Stars
🧠 Curated collection of system prompts for top AI tools. Perfect for AI agent builders and prompt engineers. Incuding: ChatGPT, Claude, Perplexity, Manus, Claude-Code, Loveable, v0, Grok, same new,…
Implementation of 17+ agentic architectures designed for practical use across different stages of AI system development.
The awesome collection of Claude Skills and resources.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Anthropic's Interactive Prompt Engineering Tutorial
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
VIP cheatsheet for Stanford's CME 295 Transformers and Large Language Models
《Machine Learning: A Probabilistic Perspective》（Kevin P. Murphy）中文翻译和书中算法的Python实现。
"Probabilistic Machine Learning" - a book series by Kevin Murphy
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
It is said that, Ilya Sutskever gave John Carmack this reading list of ~ 30 research papers on deep learning.
🤗 smolagents: a barebones library for agents that think in code.
Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 rep…
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
A comprehensive guide to LLM evaluation methods designed to assist in identifying the most suitable evaluation techniques for various use cases, promote the adoption of best practices in LLM assess…
中文翻译的 Hands-On-Large-Language-Models (hands-on-llms)，动手学习大模型
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
A one stop repository for generative AI research updates, interview resources, notebooks and much more!
Introduction to Machine Learning Systems
12 Weeks, 24 Lessons, AI for All!
A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)