What is this?
An opinionated, actively maintained collection of AI learning resources — courses, books, papers, and guides — for software developers building generative AI and agentic systems. It has over 16,600 GitHub stars.
An opinionated, actively maintained collection of AI learning resources — courses, books, papers, and guides — for software developers building generative AI and agentic systems. It has over 16,600 GitHub stars.Is this a huge directory of everything AI, or a hand-picked list?Deliberately hand-picked, not a catch-all directory: every entry must clear an absolute quality bar for technical depth, practical value, evidence, and distinctiveness, and categories are not padded just to hit quotas.
Deliberately hand-picked, not a catch-all directory: every entry must clear an absolute quality bar for technical depth, practical value, evidence, and distinctiveness, and categories are not padded just to hit quotas.What's inside, and how is the material organized?Three main sections: Learn (books, courses, foundational papers), Build AI systems (guides, agent frameworks, retrieval, evals, deployment and observability), and Agentic software engineering (coding agents, agent skills, orchestration).
Three main sections: Learn (books, courses, foundational papers), Build AI systems (guides, agent frameworks, retrieval, evals, deployment and observability), and Agentic software engineering (coding agents, agent skills, orchestration).Is everything on the list free?No — the list itself is free and MIT-licensed, but entries mix free and paid resources: the AI Engineer program is paid, while fast.ai, the Hugging Face LLM Course and Karpathy's Zero to Hero are free.
No — the list itself is free and MIT-licensed, but entries mix free and paid resources: the AI Engineer program is paid, while fast.ai, the Hugging Face LLM Course and Karpathy's Zero to Hero are free.I'm a backend dev — is this for me, or only for ML researchers?It targets working developers: people building applications with language models, retrieval, tools and agents, evaluating and deploying AI systems in production, and using coding agents for everyday engineering — not just researchers.
It targets working developers: people building applications with language models, retrieval, tools and agents, evaluating and deploying AI systems in production, and using coding agents for everyday engineering — not just researchers.Is the list still actively maintained?Yes. It is reviewed weekly by an evidence-backed automation that independently reviews, validates, and merges small changes, and entries are expected to stay current and maintained unless their value is foundational.
Yes. It is reviewed weekly by an evidence-backed automation that independently reviews, validates, and merges small changes, and entries are expected to stay current and maintained unless their value is foundational.Where should a beginner start?The README points to a free AI Engineer starter pack — agent skills, software discounts, and tutorial code — as a guided starting point. Beginner-friendly courses like fast.ai and Karpathy's Zero to Hero in the Learn section work too.
The README points to a free AI Engineer starter pack — agent skills, software discounts, and tutorial code — as a guided starting point. Beginner-friendly courses like fast.ai and Karpathy's Zero to Hero in the Learn section work too.Does it cover coding agents like Claude Code or Aider?Yes — a whole section lists terminal agents such as Claude Code, Codex CLI, Gemini CLI, Cursor CLI, Aider, OpenCode, OpenHands and Cline, and points to SWE-bench and Terminal-Bench for independent capability comparisons.
Yes — a whole section lists terminal agents such as Claude Code, Codex CLI, Gemini CLI, Cursor CLI, Aider, OpenCode, OpenHands and Cline, and points to SWE-bench and Terminal-Bench for independent capability comparisons.Which foundational papers does it include?Attention Is All You Need, Scaling Laws, Language Models are Few-Shot Learners, Retrieval-Augmented Generation, LoRA, the InstructGPT RLHF paper, ReAct, Constitutional AI, and Direct Preference Optimization.
Attention Is All You Need, Scaling Laws, Language Models are Few-Shot Learners, Retrieval-Augmented Generation, LoRA, the InstructGPT RLHF paper, ReAct, Constitutional AI, and Direct Preference Optimization.What are similar projects?transferlearning, awesome-deep-learning, DeepLearning, awesome-ai-engineering, awesome-rsi, awesome-deep-learning-papers, Machine-Learning-Tutorials, awesome-computer-vision
transferlearning, awesome-deep-learning, DeepLearning, awesome-ai-engineering, awesome-rsi, awesome-deep-learning-papers, Machine-Learning-Tutorials, awesome-computer-visionWhich topics is it in?unsupervised-learning, papers, coding-agents, reinforcement-learning, neural-network, artificial-intelligence, agentic-ai, generative-ai, learning-resources, developers, awesome-list, curated-list
unsupervised-learning, papers, coding-agents, reinforcement-learning, neural-network, artificial-intelligence, agentic-ai, generative-ai, learning-resources, developers, awesome-list, curated-list