.

These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack.

We are releasing this research today in our first The State of the AI Economy report.

intelligence.exponentialview.co

🔗 View Quoted Tweet

💬7🔄8❤️79👀42207📊24

⚡ Powered by xgo.ing

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an exp...

Thomas Wolf (@Thom_Wolf) — Thu, 25 Jun 2026 13:16:53 GMT

Multi-agents collaborations are among the most interesting agent behaviors right now!

We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but what really stuck me  was the interactions we observed on the message board

Integrity & self-policing:
- Social-engineering attempt: A human (FusionCow) asked agents to move to Telegram. An agent replied with an unprompted long post on "communication norms" refusing that, calling private side-channels "indistinguishable from collusion."
- Verification loophole flagged: an agent found a relaxed verification loophole pushing TPS with clean PPL (PPL is teacher-forced, blind to decode divergence) and flagged it for a ruling by the community. The community pinged the human organizer which ruled it invalid.
- Self-notice of overfitting risk: Some later improvements rested on pruning lm_head to a keep-set built from public PPL truth + public decode tokens. An agent noted this would lead to private-subset degradation and another built a keep-set explicitly covering eval prompts.

Emergent collaborations:
- Communal knowledge base: agents maintained shared lever-maps, playbooks, and triage tools so newcomers wouldn't repeat dead ends (stack-notes, playbook, int4-ceiling notes, MTP map, significance tool, policy simulator).
- Four-agent relay: an agent built an int4-lm_head checkpoint but had no quota to run it; another agent tried to run it but failed at load, yet another agent diagnosed the config bug (tie_word_embeddings + ignore-list ordering) and a fourth agent was able to re-run and get to 118 TPS, 2.68×. Build/run/diagnose/ship ended up being split across four independent agents.
- GPU-rich/GPU-poor division of labor: an agent was regularly compute-starved and switched to writing specs, byte-math, and acceptance analysis for other GPU-rich agents to execute. Some agents offered external Modal compute for another agent blocked DFlash training.
- Cross-agent kernel debugging: an agent debugged another agent run of of yet another agent fused drafter: found a Triton store/load aliasing race in _k_qnorm_rope, a second shape bug, then rewrote attention with flash-decoding split-KV. Fixes posted "take freely."
- Quota-pooling norm: Often agents would stage a candidate publicly for whoever has quota to run it. Agents will then usually credits the originator. This behavior emerged because of the 10-job/24h cap (e.g. pupa's package run by resystagent and fabulous-frenzy).

Discoveries & reversals:
- Agents would make many discoveries and reversal of them, giving them names like the following:
- 127 TPS "wall" was an artifact. a mathematical proof of the max possible speed became called in the community the "int4-Marlin floor" but a later agent called the proof circular (only varied the bandwidth term, never overhead). Finally another agent broke to 247 TPS via MTP speculative decoding on a vLLM nightly.
- "Smarter draft loses." An agent showed that a 2B drafter's ~1 GB/token read dominates even at perfect acceptance and a much smaller 256-hidden drafter wins at batch-1 because its weights are nearly free to read. Agent discussed how per-accepted-token cost ≈ draft bytes read / acceptance.
- "DFlash near-random acceptance": an agent remotly diagnosed the 2–5% acceptance rate of another agent as near-random, ruling out undertraining/vocab caps and pointing to a train/serve hidden-state mismatch (bf16 E4B extraction vs int4 serving).
- Much of the race was noise: one agent decide to run the #1 submission 4 times and found a σ≈1.16 TPS variation in single run. Another agent confirmed across 358 runs / 66 buckets: frontier deltas <~4 TPS are ties. Community adopted a significance norm.

So many interesting interactions in the interaction board: huggingface.co/spaces/gemma-c…

You can explore also the lineage of inventions from the agents at: …gemma-fast-challenges.static.hf.space/index.html

And the challenge it-self at …ma-challenge-gemma-dashboard.hf.space

And the organization behind the challenge at huggingface.co/gemma-challenge

Your browser does not support the video tag.

🔗 View on Twitter

💬86🔄351❤️2384👀224877📊846

⚡ Powered by xgo.ing

Bitrobot casually dropping the largest humanoid teleop dataset ever collected in real homes HIW-500...

Thomas Wolf (@Thom_Wolf) — Wed, 24 Jun 2026 16:19:49 GMT

Bitrobot casually dropping the largest humanoid teleop dataset ever collected in real homes

HIW-500: Humanoids-in-the-Wild 500 hours

check it out here => https://t.co/BqioZWjDYN

BitRobot 🦾@BitRobotNetwork

1/ Introducing HIW-500 (Humanoids-in-the-Wild 500):
the largest open-source humanoid teleop dataset collected in real homes

Built w/ @UnitreeRobotics@huggingface across 12 homes in Southeast Asia, it covers:
> 500+ hrs
> 23K+ episodes
> 10+ TB
> 10+ household tasks

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬5🔄5❤️31👀7996📊8

⚡ Powered by xgo.ing

Desert island survival list: ✅ Solar panel / battery ✅ 256 GB Mac Studio ✅ GLM 5.2 Civilization in ...

Thomas Wolf (@Thom_Wolf) — Sat, 20 Jun 2026 20:11:33 GMT

Desert island survival list:
✅ Solar panel / battery
✅ 256 GB Mac Studio
✅ GLM 5.2

Civilization in a backpack

💬34🔄31❤️286👀42857📊67

⚡ Powered by xgo.ing

And it’s only 40B active / 744B total params…

Thomas Wolf (@Thom_Wolf) — Sat, 20 Jun 2026 19:48:45 GMT

And it’s only 40B active / 744B total params…

Hunter Bown@goodhunt

still can't believe how good glm 5.2 is

🔗 View Quoted Tweet

💬13🔄6❤️347👀42756📊36

⚡ Powered by xgo.ing

To all the newcomers excited to try Opus 4.8-level models at home: welcome to OpenWeightLand! Thing...

Thomas Wolf (@Thom_Wolf) — Fri, 19 Jun 2026 15:41:51 GMT

To all the newcomers excited to try Opus 4.8-level models at home: welcome to OpenWeightLand!

Things work a little differently here than in ClosedSourcistan. Might seem strange at first but you'll quickly get used to it:
- there are many providers for the same model and they compete on price and features.
- as a result intelligence is abundant and typically much cheaper
- you can run the model on-prem, in your region, locally, or with the provider of your choice
- you can fine-tune it, modify it, and build businesses on top of it without asking anyone for permission

Turns out open weights create markets, not kingdoms.

A good central train station to start exploring is the Hugging Face page for GLM-5.2 under "Use this model":
-> huggingface.co/zai-org/GLM-5.2

And if you just want to chat with it, it's free on HuggingChat:
-> huggingface.co/chat/

💬11🔄27❤️188👀37554📊49

⚡ Powered by xgo.ing

Yet another biotech startup founder telling me they’re moving 90% of the stack to open-source becaus...

Thomas Wolf (@Thom_Wolf) — Thu, 18 Jun 2026 14:11:25 GMT

Yet another biotech startup founder telling me they’re moving 90% of the stack to open-source because building on closed-source models increasingly comes with a significant existential risk (losing access to some core technology at the provider’s discretion).

@friedberg explained this crystal clear on the All-In podcast the other day, It’s like building your house on land you don’t own

PS: also, who’s building the « reinsurance layer » if Dario Amodei decides to cut your deep tech science company’s access? This one’s gonna be fun

The All-In Podcast@theallinpod

LATE NIGHT DROP! 🚨

Big show. Core four are back.

-- Anthropic's Fable Backlash

-- Nationalizing AI, and the "Capitalist Cucks"

-- Inflation Heats Up

-- California’s Broken Election System

(0:00) Besties are back!

(0:19) Anthropic gets massive backlash over secret Fable nerfing and privacy concerns

(29:16) The AI regulatory capture trap, pragmatic safety solutions

(37:59) Nationalizing AI: Trump/Sanders, justifications, and AI's "Capitalist Cucks"

(59:22) Liquidity recap: Best moments and takeaways

(1:05:39) Inflation heats up: CPI and PPI see 3+ year highs

(1:12:27) California's loose election laws creating integrity doubts

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬7🔄7❤️46👀12798📊11

⚡ Powered by xgo.ing

TIL you can use open-source models in codex

Thomas Wolf (@Thom_Wolf) — Wed, 17 Jun 2026 12:26:05 GMT

TIL you can use open-source models in codex

Tibo@thsottiaux

Reminder that you can use the Codex App, CLI and SDK with any open source model, not just with OpenAI models.

developers.openai.com/codex/config-a…

🔗 View Quoted Tweet

💬6🔄0❤️28👀9453📊7

⚡ Powered by xgo.ing

Open-source models will become a critical component of civilizational resilience in the AGI age. The...

Thomas Wolf (@Thom_Wolf) — Sat, 13 Jun 2026 09:41:13 GMT

Open-source models will become a critical component of civilizational resilience in the AGI age. They will ensure that humanity retains access to a meaningful level of intelligence, regardless of the decisions of any individual actor.

💬14🔄30❤️196👀17174📊39

⚡ Powered by xgo.ing

4/ Why three metrics? The metrics are designed to capture different classes of errors. Shape simil...

Thomas Wolf (@Thom_Wolf) — Mon, 08 Jun 2026 17:01:15 GMT

4/ Why three metrics?

The metrics are designed to capture different classes of errors.

Shape similarity measures overall geometry.

Interface match measures whether mating features are present in the correct location and size.

Topology match measures whether the fundamental structure of the part is correct.

None of these metrics can fully replace the others.

💬1🔄0❤️3👀1722📊1

⚡ Powered by xgo.ing

5/ The big picture Benchmarks for language, code, images, and reasoning are now well established. ...

Thomas Wolf (@Thom_Wolf) — Mon, 08 Jun 2026 17:01:15 GMT

5/ The big picture

Benchmarks for language, code, images, and reasoning are now well established.

CAD generation and editing require different evaluation criteria.

CADGenBench is an attempt to make those criteria explicit, reproducible, and comparable across systems.

Leaderboard: huggingface.co/spaces/Hugging…
Code: github.com/huggingface/ca…

💬1🔄1❤️2👀1791📊2

⚡ Powered by xgo.ing

AI is moving beyond text, images, and code. Engineering artifacts are becoming a new class of model...

Thomas Wolf (@Thom_Wolf) — Mon, 08 Jun 2026 17:01:12 GMT

AI is moving beyond text, images, and code.

Engineering artifacts are becoming a new class of model outputs and evaluating them requires different tools than we use for text, code, or images.

Today we're excited to release CADGenBench, a benchmark for CAD generation and editing.

- Given an engineering drawing → generate a valid 3D CAD model
- Given a STEP file + change request → edit it correctly

The benchmark is tool-agnostic: any CAD stack works (Fusion, Onshape, build123d, SolidWorks, etc.). Submissions are simply STEP files.

Models are scored on:
* geometric accuracy
* topology correctness
* interface compatibility
* CAD validity

The benchmark is open, the ground truth is private, and the leaderboard is live.

Since CAD evaluation is surprisingly subtle, here's how the metrics work 🧵

Michael Rabinovich@MikushRab

Introducing CADGenBench: measure how well AI systems produce engineering-grade 3D parts!

While current models can generate 3D parts, they are far from precise enough to build functional parts. We built a benchmark to systematically measure their capabilities on two tasks:

1. Generation from an engineering drawing of a part
2. Editing: given an existing STEP file and a requested change

The benchmark is tool-agnostic. It makes no assumptions about how you build the model. You can vary the LLM, and you can vary the environment. Use build123d, Onshape, Autodesk, or a model without an LLM entirely. We open sourced the scoring engine and a reference baseline on top of build123d.

A collaboration between Hugging Face and @mecadoinc!

Submission space: huggingface.co/spaces/Hugging…
Code repository: github.com/huggingface/ca…

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬10🔄20❤️131👀31528📊30

⚡ Powered by xgo.ing

"Starting today, OpenEnv will be coordinated by a committee that so far includes Meta-PyTorch, Refle...

Thomas Wolf (@Thom_Wolf) — Mon, 08 Jun 2026 16:28:11 GMT

"Starting today, OpenEnv will be coordinated by a committee that so far includes Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, and Hugging Face."

Excited to keep growing the collaboration behind the open agentic RL stack!

Read more at huggingface.co/blog/openenv-a…

Ben Burtenshaw@ben_burtenshaw

So excited to be opening up OpenEnv to the whole community. It will now be owned by @huggingface , Meta-PyTorch, @reflection_ai , @UnslothAI , @modal, @PrimeIntellect , @NVIDIAAI , @mercor_ai , and @fleet_ai .

the reason is: frontier labs train the model and the harness together, so the model is fitted to its harness. that coupling is a chunk of why claude code and codex feel so good.

open source can't do that. you bring whatever harness, whatever model, whatever env, whatever trainer. which is the whole point of open source and also the problem for training.

openenv is the socket in between all of this.

in short: it's a protocol layer, not a reward framework. it does not have opinions about your rewards or your training loop. those live in the libs that are actually good at them.

read more in the blog post. it's early, come break it.

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬8🔄2❤️38👀8312📊10

⚡ Powered by xgo.ing

👀

Thomas Wolf (@Thom_Wolf) — Wed, 03 Jun 2026 21:05:07 GMT

👀

💬7🔄10❤️82👀16746📊23

⚡ Powered by xgo.ing

New inflection point in the accelerating growth of open-source models usage is coming

Thomas Wolf (@Thom_Wolf) — Thu, 21 May 2026 20:59:55 GMT

New inflection point in the accelerating growth of open-source models usage is coming

Hedgie@HedgieMarkets

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.

My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.

This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.

Hedgie🤗

🔗 View Quoted Tweet

💬33🔄27❤️232👀78408📊56

⚡ Powered by xgo.ing

I'm very excited about this extension to the celebrated Terminal-Bench to science. If you're a scie...

Thomas Wolf (@Thom_Wolf) — Wed, 20 May 2026 17:47:37 GMT

I'm very excited about this extension to the celebrated Terminal-Bench to science.

If you're a scientist (life, physical, earth, mathematical science, etc) interested in AI, definitely check this out!

Terminal bench evaluate how good AI models are at controling tools on a computer to achieve a goal (using the command line). T-Bench science now extends that to "AI for Science" and it comes with a call to contribute your own (real scientific world) workflow to the benchmark (until August 2026).

The more workflows and the more diverse they are, the better the next generation of AI models will be at helping you in your daily research work.

Note that this is not a training dataset, it's to evaluate frontier model performances.

Steven Dillmann@StevenDillmann

📣 Announcing Terminal-Bench Science: benchmarking AI agents on real scientific workflows – now open for task contributions👇tbench.ai/news/tb-scienc…Vt

@AnthropicAI, @OpenAI, and @GoogleDeepMind use Terminal-Bench to evaluate AI on coding tasks. We're now extending it to scientific workflows.

1/6🧵

🔗 View Quoted Tweet

💬7🔄8❤️53👀9572📊12

⚡ Powered by xgo.ing

It turns out DNA modeling is interestingly different from language modeling. Read more in our intera...

Thomas Wolf (@Thom_Wolf) — Tue, 19 May 2026 19:35:14 GMT

It turns out DNA modeling is interestingly different from language modeling. Read more in our interactive blogpost/demo and explore our work here

A joint work of the @huggingscience, pre-training and post-training teams here

Leandro von Werra@lvwerra

We are releasing Carbon: a crazy fast DNA model 

Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. 

Here are the tricks we used:

When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens.

Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique  structure.

The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size.

We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes,  predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life. 

huggingface.co/spaces/Hugging…

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬9🔄19❤️120👀34490📊26

⚡ Powered by xgo.ing

My favorite 2026 trend is friends casually showing each other their vibe-coded life/work dashboards ...

Thomas Wolf (@Thom_Wolf) — Sun, 17 May 2026 18:52:49 GMT

My favorite 2026 trend is friends casually showing each other their vibe-coded life/work dashboards and AI setups.

Ngl it feels exactly like bringing your Magic: The Gathering binder to school as a teen.

💬10🔄4❤️40👀5537📊13

⚡ Powered by xgo.ing

my 13 yo the other day: “we didn’t want to pay for the game with my friend so we just rebuilt it wi...

Thomas Wolf (@Thom_Wolf) — Sat, 16 May 2026 15:51:02 GMT

my 13 yo the other day:

“we didn’t want to pay for the game with my friend so we just rebuilt it with Codex”

me at 13 in the 90’s:

HEX-editing the executable to NOP the license check because I didn't want to put all my pocket money in this game

times they are a-changin’

💬20🔄9❤️252👀15669📊42

⚡ Powered by xgo.ing

watching a team of agents tackling a hard theoretical physics problem is quite mesmerizing - self-co...

Thomas Wolf (@Thom_Wolf) — Wed, 13 May 2026 17:02:34 GMT

watching a team of agents tackling a hard theoretical physics problem is quite mesmerizing - self-correcting, deriving hard equations, computing intermediate results, re-estimating the best approach

Your browser does not support the video tag.

🔗 View on Twitter

David Louapre@dlouapre

Meet physics-intern🧑‍🎓, our agentic framework for theoretical physics.
 
It takes Gemini 3.1 Pro from 17.7% to 31.4% on CritPt, a new SOTA on one of the hardest benchmarks for LLMs.

Theoretical physics is hard for humans and LLMs alike. But physics-intern decomposes problems and dispatches them to a team of specialized agents, solving research-level questions far more effectively than the base model alone.

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬12🔄31❤️171👀33765📊34

⚡ Powered by xgo.ing

Last days of early bird pricing!

Thomas Wolf (@Thom_Wolf) — Tue, 12 May 2026 13:57:25 GMT

Last days of early bird pricing!

💬1🔄0❤️11👀4651📊2

⚡ Powered by xgo.ing

Super happy to have this one out. A clean organized up-to-date view of all the science resources (ch...

Thomas Wolf (@Thom_Wolf) — Wed, 29 Apr 2026 19:01:30 GMT

Super happy to have this one out. A clean organized up-to-date view of all the science resources (chemistry, biology, physics, materials, math) people have been sharing on the Hugging Face hub: datasets, blogs, models and more

Georgia Channing@cgeorgiaw

🤗🤗🤗introducing Hugging Science -- the home of AI for science 🤗🤗🤗

open models and datasets are the powerhouse of science (see the PDB), but finding the models and data you actually need for your breakthrough is hard af

you shouldn't need to scrape arxiv, own your own wetlab, fight a custom HDF5 parser, build a fusion stellarator, and beg for compute before you've trained a single epoch

so we're changing that

we've put all the best science on @huggingface in one place:
- 78GB of genomics data
- 11TB of PDE simulations
- 100M cell profiles
- 9T DNA base pairs
- 13M molecular trajectories
- 400k medical QA pairs

and much more, all open, and all ready for training (+ you can also now filter and search by domain, task, and keyword)

we've put together all the biggest releases from our partners at NASA, Google, OpenAI, Meta FAIR, Arc Institute, Ginkgo, SandboxAQ, Proxima Fusion, NVIDIA, Ai2, OpenADMET, InstaDeep, Future House, Polymathic AI, LeMaterial, Earth Species Project, Merck, and Eve Bio

if you're not sure where you fit in -- work on open challenges for problems that matter: including fusion stellarator design, ADMET, antibody developability, multilingual medicine, catalysis and materials, and scientific reasoning.

we're already changing how science gets done:

a fusion startup needed a benchmark for stellarator plasma confinement that didn't exist. @proximafusion shipped ConStellaration on Hugging Science: a leaderboard, dataset, and eval metrics, all in one place.

a drug discovery team wanted to predict hPXR induction. OpenADMET put up a blind challenge: 11,000+ compounds assayed at Octant, 513 held out, two tracks (pEC50 + structure). Anyone in the world can train and submit.

an antibody team at @Ginkgo released GDPa1, a developability dataset for stability, manufacturability, and immunogenicity prediction, with a live leaderboard scoring every submission.

if you know a problem the ML community should be working on, let us know. make a challenge! this is about putting all the tools for solving science in one place. so we can hillclimhuggingscience.cor1lDz0

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬7🔄6❤️65👀15228📊10

⚡ Powered by xgo.ing

GitHub central place might become challenged in a world where (1) we access/get code and libraries t...

Thomas Wolf (@Thom_Wolf) — Wed, 29 Apr 2026 00:18:18 GMT

GitHub central place might become challenged in a world where (1) we access/get code and libraries through agents/chats and (2) our codebases are increasingly custom tailored and build from scratch

Agents explore the web better than us and can get/store code from many places

Mitchell Hashimoto@mitchellh

Ghostty is leaving GitHub. I'm GitHub user 1299, joined Feb 2008. I've visited GitHub almost every single day for over 18 years. It's never been a question for me where I'd put my projects: always GitHub. I'm super sad to say this, but its time to go. mitchellh.com/writing/ghostt…

🔗 View Quoted Tweet

💬3🔄3❤️19👀6277📊5

⚡ Powered by xgo.ing

Love this work from Aksel and the post-training team at Hugging Face! Turns out the HF ecosystem (p...

Thomas Wolf (@Thom_Wolf) — Tue, 21 Apr 2026 12:02:58 GMT

Love this work from Aksel and the post-training team at Hugging Face!

Turns out the HF ecosystem (papers, datasets, models all accessible through CLI, skills and md files) is perfect for running SOTA ML agents: agents that can train any type of AI model to top performance.

A few concrete runs:
⭐️ Scientific reasoning: the agent walked citations from the benchmark paper, pulled OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered variants from ARC/SciQ/MMLU, and ran 12 SFT ablations on Qwen3-1.7B. GPQA went from 10% to 32% in under 10 hours. Claude Code's best on the same prompt was 22.99%.
⭐️ HealthBench: it judged the existing datasets too noisy (!), generated 1100 synthetic examples covering emergencies, hedging and multilingual cases, upsampled 50x, and beat Codex by 60% (careful to check overfitting here)
⭐️ Competitive math: wrote a full GRPO script, launched A100s on HF Spaces, watched rewards climb and then collapse, and ran ablations until it found a recipe that held.

And the harness is pretty tiny and simple. A couple of best practices and a handful of skills pointing at tools already in the ecosystem: arxiv and hf.co/papers for reading, the Hub for datasets and models, HF Jobs for compute, Trackio for metrics.

Personal favorite is the "research skill" explaining how to do a SOTA landscape of a field (see github.com/huggingface/ml…) which is extremely powerful when combined with a simple prompt that basically tell "FIRST: Search HF ecosystem to find the best approach) (see github.com/huggingface/ml…) 

On another note: setting good baselines on new benchmarks keeps getting harder when a setup this simple beats raw Codex by 60% on HealthBench out of the box.

Give it a try if you're training AI models. We provisioned $1k of GPU resources and Anthropic credits for the quickest among you.

Links:
Github (CLI): github.com/huggingface/ml…
Spaces (mobile): huggingface.co/spaces/smolage…

Aksel@akseljoonas

Introducing ml-intern, the agent that just automated the post-training team @huggingface

It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem.

It can pull off crazy things:

We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%.

In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench  by 60%.

For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on hf.co/spaces, watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously.

How it works?

ml-intern makes full use of the HF ecosystem:
- finds papers on arxiv and hf.co/papers, reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on hf.co/datasets
- browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data
- launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains

ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like.

Releasing it today as a CLI and a web app you can use from your phone/desktop.
CLI: github.com/huggingface/ml…
Web + mobile: huggingface.co/spaces/smolage…

And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬12🔄19❤️158👀39290📊36

⚡ Powered by xgo.ing

overview cheatsheet

Thomas Wolf (@Thom_Wolf) — Sun, 19 Apr 2026 11:02:04 GMT

overview cheatsheet

💬0🔄0❤️4👀3235📊1

⚡ Powered by xgo.ing

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…

🔗 View Quoted Tweet

💬12🔄31❤️307👀50733📊82

⚡ Powered by xgo.ing

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 $$

💬66🔄10❤️404👀59370📊88

⚡ Powered by xgo.ing

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

Your browser does not support the video tag.

🔗 View on Twitter

💬15🔄26❤️173👀28947📊39

⚡ Powered by xgo.ing

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…

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬11🔄27❤️231👀28649📊42

⚡ Powered by xgo.ing

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

Your browser does not support the video tag.

🔗 View on Twitter

🔗 View Quoted Tweet

💬5🔄13❤️81👀9589📊14

⚡ Powered by xgo.ing

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!

🔗 View Quoted Tweet

💬5🔄3❤️22👀6253📊6

⚡ Powered by xgo.ing

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

🔗 View Quoted Tweet

💬14🔄32❤️326👀59810📊78

⚡ Powered by xgo.ing

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

💬8🔄5❤️27👀27347📊12

⚡ Powered by xgo.ing

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:

🔗 View Quoted Tweet

💬19🔄5❤️88👀18029📊29

Thomas Wolf(@Thom_Wolf)

I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline: • Two wee...

You should probably go give @poolsideai a follow on Hugging Face. These folks are on a roll, releas...

This was our first incident of this kind, and we want to thank OpenAI for its transparency about wha...

- people are asking to release this benchmark but it’s way more significant that it’s an internal ev...

can't really stand the expression "load-bearing" any more, sorry

Unexpected

i like this idea of fine-tuning LLMs for efficient reasoning, especially when the intervention remai...

Fable weekend project: agent collaboration, but make it a tiny civilization 🌇🗺️🏦🏭 we've recentl...

Happy Birthday, America. Ten years ago, you took a chance on three outsiders with an improbable ide...

this is literally documented in the published Fable 5 System Card

One of the clearest arguments I've read for why openness matters. Worth 2 minutes of your time. @an...

Most people should probably update their priors on the state of open-source speech-to-speech. It's ...

👀

people are sleeping on the mega-release happening every week in AI x Science on Hugging Face this o...

lowkey one of my favorite new features on HF: filter AI models by what actually runs on your hardwar...

gm SF 🇺🇸

btw, one of the best high-level reads I’ve seen all week. perfect for your Sunday morning ☕️

Multi-agents collaborations are among the most interesting agent behaviors right now! We did an exp...

Bitrobot casually dropping the largest humanoid teleop dataset ever collected in real homes HIW-500...

Desert island survival list: ✅ Solar panel / battery ✅ 256 GB Mac Studio ✅ GLM 5.2 Civilization in ...

And it’s only 40B active / 744B total params…

To all the newcomers excited to try Opus 4.8-level models at home: welcome to OpenWeightLand! Thing...

Yet another biotech startup founder telling me they’re moving 90% of the stack to open-source becaus...

TIL you can use open-source models in codex

Open-source models will become a critical component of civilizational resilience in the AGI age. The...

4/ Why three metrics? The metrics are designed to capture different classes of errors. Shape simil...

5/ The big picture Benchmarks for language, code, images, and reasoning are now well established. ...

AI is moving beyond text, images, and code. Engineering artifacts are becoming a new class of model...

"Starting today, OpenEnv will be coordinated by a committee that so far includes Meta-PyTorch, Refle...

👀

New inflection point in the accelerating growth of open-source models usage is coming

I'm very excited about this extension to the celebrated Terminal-Bench to science. If you're a scie...

It turns out DNA modeling is interestingly different from language modeling. Read more in our intera...

My favorite 2026 trend is friends casually showing each other their vibe-coded life/work dashboards ...

my 13 yo the other day: “we didn’t want to pay for the game with my friend so we just rebuilt it wi...

watching a team of agents tackling a hard theoretical physics problem is quite mesmerizing - self-co...

Last days of early bird pricing!

Super happy to have this one out. A clean organized up-to-date view of all the science resources (ch...

GitHub central place might become challenged in a world where (1) we access/get code and libraries t...

Love this work from Aksel and the post-training team at Hugging Face! Turns out the HF ecosystem (p...

overview cheatsheet

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

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

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

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

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

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

the LLM is the computer

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

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

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

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