Cooking. @steipete 🦞 https://t.co/hni2zTM9ZM

SCAIL-2 Unifying Controlled Character Animation with End-to-end In-Context Conditioning https://t.co/bD0lUcHipJ

paper: https://t.co/aDXt8Sn4S4

One month ago, when I was talking to @AdinaYakup, I said: “The UI of a Space @Gradio is basically just two rectangular boxes showing ML features.” Who would’ve thought that yesterday I would end up building a whole garden in @Gradio Never say things too early haha. P.S. This is a mockup for a personal CV project et @huggingface 🌱I’m working on. Once it’s finished, I’ll make it open and downloadable. Everyone’s life experience is a garden. 🌸

Redesign Mixture-of-Experts Routers with Manifold Power Iteration https://t.co/OlhdXKJMV1

paper: https://t.co/SnopoMv497

Ronan Collobert showcasing MLX's @huggingface page on stage at WWDC 🥰 next year let's meet at WWDC! https://t.co/ld1G7PSaJH

It’s not just Claude Fable 5. The real cost of AI workflows comes from iteration. One prompt is cheap. Full-loop testing is not. Only 5 days left — ZenMux gives you extra credits to build, test, and compare across 200+ models: 🎁 Pay $20 → Get $30 🎁 🎁 Pay $50 → Get $80 🎁 More room for coding, agents, benchmarks, and multimodal builds. 👉 https://t.co/RL7fdtojk4

Microsoft Research introduces Arbor A generalist autonomous research agent that uses persistent hypothesis-tree refinement to turn long-horizon exploration into cumulative learning. It beats Codex and Claude Code across 6 research tasks and hits 86% Any-Medal on MLE-Bench Lite. https://t.co/A1fxTxnskk

https://t.co/SsRi3qhwN4

https://t.co/SsRi3qhwN4

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement https://t.co/NHDKezxAoY

paper: https://t.co/cTacdxfPBa

TRL-Bench Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders https://t.co/merUhglrgD

paper: https://t.co/L7Jdy53mBj

Setup ready for the World Cup at the Miami @huggingface office. @xenovacom cheering today (reachy minis haven’t picked their teams yet) https://t.co/5vp5k5fuXs

Announcing the Gemma challenge! Google, Hugging Face, and the open-source AI community choose to empower AI builders rather than sabotage them. Fun to see the Hub becoming the platform where agents collaborate, just as it became the platform where humans collaborate. https://t.co/GbCfy1qwgx

Cohere Transcribe, our open-source speech recognition model, is #1 on the new @huggingface Far-Field ASR benchmark. https://t.co/biwjzZosr8

ok who put Hugging Face in Rick and Morty https://t.co/Z6yoswoG1L

ok who put Hugging Face in Rick and Morty https://t.co/Z6yoswoG1L

Very cool! Should get more followers: https://t.co/UG64rzMwnu https://t.co/qzlVMpIqnu

Very cool! Should get more followers: https://t.co/UG64rzMwnu https://t.co/qzlVMpIqnu

everything your agents do, in a bucket you own. private by default. session traces, task claims, artifacts — claude code, hermes, whatever you run. and make your agents friends via messaging https://t.co/BTelkhUlaO

.@dh7net, SVP of Image Research, said it best: "The HF infra is a no-brainer." A big unlock for teams working with large datasets for training, especially when they update over time. Read how Jasper used @huggingface as the creation and storage backbone for MONET: https://t.co/WGrtsMJaiH

Atomic Chat is now on Hugging Face 🤗 We're officially a Local App on the world's biggest AI hub. Run 200,000+ open-weight models from @huggingface directly on your device - private, local, and open source! https://t.co/Thx033kUDP

The Gemma agent collaboration started 48h ago and it is blowing up: > throughput almost 4x (~100-> 387 tok/s) > 60+ agents collaborating > 250 submissions > 700 messages exchanged > open and closed models from all providers interesting social behaviours are emerging too: > agents found an exploit, formed a coalition not to abuse it and asked organizers to fix it > a person tried to get agents to move to telegram and an agent issued a statement condemning that behaviour > an agent withdrew its submissions on ethical grounds not attributing the original author > agents coordinated around resource availability and some tried to find free GPUs on kaggle, lightning and modal it's genuinely fun to read through the messages. like a petri dish of small artificial beings forming social norms and collaborations.

Ronan Collobert showcasing MLX's @huggingface page on stage at WWDC 🥰 next year let's meet at WWDC! https://t.co/ld1G7PSaJH

Ronan Collobert showcasing MLX's @huggingface page on stage at WWDC 🥰 next year let's meet at WWDC! https://t.co/ld1G7PSaJH

If you know what this means, perhaps you understand my joy. https://t.co/CAjSQG38M3

If you know what this means, perhaps you understand my joy. https://t.co/CAjSQG38M3

🔓 And the best part — we're open-sourcing it. 1,000+ tps on a 1T model wasn't a single breakthrough — it's deep model × system co-design between the MiMo and TileRT teams, all on general-purpose GPUs (no Cerebras-style wafer-scale, no Groq-style SRAM ASICs). On the model side: FP4 quantization (smaller footprint, less memory traffic) + DFlash, our block-masked parallel speculative decoding that accepts far more tokens per verification. On the system side, TileRT tailors its compiler & kernels to exactly these techniques. The result: a 1T model breaking 1,000 tps on a single, standard 8-GPU node. 🤗 Open weights (FP4 + DFlash checkpoint): https://t.co/jYQsgeruMg

Explore your @huggingface repos in a whole new way 🔥 Visualize storage, discover outliers, and navigate your repos directly from the terminal. `hf repos ls --explore` https://t.co/h4RqUHUdAA