Awesome Artificial IntelligenceA 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.📚 BooksModern & Practical - Designing Machine Learning Systems — Scalable, maintainable ML pipelines (Chip Huyen). - Generative Deep Learning (2nd Edition) — GANs, VAEs, diffusion models (David Foster). - AI Engineering — End-to-end AI product building (Chip Huyen). - 100 Page Language Models Book — This book guides you through the evolution of language models, starting from machine learning fundamentals.Foundational - Artificial Intelligence: A Modern Approach — Comprehensive AI theory (Russell & Norvig). - Deep Learning — Neural networks & architectures (Goodfellow, Bengio, Courville). - Reinforcement Learning: An Introduction (2nd Edition) — RL fundamentals (Sutton & Barto).🏗 AI EngineeringFrameworks 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) — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents.

OpenAI Agents Guide — Practical guide on building agents

Google AI Agents Paper - Practical guide to building AI agents from Google

Google Agents Companion Paper - Guide from Google

OpenAI Cookbook — Example code, recipes, and best practices for working with OpenAI APIs.

LLM Engineer Handbook — A goldmine of useful links for AI engineers

🤖 Frameworks

PocketFlow — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn.

Google ADK — Google's Agent Development Kit (Python, Java). Great local development experience + A2A + MCP.

Pydantic-AI — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs.

LangGraph — Build multi-agent workflows with stateful graphs on top of LangChain.

CrewAI — Agent orchestration with structured tasks and human-in-the-loop controls.

AutoGen — Microsoft’s framework for multi-agent conversation and collaboration.

📦 Retrieval-Augmented Generation (RAG)

LlamaIndex — Data framework for ingesting, indexing, and querying private data with LLMs.

Haystack — Open-source search/RAG framework with modular pipelines.

Docling — Great library for ingesting any kind of document for RAG ⭐

Evals

OpenAI Evals — OpenAI's framework for writing evals

📄 Landmark PapersResearch that shaped modern AI — worth reading to understand the "why" behind today’s architectures. - Attention Is All You Need — Transformer architecture. - Scaling Laws for Neural Language Models — Model/data/compute scaling. - Language Models are Few-Shot Learners — GPT-3 capabilities. - Constitutional AI — Safer model alignment.🎓 CoursesLearn from the best — structured content for every level.Beginner - Google Generative AI Learning Path - Hugging Face LLM Course - Fast.ai — Practical Deep LearningIntermediate / Advanced - Stanford CS324: Large Language Models - Full Stack Deep Learning - MIT 6.S191: Intro to Deep LearningFocused - DeepLearning.AI Short Courses

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 Systems — Scalable, maintainable ML pipelines (Chip Huyen). - Generative Deep Learning (2nd Edition) — GANs, VAEs, diffusion models (David Foster). - AI Engineering — End-to-end AI product building (Chip Huyen). - 100 Page Language Models Book — This book guides you through the evolution of language models, starting from machine learning fundamentals.

Foundational - Artificial Intelligence: A Modern Approach — Comprehensive AI theory (Russell & Norvig). - Deep Learning — Neural networks & architectures (Goodfellow, Bengio, Courville). - Reinforcement Learning: An Introduction (2nd Edition) — 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) — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents.
OpenAI Agents Guide — Practical guide on building agents
Google AI Agents Paper - Practical guide to building AI agents from Google
Google Agents Companion Paper - Guide from Google
OpenAI Cookbook — Example code, recipes, and best practices for working with OpenAI APIs.
LLM Engineer Handbook — A goldmine of useful links for AI engineers

🤖 Frameworks

PocketFlow — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn.
Google ADK — Google's Agent Development Kit (Python, Java). Great local development experience + A2A + MCP.
Pydantic-AI — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs.
LangGraph — Build multi-agent workflows with stateful graphs on top of LangChain.
CrewAI — Agent orchestration with structured tasks and human-in-the-loop controls.
AutoGen — Microsoft’s framework for multi-agent conversation and collaboration.

📦 Retrieval-Augmented Generation (RAG)

LlamaIndex — Data framework for ingesting, indexing, and querying private data with LLMs.
Haystack — Open-source search/RAG framework with modular pipelines.
Docling — Great library for ingesting any kind of document for RAG ⭐

Evals

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 — Transformer architecture. - Scaling Laws for Neural Language Models — Model/data/compute scaling. - Language Models are Few-Shot Learners — GPT-3 capabilities. - Constitutional AI — Safer model alignment.

🎓 Courses

Learn from the best — structured content for every level.

Beginner - Google Generative AI Learning Path - Hugging Face LLM Course - Fast.ai — Practical Deep Learning

Intermediate / Advanced - Stanford CS324: Large Language Models - Full Stack Deep Learning - MIT 6.S191: Intro to Deep Learning

Focused - DeepLearning.AI Short Courses