Turns any website into a structured, text-like interface that LLM agents can read and act on, handling clicks, forms, scraping, anti-detection and CAPTCHAs. Ships as an open-source Python library plus a hosted cloud API for running browser agents at scale.
High-accuracy biomolecular structure prediction suite: open-source models (protenix-v2/v1), a benchmark/evaluation toolkit, and a web server for inference. Targets protein/antibody–antigen and ligand-aware predictions with inference-time sampling and constraint support.
A GitHub repository of learning notes and code dedicated to ML + SYS (machine learning systems). It collects tutorials, code walkthroughs and engineering notes on RLHF, distributed training (FSDP, Megatron), inference and scheduling (SGLang, vllm), quantization, CUDA/GPU optimization, system design, and practical engineering.
Framework for building and orchestrating multi-agent LLM systems, with agent types, tool integration, and human-in-the-loop workflows. Supports multi-agent conversation patterns, multiple LLM providers, and RAG-style tooling for research and prototyping agentic workflows.
Translates scientific PDFs while keeping the original layout intact: parses text, tables, and figures, then re-renders bilingual or monolingual output via any OpenAI-compatible LLM. Tuned for English-to-Chinese papers, with CSV glossary support.
Extends the Wand (WeMod) desktop client’s local configuration and UI with a remote web panel, injected renderer scripts, automated compatibility patches and client-side AI features; runs entirely locally and does not publish official executables (build your own).
Predicts 3D structures of proteins, nucleic acids, and small-molecule complexes, the first fully open-source model to approach AlphaFold3 accuracy. Boltz-2 adds binding-affinity prediction that nears FEP simulation accuracy at ~1000x the speed.
Orchestrates configurable deep-research agent workflows that combine LLMs, web search, and MCP tools to produce structured research reports and evaluation outputs. Supports LangGraph Studio, multiple model providers (OpenAI, Anthropic, local models), and Deep Research Bench evaluation for benchmarked comparisons.
Brings an agentic chat experience to the terminal: describe a task in natural language and it plans, edits files, and runs commands to build the app. Written in Rust, ships on macOS and Linux. Now succeeded by the closed-source Kiro CLI.
Custom ComfyUI nodes that run Lightricks' LTX-Video diffusion-transformer models for text-to-video and image-to-video, adding IC-LoRA control over depth, pose, edges, and motion plus distilled and low-VRAM variants for node-based workflows.
Official remote MCP servers that let AI agents read and change Cloudflare config in natural language — managing Workers and bindings, querying observability and DNS analytics, searching docs. Each capability is a separate scoped server.
Runs text-to-speech, speech-to-text, and speech-to-speech models natively on Apple Silicon via MLX — no CUDA or cloud. Supports 20+ TTS and 15+ STT models (Kokoro, Whisper, Qwen3), low-bit quantization, an OpenAI-compatible API, and a Swift package.