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Connects multiple Macs and Linux machines into one cluster to run models too large for any single machine. Auto-discovers peers, shards a model across them via tensor parallelism, and exposes OpenAI-, Claude-, and Ollama-compatible APIs.
Visually edit Next.js + Tailwind projects in the browser like Figma, with every change written straight back to your real React code. Pairs a DOM-level visual canvas with AI chat that scaffolds and edits components, plus branching and one-click deploy.
Provides code, pretrained weights, and tooling for protein language models and structure prediction — including ESMC, ESMFold2, sparse autoencoders (SAEs), and the ESM Atlas. Includes model checkpoints, tutorials, Hugging Face & Biohub integration, and an MIT license.
Disaggregated LLM serving architecture that splits prefill and decode into separate clusters and pools spare CPU, DRAM, and SSD into a distributed KVCache. Powers Kimi in production, handling 75% more requests under the same SLOs.
A research codebase and model family for vision–language models that experiments with data‑centric post‑training strategies and long‑context multimodal reasoning. Includes model reports, released research weights (non‑commercial), grounding tools (LocateAnything) and integrations for inference/optimization.
Official code companion to the O'Reilly book by Jay Alammar and Maarten Grootendorst: 12 chapters of runnable notebooks on tokens, embeddings, Transformers, text classification, clustering, prompt engineering, semantic search, RAG, and fine-tuning.
Publishes a structured open textbook on large language model foundations, covering language modeling, LLM architectures, prompt engineering, PEFT, model editing, and RAG.
Open-source TTS that clones a voice from 3-10s of audio and synthesizes cross-lingual speech in 9 languages and 18+ Chinese dialects. Supports streaming at ~150ms latency with instruction control over emotion, speed, and accent.
Converts PDFs, images, and Office documents into Markdown or JSON for retrieval, extraction, and agent workflows, with OCR, layout analysis, formula handling, and multiple runtime modes.
Edits a codebase from natural-language prompts in the terminal, coordinating specialized sub-agents — file picker, planner, editor, reviewer — instead of one model. Beats Claude Code 61% vs 53% on its own evals; agents scriptable in TypeScript.
Routes each user query to the most suitable agent via a classifier that weighs agent profiles and conversation history, keeping context shared across handoffs. Python and TypeScript, with a SupervisorAgent that runs sub-agents in parallel.
Drives UI automation from screenshots alone: describe steps in natural language and a vision model acts on what it sees, no DOM selectors. One API spans web, Android, iOS, HarmonyOS and desktop; plugs into Playwright/Vitest or runs autonomously.