Hands-on coding tutorial series for large language models with slides and runnable notebooks covering fine-tuning, prompting, RLHF, safety, steganography, watermarking, multimodal models, GUI agents, and deployment. Community-maintained, free course materials for students and researchers.
Publishes a structured open textbook on large language model foundations, covering language modeling, LLM architectures, prompt engineering, PEFT, model editing, and RAG.
Seven-week course that builds a production RAG system from scratch — an arXiv paper assistant that starts with BM25 keyword search, then layers hybrid vector retrieval, local-LLM generation, Langfuse monitoring, and an agentic LangGraph Telegram bot.
Teaches AI agent principles and practice through a structured Chinese curriculum, pairing theory with runnable code so learners can build, debug, and extend agent systems step by step.
A step-by-step, beginner-first programming course that teaches 'vibe coding'—conversational workflows to turn ideas into AI-enabled web and full‑stack prototypes. Features interactive simulated coding, multi-language docs, stage-based projects (from simple demos to SaaS capstones) and advanced agent/Claude Code guidance.
AI Agent2026
Turns any topic or document into an interactive, multi-agent classroom that generates slides, quizzes, interactive simulations and project-based learning activities. Includes real-time AI teachers/classmates, whiteboard drawing, TTS/ASR, PPTX/HTML export and chat-app integration via OpenClaw.
Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.