🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
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This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent 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)
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
A straightforward method for training your LLM, from downloading data to generating text.
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
- pytorch
- pytorch-tutorials
- pytorch-tutorials-cn
- deep-learning
TranslatedBuild your neural network easy and fast, 莫烦Python (Morvan Python) Chinese teaching
- neural-network
- python
- pytorch-tutorial
- batch-normalization
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Codebase for the Super Rentals official tutorial. Generated by https://github.com/ember-learn/super-rentals-tutorial/. Changes must be made there.
A list of general purpose tricks and techniques for saving tokens in PICO-8.
- pico-8
- optimization-methods
Detailed guide for how to get started with beatoraja or BMS in general. -- For example: What is BMS, How do I set it up, Where do I download songs, How does the community work, How does the difficulty system work
Constructive Generation Methods for Dungeons and Levels
Simple Genetic Algorithm for demonstration purposes
- genetic-algorithm
- evolutionary-algorithms
- evolutionary-algorithm
- evolutionary-computation