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🚀 A curated collection of AI engineering resources, roadmaps, tools, and projects covering LLMs, RAG, AI Agents, MCP, fine-tuning, evaluations, and production AI systems.

TranslatedProgrammer Yupi's (程序员鱼皮) complete AI resource collection + zero-basics Vibe Coding tutorial. Shares step-by-step OpenClaw tutorials, ways to use large models (DeepSeek / GPT / Gemini / Claude / GLM), the latest AI news, a complete collection of Prompts, an AI knowledge encyclopedia (Agent Skills / RAG / MCP / A2A), AI programming tutorials (Harness Engineering), guides to using AI tools (Cursor / Claude Code / TRAE / Codex / Copilot), AI development framework tutorials (Spring AI / LangChain), and a guide to monetizing AI products, helping you quickly master AI technology and stay at the forefront of the times. This project is the open-source documentation aiguide, which has been upgraded to the Yupi AI Navigation website (鱼皮 AI 导航网站)

TranslatedThis project once rose to number one in the world. A collection of high-quality content is at the very bottom of this page. In addition, the complete and beautifully made print edition of 《编程之法:面试和算法心得》 (The Art of Programming: Interview and Algorithm Insights) is now on sale at JD.com and Dangdang.

❤️ 1000+ Hand-Crafted Go Examples, Exercises, and Quizzes. 🚀 Learn Go by fixing 1000+ tiny programs.

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

TranslatedIllustrated computer networks, operating systems, computer organization, and databases, with 1,000 diagrams + 500,000 characters, breaking down obscure and hard-to-understand computer fundamentals, so that no "bagu wen" (standard interview question essays) is hard to understand in the world! 🚀 Read online: https://xiaolincoding.com

  • java
  • cpp
  • python
  • c

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services