[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9634":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":37,"readmeContent":38,"aiSummary":39,"trendingCount":15,"starSnapshotCount":15,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},9634,"awesome-artificial-intelligence","owainlewis\u002Fawesome-artificial-intelligence","owainlewis","A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.","",null,14055,2313,666,35,0,5,42,352,26,45,"MIT License",false,"master",true,[26,27,28,29,30,31,32,33,34,35,36],"ai","artificial-intelligence","deep-learning","intelligent-machines","intelligent-systems","machine-intelligence","machine-learning","neural-network","reinforcement-learning","statistical-learning","unsupervised-learning","2026-06-12 02:02:10","# Awesome Artificial Intelligence\n\nA curated collection of **must-use, actively maintained resources** for building and shipping AI systems.  \n\nFocus: **AI engineering** (RAG, agents, evals, guardrails, deploy) plus the best books, guides, papers, and a *carefully selected* set of tools.\n\n![](https:\u002F\u002Fmedia.giphy.com\u002Fmedia\u002FjeAQYN9FfROX6\u002Fgiphy.gif)\n\n---\n\n## 🏛 Core Resources (Evergreen)\n\n_The foundations — these will still be valuable five years from now, even if today’s tools are gone._\n\n### 📚 Books\n**Modern & Practical**\n- [Designing Machine Learning Systems](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fdesigning-machine-learning\u002F9781098107956\u002F) — Scalable, maintainable ML pipelines (Chip Huyen).\n- [Generative Deep Learning (2nd Edition)](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fgenerative-deep-learning\u002F9781098134174\u002F) — GANs, VAEs, diffusion models (David Foster).\n- [AI Engineering](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fai-engineering\u002F9781098166298\u002F) — End-to-end AI product building (Chip Huyen).\n- [100 Page Language Models Book](https:\u002F\u002Fwww.thelmbook.com\u002F) — This book guides you through the evolution of language models, starting from machine learning fundamentals.\n\n**Foundational**\n- [Artificial Intelligence: A Modern Approach](https:\u002F\u002Faima.cs.berkeley.edu\u002F) — Comprehensive AI theory (Russell & Norvig).\n- [Deep Learning](https:\u002F\u002Fwww.deeplearningbook.org\u002F) — Neural networks & architectures (Goodfellow, Bengio, Courville).\n- [Reinforcement Learning: An Introduction (2nd Edition)](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fpsych209\u002FReadings\u002FSuttonBartoIPRLBook2ndEd.pdf) — RL fundamentals (Sutton & Barto).\n\n---\n\n### 🏗 AI Engineering\n_Frameworks and design patterns for building robust, production-grade AI systems._  \n_Personal note: you don't need tons of frameworks — start with simple LLM calls and work up._\n\n#### 📖 Guides & Playbooks\n- **[Building Effective Agents (Anthropic)](https:\u002F\u002Fwww.anthropic.com\u002Fengineering\u002Fbuilding-effective-agents)** — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents.\n- [OpenAI Agents Guide](https:\u002F\u002Fcdn.openai.com\u002Fbusiness-guides-and-resources\u002Fa-practical-guide-to-building-agents.pdf) — Practical guide on building agents\n- [Google AI Agents Paper](https:\u002F\u002Fwww.kaggle.com\u002Fwhitepaper-agents) - Practical guide to building AI agents from Google\n- [Google Agents Companion Paper](https:\u002F\u002Fwww.kaggle.com\u002Fwhitepaper-agent-companion) - Guide from Google\n- [OpenAI Cookbook](https:\u002F\u002Fcookbook.openai.com\u002F) — Example code, recipes, and best practices for working with OpenAI APIs.\n- [LLM Engineer Handbook](https:\u002F\u002Fgithub.com\u002FSylphAI-Inc\u002FLLM-engineer-handbook) — A goldmine of useful links for AI engineers\n\n#### 🤖 Frameworks \n- [PocketFlow](https:\u002F\u002Fthe-pocket.github.io\u002FPocketFlow\u002F) — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn.\n- [Google ADK](https:\u002F\u002Fgoogle.github.io\u002Fadk-docs\u002F) — Google's Agent Development Kit (Python, Java). Great local development experience + A2A + MCP.\n- [Pydantic-AI](https:\u002F\u002Fai.pydantic.dev\u002F) — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs.\n- [LangGraph](https:\u002F\u002Fwww.langchain.com\u002Flanggraph) — Build multi-agent workflows with stateful graphs on top of LangChain.\n- [CrewAI](https:\u002F\u002Fwww.crewai.com\u002F) — Agent orchestration with structured tasks and human-in-the-loop controls.\n- [AutoGen](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F) — Microsoft’s framework for multi-agent conversation and collaboration.\n\n#### 📦 Retrieval-Augmented Generation (RAG)\n- [LlamaIndex](https:\u002F\u002Fwww.llamaindex.ai\u002F) — Data framework for ingesting, indexing, and querying private data with LLMs.\n- [Haystack](https:\u002F\u002Fhaystack.deepset.ai\u002F) — Open-source search\u002FRAG framework with modular pipelines.\n- [Docling](https:\u002F\u002Fgithub.com\u002Fdocling-project\u002Fdocling) — Great library for ingesting any kind of document for RAG ⭐\n\n#### Evals \n\n- [OpenAI Evals](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fevals) — OpenAI's framework for writing evals\n\n---\n\n### 📄 Landmark Papers\n_Research that shaped modern AI — worth reading to understand the \"why\" behind today’s architectures._\n- [Attention Is All You Need](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03762) — Transformer architecture.\n- [Scaling Laws for Neural Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.08361) — Model\u002Fdata\u002Fcompute scaling.\n- [Language Models are Few-Shot Learners](https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.14165) — GPT-3 capabilities.\n- [Constitutional AI](https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.08073) — Safer model alignment.\n\n---\n\n## 🎓 Courses\n_Learn from the best — structured content for every level._\n\n**Beginner**\n- [Google Generative AI Learning Path](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fpaths\u002F118)\n- [Hugging Face LLM Course](https:\u002F\u002Fhuggingface.co\u002Flearn\u002Fllm-course\u002Fchapter1\u002F1)\n- [Fast.ai — Practical Deep Learning](https:\u002F\u002Fcourse.fast.ai\u002F)\n\n**Intermediate \u002F Advanced**\n- [Stanford CS324: Large Language Models](https:\u002F\u002Fstanford-cs324.github.io\u002Fwinter2022\u002F)\n- [Full Stack Deep Learning](https:\u002F\u002Ffullstackdeeplearning.com\u002F)\n- [MIT 6.S191: Intro to Deep Learning](https:\u002F\u002Fintrotodeeplearning.com\u002F)\n\n**Focused**\n- [DeepLearning.AI Short Courses](https:\u002F\u002Flearn.deeplearning.ai\u002F)\n- [Google Deepmind| Introduction to Reinforcement Learning](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)\n- [Karpathy’s LLM Zero-to-Hero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)\n- [Neural Nets - Zero-to-Hero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)\n\n---\n\n## 📰 Newsletters\n_Stay current with AI developments without drowning in noise._\n- [The Rundown AI](https:\u002F\u002Fwww.therundown.ai\u002F)\n- [AlphaSignal](https:\u002F\u002Falphasignal.ai\u002F)\n- [Superhuman AI](https:\u002F\u002Fwww.superhuman.ai\u002F)\n- [AI Engineer](https:\u002F\u002Fnewsletter.owainlewis.com)\n\n## ⚡ Tools\n\nTools for building and deploying AI applications. \n\n### 💬 Models\n- [ChatGPT](https:\u002F\u002Fopenai.com\u002Fchatgpt\u002Foverview\u002F) — Best for general coding + reasoning.\n- [Claude](https:\u002F\u002Fwww.anthropic.com\u002Fclaude) — Best for long-context analysis and structured thinking.\n- [Gemini](https:\u002F\u002Fgemini.google.com\u002F) — Best for Google ecosystem integration.\n- [Perplexity](https:\u002F\u002Fwww.perplexity.ai\u002F) — Best for quick research with live citations.\n- [Cohere](https:\u002F\u002Fcohere.com\u002F) — Best for enterprise LLMs with strong retrieval-augmented generation APIs.\n- [Mistral](https:\u002F\u002Fmistral.ai\u002F) — Best for lightweight, high-performance open-weight models.\n- [Qwen](https:\u002F\u002Fqwenlm.github.io\u002F) — Best for multilingual and Chinese-first applications.\n- [DeepSeek](https:\u002F\u002Fdeepseek.com\u002F) — Best for efficient, cost-optimized large models with competitive reasoning.\n  \n### 👨‍💻 Code & Developer Tools\n- [Claude Code](https:\u002F\u002Fwww.anthropic.com\u002Fclaude) — IDE extensions with long-context code edits.\n- [GitHub Copilot](https:\u002F\u002Fgithub.com\u002Ffeatures\u002Fcopilot) — In-IDE code completion, chat, and refactors.\n- [Cursor](https:\u002F\u002Fcursor.sh\u002F) — LLM-powered IDE for multi-file edits and codebase-aware chat.\n  \n### 🎨 Multimedia AI Tools\n\n#### 🖼 Image\n- [ChatGPT-4o Image Generation](https:\u002F\u002Fopenai.com\u002Fchatgpt) — Integrated image creation with style control.\n- [Midjourney](https:\u002F\u002Fwww.midjourney.com\u002F) — Artistic and photorealistic images and video.\n- [Adobe Firefly](https:\u002F\u002Fwww.adobe.com\u002Fsensei\u002Fgenerative-ai\u002Ffirefly.html) — Integrated into Creative Cloud.\n- [Ideogram](https:\u002F\u002Fideogram.ai\u002F) — Precise, legible text in generated images.\n- [Flux](https:\u002F\u002Fblackforestlabs.ai\u002F) — High-res, prompt-editable images.\n\n#### 🎥 Video\n- [Kling](https:\u002F\u002Fklingai.com\u002F) — Cinematic, realistic video generation.\n- [Google Veo 3](https:\u002F\u002Fdeepmind.google\u002Ftechnologies\u002Fveo\u002F) — High-quality video with synchronized audio.\n- [Runway](https:\u002F\u002Frunwayml.com\u002F) — Video editing + generation.\n\n#### 🎙 Audio\n- [ElevenLabs](https:\u002F\u002Felevenlabs.io\u002F) — High-quality text-to-speech.\n- [Suno](https:\u002F\u002Fsuno.ai\u002F) — AI music from text prompts.\n- [Aiva](https:\u002F\u002Fwww.aiva.ai\u002F) — Music composition for media.\n\n---\n","该项目是一个精心整理的人工智能资源列表，涵盖了AI课程、书籍、视频讲座和论文。它专注于AI工程领域，包括可检索增强生成（RAG）、代理构建、评估方法、安全措施及部署策略等内容，并提供了一系列精选工具。此外，还列举了现代与实用的书籍如《设计机器学习系统》等，以及基础性教材如《人工智能：一种现代方法》等。适合希望深入理解和实践AI技术的研究者、工程师或学生在学习和工作中参考使用。",2,"2026-06-11 03:23:53","top_topic"]