[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODIyODA","title","The Reluctant Engineer","state","published","contactAuthorMethod","email","aboutText","

You're not wrong about the slop. You're wrong about where it comes from.

\"Vibe coding\" does ship garbage. Half the demos die at the first edge case. The threads are

unbearable. Keep that radar on, well-calibrated skepticism is engineering.

But slop is a process defect, not a property of the model. It shows up exactly where

verification, permission layers, a clean context window, and someone actually reading the diff

are missing. \"AI writes crap code\" is like \"juniors write crap code\": sometimes true, and the

answer was never to fire the juniors. It was code review, tests, and clear boundaries.

This book builds that process around a non-deterministic tool.

It will not ask you to lower your standards or become an enthusiast. It asks the opposite:

raise the controls. Permission layers, verification, clean context, and a hard line between

what has to be deterministic and what can't be, then use Claude Code where it actually gives

leverage, without letting it near your craft.

What you'll work through

Seven chapters, dense on purpose:

You're half right: where your skepticism is correct, where it's costing you, and why the model is the same for everyone while the context isn't.
Keep control: API keys, `.env`, the permission modes and the one that will bite you, where your data actually goes, trust levels, agent teams, scheduled automations, and token and prompt-cache economics.
Keep quality: the four levers of prompting, with verification as the one that moves a first attempt from \"60% and you fix it by hand\" to \"80% and you review it\"; taste, and iteration speed as the real differentiator
Context is the job: prompt engineering versus context engineering, managing the window before it degrades, and the three different things people mean by \"memory\".
Orchestration: sub-agents that burn tokens in *their own* window and hand back only a summary; skills that encode a process once and load by progressive disclosure.
Ship with guardrails: CLAUDE.md and the .claude folder as real project configuration, and a full path from commit to something live.
Systems that compound: a second brain in five levels, and the role you're left holding at the end of all this: senior reviewer.

Plus a plain-language glossary of every term used, and a resources chapter of hand-picked, verified links to official docs and tools.

Who it's for

Engineers who already know how to build things and have no interest in an \"AI is magic\" pitch.

Beginner basics are deliberately stripped out: this edition is around 16,000 words and assumes

you can read a diff, live in a terminal, and smell a bad abstraction from across the room. If

you want the longer, gentler path, look at the other titles in the AI-Native Series.

How it's built

Every lesson ends with hands-on exercises you run for real inside Claude Code, plus a short

\"check yourself\" quiz with the answers inline. Keep a terminal open next to the book.

\"You can outsource thinking, but you can never outsource understanding.\"

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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