Learning

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

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.

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

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.

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

A community knowledge base that reverse-engineers hundreds of GPT-Image2 examples into structured, agent-ready prompt protocols.

A curated index of free machine learning courses on YouTube, organized by topic so you don't have to trust the algorithm.

An open-source Chinese textbook that binds math, code, and critical thinking into a single, living resource for deep learning.

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

A readable reference for how classic machine learning actually works under the hood, from backprop to genetic algorithms.

It collects copy-paste role-play prompts that cast ChatGPT as a Linux terminal, proofreader, or interviewer for Chinese-speaking users.

Because 'make it pretty' is not a prompt, and this repo treats image generation like a production pipeline, not a toy.

Because 'just ask nicely' is not a production strategy, this is a curated, living reference for systematic LLM prompting.

This repo exists because the LLM learning curve is a scattered mess of blog posts, so it organizes the field into three distinct tracks—fundamentals, scientist, and engineer—each paired with runnable Colab notebooks.

A systematic Chinese tutorial for developers who want to stop treating LLMs as black boxes and hand-build a 215-million-parameter model from the ground up.

Official Python notebooks and guides for common OpenAI API tasks.

A massive collection of extracted system prompts and internal models from more than two dozen commercial AI coding agents and assistants.

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

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

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.