An interactive, full-screen field guide to mechanistic interpretability — reading a transformer not as a stack of layers but as a residual stream that named circuits read from and write to. Framed the way you'd reverse-engineer a piece of malware.
One complete, practical guide to fine-tuning a language model for your own task — when to fine-tune versus prompt or retrieve, why you adapt a small slice (LoRA) rather than retrain, the five steps end to end, the settings that matter, and a full production case study where a fine-tuned model replaced a general LLM agent on a narrow job.
A section-by-section walk-through of the methodology in "Cost Modeling for Public-Facing LLM Chat Applications" — the naive per-token formula and why it lies, the structural corrections that break it (caching, traffic shape, segments, daily caps), the six equations that replace it, and the worked geospatial-Q&A example that ties them together.
Twenty-three interactive chapters that walk language models end-to-end — from what tokens are and how attention works, through retrieval and context engineering, to the production techniques (caching, tools, evals), inference and serving, and the choice of model itself, closing with foundation models beyond language. Read in order or jump to the chapter that matches what you are building.
A single-page, progressive tutorial for engineers and analysts who want to build their own AI-agent cost calculator. Starts at "what is a token?" and ends at a reference architecture you can clone — data model, formula library, and a validation harness. Implementation-agnostic; one worked reference lives at calc.ajinkya.ai.