Google released an app that allows you to run... LLMs from Hugging Face, fully privately and 100% local 🔥 > Generate code on-the-fly > Chat with images > Supports multi-turn conversations > Choose any model from Hugging Face > Based on LiteRT 🔥 > Sign in with HF Support for iOS coming soon! - exciting times for LiteRT and LocalLlama community! 💥show more

After months of work, and with the help of... our awesome community, we're excited to finally share LM Studio 0.3.0! 🎉 🔥 What's new: - Built-in Chat with Documents, 100% offline - OpenAI-like 'Structured Outputs' API with any local model - Total UI revamp (with dark/light/sepia themes) - Load & serve multiple LLMs *on the local network* - Available in 7 languages! 🌎🌍🌏 - Download any supported model from Hugging Face - Update LLM runtimes (llama.cpp) separately from the app ... and tons more goodies! Let us know how you like it! 👾🤝show more

chat with papers for any arXiv link to HF... paper you can now chat using Hugging Chat All Hugging Face Papers now include a built-in assistant, powered by HuggingChat and the Hugging Face MCP server. It helps you quickly understand papers by answering questions, summarizing key ideas, and providing context as you browse the latest researchshow more

LM Studio 0.3.4 ships with Apple MLX 🚢🍎 Run... on-device LLMs super fast, 100% locally and offline on your Apple Silicon Mac! Includes: > run Llama 3.2 1B at ~250 tok/sec (!) on M3 > enforce structured JSON responses > use via chat UI, or from your own code > run multiple models simultaneously > download any model from Hugging Face Video at 1x speed.show more

This is RICULOUSLY good, TRELLIS 3D Generation model by... Microsoft! 🔥 Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!show more

Let’s fucking goooo, starting today you can directly try... out AI models on FREE Colab notebooks from Hugging Face 🔥 Continuing with our mission to make AI accessible to the masses - we’re excited to support Colaboratory for fast exploration and rapid prototyping! BONUS: you can put a custom “notebook.ipynb” in your model repo and we’ll serve that directly!show more

You can now route each request to the "fastest"... ⚡ or "cheapest" 💸 provider with Hugging Face Inference Providers in Copilot Chat on Visual Studio Code!🤗 For each model, you can explicitly choose "cheapest" when price matters - or "fastest" when throughput is the priority. thanks to Simon Brandeis for shipping this!show more

Litter iOS just got a beta (TestFlight) release so... now you can install it and try it out. It's a remote for codex! - Finds all the computers on your local network (or tailscale) that run codex app-server or ssh - Lets you continue any session or start a new one - Experimental local codex with oauth support Free, fully open-source, fully native, with android release coming soon! Thanks to Maky drumroll.dev ☄️ PincentΞ for the contributions! TestFlight ⬇️show more

Use Your Private Models on Comfy Cloud! Continue client... work safely — or generate with your experimental private models. You can now import models directly from private Hugging Face or Civitai repos. Just add your private key and model URL, and we will bring the models to your workflow! Everything you import stays 100% private to your account.show more

As announced in partnership with NVIDIA at CES, we’re... excited to introduce Stable Point Aware 3D (SPAR3D), setting a new standard in 3D generation. Ideal for running on NVIDIA RTX AI PCs, SPAR3D enables real-time editing and complete structure generation of 3D objects from a single image in under a second. You can download the weights on Hugging Face and code on GitHub, or access the model through the Stability AI API. Learn more here: (1/3)show more

⭐The Year of Inference is here. Featherless is now... an official inference provider on Hugging Face, unlocking 6,700+ LLMs for anyone to run, eval, and deploy instantly. It all starts with accessibility. From DeepSeek to Mistral, LLaMA to Qwen — powerful LLMs are one click away. We believe the future of AI is shaped by the long tail: personalized, specialized models tuned to real people’s needs. To get there, inference must be open, affordable, and usable by all. Whether you're fine-tuning, prototyping, or scaling a product, this moment is for you. 🫱🏻‍🫲🏻Let’s make inference the easiest part of building with AI. 📢 Share this so more builders know what’s now possible. Excited to be partnering with clem 🤗 Julien Chaumond Vaibhav (VB) Srivastav Simon Brandeis & Hugging Face team to take this to the next level!show more

If you’re designing an app with audio, get ready... for the most feature-rich audio support coming in our next release! Some highlights: › Upload audio files and access the microphone for input › Generate real-time waveform data based on input (audio or mic) › currentTime(), TotalTime(), volume, pan, rate, numberOfLoops supported for interactions › Sync multi tracks This release will have mic support and the speech framework will be supported in the future. This project and others will be available in the gallery for you to duplicate and make your own!show more

Introducing ml-intern, the agent that just automated the post-training... team Hugging Face 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 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 reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - 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: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.show more

Hackathon participants get: - Guide on how to get... 100 paying customers for any app (has community, content, and product tactics we used to get our first 1000+ customers) - How to rank top 5 on Product Hunt (what we did) - 50 validated app ideas that are easy to build and can make you your first $10K. - Advanced Vibe Coding Guide: technical roadmap for going from idea to real app. - $100 discount on Anything Max and a motivated group chat building alongside you Retweet and comment “LFG” on the launch video (above), and we’ll send you a $100 discount code and the link to participate.show more

Working on a “Hand-Off to Mac” way to load... the chats from the Codex Remote Control for iOS app to the Codex App Codex app-server doesn’t quite support a live update So I decided to find a workaround A button that forces the closing of the Codex App and reopening it in the chat created from iPhone Would you use this button?show more

Heyo exciting news! Just released my react-native-godot library to... the world! Available on npm, just `yarn add react-native-godot` and you’re (almost) good to go :) Basically, you can embed any Godot projects on iOS (Android coming soon) into your React Native app, It supports multiple Godot views at the same time, full styling (you get flex box multiple Godot views and update their layout dynamically) Still pretty early but I think it’s already enough to hack around with it. If you have any questions or issues feel free to dm me. Alsoo, give us a star on Github if you support the initiative 😊show more

parakeet.cpp: native C++/ggml (ggml) inference for NVIDIA AI Developer's... Parakeet, one of the best speech-to-text models out there, from the LocalAI team. Every Parakeet model (TDT/CTC/RNNT/hybrid + cache-aware streaming), byte-for-byte identical output to NeMo, now running anywhere with no Python and even a bit faster, on CPU and GPU. Quantized GGUF on Hugging Face 🤗 Huge thanks to Georgi Gerganov for ggml and to NVIDIA AI Developer for releasing Parakeet! 🧵show more

OpenAI's Deep Research is getting a run for its... money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!show more

Introducing Replit ModelFarm, the fastest and safest way to... build your next Generative AI app. Available for free on Hacker and Pro plans till October 15th. It requires zero setup, zero configuration, and zero API keys. With Replit ModelFarm, you can build a working Gen AI app in as little as 3 lines of code. Get started by installing the Replit AI library in any Python, JavaScript, or TypeScript Repl. The library implements an API for text completion, chat completion, and text embeddings. It supports streaming so your users can see model responses in real-time rather than waiting on a single output. All Hacker and Pro builders will have free access to a selection of Gen AI models offered by Google Cloud Vertex AI through Replit ModelFarm. All models are accessible from the development environment and any deployed app.show more

Train a TensorFlow object detection model – then deploy... it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:show more

My OpenClaw bot scraped 1,000+ local business websites, took... screenshots of every single one, and used them to train an AI model that scores website design quality automatically Here's what it did: → Scraped 1000+ local businesses from Google Maps → Screenshotted every website → Trained an AI model on 1,000+ sites to score design 0–100 → Flagged the worst ones as hot leads No more manually finding clients with bad websites, it finds them for you in seconds Reply "OpenClaw" and I'll send you the whole system for freeshow more