Vibe Coding Your MVP: What Works, What Doesn't, and When to Hire

Where the Term Comes From

In February 2025, Andrej Karpathy posted about a new relationship with coding. He described using AI assistants so heavily that he had largely stopped reading the code being generated. He would describe what he wanted, accept or reject the output based on whether the behavior looked right, and move on. He called it vibe coding.

Karpathy was describing his experience as a world class engineer using AI to move fast on personal projects. The internet took the concept and applied it to something different: non technical founders using Lovable, Bolt, v0, and Replit to build their entire startup without writing a single understood line of code.

Both are real phenomena. Both have different implications. This guide is honest about what each approach actually produces.

The Tools and What They Are Actually Good At

Cursor

Cursor is a code editor built on top of VS Code with deep Claude and GPT integration. It can read your entire codebase, answer questions about it, and generate changes across multiple files simultaneously. It is designed for developers who can read and understand the code being generated.

What it is good at: Accelerating developers. A competent developer using Cursor ships roughly two to four times faster than without it on most tasks. It handles boilerplate exceptionally well. Refactoring, test writing, and documentation are all strong.

What it struggles with: Generating code that requires deep domain knowledge the model was not trained on, debugging complex runtime issues where the error is far from the source, and maintaining consistency across very large codebases (it starts to lose context of decisions made earlier in the project).

Lovable

Lovable generates full stack applications from natural language descriptions. You describe a feature, it generates the frontend and often wires up a Supabase backend. The output is deployable React with Tailwind.

What it is good at: Getting to a visually coherent UI fast. If your MVP is primarily a data display and entry interface (think internal tools, simple SaaS dashboards, booking systems), Lovable can get you to something real in hours.

What it struggles with: Complex business logic, custom authentication flows, non standard integrations, and anything that requires understanding the generated code to extend it. When Lovable generates something wrong, fixing it often requires going outside the tool.

Bolt and v0

Bolt is Stackblitz's AI coding environment. v0 is Vercel's component generator. Both excel at UI component generation from descriptions. Bolt can also run the generated code directly in the browser.

What they are good at: One off UI components, landing page sections, prototype screens. If you know what you want visually and want to skip the CSS, these tools are fast.

What they struggle with: Stateful application logic, backend integration, and anything that requires coordination between multiple components.

Replit Agent

Replit has positioned itself as the vibe coding platform for non technical founders. The agent can build and deploy a full application from a description, running on Replit's infrastructure.

What it is good at: Zero to deployed in under an hour for simple CRUD applications. The infrastructure is handled. No deployment configuration required.

What it struggles with: Production grade reliability, custom domain setup beyond basics, and the same complex logic issues that affect every AI code generation tool.

What You CAN Build

Let us be specific, because the tools are genuinely useful for real categories of MVPs.

Validation landing pages: Describe your product, ask for a landing page with email capture, deploy it. This is table stakes. There is no reason to hire anyone for a landing page in 2026.

Internal dashboards: A dashboard that reads from a database and displays metrics is exactly the kind of thing Lovable or Cursor can build well. Standard components, standard data flow, nothing unusual.

Simple CRUD applications: If your MVP is fundamentally a form that writes to a database and a list that reads from it, vibe coding tools handle this reliably.

Prototypes for user testing: You need something to put in front of users to test your assumptions. It does not need to be production quality. AI generated code is perfectly adequate for this.

Marketing pages: Multi section marketing sites, pricing pages, blog layouts. These are solved problems for AI tools.

What BREAKS

Here is where founders get in trouble. The tools work smoothly for the categories above and then hit hard walls elsewhere.

Authentication and security: AI generated auth code is frequently wrong in subtle ways. Sessions are stored insecurely. CSRF protections are missing. Password reset flows have timing attack vulnerabilities. The model generates code that looks correct but has edge cases it did not test. This is the category most likely to cause a production security incident.

Payment integrations: Stripe, Paddle, and Polar have complex webhook handling requirements. The model knows the happy path. It does not reliably generate the error handling, idempotency checks, and webhook signature validation that make payments work correctly in production.

Complex business logic: When your rules are specific to your domain and not well represented in training data, the model invents plausible looking logic that is wrong. It looks right until a user triggers the edge case.

Database migrations: AI tools generate initial schemas reasonably well. They are unreliable at generating safe migrations on existing data. A wrong migration drops a column with user data in it. This has happened.

Third party API integrations: Any API with non standard auth flows, complex rate limiting requirements, or significant undocumented behavior is a risk. The model has seen the API documentation but not the real world failure modes developers encounter.

Performance at scale: AI generated code is rarely optimized. Missing database indexes, N+1 query patterns, and unnecessary re renders are common. For a prototype this is fine. For a product with real load, it causes problems.

The Last Mile Problem

The most consistent failure mode with vibe coding is the 80% problem.

AI tools get you to a working prototype fast. The first 80% of features are generated quickly and work reasonably well. Founders show the prototype to users, users get excited, the founder decides to launch.

Then reality arrives. The remaining 20% is:

• Error states that the happy path demo never hit
• Edge cases that real users trigger immediately
• Security hardening before you can take payments
• Performance issues that appear under real load
• Integrations that require reading documentation carefully
• Production deployment configuration (environment variables, secrets management, CORS, SSL)
• Monitoring so you know when things break

This 20% takes as long or longer than the first 80%. The tools are less helpful here because the problems are specific to your exact codebase, your exact user patterns, and your exact infrastructure. The model cannot generate a fix for a bug it does not understand.

80% done is not 80% of the way to shipped. For many founders, 80% done is 0% shipped because the product is not production ready.

The Compounding Technical Debt Problem

There is a structural problem with vibe coded applications that gets worse over time. AI generated code accumulates technical debt faster than handwritten code because the developer generating it did not fully understand what they were accepting.

When a real developer writes code they do not understand, they either learn it or flag it. When a vibe coder accepts code they do not understand, they build on top of it. Three months later, the application has five layers of AI generated code that nobody fully understands, and adding any new feature breaks two existing ones.

This is not hypothetical. It is the consistent experience of founders who vibe code their MVP and then try to scale it.

The fix is expensive: either a developer spending weeks understanding and untangling the codebase before they can extend it, or a rewrite. Neither is cheap.

When to Hire Instead

Use vibe coding for:

• Landing pages and marketing
• Prototypes you will throw away after user testing
• Internal tools where security requirements are low
• Simple CRUD features with no unusual requirements

Stop vibe coding and bring in a team when:

• You have paying customers and cannot afford downtime or security incidents
• Users are waiting on features your tools cannot generate reliably
• Your codebase has become unpredictable (new features break old ones)
• You need integrations that require deep technical understanding
• You are about to take payments or handle sensitive user data

When you hit that point, how to build an MVP gives you the full professional development process. For choosing the right production stack, our tech stack recommender provides a specific recommendation based on your product requirements.

The HouseofMVPs team has rebuilt vibe coded MVPs into production grade products many times. The pattern is consistent: the founder got a working prototype fast, the prototype validated the idea, real users arrived, and the codebase could not keep up.

The cheapest version of this story is catching it before the rewrite becomes urgent. If your product has found users and you are still running on AI generated code, now is the time to bring in real engineering before the debt compounds further.

Use the MVP Cost Calculator to compare the real cost of vibe coding through the problems versus hiring a team to build it right. The math often surprises founders who have not accounted for their own time and the cost of the eventual rewrite.

See also How to Build an MVP and How to Choose a Tech Stack for Your MVP for the foundational decisions that come before you start generating code.