AI fundamentals, for people who already ship software
Read it once, in order,
and go one layer deeper without losing your place.
Every technical term in the prose is a link that opens a short explainer beside the text instead of navigating away. 22 topics, 92 terms, about 2 hours. It assumes you know distributed systems, caching and API design, and assumes nothing about machine learning.
Phases
How does text become something a model can compute on?
Before any model does anything interesting, your strings have to become numbers. This phase is about that conversion and everything it quietly decides for you.
Phase 2 · The model
🔒 LockedWhat is actually happening inside, and what does that cost?
A transformer is a fixed amount of arithmetic per token. Once you can count that arithmetic, latency, memory and price stop being mysterious.
Unlocks at 4 of 4 topics in Phase 1 — 0 done. Unlock anyway.
Phase 3 · Building with it
🔒 LockedHow do I compose a system around a non-deterministic component?
The model is one call in a larger system. This phase is the engineering around it: what you put in the context, what you get back, and how you know it works.
Unlocks at 5 of 6 topics in Phase 2 — 0 done. Unlock anyway.
Phase 4 · Systems
🔒 LockedHow do I run this in production?
Loops, tools, queues, budgets and the failure modes that only show up under load. Ordinary distributed systems work, with one unusual component in the middle.
Unlocks at 5 of 6 topics in Phase 3 — 0 done. Unlock anyway.
Local-first
Progress, highlights and notes live in this browser. No account, no server, no telemetry. Export them to Markdown or JSON whenever you like.
Level-gated, not locked
A phase opens at 80% of the one before it. If you already know the material, there is an unlock switch in settings — the progress data stays meaningful either way.
A diagram per concept
Every topic carries one hand-drawn picture of the mental model, placed before the detail rather than after it, because a diagram is scaffolding and not decoration.