On writing a handbook that refuses to sit still.
Every technology story eventually gets a tidy narrative: the lone inventor, the garage, the breakthrough, the fortune. The AI story refuses that tidiness. There is no one inventor. There is no one breakthrough. There are thousands of researchers, dozens of labs, hundreds of papers, and — if you are reading this in April 2026 — an uncomfortable sense that the road ahead is getting steeper, not flatter.
I wrote this handbook because I wanted a single place where a working developer could get the shape of what happened, the meaning of each concept, and the practice of using it. Most AI books fall into one of three traps:
This handbook tries for a middle path: linear enough to be a story, technical enough to be useful, and opinionated enough to be worth disagreeing with.
Part I is history. How we got from AlexNet in 2012 to ChatGPT in 2022 to Opus 4.7 in 2026. Read it once, then forget the dates; keep the intuitions.
Part II is the scaling era — the machinery that made modern LLMs possible, the open-source counter-movement that democratized them, and the core skills (prompting, retrieval, fine-tuning) you will use every week.
Part III is the agent era — where models stop being chatbots and start being software. Tools, multi-agent systems, and the Model Context Protocol.
Part IV is a focused profile of the Claude family, ending at Opus 4.7.
Part V is practical. What to install, what to build, how to wire AI into Python, Java, the web, GCP, and AWS.
Part VI is career. A twenty-four-month roadmap for a mid-level developer who wants to still be valuable when the agents can write most of the code.
The appendices are where you'll actually live day to day — glossary, papers, videos, communities, and a one-page cheat sheet.
As a story, front-to-back. As a reference, via the index. As a training plan, via the roadmap in Chapter 18. As a reading list, via the "Further reading" sections at the end of every chapter — those are the most important links in the handbook.
Most of all: read with a terminal open. Try every concept on a real codebase. The difference between someone who has read about RAG and someone who has shipped a RAG service is the difference between a tourist and a resident.
This handbook is dated. Anything I say about a specific product, model, or benchmark is a photograph of April 2026. The underlying concepts — tokens, attention, retrieval, tool use, agents, evals — will outlive every model mentioned here. Lean on the concepts.
I will not pretend to be neutral. Where I think a tool is good, I'll say so. Where I think a practice is a waste of time, I'll say that too. Disagree freely. The field is young enough that your opinions are as likely to be right as mine.
To the researchers who published in the open when they didn't have to. To the engineers who wrote the blogs that taught me. To the communities on Hacker News, r/LocalLLaMA, and a dozen Discords where the real-time knowledge actually lives. And to the reader, who — if you make it to the end and ship something — will have made writing this worth it.
— April 2026