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.
Visual, example-driven guide for using Claude Code: structured learning path, copy‑paste templates, and diagrams that show how to combine slash commands, hooks, subagents and MCP into production workflows.
Fifteen reusable agent skills for curating LLM context windows, treating attention decay—not token capacity—as the real constraint. A routing layer benchmarked at 0.92 top-1 accuracy selects the right skill for each task.
Encodes production-grade engineering workflows (spec, plan, build, test, review, ship) as reusable "skills" so AI coding agents follow consistent development practices. Packaged as per-skill SKILL.md files and slash commands for integration with agents and CLIs. Suited for teams embedding engineering guardrails into agent-driven dev workflows.
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.
Collection of hands-on workshop materials and sample code from Anthropic's "Code with Claude" series, covering Claude Managed Agents, memory (Dreaming Service), eval-driven agent development, and multi-agent patterns. Not maintained and not accepting contributions.