Learning

A structured 12-week curriculum that teaches symbolic AI, neural nets, and even genetic algorithms—without pretending deep math or cloud ML don't exist.

This repo open-sources a full Chinese textbook on AI agent engineering—complete with Markdown source, compiled PDF, and runnable Python demos for every chapter.

It exists to teach developers how to build generative AI applications through 21 modular lessons that mix conceptual explainers with working Python and TypeScript code.

Why rebuild the same RAG pipeline or agent loop from scratch when you can fork a working template instead?

Hello-Agents is a free, 16-chapter curriculum that teaches developers to construct truly AI-driven agents from first principles rather than wiring no-code workflows.

To turn graduate-level NLP and AI safety lectures into runnable Jupyter notebooks that anyone can break, watermark, or align.

It teaches how LLMs work by implementing tokenization, attention, pretraining, and finetuning in pure PyTorch, one notebook at a time.

This curriculum closes the gap between calling AI APIs and understanding the loss curves underneath.

To teach agentic design patterns through videos, notebooks, and documentation auto-translated into more than 50 languages.

A living archive of extracted system prompts from Anthropic, OpenAI, Google, and xAI that exposes the hidden instructions shaping tone, tools, and guardrails.

A curated registry of reusable instruction packages that keep Claude and other agents from reinventing the wheel on every task.

This repo exists to sort the scattered open-source AI agent ecosystem into an industry-by-industry directory, saving you from GitHub search purgatory.

It collects official skill definitions from engineering teams so AI agents don't have to improvise.

Official Jupyter notebooks demonstrating how to wire Claude into production tasks like RAG, SQL queries, and multimodal pipelines.

A curated field guide for turning Claude Code from a chatty assistant into a repeatable engineering agent.

A free, 26-lesson curriculum marches beginners through classic machine learning the old-fashioned way: with homework, quizzes, and Scikit-learn.

MiniMind is an educational training ground that rebuilds every stage of a modern language model—from tokenizer to RLHF—in raw PyTorch so you can see the gears turning instead of just calling high-level APIs.

It catalogs legitimate services offering free API access to large language models, complete with rate limits, model lists, and data-privacy caveats.

Open-sources the exact LLM prompts and agent skills that researchers at MSRA, ByteDance Seed, and top Chinese universities use to write and polish papers.

A crowdsourced archive of leaked and reverse-engineered system prompts that exposes the hidden scaffolding behind major AI models and agents.