In this video, I tried to recreate that “simple app making insane money” vibe using a super practical stack: ChatGPT for prompt engineering, Antigravity for generating the React app, and Supabase for auth + database. I start by writing a messy, human-style idea (dashboard, today’s workout logger, BMI tracking by date, goals + veg/non-veg diet suggestions, history, weekly/monthly reports). Then I use ChatGPT to convert that into a clean, well-defined prompt—because the tighter the prompt, the less time you waste fixing random AI mistakes later. Once I paste the prompt into Antigravity, it generates an implementation plan, asks a few clarifying questions, and then builds the app UI with cards for calories, exercises, active time, BMI, goals, streaks, and weekly score. At first, the data sits in local storage (so it breaks across devices), so I push it further: I tell Antigravity to add login/signup + sign out, then connect Supabase using the project URL and anon public key. Since tables don’t exist automatically, I ask for SQL, run it in Supabase, and verify everything is actually writing to tables like BMI history, goals, profiles, workouts, and workout exercises. The main takeaway: prompt precision is the multiplier—ChatGPT helps you ship faster with fewer debug loops.