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Audit a Course-prepared small App build with AI, then test, improve, review, and hand off only what the evidence supports.
You have an idea for a small App. An AI builder can turn a few sentences into a polished screen surprisingly quickly—but a screen that looks finished may still behave differently from the brief.
This Course is a guided audit of one end-to-end, low-risk project: a mobile-first checklist for preparing and closing a community book-swap event. The Course supplies twelve synthetic tasks containing no real personal or organization data. Before each production result is revealed, you make the relevant scope, testing, repair, approval, or handoff decision and compare it with evidence from the real run.
You will first compare the main AI coding work modes and representative products, then audit one continuous Replit-to-Codex path. The recorded build begins in Replit because there is no existing code and the first need is a runnable user path with little setup. You will turn requirements into observable checks, classify the results, and use a real failure to form one bounded repair request instead of repeatedly asking AI to “make it better.”
Later, you inspect a disagreement between the visible App and its files that the Preview alone could not settle. That concrete evidence—not the idea that a Coding Agent is always better—determines whether a move to Codex is justified. You judge how the working state should be preserved, approve an exact inspection and edit scope, check the actual difference, and interpret both successful checks and checks that could not run honestly.
The learning investigation ends with a reviewable, non-public demonstration and a clear handoff, even though the original App brief is not fully satisfied. You leave with a transferable method for choosing an AI coding tool, limiting a first version, testing behavior, requesting focused changes, checking Agent work, and stopping where the evidence stops.
No. The Course assumes no programming experience and explains only the project, file, test, and change concepts needed for the decisions you make. You are still responsible for checking observable behavior and deciding what AI may change.
No. The guided Lessons use Course-prepared evidence from a real Replit-to-Codex run, so you do not need an existing codebase or local development setup to follow the investigation. Repeating the build outside the Course depends on the products' current access and terms.
The Lessons follow one supplied checklist so the build, failures, fixes, and handoff remain consistent. You can transfer the method to another small, low-risk project, but you must define and test that project's own requirements rather than treating the Course result as evidence for it.
No. The Course ends with a reviewable, non-public demonstration that still has known limitations. Any decision to publish, collect real data, or expand the App requires new checks and remains your responsibility.
Lesson 1: What Is Vibe Coding?
Decide whether a small software task fits Vibe Coding and identify the decisions that still belong to you.
Lesson 2: Choose a Vibe Coding Tool
Choose an AI coding starting point from the task's current state, setup needs, and required level of control.
Lesson 3: Plan Your First App
Turn a vague App idea into a bounded brief with a complete user path and observable success conditions.
Lesson 4: Build Your First App with Replit
Follow a bounded Replit build and inspect the first runnable Preview without treating it as a finished product.
Lesson 5: Test Your Replit App
Run observable checks against the original brief and record what passed, failed, or remains unverified.
Lesson 6: Fix One Problem with Replit
Turn one observed Reset failure into a focused repair request, then retest the repaired path without hiding what remains unknown.
Lesson 7: Hand Off a Replit Project to Codex
Inspect project evidence, identify a genuine file-level need, and prepare a bounded Replit-to-Codex handoff.
Lesson 8: Verify and Hand Off a Codex Change
Approve one exact Codex change, review its diff and checks, and hand off the working state with honest limitations.
AILesson helps AI beginners build practical, transferable skills through interactive, task-based courses.
