Your Bolt project looks complete — until you check what's behind the curtain

Bolt and Lovable take a fundamentally different approach from other AI tools — they generate entire full-stack applications in one shot. This creates an illusion of completeness that is more dangerous than obviously incomplete code. When Cursor gives you a broken file, you know it's broken. When Bolt gives you a full application with a login page, a dashboard, API routes, and a database schema, you assume it works. The surface area looks production-ready while the implementation depth is prototype-grade throughout.

The pattern these tools follow is scaffolding-first development. They create the file structure, the route definitions, the component hierarchy, and the database schema — then fill in each endpoint and component with the minimum viable logic to make the demo path work. Your user registration endpoint might hash a password and insert a row, but it doesn't check for duplicate emails, validate input length, or handle database connection failures. Your API routes return data, but they don't implement pagination, filtering, or authorization checks. Every feature is a thin veneer over a `// TODO: implement` that was replaced with just enough code to not crash on the happy path.

Supabase integration is where Bolt and Lovable projects most commonly fall apart in practice. Both tools love to scaffold Supabase auth and database access, but they generate Row Level Security policies that are either too permissive (anyone can read anything) or too restrictive (nothing works when you're actually logged in). The Supabase client is often initialized multiple times with different configurations, auth state listeners conflict with each other, and the realtime subscriptions they set up create memory leaks because they never unsubscribe. It looks like a complete backend integration — it's actually a collection of Supabase API calls that happen to not throw errors during a demo.