Build a modern LLM from scratch. Every line commented. Explained like we are five.
This is a 12-chapter, 3,900+ line interactive textbook that teaches you how to build, train and run a modern language model from absolute scratch.
You won't just read about Transformers. You'll write every line yourself: tokenizer, embeddings, attention, training loop, inference engine. Every single line annotated to explain what it does and why it's there.
The goal: After finishing, you won't just know that attention "works". You'll understand the variance argument behind 1/√d_k. How RoPE captures relative position through rotation. Why pre-norm beats post-norm for deep networks. And exactly where every gradient flows during backpropagation.
