Awesome Artificial Intelligence
An opinionated, actively maintained collection of resources for software developers learning to build and ship generative AI and agentic systems.
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.
For a guided starting point, get the free AI Engineer starter pack, which includes agent skills, $1M+ in software discounts and tutorial code.
Learn
Books
- Artificial Intelligence: A Modern Approach: The broad reference for classical AI, including search, reasoning, planning, learning, and robotics.
- Reinforcement Learning: An Introduction: Sutton and Barto's foundational treatment of reinforcement learning concepts and algorithms.
- Machine Learning Bookcamp: A project-based introduction to building and deploying machine learning systems by Alexey Grigorev.
- Designing Machine Learning Systems: Scalable, maintainable machine learning systems by Chip Huyen.
- AI Engineering: Building applications with foundation models by Chip Huyen.
- Build a Large Language Model from Scratch: Implement transformers in PyTorch with Sebastian Raschka.
- Hands-On Large Language Models: A visual and practical guide by Jay Alammar and Maarten Grootendorst.
- LLM Engineer's Handbook: LLMOps, fine-tuning, serving, and production workflows.
- The 100-Page Language Models Book: A concise, technical introduction by Andriy Burkov.
- Deep Learning: Mathematical foundations by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
- Deep Learning: Foundations and Concepts: A probability-grounded treatment by Christopher and Hugh Bishop.
- Understanding Deep Learning: Theory, intuition, and practical notebooks by Simon Prince.
- Speech and Language Processing: The continuously updated NLP reference by Dan Jurafsky and James Martin.
Courses
Foundational papers
Build AI systems
Guides and playbooks
LLM application engineering
Protocols and interoperability
- Model Context Protocol: The open specification for connecting AI applications to external tools, data sources, prompts, and interactive apps.
- Agent2Agent Protocol: A vendor-neutral specification for agent discovery, task delegation, streaming, asynchronous updates, and cross-platform communication.
Agent frameworks
Durable and asynchronous agents
Retrieval and data
- LlamaIndex: Data ingestion, indexing, retrieval, and agent workflows.
- Haystack: Modular pipelines for retrieval and generative AI applications.
- Docling: Document parsing and conversion for AI applications.
Evals and reliability
- Demystifying Evals for AI Agents: A practical method for building task suites, graders, transcripts, and evaluation harnesses.
- OpenAI Evals: An open-source framework and registry for evaluating language models and systems.
- Promptfoo: Test cases, assertions, model comparisons, and red-team checks for LLM applications.
- Ragas: Evaluation and experimentation for retrieval and generative AI applications.
Deployment and observability
- Langfuse: Tracing, evaluation, prompt management, and metrics for LLM applications.
- vLLM: An inference and serving engine for language models.
- LiteLLM: A model gateway and unified interface for multiple model providers.
Agentic software engineering
Coding agents help developers plan, implement, review, test, and debug software. For independent capability comparisons, see SWE-bench and Terminal-Bench.
Coding agents
- Neo: An open-source, workflow-first terminal coding agent with subagents, skills, sandboxed tools, and multiple model providers.
- Claude Code: A terminal agent with hooks, subagents, skills, and repository-level instructions.
- Codex CLI: An open-source terminal agent with sandbox and approval controls.
- Gemini CLI: An open-source terminal agent built around Gemini and extensible tools.
- Cursor CLI: A terminal agent connected to Cursor's editor and cloud workflows.
- GitHub Copilot coding agent: An asynchronous agent that works from GitHub issues and opens pull requests.
- Aider: An open-source pair programmer with Git integration and broad model support.
- OpenCode: An open-source, provider-independent terminal agent with a client-server architecture.
- OpenHands: An open-source platform for running software development agents locally or in the cloud.
- Cline: An open-source coding agent available as an editor extension, CLI, and SDK.
- Continue: Open-source coding agents for IDE and CI workflows with source-controlled configuration.
For a worked implementation, watch Build Your Own Coding Agent Like Pi, which builds a minimal Go harness around the agent loop, tools, files, and terminal commands.
Agent skills and workflows
- Blueprint: An open-source set of focused agent skills for designing, implementing, testing, reviewing, and shipping software changes.
Software factories and agent orchestration
Factory is a developer-preview control plane for scheduling and coordinating Pi, Codex, and Claude Code workers across Git repositories.
Contributing
Suggestions are welcome, but this list is intentionally selective. Read the contribution guide and the curation policy before opening an issue or pull request.
A proposed resource should:
- serve software developers learning or practising AI engineering;
- provide technical or practical value beyond a product homepage;
- be current and maintained, unless its value is foundational and durable;
- add something meaningfully different from the existing entries;
- use a factual description supported by a primary source.
Explain which developer problem the resource solves and why it clears the rubric. If it overlaps an existing entry, explain why it is materially better. Disclose any affiliation with the resource.
For ongoing practical tutorials about coding agents and AI engineering, visit the AI Engineer YouTube channel.
一份立场鲜明、持续活跃维护的 AI 学习资源精选合集——涵盖课程、书籍、论文和指南——面向构建生成式 AI 和智能体系统的软件开发者。它在 GitHub 上已有超过 16,600 颗星。