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Published: March 26, 2025 After three months taking a Lovable prototype to over a thousand users with Techstars alumni GrowthMatch, I’ve seen firsthand what happens when the “vibe coding” dream meets production reality. This is the post-mortem on that journey - the technical challenges, the human factors, and the uncomfortable truths about AI-generated codebases. The story starts with Snacker.ai, a video creation platform that began as a Lovable prototype and ended up serving real customers with real money on the line. Along the way, we discovered exactly where the AI-to-production path breaks down, what it actually costs, and why the marketing promises don’t match the reality.

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The Setup: Why We Thought It Would Work

Full transparency: Taking Lovable to production is technically possible but rarely advisable I’m sharing this experience because you deserve to know what you’re signing up for. The technical workarounds exist, but the real cost comes in ways you won’t expect.
After years in the engineering trenches, I believe in democratizing information. This article shares a path I wouldn’t personally recommend, but if you’re determined to try it, you should know what’s coming. Some teams will make it work. For specific narrow use cases, it might even be perfect. But you need the full picture to make an informed decision.

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What Actually Happened: The Snacker Story

The Snacker.ai prototype looked solid on the surface. It had an in-browser video recorder, a substantial Supabase schema, and from the outside, everything worked. The founder had built something impressive using Lovable - demos went smoothly, investors were interested, and early users were excited. But underneath was a different story. The codebase was a patchwork of localStorage hacks mixed with insecure Supabase calls. Components followed different patterns depending on which AI prompt had generated them. There was no consistent state management, no unified patterns, nothing that would scale. The founder was unaware of these issues, and that’s not a failing on his part. Lovable abstracts away the code quality, and even if it showed the code, non-engineers wouldn’t recognize the problems. The tool’s entire value proposition is that you don’t need to understand what’s underneath. When I recommended a two-week rebuild to establish solid foundations, the response was understandable: “I don’t want to do the rebuild. I want to keep pushing for features.” The pressure to ship was real - there were customers waiting, investors watching, and competition moving fast. So we deferred the rebuild. The cost of that decision became clear immediately. Every feature took three times longer to implement because we had to navigate through the architectural chaos. Simple changes required untangling complex dependencies. Bug fixes introduced new bugs. The velocity we thought we were preserving by avoiding a rebuild was actually being destroyed by technical debt. The breaking point came with “Snacker for Teams.” What should have been a straightforward enterprise feature - adding workspaces, team management, and proper payment flows - became a massive undertaking. We were bolting critical business logic onto a foundation that couldn’t support it.

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The Technical Workaround We Tried

The Lovable + Production Architecture For those determined to try this path, there are ways to maintain Lovable collaboration while building production infrastructure. It involves Git submodules, monorepos, and careful orchestration. But the technical setup is the easy part.
We attempted to create a hybrid approach where the Lovable app could continue evolving while we built production infrastructure around it. The idea was to use Git submodules to embed the Lovable repository inside a Turborepo monorepo, allowing both workflows to coexist. The technical setup itself is straightforward - connect Lovable to GitHub, create a monorepo, add the Lovable app as a submodule, build your backend services around it. In theory, this lets non-technical team members continue prototyping while developers handle the production requirements. But here’s what we learned: the technical architecture wasn’t the problem. The real challenges were human and organizational.

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The Real Challenges Nobody Talks About

The marketing pitch for Lovable-to-production sounds compelling: maintain the collaborative prototyping while building real infrastructure. Non-technical team members keep using the tool they love, developers add the production layer, everyone wins. Here’s what actually happens:

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The Code Quality Cliff

Every new AI tool promises that coding is over, that everyone can be a developer now. The marketing is everywhere, and it’s compelling. But when you’re three months into production with real customers, the reality hits differently. People using Lovable burn through credits building features, then hit walls they can’t conceptualize. A caching issue. A memory leak. A race condition. These aren’t just bugs - they’re concepts that require years of experience to recognize and fix. The tool that promised to eliminate the need for engineers suddenly requires one urgently. With Snacker, we saw this pattern repeatedly. Lovable generates code by stacking features on top of each other. Each new request gets implemented with another layer of conditional logic. There’s no refactoring, no architectural planning, just accumulation. The result is code that works until it doesn’t, and when it stops working, you need deep expertise to fix it. The knowledge gap is real. Engineering is difficult because it requires understanding systems, not just syntax. When you hit production issues - and you will - you need someone who can diagnose problems across the entire stack. Take Snacker’s video processing requirements. Recording a video seems simple until you realize you need to handle 10-20 minute files. Those files need proxy versions for editing. You can’t send 500MB files to transcription services. You need transcoding, caching, CDN delivery. Developers see these requirements coming from experience. It’s our job to anticipate and plan for them. But Lovable makes it easy to feel powerful right up until you hit these walls. Then you discover that neither Lovable nor ChatGPT can solve these problems, and you don’t have the vocabulary to even articulate what you need. The financial reality is brutal. Founders think they’re saving $150-200/hour on developer costs. But after putting in 90 hours of their own time (which has value too), they hit technical limitations that require a developer anyway. That developer looks at the codebase and delivers one of three responses: they won’t touch it, they’ll rebuild it first, or they’ll charge triple their normal rate to deal with the mess. Meanwhile, every new feature takes three times longer to implement because of the technical debt. The velocity loss compounds until it becomes untenable.

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The Harsh Reality of Hybrid Approaches

We tried to make the hybrid approach work - keeping Lovable for rapid iteration while building production infrastructure around it. The theory was sound: use Git submodules, maintain synchronization, let everyone work with their preferred tools. In practice, it created more problems than it solved. The Lovable-generated code and the production code lived in parallel universes with different standards, patterns, and assumptions. Every sync introduced potential conflicts. Every feature required translation between the two worlds. What we encountered in the Snacker codebase was typical of AI-generated code:
  • Conditional logic stacked on conditional logic
  • No consistent architectural patterns
  • API keys exposed in frontend code
  • Missing error handling
  • Performance bottlenecks invisible to non-engineers
Each of these issues alone is fixable. Together, they create a maintenance nightmare that gets worse with every new feature.

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The Synchronization Nightmare

The promise of maintaining both Lovable and production workflows sounds elegant. The reality is managing constant conflicts between auto-generated changes and manual edits. Git submodules fail to update. Branches diverge beyond recognition. Work gets lost in sync conflicts. You end up playing ten games of chess simultaneously: managing Lovable’s patterns, production architecture standards, framework best practices, security requirements, performance optimization, database evolution, API versioning, testing strategies, deployment pipelines, and user data protection. Each has its own rules, and they often conflict.

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When Production Reality Hits

Deployment is where everything falls apart. Lovable apps aren’t built for multiple environments. They have hardcoded values where you need configuration. They don’t distinguish between build-time and runtime variables. Secrets end up exposed in client-side bundles. The build process breaks in ways you can’t anticipate. Memory limits get exceeded. Dependencies that worked locally don’t exist in production. Build caching fails with monorepo structures. Then come the runtime surprises: CORS issues, database connection pooling problems, rate limiting from APIs, memory leaks from unoptimized code, cold start times that destroy user experience. There’s no error tracking, no performance monitoring, no way to debug production issues. Logs are scattered across services with no alerting when things break. The app that worked perfectly on localhost:3000 crumbles under real-world conditions. With Snacker, we spent more time debugging these deployment issues than we would have spent building from scratch. Every production incident required detective work across multiple layers of abstraction.

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The Three-Month Results

After three months with Snacker, we had over a thousand users and paying customers. By any metric, that’s a success. But the cost of maintaining and scaling the Lovable-based architecture was becoming unsustainable. Every new feature request required navigating through layers of technical debt. Simple changes that should have taken hours took days. Complex features that should have taken days took weeks. The founder’s urgency to ship was constantly at odds with the reality of the codebase.

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What I’d Do Differently

The hard truth about Lovable in production After taking multiple Lovable apps to paying customers, the lesson is clear: the hybrid approach is a workaround, not a solution.
Lovable-generated code should be treated as disposable - it has a finite lifespan and that’s intentional. Use it for getting ideas out quickly, testing concepts with users, building demos for investors, or internal tools where imperfect is better than nothing. But as soon as you’re serious about production, start fresh. As an engineer, I wouldn’t bring another developer onto a Lovable codebase. It’s not about pride - it’s about practicality. Developers accustomed to clean, sustainable architectures find it painful to work in AI-generated chaos. Before I joined the Snacker project, another developer had already tried and failed to untangle the mess. I ended up fixing both the original Lovable code and their attempts to work with it.

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The Two-Week Rebuild That Saves Months

From experience, a clean rebuild takes 2-3 weeks and saves months of technical debt. It’s rare to build something in Lovable so complex that an experienced developer can’t recreate it properly in less time than you’ll spend fighting the hybrid approach. The path that actually works:
  1. Use Lovable to validate your idea and get user feedback
  2. Once you have product-market fit signals, hand the validated concept to an experienced developer
  3. Let them build it right from the ground up
  4. Keep the Lovable prototype as a reference, not as production code
The key is finding the right technical partner - someone who understands your domain, has built similar systems, can explain trade-offs clearly, and will push back when necessary. Without that expertise, you’ll make expensive mistakes that could have been avoided. Once you’re thinking about hundreds or thousands of customers, real-time operations, complex business logic, or security requirements, the Lovable-to-production path becomes untenable. The technical debt compounds faster than you can pay it down.

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The Advice I Wish Someone Had Given Me

If you’re thinking about building your startup with Lovable, go ahead and try. It’s empowering to build something yourself, and you’ll learn a lot about your product in the process. But understand what you’re signing up for. At some point, you’ll paint yourself into a corner. The frustration will kill your momentum. No amount of determination can bridge fundamental knowledge gaps. Lovable is an exceptional prototyping tool - more interactive than Figma, perfect for exploring ideas and showing developers what features should look like. That’s incredibly valuable. Use it for that. But the marketing promises that you can build production applications without engineers are fantasy. You’ll make compromises that wouldn’t exist with proper architecture. You’ll hit walls that require deep expertise to overcome. Software engineering is a specialized profession for good reasons. The problems are complex, the edge cases are numerous, and the cost of mistakes is high. When you’re ready for production, you need someone with years of experience navigating these challenges. Trust your technical partners. Don’t paste ChatGPT solutions thinking you’re helping. Let the experts who’ve spent years in this domain guide you through it. Your role as a founder is to understand the business, theirs is to understand the technology. Respect that division of expertise.

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The Real Lessons Learned

After three months and over a thousand users, here’s what taking Lovable to production taught me: The tools work exactly as advertised - for prototyping. Lovable is exceptional at rapid iteration, testing ideas, and getting something in front of users quickly. That’s valuable and shouldn’t be dismissed. But production is a different discipline with different requirements. The skills, patterns, and architectures that make applications maintainable, scalable, and reliable can’t be generated by AI yet. They require human judgment, experience, and the ability to see around corners. The two-week rebuild I recommended would have saved us months of fighting technical debt. Every shortcut we took to avoid that rebuild cost us triple in the long run. The founder’s urgency was understandable, but the price was paid in engineering velocity that never recovered. Maybe these tools will mature to handle production requirements. I hope they do. But today, the gap between prototype and production remains real, and pretending otherwise leads to expensive failures.

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Key Takeaways

For Founders:
  • Use Lovable for validation, not production
  • Budget for a proper rebuild once you have product-market fit
  • The two-week rebuild will save months of technical debt
  • Your time has value too - factor it into the cost equation
For Developers:
  • Set clear expectations about Lovable’s limitations
  • Advocate strongly for clean rebuilds when appropriate
  • Document the technical debt clearly and its impact on velocity
  • Be patient with founders discovering these limitations for the first time
For Teams:
  • Treat Lovable prototypes as throwaway code
  • Plan for the transition from prototype to production
  • Don’t try to maintain parallel workflows - pick one
  • Respect the expertise required for production systems
The gap between “it works” and “it works in production” remains vast. Until that changes, treat prototyping tools as exactly that - tools for prototyping, not production.
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