The AI community building the future. https://xitter.cf/t.co/TpiXQMQ9rZ
NYC and Paris and 🌏
Joined September 2016
- Tweets14.4K
- Following227
- Followers825K
- Likes9.5K
Hugging Face retweeted
Introducing SimReady Central Library. 👋
This library brings 2,000+ simulation-ready 3D assets to @huggingface, open source for developers building robotics simulations and AI factory digital twins.
Now, developers can spend less time preparing assets while equipment manufacturers can help customers evaluate equipment in simulation before real-world deployment.
Get started 👉 nvda.ws/3Wl6LCi
Come and build models, datasets, and spaces on the hub, to win $20k in cash and credits!
We teamed up with @huggingface to give away $20,000 to anyone who's building with Beam 🤗
Use Beam for any project - make a demo or a dataset, benchmark the model, create an RL environment - and comment your link in the model's repo page by Monday, November 2.
We choose the winners. Winners choose cash or credits. Claim $25 in Hugging Face credits to build with Beam and enter the competition 👇
huggingface.co/reflection/Be…
Hugging Face retweeted
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Hugging Face retweeted
When @jensenhuang asks for a somersault, you deliver. 🦆
See how AI agents on NVIDIA RTX Spark help bring @huggingface's Microduck from simulation to the real world.
Hugging Face retweeted
Today we’re releasing two decision models, elephant-1 (9B) and mammoth-1 (27B), trained by the @baselabs team, available for free for one week on our Model APIs.
mammoth-1 is #1 on the Jev Decision Index and elephant-1 is tied for #1 on the Artificial Analysis Decision Index.
It’s a new model, but more importantly, this is an open-source model you can access, deploy, and post-train on Baseten (or wherever you do your best work).
The free week ends 10/27/26 at 11:59 PM PT. Try it and let us know what you think.
Try mammoth-1 here: baseten.co/library/mammoth-1…
Try elephant-1 here:
baseten.co/library/elephant-…
Hugging Face retweeted
It feels pretty fitting for @JensenHuang & @nvidia to end their European tour with Microducks on stage.
Microduck was designed and engineered in Europe (more precisely Bordeaux, France) by the @pollenrobotics and @huggingface teams. With American capital and ambition + Chinese manufacturing genius, it became the most popular robot of 2026 with over 15,000 sold all over the world!
I believe Europe has more opportunity to shine in AI than in any previous tech wave. We intend to prove it with @huggingface @nvidia and the whole open-source AI community in the coming years!
Let's go European AI 🇪🇺🇪🇺🇪🇺
Hugging Face retweeted
Here is the Microduck up close
You can program it work DGX spark
This baby duck represents the future of physical Ai
it relies on reinforcement learning to transition AI-trained movements from digital simulations into the real world
Small duck now large robots later 🐥🤖💚
Hugging Face retweeted
So many SO-ARMs! 💪
Here is the robotics workshop organized at @NVIDIAGTC Berlin that uses SO-ARM101 and LeRobot to control real and simulated robotic arms. The light box makes it easier to get policies working in any location and light conditions.
Hugging Face retweeted
Microducks on stage for Jensen Huang’s famous keynote!
We couldn’t be prouder of our little ones. 🥹
Hugging Face retweeted
I've made some big improvements to the PDF reader on Papers with Code!
> The AI assistant now cites its sources, full RAG-style
> This is powered by Chandra-OCR 2 by @datalabto which is openly available on @huggingface
> This model returns bounding boxes besides markdown
> Allows you to click on citations and highlight sections
Try it out on any PDF, like paperswithcode.co/pdf/2610.1…
Hugging Face retweeted
🧵 I've just released Sentence Transformers v6.2.0!
Featuring model merging with @arcee_ai mergekit, load models faster, & evaluators share work across GPUs automatically.
Plus lower-memory losses, multi-vector improvements & plenty of fixes.
Thread with highlights 👇
Hugging Face retweeted
Jensen spent 10 min of his keynote asking microducks to sit, then explained Nvidia's roadmap to them 🥹
Hugging Face retweeted
We built a robotics data engine with Astra-level performance at 125x lower cost. And we are open-sourcing the weights today! Introducing Flora, a suite of open models to parse the physical world and turn raw footage into rich training signals.
Flora already powers our in-house data engine, labeling 200K hours of videos a day on 500 GPUs. We created it to scale the frontier for robot learning. Now you own the weights too.
Flora gives you:
- High-quality, timestamped captions to curate and filter human egocentric and robot data;
- Dense progress rewards for quality control and RL post-training;
- Rubrics scoring for real-world benchmarking
Try it today, links in thread:
Hugging Face retweeted
Meet Fibo 2.0 from @bria_ai_ 🔥
The #1 *open weights* model in image editing is supported in Diffusers (day-0, of course)!
1. JSON-native control
2. Pretty good text rendering
3. Comes in two variants: Turbo and Dev
Check out the model here:
huggingface.co/docs/diffuser…
Hugging Face retweeted
Agent Manager is finally out!
Orchestrate dozens of agents across harnesses (Claude Code, Codex, OpenCode etc.) effortlessly.
Agents communicate and self-organize to help you achieve complex tasks and always keep running on HF spaces.
Try it: huggingface.co/spaces/lvwerr…
Hugging Face retweeted
Muse Spark open weights (even in NVFP4) is an amazing computer use agent
I shipped an e2e tutorial on building an env for a browser agent
(with OpenEnv, Helium and @huggingface Sandboxes)
enjoy 🤗 here's a small visualized run ⤵️
Local AI 202: Get Massive Speed-ups with Speculative Decoding xitter.cf/i/broadcasts/1NGaroZve…
Hugging Face retweeted
Fun and crazy napkin math: Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, Mistral and ~30 other labs have shared close to $1B worth of training compute on the Hugging Face Hub
Free to download
End of 2023 it was under $40M
Wikipedia was written in volunteer hours. This AI age commons is written in GPU-hours and data
(it's a floor btw: about half comes from official compute numbers rest is educated guess and it still miss the ~900k community fine-tunes)
Space with data to explore: thomwolf-hub-dollars.static.…
