obra / superpowers
A composable skills framework and development methodology that guides coding agents through design, planning, TDD, and subagent-driven implementation.
obra / superpowers
msitarzewski / agency-agents
A collection of role-specific AI agent personas with defined processes and deliverables, installable into tools like Claude Code, Cursor, and Gemini CLI.
rohitg00 / ai-engineering-from-scratch
A free, MIT-licensed course of 542 lessons on AI engineering, implementing model internals, retrieval, and agent runtimes from scratch.
Donchitos / Claude-Code-Game-Studios
A set of 49 Claude Code subagents and 74 slash-command workflows that structure an AI session into a game dev studio with defined roles.
jackfrued / Python-100-Days
A free Chinese-language 100-day Python course, from basics to advanced topics, with lessons and code examples.
HandsOnLLM / Hands-On-Large-Language-Models
Code examples for the O'Reilly book Hands-On Large Language Models, covering LLM concepts and practical techniques in Jupyter notebooks.
danielmiessler / SecLists
A collection of security testing wordlists covering usernames, passwords, URLs, fuzzing payloads, web shells, and sensitive data patterns.
chiphuyen / aie-book
Companion repository for Chip Huyen's book AI Engineering, with chapter summaries, study notes, prompt examples, and case studies.
alirezadir / AIMLInterviews
A study guide and interview-prep curriculum for AI/ML technical roles at big tech, covering coding, ML breadth, system design, and behavioral rounds.
Liquid4All / cookbook
A collection of examples, tutorials, and apps for building with Liquid AI's open-weight LFM models and the LEAP SDK on laptops, mobile, and edge devices.
openai / openai-cookbook
Example notebooks and guides showing how to accomplish common tasks with the OpenAI API.
microsoft / mcp-for-beginners
A beginner curriculum teaching the Model Context Protocol (MCP) with hands-on code examples in C#, Java, JavaScript, Rust, Python, and TypeScript.
exacity / deeplearningbook-chinese
Chinese translation of the Deep Learning textbook by Goodfellow, Bengio, and Courville, with community proofreading.