Mercury 2 is live 🚀🚀
The world’s first reasoning diffusion LLM, delivering 5x faster performance than leading speed-optimized LLMs.
Watching the team turn years of research into a real product never gets old, and I’m incredibly proud of what we’ve built.
We’re just getting started on what diffusion can do for language.
The world isn't ready for this. But I am.
Seedance 2.0 is going to drastically change filmmaking forever. I'm not being hyperbolic. This is 1 detailed prompt with 5 cuts specified and a single starter image. That's it.
It's time to start directing.
Auto rigging without manual weight painting or skeleton construction. Creators/game devs are going to love this.
SkinTokens converts skinning to discrete token sequences. uses Qwen3-0.6B for precise skeleton and weight gen across any 3D asset.
https://t.co/aVMhKmOMGh
Severus Snape - ALWAYS (LIVE at Hogwarts) 🔥🔥🔥
This is a MASTERPIECE. The music, the imagery, the lyrics, the setting, the accuracy to the story, it's PERFECT. Chef's kiss. Peak AI.
📸: WickedAI (YT)
Introducing FLUX.2 [klein]. Blazing fast. Beautiful.
Generate stunning images in under a second while maintaining exceptional quality.
Great for fast editing, changing styles, and developing ideas from 0 → 1.
Available via API, or run it locally - Klein 4B under Apache 2.0, Klein 9B as open weights.
Try it for free in our demo app (link in the thread).
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 research
InfiniDepth
Neural implicit fields for monocular depth estimation that enable arbitrary-resolution and fine-grained depth maps. Query depth at any continuous 2D coordinate, scaling from 4K to 16K and beyond.
🚨 Hunyuan 3D 3.0 is now live on fal!
🎯 3x modeling accuracy with 3.6B voxels
✨ Ultra-high resolution (1536³) photorealistic details
🎨 Text-to-3D, Image-to-3D & Sketch-to-3D
⚡ Professional-grade assets in minutes
Excited to release new repo: nanochat!
(it's among the most unhinged I've written).
Unlike my earlier similar repo nanoGPT which only covered pretraining, nanochat is a minimal, from scratch, full-stack training/inference pipeline of a simple ChatGPT clone in a single, dependency-minimal codebase. You boot up a cloud GPU box, run a single script and in as little as 4 hours later you can talk to your own LLM in a ChatGPT-like web UI.
It weighs ~8,000 lines of imo quite clean code to:
- Train the tokenizer using a new Rust implementation
- Pretrain a Transformer LLM on FineWeb, evaluate CORE score across a number of metrics
- Midtrain on user-assistant conversations from SmolTalk, multiple choice questions, tool use.
- SFT, evaluate the chat model on world knowledge multiple choice (ARC-E/C, MMLU), math (GSM8K), code (HumanEval)
- RL the model optionally on GSM8K with "GRPO"
- Efficient inference the model in an Engine with KV cache, simple prefill/decode, tool use (Python interpreter in a lightweight sandbox), talk to it over CLI or ChatGPT-like WebUI.
- Write a single markdown report card, summarizing and gamifying the whole thing.
Even for as low as ~$100 in cost (~4 hours on an 8XH100 node), you can train a little ChatGPT clone that you can kind of talk to, and which can write stories/poems, answer simple questions. About ~12 hours surpasses GPT-2 CORE metric. As you further scale up towards ~$1000 (~41.6 hours of training), it quickly becomes a lot more coherent and can solve simple math/code problems and take multiple choice tests. E.g. a depth 30 model trained for 24 hours (this is about equal to FLOPs of GPT-3 Small 125M and 1/1000th of GPT-3) gets into 40s on MMLU and 70s on ARC-Easy, 20s on GSM8K, etc.
My goal is to get the full "strong baseline" stack into one cohesive, minimal, readable, hackable, maximally forkable repo. nanochat will be the capstone project of LLM101n (which is still being developed). I think it also has potential to grow into a research harness, or a benchmark, similar to nanoGPT before it. It is by no means finished, tuned or optimized (actually I think there's likely quite a bit of low-hanging fruit), but I think it's at a place where the overall skeleton is ok enough that it can go up on GitHub where all the parts of it can be improved.
Link to repo and a detailed walkthrough of the nanochat speedrun is in the reply.
Excited to drop Falls of Fortune! 🎰 This social casino mashup packs coin-dropping action, epic slots, and resort-building vibes. Team up with Maggie Wilson to turn her estate into a fortune empire. Check the screenshots 🚀 #SocialCasino#GamingNews