TL;DR: I built VibeBoost, a full X growth tool with auth, payments, a live X API integration and a Cloudflare deploy, in Lovable, as someone who can't write production code. The method that actually worked: build one screen at a time, hand it exact component code (not vague descriptions) when you care about the result, keep files small, and treat every fix as "change this one thing." Here's the real teardown, gotchas included.
Most "I built X with AI" posts are hype. This is the boring, specific version, because the specifics are the whole value.
The core method: one screen, one prompt, verify, repeat
The single biggest mistake with any AI builder is asking for too much at once. "Build me an X growth tool" gives you an unfixable mess. What worked was treating the app as a stack of screens and building them one at a time:
- Describe one screen in plain language, with the states it needs.
- Let it build, then actually click through it.
- Fix exactly one thing at a time until it's right.
- Only then move to the next screen.
Slow feels wrong. It's the fastest path, because you never end up with a giant broken thing you can't debug.
When to describe, and when to hand it exact code
This is the lesson that saved me the most time. There are two modes:
- Describe it when you're exploring or the exact look doesn't matter yet. Good for a first pass.
- Hand it the exact component code when you know precisely what you want. If I already had the markup and styles for a card, I pasted the real component and said "build this, wire it to real data," instead of describing it and watching the AI drift.
Early on I described things and the output kept drifting from what I wanted, small font changes, spacing off, the wrong interaction. The fix was to stop describing and start handing over the exact code, then let it do the wiring. Descriptions are for ideas. Code is for pixels.
Screen by screen
The reply session (the core). This is the one screen the whole product lives or dies on. I built it in isolation first: the source post rendered like a real tweet, the drafted reply, the "copy and reply on X" button, the refine controls. I handed over the exact card component so it looked right, then wired it to the real generation function. Everything else is decoration around this one moment.
Auth and billing. Handed to the AI as standard flows (Supabase auth, Stripe checkout with a card-required trial). This is where AI builders shine, the boring plumbing that used to eat weeks is a few prompts.
The dashboard, insights, schedule, studio. Built each as its own screen, reusing shared components. The trick was making a small set of styled building blocks (a card, a stat, a tab bar) once, then telling the AI to use them everywhere, so the app stayed consistent instead of every screen looking slightly different.
Settings and onboarding. Built last, because they're where you wire together everything else. Onboarding is where I later found my biggest leak: it set up a watchlist but dropped the user before the first drafted reply, so most signups never saw the magic. That's a product lesson, not a Lovable one, but it's the kind of thing you only see once the screens exist and you watch real behavior.
The gotchas nobody warns you about
- Drift. The AI slowly wanders from your intent across prompts. Counter it by handing over exact code for anything you care about, and by verifying every screen instead of trusting the summary.
- Big files break tooling. Some of my files got large enough that they couldn't be read in one pass. Keep components small and split logic into separate files. Future-you debugging will thank present-you.
- Dev vs production differences. My server entry ran fine in dev but I had to confirm the code actually got bundled into the production worker. Always verify the deployed thing, not just the preview.
- Costs hide in the plumbing. The API integration the AI wired up worked, but it wasn't cost-aware. It re-read data it already had. AI builds features fast, it does not think about your bill. That's on you.
- Publishing is a separate step. Building in the editor is not the same as it being live. Deploy, then verify the real URL.
What I'd do differently
Instrument costs from the first API call, not after the bill scared me. Build the onboarding-to-first-value path early instead of last. And commit to handing over exact code sooner, I wasted days describing things and fighting drift before I learned that lesson.
FAQ
Do you need to know how to code to use Lovable? No, but you need product judgment and the patience to verify and direct. You'll also hit real engineering problems (costs, deploys) that need thinking.
How do you stop the AI from breaking working features? Change one thing at a time, verify after each change, and keep files small so a fix can't ripple everywhere.
Is the output actually production-ready? It runs a live product with paying users. It's not flawless, you direct it and catch its mistakes, but it's real.