A free, open textbook on engineering ML systems — building efficient, reliable AI from a single GPU up to warehouse-scale clusters. Goes beyond model design and MLOps tooling to the underlying science: scheduling, quantization, data pipelines, serving.
AI Infra2023
Curated learning hub that aggregates roadmaps, tutorials, bootcamps, books, projects, and tool recommendations for learning data engineering and production data infrastructure. Focuses on practical applied learning (projects, interview prep, community links) rather than code libraries.
Nine-chapter course teaching prompt engineering for Claude: from basic prompt structure through roles, output formatting, and hallucination control to complete prompts for chatbot, legal, finance, and coding tasks. Runs as editable Jupyter notebooks.
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.