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--- visibility: public --- # Artificial Intelligence **repo:** [owainlewis/awesome-artificial-intelligence](https://github.com/owainlewis/awesome-artificial-intelligence) **category:** [[theory|Theory]] **related:** [[deep-learning|Deep Learning]] · [[machine-learning|Machine Learning]] --- # Awesome Artificial Intelligence A curated collection of **must-use, actively maintained resources** for building and shipping AI systems. Focus: **AI engineering** (RAG, agents, evals, guardrails, deploy) plus the best books, guides, papers, and a *carefully selected* set of tools.  --- ## 🏛 Core Resources (Evergreen) _The foundations — these will still be valuable five years from now, even if today’s tools are gone._ ### 📚 Books **Modern & Practical** - [Designing [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/) — Scalable, maintainable ML pipelines (Chip Huyen). - [Generative [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) (2nd Edition)](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/) — GANs, VAEs, diffusion models (David Foster). - [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/) — End-to-end AI product building (Chip Huyen). - [100 Page Language Models Book](https://www.thelmbook.com/) — This book guides you through the evolution of language models, starting from [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) fundamentals. **Foundational** - [Artificial Intelligence: A Modern Approach](https://aima.cs.berkeley.edu/) — Comprehensive AI theory (Russell & Norvig). - [Deep Learning](https://www.deeplearningbook.org/) — Neural networks & architectures (Goodfellow, Bengio, Courville). - [Reinforcement [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning): An Introduction (2nd Edition)](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf) — RL fundamentals (Sutton & Barto). --- ### 🏗 AI Engineering _Frameworks and design patterns for building robust, production-grade AI systems._ _Personal note: you don't need tons of frameworks — start with simple LLM calls and work up._ #### 📖 Guides & Playbooks - **[Building Effective Agents (Anthropic)](https://www.anthropic.com/engineering/building-effective-agents)** — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents. - [OpenAI Agents Guide](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf) — Practical guide on building agents - [Google AI Agents Paper](https://www.kaggle.com/whitepaper-agents) - Practical guide to building AI agents from Google - [Google Agents Companion Paper](https://www.kaggle.com/whitepaper-agent-companion) - Guide from Google - [OpenAI Cookbook](https://cookbook.openai.com/) — Example code, recipes, and best practices for working with OpenAI APIs. - [LLM Engineer Handbook](https://github.com/SylphAI-Inc/LLM-engineer-handbook) — A goldmine of useful links for AI engineers #### 🤖 Frameworks - [PocketFlow](https://the-pocket.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/PocketFlow/) — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn. - [Google ADK](https://google.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/adk-docs/) — Google's Agent Development Kit (Python, Java). Great local development experience + A2A + MCP. - [Pydantic-AI](https://ai.pydantic.dev/) — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs. - [LangGraph](https://www.langchain.com/langgraph) — Build multi-agent workflows with stateful graphs on top of LangChain. - [CrewAI](https://www.crewai.com/) — Agent orchestration with structured tasks and human-in-the-loop controls. - [AutoGen](https://microsoft.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/autogen/) — Microsoft’s framework for multi-agent conversation and collaboration. #### 📦 Retrieval-Augmented Generation (RAG) - [LlamaIndex](https://www.llamaindex.ai/) — Data framework for ingesting, indexing, and querying private data with LLMs. - [Haystack](https://haystack.deepset.ai/) — Open-source search/RAG framework with modular pipelines. - [Docling](https://github.com/docling-project/docling) — Great library for ingesting any kind of document for RAG ⭐ #### Evals - [OpenAI Evals](https://github.com/openai/evals) — OpenAI's framework for writing evals --- ### 📄 Landmark Papers _Research that shaped modern AI — worth reading to understand the "why" behind today’s architectures._ - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) — Transformer architecture. - [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) — Model/data/compute scaling. - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) — GPT-3 capabilities. - [Constitutional AI](https://arxiv.org/abs/2212.08073) — Safer model alignment. --- ## 🎓 Courses _Learn from the best — structured content for every level._ **Beginner** - [Google [Generative AI](/@harrisonqian/awesome/wiki/computer-science/generative-ai) [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Path](https://www.cloudskillsboost.google/paths/118) - [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter1/1) - [Fast.ai — Practical Deep Learning](https://course.fast.ai/) **Intermediate / Advanced** - [Stanford CS324: Large Language Models](https://stanford-cs324.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/winter2022/) - [Full Stack Deep Learning](https://fullstackdeeplearning.com/) - [MIT 6.S191: Intro to Deep Learning](https://introtodeeplearning.com/) **Focused** - [DeepLearning.AI Short Courses](https://learn.deeplearning.ai/) - [Google Deepmind| Introduction to Reinforcement Learning](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) - [Karpathy’s LLM Zero-to-Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) - [Neural Nets - Zero-to-Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) --- ## 📰 Newsletters _Stay current with AI developments without drowning in noise._ - [The Rundown AI](https://www.therundown.ai/) - [AlphaSignal](https://alphasignal.ai/) - [Superhuman AI](https://www.superhuman.ai/) - [AI Engineer](https://newsletter.owainlewis.com) ## ⚡ Tools Tools for building and deploying AI applications. ### 💬 Models - [ChatGPT](https://openai.com/chatgpt/overview/) — Best for general coding + reasoning. - [Claude](https://www.anthropic.com/claude) — Best for long-context analysis and structured thinking. - [Gemini](https://gemini.google.com/) — Best for Google ecosystem [integration](/@harrisonqian/awesome/wiki/platforms/integration). - [Perplexity](https://www.perplexity.ai/) — Best for quick research with live citations. - [Cohere](https://cohere.com/) — Best for enterprise LLMs with strong retrieval-augmented generation APIs. - [Mistral](https://mistral.ai/) — Best for lightweight, high-performance open-weight models. - [Qwen](https://qwenlm.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) — Best for multilingual and Chinese-first applications. - [DeepSeek](https://deepseek.com/) — Best for efficient, cost-optimized large models with competitive reasoning. ### 👨💻 Code & Developer Tools - [Claude Code](https://www.anthropic.com/claude) — IDE extensions with long-context code edits. - [GitHub Copilot](https://github.com/features/copilot) — In-IDE code completion, chat, and refactors. - [Cursor](https://cursor.sh/) — LLM-powered IDE for multi-file edits and codebase-aware chat. ### 🎨 Multimedia AI Tools #### 🖼 Image - [ChatGPT-4o Image Generation](https://openai.com/chatgpt) — Integrated image creation with style control. - [Midjourney](https://www.midjourney.com/) — Artistic and photorealistic images and video. - [Adobe Firefly](https://www.adobe.com/sensei/generative-ai/firefly.html) — Integrated into Creative Cloud. - [Ideogram](https://ideogram.ai/) — Precise, legible text in generated images. - [Flux](https://blackforestlabs.ai/) — High-res, prompt-editable images. #### 🎥 Video - [Kling](https://klingai.com/) — Cinematic, realistic video generation. - [Google Veo 3](https://deepmind.google/technologies/veo/) — High-quality video with synchronized audio. - [Runway](https://runwayml.com/) — Video editing + generation. #### 🎙 Audio - [ElevenLabs](https://elevenlabs.io/) — High-quality text-to-speech. - [Suno](https://suno.ai/) — AI [music](/@harrisonqian/awesome/wiki/media/music) from text prompts. - [Aiva](https://www.aiva.ai/) — [Music](/@harrisonqian/awesome/wiki/media/music) composition for media. ---