CodeFast

You start by shrinking your idea to a small, usable slice—a single screen, a simple flow, a first version that proves value. The lessons give you just enough context to move, then you build inside your own project so every minute pushes the product forward.

A typical working block looks like this: watch a short clip to learn a pattern, apply it immediately, ask the AI assistant to scaffold a component or clarify an approach, run the app, and fix what breaks. You repeat this loop for navigation, UI state, basic data handling, and simple server endpoints until a thin, end‑to‑end path is working.

When errors show up, you paste logs or snippets into the AI and request a diagnosis and a minimal fix. If performance drags or the code gets messy, you ask for a refactor plan and safer tests. When a feature feels unclear, you prompt for alternative implementations and pick the smallest option that ships today. This keeps you moving without detouring into theory.

Once a core slice is stable, you push to production. The workflow covers prepping environment settings, running a build, deploying, and sanity‑checking the live app. From there, you iterate: log feedback, queue tiny improvements, and let the AI help generate patches, test cases, and release notes so you can ship more often.

If you hit a wall, you post the problem to the private community or ask the instructor for direction and an example that unblocks you. Over a few focused weeks, this rhythm turns a rough concept into something customers can use, and you leave with a process you can run again for the next feature—or the next product.