Discover the Best AI Resources

A 236B-parameter Mixture-of-Experts LLM that activates only 21B parameters per token, cutting training cost 42.5% versus a dense 67B model and shrinking the KV cache 93.3% via Multi-head Latent Attention, with 128K context.

Builds real-time multimodal conversational AI agents with voice-assistant examples, VAD, turn detection, RTC/WebSocket transport, avatars, transcription, and edge-device demos.

Pydantic AI

Provides a Python framework for building generative-AI agents and workflows with Pydantic-style type safety and composable capabilities. Model-agnostic provider support, built-in observability, human-in-the-loop tool approval, and durable execution for production use cases.

hf-audio/open-asr-leaderboard

Provides leaderboard-ready test splits for the Open ASR Leaderboard: converts unsafe custom loaders to Parquet, sorts samples by audio length, and packages eight ESB test sets (LibriSpeech, Common Voice, GigaSpeech, SPGISpeech, etc.) for reproducible ASR benchmarking.

exo

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.

Onlook

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.

ESM (Biohub/esm)

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.

Mooncake

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.

Eagle: Frontier Vision-Language Models with Data-Centric Strategies

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.

Hands-On Large Language Models

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.

genlayer-project-boilerplate

Provides a GenLayer DApp boilerplate implementing a football-bets intelligent contract with web and LLM integration. Bundles fast in-memory direct tests with web/LLM mocking, a GenVM linter, integration test workflows, CI, and a Next.js 15 TypeScript frontend.

Publishes a structured open textbook on large language model foundations, covering language modeling, LLM architectures, prompt engineering, PEFT, model editing, and RAG.