Lovable, Bolt, Replit and Cursor: getting your AI-generated code reviewed

Every AI coding tool has its own personality, if you can call it that. Lovable builds fast, polished-looking apps. Bolt.new spins up a working full-stack project practically instantly. Replit feels like a playground where anything's possible. Cursor sits closer to how a real developer works, one file at a time, inside an actual editor.

And each one, in its own particular way, tends to leave behind similar kinds of problems.

This guide goes tool by tool through what we typically see in codebases built with each one, and what's worth checking before you launch. If you used more than one tool, or switched partway through a project (very common, by the way), read the sections for each one you touched.

#Why the tool you used actually matters for a review

It might seem like AI-generated code is AI-generated code, regardless of which product wrote it. In practice, each tool has its own training patterns, its own defaults, and its own blind spots. Lovable, for instance, is optimized heavily for visual polish and fast iteration on the frontend, which sometimes means backend security gets less attention than it should. Bolt.new generates a full stack in one shot, which is impressive, but full-stack generation in a single pass tends to produce more duplicated logic than a slower, file-by-file approach would.

Knowing which tool built which part of your app tells you where to look first.

Think of it a bit like knowing which contractor worked on which part of a house. The electrician and the plumber both do good work, but they make different kinds of mistakes, and a home inspector who knows that checks different things depending on who did what. Same idea here. A reviewer who knows your auth system came from Bolt and your dashboard came from Lovable already has a head start on where the actual problems are likely hiding.

#Lovable code review and audit

Lovable apps tend to look genuinely production-ready from the outside, which is both the appeal and the risk. The visual layer is strong, so it's easy to assume the backend logic underneath is equally solid. It often isn't, not because Lovable is bad at backend work, but because visual polish gets prioritized quickly and functional depth catches up over several iterations.

#Common Lovable app issues we see

• Supabase or database row-level security left at default, overly permissive settings
• Frontend components calling the database directly without a proper API layer in between
• Forms that validate visually but don't re-validate on the server side
• Auth flows that look complete in the UI but skip role-based checks underneath

#Lovable security audit checklist

Check your Supabase (or equivalent) row-level security policies specifically. This is the single most common gap in Lovable apps we've seen: the database is technically reachable from the client, and if row-level security isn't configured tightly, any authenticated user can potentially read or modify data they shouldn't be able to touch.

#Bolt.new code review and audit

Bolt.new's strength is speed. It'll generate a working full-stack app, frontend and backend together, often in one go. That single-pass approach is genuinely impressive, but it also means less opportunity for the kind of incremental refinement that catches small mistakes along the way.

Apps built with Bolt tend to have decent structure early on but accumulate duplicated logic fast once you start iterating and re-prompting specific features. If you asked Bolt to "add" or "fix" the same feature more than a couple of times, there's a good chance old versions of that logic are still sitting somewhere unused.

#Replit code review and audit

Replit sits in an interesting spot. It's used by everyone from students learning to code to non-technical founders building real products, and the audits we do on Replit apps reflect that range quite a bit.

The most common issue isn't really about the AI-generated code itself. It's environment and deployment hygiene. Replit makes it very easy to just run something and share the link, which is great for speed and genuinely terrible for security if secrets end up hardcoded because there was never a clear "this is now production" moment that forced a proper setup.

Check your Replit project's environment variables specifically, and make sure nothing sensitive is sitting in a config file that got committed alongside the rest of the code.

#Cursor-generated code review

Cursor works differently from the app generators. It's an AI-powered editor, so code tends to get written and reviewed incrementally, file by file, closer to how a human developer actually works. This generally produces cleaner architecture than one-shot generators.

That said, Cursor-generated code still needs the same core checks. Auth, input validation, and error handling don't become less important just because the generation process felt more controlled. If anything, the more "developer-like" the process feels, the easier it is to assume the output has been reviewed the way a human developer's code would be, when it hasn't.

#ChatGPT and Claude-generated code review

A lot of people don't use a dedicated coding tool at all. They just paste requirements into ChatGPT or Claude and copy the output straight into their project. This is extremely common, and it comes with its own specific risk: context loss.

When you're pasting code back and forth across a long conversation, the model doesn't always have full visibility into your entire codebase. It sees what you've shown it. That means auth checks or validation logic that exist in one file might get quietly duplicated or contradicted in another file the model never saw. Reviewing ChatGPT or Claude-generated code means paying extra attention to consistency across files, not just correctness within any single response.

#Framework notes: React and Next.js-specific issues

Regardless of which tool generated your frontend, a few React and Next.js patterns show up constantly:

• Environment variables meant to stay server-side accidentally exposed to the client through a naming mistake
• API routes with no rate limiting, especially ones calling an LLM
• Client-side-only validation with no matching server-side check
• useEffect hooks calling APIs without proper cleanup, leading to memory leaks on long sessions

#Comparison: what each tool tends to get wrong

None of these tools are bad. Each one is genuinely good at what it's built for. The gaps above aren't flaws so much as predictable side effects of how each tool works, and knowing them in advance means you know exactly where to look first instead of guessing.

#A real example worth mentioning

A small team building a local services marketplace, the kind of app that connects customers with electricians, plumbers, that sort of thing, started in Bolt.new to get a working prototype fast. It took them a weekend. Genuinely impressive for a weekend's work, booking flow, payments, provider profiles, all functional.

They moved to Cursor a few weeks later once they wanted more control over the payment logic specifically. That's usually a smart move. But nobody went back and checked whether the auth system Bolt originally generated still matched the new payment routes Cursor added on top of it. It mostly did. Mostly. One provider-only route had been left reachable by any logged-in user, a gap that only existed because it sat exactly at the seam between what Bolt built and what Cursor extended later.

This is a genuinely common story, not a rare one. Basically anyone who switches tools partway through a build ends up with some version of this seam problem. It's rarely catastrophic on its own, but it's exactly the kind of thing that's invisible in a demo and very visible in a proper review.

#A note on switching tools mid-project

Plenty of people start in Bolt or Lovable for speed, then move to Cursor once the project gets serious enough to need more control. If that's you, pay extra attention to the seams, the parts of the codebase where one tool's output meets another's. Auth logic written by one tool and extended by another is a classic place for inconsistencies to sneak in, since neither tool has full visibility into what the other one built.

#Frequently asked questions

#One last thing worth checking

Whatever tool you used, go back and look at your git history if you have one, even a rough one. Sometimes the fastest way to spot a tool-specific issue is seeing exactly when a particular file or feature was added and cross-referencing that against which tool you were using at the time. It won't always be clean, plenty of projects jump between tools without much record keeping. But even a partial timeline helps a reviewer, or you, know where to look first.

#Getting your code checked, whichever tool you used

The tool matters less than the review, honestly. Every one of these platforms can produce a genuinely solid app, and every one of them can also produce something with a serious gap hiding just under the surface. If you're not sure which category your app falls into, a vibe coding audit matched to your specific stack will tell you, tool-specific blind spots included.