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Tools for AI work

Start with these 16

• Intro to Large Language Models: Karpathy's one-hour talk

  A single free talk that explains an LLM as an artifact (a big parameter file plus a small program that runs it), how training and fine-tuning produce one, and where the security problems come from. Start here if you use models daily but have never looked inside.

  youtube.comLLMsBeginner

• The Illustrated Transformer

  Jay Alammar's diagram-driven walkthrough of the architecture from the original transformer paper: self-attention, multi-head attention, positional encoding and the encoder and decoder stacks. Read it before the paper, then keep it open next to the paper.

  jalammar.github.ioLLMsIntermediate

• Attention Is All You Need, the original transformer paper

  The 2017 paper that introduced the transformer and dropped recurrence in favour of attention. Dense in places, but short, and nearly every modern language model descends from it. Pair it with the illustrated walkthrough.

  arxiv.orgPapersAdvanced

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

  The paper that named RAG: a generator combined with a dense vector index over Wikipedia, trained together. Useful for understanding what the acronym originally meant before it became a catch-all for "put documents in the prompt".

  arxiv.orgPapersIntermediate

• Hugging Face LLM Course

  A free, hands-on course on using and fine-tuning transformer models with the Hugging Face libraries. Good first stop if you want to move from calling an API to understanding tokenizers, models and training loops.

  huggingface.coCoursesBeginner

• Hugging Face Agents Course

  A free course on building, running and evaluating AI agents, with practical units rather than only theory. Reach for it when you want a structured path into agents instead of piecing blog posts together.

  huggingface.coCoursesBeginner

• Practical Deep Learning for Coders (fast.ai)

  A free, code-first course that teaches deep learning top-down: you build working models early and learn the theory underneath as you go. A strong fit for developers who learn by running things.

  course.fast.aiCoursesIntermediate

• Neural Networks: Zero to Hero

  Free video series by Andrej Karpathy that builds from backpropagation by hand up to a GPT-style language model in code. Slow and rewarding: the best way to make the maths feel concrete if you are willing to type along.

  karpathy.aiCoursesIntermediate

• Generative AI for Beginners from Microsoft

  A free, open-source 21-lesson course. Lessons are either "Learn" (concepts) or "Build" (code in Python and TypeScript where possible). Pick the lessons you need; they do not have to be taken in order.

  github.comCoursesBeginner

• Prompt Engineering Guide by DAIR.AI

  A free, community-maintained reference covering prompting techniques, with examples and links to the papers behind them. Use it as a lookup when you want to name and try a technique such as few-shot, chain-of-thought or ReAct.

  promptingguide.aiPromptingBeginner

• Anthropic prompt engineering documentation

  Vendor docs, but the habits transfer to other models: be explicit, show examples, structure your inputs clearly, and iterate against a small set of test cases. Free to read; running the examples needs API access.

  platform.claude.comPromptingBeginner

• OpenAI prompt engineering guide

  OpenAI's official guidance on writing prompts and structuring messages. Read it alongside another vendor's guide: where the two agree, you can trust the advice to travel across models. Free to read; API use is billed.

  developers.openai.comPromptingBeginner

• Building Effective Agents: workflows vs agents

  A calm essay that separates predefined workflows from model-directed agents and lays out five composable patterns. Its main advice is to start with the simplest thing that works and add complexity only when it measurably helps.

  anthropic.comAgentsIntermediate

• LLM Powered Autonomous Agents by Lilian Weng

  A thorough overview of agents built around a language model, organised around planning, memory and tool use, with pointers to the research behind each. A good map of the territory before you pick a framework.

  lilianweng.github.ioAgentsIntermediate

• LangGraph: stateful, long-running agent orchestration

  MIT-licensed, low-level framework for agents modelled as stateful graphs, with durable execution so a run can resume after a failure. Reach for it when a plain loop stops being enough and you need control over state and flow.

  github.comAgentsAdvanced

• Model Context Protocol: the official introduction

  Home of the open protocol that lets AI applications connect to tools, data and prompts through a common interface. Start with the introduction and architecture pages, then build a small server of your own.

  modelcontextprotocol.ioMCPBeginner