Most developers use an AI editor the way they used autocomplete: type a little, accept what appears, and hope. The results feel fast and quietly rot bloated context, unreviewed changes, refactors nobody can explain. This book replaces that habit with a deliberate, advanced workflow built around one real project.

The project is Ledgerline, a small TypeScript invoice engine that reads usage events, validates them, prices them in integer cents, and renders invoices. It is narrow enough to reason about and rich enough to expose every failure mode that matters: context limits, unsafe refactors, runtime bugs, weak tests, and maintenance drift.

Each chapter installs one professional discipline. You will treat context as a budget, write rules that genuinely change the model’s behavior, and plan before you build. Then you will refactor without regret, debug from evidence, test the contract your code promises, and review the machine’s work the way a senior engineer reviews a colleague’s.

Inside, you will find:
A deliberate workspace and context treated as a budget
Editor rules that actually change the AI’s behavior
Planning before building, then refactoring without regret
Debugging with evidence instead of guesses
Testing the contract and reviewing the machine’s work
Running agents in parallel and closing the verification loop
Automation with skills and hooks while protecting code and data

This book is for developers who already write code and already use Cursor, and who want output they can defend in review. It assumes programming experience, not AI expertise, and every technique is shown on the Ledgerline codebase rather than in the abstract.

Work through it and your editor stops being a slot machine. It becomes an instrument: predictable inputs, verified outputs, and a workflow you can evaluate and improve over time. An independent guide, not affiliated with or endorsed by Anysphere.

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