Now this is an ad. https://t.co/TN9oz2VHMv

Now this is an ad. https://t.co/TN9oz2VHMv

SkillClaw Let Skills Evolve Collectively with Agentic Evolver paper: https://t.co/fyZT9YqUP6 https://t.co/zN4KuczCbA

Rethinking Generalization in Reasoning SFT A Conditional Analysis on Optimization, Data, and Model Capability paper: https://t.co/AFqLfOfK3R https://t.co/j7gHnlDofv

HY-Embodied-0.5 Embodied Foundation Models for Real-World Agents paper: https://t.co/ocajaLYTLl https://t.co/MOqXLCTcLE

MegaStyle Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping paper: https://t.co/szeFIYDvcN https://t.co/kiMko1Q3Jk

void-model has been awesomely trending on @huggingface this week! Please use it and let us know how we can make it better 🤗 https://t.co/4BwZKr975B https://t.co/r8XtGBglds

🤯NEW JACKRONG GEMOPUS-4 26-A4B DROPPED The Model Magic 🪄 🧠 Google’s Gemma 4 26B MoE base 4B Active Params 🤖Full Claude Opus-style reasoning distillation Beast Mode Performance🚨 🔥 75 tokens/sec at Q6_K ✅ Just 22.7 GB VRAM 🚨 Full 131k context window Pair this with HemresAgent and run it locally Makes it VERY capable! Tweak this for your use case Try it now 👇🏻 https://t.co/GZI6yPXnHm

1/3 Money, money, money, moneyyyyy 💸💸💸💸 Today we’re making it possible for you to earn actual money from your Pika AI Self agent. Because we think your agent should work FOR you in every sense of the phrase. Every time someone talks with them, or uses one of their skills, you earn tokens redeemable for cash. Say goodbye to those deadbeat agents.

👀some thoughts: https://t.co/bisWAsEQQn

👀some thoughts: https://t.co/bisWAsEQQn

NVIDIA just released Kimodo on Hugging Face A kinematic motion diffusion model trained on 700 hours of optical motion capture to generate 3D human and robot motions controlled by text and kinematic constraints. https://t.co/s7foOXXqqB

Speculative decoding for Gemma 4 31B (EAGLE-3) A 2B draft model predicts tokens ahead; the 31B verifier validates them. Same output, faster inference. Early release. vLLM main branch support is in progress (PR #39450). Reasoning support coming soon. https://t.co/PoK8zbA7li

RIP my huggingface storage 🪦 https://t.co/yBCMZ2f2sg

RIP my huggingface storage 🪦 https://t.co/yBCMZ2f2sg

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 https://t.co/0m65ozY8JA

NVIDIA's Nemotron 3 Super can be found here: https://t.co/VXMrQHxXLS Fully open AI btw

NVIDIA's Nemotron 3 Super can be found here: https://t.co/VXMrQHxXLS Fully open AI btw

Ran autoresearch on hf to see whether anything can beat MuonAdamW baseline Biggest takeaway: NS orthogonalization is a very strong attractor that absorbs most gradient modifications you throw at it. See all the artifacts at https://t.co/S5DY7MezUp https://t.co/XyIEMeZ4Ft

Claude Opus power, but tiny & local GemOpus-4 26-A4B - Gemma 4 + Opus-style reasoning. 4B active params, 75 tok/s on just 22.7GB VRAM https://t.co/GxutcyRSOR https://t.co/kYZbrQmDdh

Google just released the dense prediction TIPSv2 models on Hugging Face A vision encoder with DPT heads for depth estimation, surface normals, and semantic segmentation — all trained on TIPSv2 B/14. https://t.co/VE9ywKqBjh

In a world where writing code to build websites and apps is trivial (thank you Lovable, Cursor, Claude,...), the real differentiation for you and your company (and what makes you successful) will be how you manage to train, run and optimize AI models yourself. That's why at Hugging Face, we're doubling down on enabling more to become AI builders rather than AI users. We're releasing this week Kernels on the Hugging Face hub. This repo type is for the hardcore AI engineers among you. Kernels are collections of optimized binary operations where hardware providers support is a first-class citizen: - CUDA - ROCm - Apple Silicon - Intel XPU Expect to see more of this repo type on Hugging Face in the coming days. Featured here: the Flash Attention kernel from @sgl_project team ❤️

🚀 NEW GEMMA 4 31B TURBO DROPPED Runs on a SINGLE RTX 5090: ⚡️18.5 GB VRAM only (68% smaller) 🧠51 tok/s single decode 💻1,244 tok/s batched 🤖15,359 tok/s prefill ← yes, fifteen thousand 🚨2.5× faster than base model with basically zero quality loss. It hits Sonnet-4.5 level on hard classification tasks… at 1/600th the cost. Local models are shipping faster than we can test 👇🏻 🔥 HF: https://t.co/XUvVZBj9AX

This is the full video of the hardest version of the task: t-shirt folding from unstructured initial states. This setting really requires at least some strategy, since the robot first has to spread the shirt before it can complete the fold. Full details on data collection strategies in the blog below. 👇

🚨REQUESTED MLX TUNE DGEMMA 4-31B LANDED 🦥@UnslothAI native 4-bit MLX for Apple Silicon 🦥 🔥Blazing fast inference on all M-series Macs 🤖Super efficient (~20GB RAM only) 🤯Strong multimodal + vision performance 🌐Full 256K context + native function calling 🔥Crushes coding, long reasoning & agents 100% LOCAL everything stays on your Mac Frontier-level quality at local speed 85.2% MMLU Pro • 80% LiveCodeBench Try it now 👇🏻 https://t.co/Sgw7Y9DDIv

Introducing gyaradax 🐉: A JAX solver for local flux-tube gyrokinetics with custom CUDA kernels for acceleration. This entire code was vibecoded by @ggalletti_ and me in a month. Validated against GKW (CPU-only Fortran code) with 10x speedups. Details and code in the replies. https://t.co/22PrHjItR5

@DanielWulikk Have to think a bit about how to best visualize it, but if you are interested, I have a working from-scratch code implementation of Gemma 4 E2B in the meantime to see how per-layer embeddings are implemented: https://t.co/jyiq1vyJnH https://t.co/fVrSBWHNHl

Google DeepMind is hosting a Gemma 4 hackathon with a $10,000 Unsloth prize! 🦥 Show off your best fine-tuned Gemma 4 model built with Unsloth. There's $200,000 total prizes to be won. Challenge info + Notebook: https://t.co/HndHPaXICT https://t.co/cBnNro1fVI

Meow Wolf is one of the most magical places on earth. We rented it out for startups. Founders: join the Google DeepMind team as we host top startups joining in Las Vegas for Next '26 for an unforgettable evening with great connections, startup Gemini Demos, food, drinks and adventure. Weds Apr 24 If you're in Vegas, attending Next, and a startup founder, RSVP today! https://t.co/YX7CiXtZoe @OfficialLoganK / @DynamicWebPaige / @osanseviero / @vadiamit / @ammaar / @harrisonfjobe / @_philschmid / @patloeber

Yes, GitHub is 18 years old today. But some things never change. https://t.co/CeDtE5ItYv

I built a physical notification device to prevent the tragedy of GitHub Copilot getting stuck waiting for user input, hidden behind dozens of windows! When it detects the "waiting for input" state, this little guy starts shaking its head and looking around for you... 3D models + firmware + step-by-step build guide here: https://t.co/tM7N0xzBOY

Accessibility work often gets stuck at triage. GitHub's team found a way to let AI handle that part. Now there's a continuous loop: feedback comes in, AI triages it, and fixes ship faster. That's a big difference for users who depend on accessible experiences every day. Here's how the team transformed their internal workflow. ⬇️ https://t.co/83nRZ4B9oc