
How to use Cursor AI to build an app without a mess
“Build me an app” is how Cursor invents a stack, extra packages, and no tests. Here is the small-app loop I actually use: one slice, your stack, a fence, then Chat, Composer, review.
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“Build me an app” is how Cursor invents a stack, extra packages, and no tests. Here is the small-app loop I actually use: one slice, your stack, a fence, then Chat, Composer, review.

Orang googling cara menggunakan Cursor AI, skip pagar, lalu Accept. Seminggu kemudian repo klien penuh package baru, utils2, dan diff yang nggak ada yang berani review. Ini loop yang saya pakai.

Empty rules plus “build me X” is how Cursor invents a package your repo does not have. Project rules are the fence. Here is what I actually put in them on client work.

Cursor Chat vs Composer: I open Chat when I am lost, Composer when I can name the files. Same model, different job. My loop on real repos.

Effective Cursor use is not a greenfield demo. It is a messy client repo: monorepo, local conventions, folders you must not touch. Here is how I scope, rule, and review so the PR still looks like the codebase a month later.

Most Cursor AI tutorials start at hello world. This one does not. If you already ship production code and you just opened Cursor, here is the first-week loop I teach: plan, scope, generate, review. Chat vs agent, one invoices CSV example, and the mistakes that make new users look fast and messy.

Orang googling apa itu Cursor AI lalu dapat jawaban “chatbot buat nge-code”. Kurang pas. Cursor itu editor. AI-nya hidup di dalam proyek yang kamu buka, bukan di tab browser. Ini penjelasan praktis yang saya kasih ke mentee sebelum mereka install.

People treat cursor ai vs copilot like a brand war. I treat it as a job split. Cursor for the agent, multi-file edits, and repo chat. Copilot when inline complete in VS Code is already enough. This is how I pick for client work.

Most people google Cursor AI pricing, see $20, and assume that is the bill. It is not. You pay a plan fee, you get a pool of included model usage, and you can go on-demand after that pool is gone.

How to use Cursor AI in a practical production workflow: plan, scoped diffs, hard review, and tests, so you ship faster without wrecking the codebase.

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