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I have spent the last year shipping AI-generated apps — building one of them, even. Some of what I expected was right. A lot was wrong. Here is the version I would send to a friend asking whether AI builders are real or hype.
Describe the app you need — we’ll build it now.
We started the prompt for you. Edit it, or describe your own app.
Where AI builders really win #
- First drafts. The blank page is the most expensive part of building. An agent that hands you a working scaffold in 90 seconds changes the calculus.
- UI polish you would otherwise skip. Empty states, hover states, edge case rendering — the things humans skip because of time, agents do because they are part of the description.
- Translation work. "Make this page match that one" and "convert this design to React" used to be 4-hour tasks. Now they are minutes.
Where they collapse #
- Long-running multi-developer maintenance. Generated code is not magically more maintainable. After three months, somebody has to actually read it.
- Anything load-bearing for security. "The agent will write the access control" is how you end up with a leaked admin endpoint.
- Domain decisions disguised as code. The agent can write the code; it cannot decide what your business rule should be.
The backend trick almost nobody talks about #
Most AI builders try to generate the entire stack — UI, data, auth, deployment. That is the wrong split. The frontend is where AI generation is genuinely a productivity unlock; the backend is where you want a battle-tested, governed, opinionated system you can reason about.
Vibe takes that side. The data, the roles, the workflows, the PDFs — all live in Tadabase. The agent only writes the frontend that talks to it. The result: AI-speed builds with the security posture of a real platform. No hand-rolled auth. No "the AI wrote the permission check, hope it is right."
So do they replace developers? #
No, but they replace the parts of a developer's day that developers were never going to defend anyway — boilerplate, scaffolding, and tedious translations. The work that survives is the work that always mattered: data modeling, edge-case judgment, and knowing what to build in the first place.