Your Loveable App Is Breaking. Here's What to Do.

You shipped in four weeks. Loveable made it possible. You had a working app, a demo that impressed your investors, and your first 100 users signing up. It felt like magic.

Now it doesn’t.

Page loads are crawling. Users report bugs you can’t reproduce. You fix one thing and break two others. The onboarding flow that worked perfectly in your demo now throws errors for half your signups. Your Supabase bill tripled last month and you’re not sure why.

You’re not imagining it. Your Loveable app is falling apart, and it’s happening faster than you expected.

Here’s the thing — this isn’t your fault. You made the right call using Loveable to validate your idea quickly. The problem isn’t that you used an AI tool to build your MVP. The problem is that Loveable optimizes for speed, not durability. What got you from zero to demo won’t get you from demo to 1,000 paying users.

You don’t need to start over. But you do need to understand what’s actually wrong and fix it systematically. That’s what this post is about.

If you’re dealing with a similar situation using other AI tools, we’ve written about fixing Cursor-built apps and fixing Bolt.new MVPs too. The patterns overlap, but each tool creates its own flavor of technical debt.

Why Loveable apps break at scale

Loveable is genuinely impressive for what it does. You describe what you want, and it generates a working React app with Supabase integration, authentication, and a decent UI. For prototyping, it’s hard to argue with the results.

But Loveable isn’t thinking about your app the way an engineer would. It’s thinking about it the way a demo builder would.

When you ask Loveable to add a feature, it generates the simplest code that makes that feature work right now. It doesn’t consider how that feature interacts with the rest of your codebase. It doesn’t think about what happens when 500 users hit that endpoint simultaneously. It doesn’t plan for the next feature you’ll need.

This is by design. Loveable is optimized for immediacy. “Does the thing I just described show up on screen?” That’s the bar. And for getting to a working prototype, that bar is fine.

The trouble starts when you keep building on top of that prototype. Each new feature Loveable adds is built in isolation. Components get duplicated instead of shared. Database queries get scattered across files with no consistency. Error handling is an afterthought — if it exists at all.

After a few weeks of iterating, you don’t have an application. You have a collection of features that happen to live in the same repository. There’s no architecture holding it together. No patterns to follow. No guardrails to prevent one change from cascading into unexpected failures.

This is why your app worked perfectly at 10 users and started breaking at 100. It’s not a scaling problem in the traditional sense. It’s a structural problem. The foundation was never meant to hold this much weight. If you want to understand how to use Loveable more effectively from the start, our guide on Loveable best practices covers the key patterns.

The five problems hiding in your Loveable codebase

Every Loveable codebase we’ve audited has some variation of these five issues. They compound on each other, which is why fixing one thing often seems to break something else.

1. Duplicated components everywhere

This is the most visible problem. Loveable creates new components for almost every feature request rather than reusing existing ones. You’ll find UserCard.tsx, UserProfileCard.tsx, and UserInfoCard.tsx all doing nearly the same thing with slightly different props and styling.

We’ve seen codebases with 40+ component files where fewer than 20 were actually unique. The rest were near-duplicates with minor variations. When you fix a bug in one, the same bug still exists in its copies. When you update styling in one place, the others look inconsistent. You end up playing whack-a-mole with your own UI.

2. No architecture to speak of

Loveable tends to generate flat file structures. Everything lives in src/components/ or gets dumped into a handful of page files. There’s no separation between business logic and UI. There’s no service layer. There’s no clear data flow.

This means when you need to change how something works — say, how user permissions are checked — you’re hunting through dozens of files to find every place that logic lives. And you’ll miss some. You always miss some.

3. Happy-path-only error handling

Loveable builds for the scenario where everything goes right. User submits a form? Great, it handles the success case beautifully. But what happens when the network request fails? What happens when the user submits invalid data? What happens when Supabase returns an unexpected response?

Usually nothing. The app either silently fails, shows a blank screen, or throws an unhandled error that crashes the component. Your users see a white page and hit refresh. If they’re patient, they try again. Most aren’t patient.

4. Database queries scattered inline

Instead of creating a proper data access layer, Loveable puts Supabase queries directly inside components. The same query might appear in three different files with slightly different filters. There’s no caching strategy. There’s no query optimization. There’s no single place to update when your data model changes.

This is where your performance problems and unexpected bills come from. Components re-fetch data on every render. Related queries run sequentially instead of in parallel. You’re making five database calls where one would do.

5. Zero test coverage

Loveable doesn’t write tests. Not unit tests. Not integration tests. Nothing. This means every change you make — whether by hand or by prompting Loveable again — is a gamble. You have no automated way to verify that existing features still work after a modification.

This is the problem that makes all the other problems worse. Without tests, you can’t refactor with confidence. Without confidence, you patch instead of fix. And patches compound into more technical debt, which makes everything harder to change, which makes you more likely to patch instead of fix. It’s a vicious cycle.

What this looks like in practice

We recently worked with a SaaS startup that had built their initial product entirely with Loveable over six weeks. They had a working app, paying customers, and a seed round closing. They also had a codebase that was actively fighting them.

Here’s what the audit revealed:

• 47 component files, 18 of which were near-duplicates of other components
• 40% code duplication across the codebase
• Zero shared utilities — the same date formatting logic appeared in 11 different files
• 4.2-second average page load on their main dashboard
• $200/month in unnecessary Supabase calls from redundant queries and missing caching
• No error boundaries — a single failed API call crashed the entire page

The founder thought they needed a complete rewrite. They were getting quotes for three-month rebuild projects. They were terrified of losing momentum right when their fundraise was closing.

They didn’t need a rewrite. They needed targeted surgery. We identified the 20% of issues causing 80% of their problems and fixed those first. Dashboard load time dropped to 900ms. Their Supabase bill dropped by 60%. Bug reports from users dropped by half in the first two weeks.

The whole thing took four weeks — not four months.

Your three options

When your Loveable app starts breaking, you have three paths forward. Only one of them makes sense for most startups.

Option A: Keep patching

This is what you’re probably doing now. Something breaks, you fix it. Another thing breaks, you fix that too. You ask Loveable to help, and it generates more code that adds to the problem.

Patching works for a while. But each patch adds complexity. Each fix that doesn’t address the root cause makes the next fix harder. You’re running faster on a treadmill. At some point, you spend more time fixing bugs than building features. That’s when your competitors pass you.

Option B: Full rewrite

This is the tempting option. Burn it down, start fresh, do it “properly” this time. It feels clean. It feels decisive.

It’s almost always wrong.

Rewrites take 3-6 months minimum. During that time, you’re maintaining two codebases — the old one that’s still serving users and the new one that’s not ready yet. Your users don’t get new features for months. Your team’s energy goes into rebuilding what you already had instead of building what you need next.

Most startup rewrites either never finish or ship with fewer features than the original. You lose momentum at the exact moment you need to be accelerating.

Option C: Targeted refactoring

This is the path we recommend for almost every Loveable-built app we see. Instead of rewriting everything, you systematically fix the highest-impact problems first.

You identify the components that are duplicated and consolidate them. You extract inline queries into a proper data layer. You add error handling to the critical user paths. You write tests for the parts that break most often.

It’s not glamorous. It’s not a clean slate. But it gets you from “breaking constantly” to “stable and maintainable” in weeks instead of months. And you keep shipping features the entire time.

How we fix Loveable apps

We’ve refined this into a four-week process that addresses the structural problems without disrupting your users or your momentum. Here’s how it works.

Week 1: Audit and plan

We read every file in your codebase. Not skimming — actually reading. We map out the component duplication, identify the missing abstractions, profile the database queries, and catalog the error handling gaps.

By the end of week one, you have a document that tells you exactly what’s wrong, why it matters, and what we’re going to do about it. No surprises. No vague promises. A concrete plan with priorities based on user impact.

Weeks 2-3: Core refactoring

This is where the real work happens. We tackle the problems in order of impact.

Component consolidation. We merge duplicated components into shared, reusable ones with proper props interfaces. Your 47 files become 22. Changes propagate correctly. Styling is consistent.

Data layer extraction. We pull every inline Supabase query out of your components and into a proper service layer. Queries are deduplicated, optimized, and cached appropriately. Your database bill drops. Your load times improve.

Error handling. We add error boundaries, loading states, and fallback UI to every critical path. When something fails — and things always fail — your users see a helpful message instead of a white screen.

State management cleanup. We untangle the prop drilling and redundant state that Loveable creates and replace it with clean, predictable data flow.

Throughout this phase, your app stays live. We refactor in small, tested increments. Your users don’t experience downtime.

Week 4: Testing and performance

We write tests for the critical paths — the ones that were breaking before and the ones that would hurt most if they broke in the future. Not 100% coverage for the sake of a number. Practical tests that catch real regressions.

We also run a performance pass. Optimize bundle sizes. Fix unnecessary re-renders. Add proper lazy loading. The goal is an app that feels fast and stays fast as you add features.

By the end of week four, you have a codebase you can actually build on. Not perfect — that’s not the point. Stable, maintainable, and ready for your next phase of growth.

If your problems go beyond the Loveable-specific issues and into deeper architectural territory, our technical debt cleanup service covers the full range of stabilization work.

Stop patching. Start fixing.

Every week you spend patching a broken Loveable codebase is a week you’re not spending on the features your users are asking for. The bugs won’t fix themselves. The architecture won’t improve on its own. The technical debt only compounds.

The good news: you don’t need a six-month rewrite. You need a focused, systematic cleanup by engineers who’ve seen this pattern dozens of times and know exactly where to cut. Start with a code audit to understand what’s there, then our vibe code cleanup process handles the restructuring.

We offer a free 30-minute code assessment where we’ll look at your Loveable codebase, identify the critical issues, and tell you honestly whether you need help or can handle it yourself. No pitch deck. No sales pressure. Just an engineer looking at your code and telling you what they see.

Book your code assessment here and stop fighting your codebase.

Built with Loveable and hitting a wall? Variant Systems helps founders turn AI-generated MVPs into production-ready products.