awesome-artificial-intelligence

# Awesome Artificial Intelligence

An opinionated, actively maintained collection of resources for software developers learning to build and ship generative AI and agentic systems.

![Artificial intelligence](https://media.giphy.com/media/jeAQYN9FfROX6/giphy.gif)

This list is for developers who want to:

- understand the foundations behind modern AI systems;
- build applications with language models, retrieval, tools, and agents;
- evaluate, observe, and deploy AI systems in production;
- use coding agents to improve software engineering work.

This is not a comprehensive directory of AI products. Every entry must clear an absolute quality bar for technical depth, practical value, evidence, and distinctiveness. Categories are not quotas, and a short category is better than one padded with weak choices.

The list is reviewed weekly by an evidence-backed automation that independently reviews, validates, and merges small changes. See [how resources are evaluated](CURATION.md).

For a guided starting point, get the [free AI Engineer starter pack](https://aiengineer.co/start), which includes agent skills, $1M+ in software discounts and tutorial code.

## Learn

### Books

- [Artificial Intelligence: A Modern Approach](https://aima.cs.berkeley.edu/): The broad reference for classical AI, including search, reasoning, planning, learning, and robotics.
- [Reinforcement Learning: An Introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf): Sutton and Barto's foundational treatment of reinforcement learning concepts and algorithms.
- [Machine Learning Bookcamp](https://www.manning.com/books/machine-learning-bookcamp): A project-based introduction to building and deploying machine learning systems by Alexey Grigorev.
- [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/): Scalable, maintainable machine learning systems by Chip Huyen.
- [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/): Building applications with foundation models by Chip Huyen.
- [Build a Large Language Model from Scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch): Implement transformers in PyTorch with Sebastian Raschka.
- [Hands-On Large Language Models](https://www.llm-book.com/): A visual and practical guide by Jay Alammar and Maarten Grootendorst.
- [LLM Engineer's Handbook](https://www.packtpub.com/en-us/product/llm-engineers-handbook-9781836200079): LLMOps, fine-tuning, serving, and production workflows.
- [The 100-Page Language Models Book](https://www.thelmbook.com/): A concise, technical introduction by Andriy Burkov.
- [Deep Learning](https://www.deeplearningbook.org/): Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- [Deep Learning: Foundations and Concepts](https://www.bishopbook.com/): A probability-grounded treatment by Christopher and Hugh Bishop.
- [Understanding Deep Learning](