The library
Pick a track to see its full contents.
Short course · intro
Neural Networks
Build a neural network from its smallest piece, one neuron at a time.
9 parts · ~51 min View contents →
Course · intro
How Language Models Actually Work
A first-principles path through tokens, embeddings, attention, and the machinery behind LLMs, built up step by step.
15 lessons · ~83 min View contents →
Course · intro
Prompt Design
Write prompts that get reliable, repeatable output from any language model: the anatomy of a prompt, clear instructions, roles, and in-context learning, from first principles.
15 lessons · ~85 min View contents →
Short course · intro
Introduction to LLM Agents
Build a working agent from scratch in plain Python, one piece at a time: a tool, the reasoning step, the call, and the loop. Then see where plain loops break and why frameworks exist.
7 parts · ~31 min View contents →
Short course · intermediate
Building Production Agents with LangGraph
Take a working agent and make it hold up in production: clean state, explicit graphs, reliable tools, recoverable failures, and the observability and hygiene that keep it maintainable.
9 parts · ~60 min View contents →
Paper explainer · intermediate
Attention Is All You Need
A visual, plain-language walkthrough of the 2017 paper that introduced the transformer, from the limits of RNNs to the architecture behind every modern LLM.
9 parts · ~27 min View contents →
Short course ☕ Brewing
Diffusion & Image Models
How models paint a picture by reversing noise, step by step.
Coming soon
Short course ☕ Brewing
Retrieval-Augmented Generation
Give a model your own documents so its answers stay grounded in your facts.
Coming soon
Course ☕ Brewing
Training & Fine-Tuning
How models learn from data, and how to adapt a pretrained model to your own task.
Coming soon