Curates 80+ hands-on LLM-powered examples, tutorials and recipes for building agents, RAG systems, voice assistants, and agentic workflows. Includes starter templates, course playlists, and reference apps for rapid prototyping and learning.
An asynchronous, high-throughput framework for large-scale reinforcement learning and agentic training that scales to 1T+ MoE models and 1000+ GPUs, with native verifiers integration, end-to-end SFT/RL/evals, and Slurm/Kubernetes deployment; requires NVIDIA GPUs.
A benchmark dataset for evaluating MLLM-driven interactive webpage code generation: provides prototyping screenshots, action.json interaction metadata, and example generation scripts across 127 webpages and 374 interactions to test dynamic UI-to-code capabilities.
Trains LLM reasoning and agentic models with fully asynchronous reinforcement learning, decoupling rollout generation from policy updates for a 2.77x speedup over synchronous RL. Covers GRPO, PPO and DAPO across Megatron, FSDP, vLLM and SGLang backends.
Curated collection of production-oriented AI projects that implement OCR, RAG, multi-agent systems, and multimodal pipelines. Each entry provides runnable code, setup notes, and engineering patterns to help developers move prototypes toward production.
Provides a local model gateway and control plane for coding agents — route Claude Code, Codex, Grok CLI, ZCode and compatible clients to multiple providers through one stable local endpoint while managing routing, failover, tools, credentials, and observability.
Walks through real LLM workflows across chat, search, deep research, file analysis, coding, voice, images, and generated podcasts. It is most useful as a field guide to the messy AI app layer.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
Simulates adversarial attacks against LLMs and AI agents to surface vulnerabilities (e.g., jailbreaks, prompt injection, PII leakage) and ships guardrails to block risky inputs/outputs; runs locally and can be driven from CLI or Python.
Collects the leaked and reverse-engineered system prompts, internal tool definitions, and model configs of 25+ proprietary AI coding assistants — Cursor, v0, Devin, Replit, Windsurf, Claude Code and more. Reveals what each is told to do.
Build and run configurable multi-agent LLM workflows and personal AI agents locally or with cloud LLMs; supports simple TOML-based LLM configuration, optional browser automation, a demo on Hugging Face, and companion RL tuning (OpenManus-RL) for agent training.
High-quality, efficiently verified and filtered web corpus for LLM pretraining — supplies ~1 trillion English tokens and ~120 billion Chinese tokens with English/Chinese Parquet splits. Designed for large-scale pretraining experiments and data-filtering research.