The Founder Who Vibe Coded Their MVP: A Postmortem and Rescue Plan

What you actually need to know

• Vibe coding produces working MVPs with specific, predictable failure patterns. The failures are not random -- they are consistent outputs of the AI generation approach.
• The most dangerous failure is security vulnerabilities in authentication and data access logic. These do not fail visibly in normal testing; they fail when attacked or misconfigured.
• The rescue plan is: audit first, stabilize second, replace third. A complete rewrite is slower and riskier than a targeted rescue of the specific failure areas.
• The engineering process that vibe coding skips -- code review, testing, staging environment -- needs to be put in place before the codebase grows further.
• A vibe-coded MVP that has been running in production with real users is not worthless. It has real usage data, real feedback, and often real revenue. The goal is to make it trustworthy, not to discard it.

The core argument

Vibe coding is real and it works. Founders who have used AI assistants to build MVPs have shipped products faster than would have been possible with traditional development. Some of those products have found product-market fit, generated revenue, and hired engineering teams. The vibe-coded MVP has been a legitimate path to market.

The problem is not that vibe coding produces software that does not work. The problem is that it produces software with a specific risk profile that the non-engineer founder is not equipped to evaluate. The code that was generated by an AI assistant may handle the happy path correctly and fail on edge cases that happen in production. The authentication logic may work for normal usage and have a bypass that an attacker can exploit. The data model may work for the first 500 users and have structural problems that cause data integrity failures at scale.

I have reviewed several vibe-coded codebases that founders brought to me after noticing production problems they could not diagnose. In every case, the codebase had real value -- working features, users who had come to depend on them, revenue that made the business real. In every case, the codebase also had specific categories of problems that required structured intervention before the product could grow without accumulating compounding technical debt.

The most common failure patterns

Authentication and authorization gaps are the highest-risk findings. AI assistants generate authentication code that demonstrates the concept -- the user logs in, the session is set, the user is logged out. What they often do not generate is the authorization layer that enforces which resources each user can access. The result is a multi-tenant product where, with the right request, User A can access User B's data. This is not a hypothetical -- it is a finding in vibe-coded multi-tenant applications often enough to be a default assumption.

Input validation absence is nearly universal. AI-generated code validates input in the UI but often does not re-validate on the server. A user who bypasses the UI -- by using the API directly or by modifying the request -- can submit data that the database receives without validation. The result: malformed data in the database, unexpected behavior, and potential injection vulnerabilities.

Error handling inconsistency is a reliability problem rather than a security problem. Some paths have try-catch blocks; others do not. Some errors are logged; others are swallowed silently. The production system that has no logging for errors has no visibility into what is failing. The founder who built with AI assistance does not know which parts of the codebase have error handling and which do not -- the AI generated it inconsistently and there was no review to catch the inconsistency.

Data model assumptions are the problems that compound most painfully. AI assistants build the data model that fits the prompt, which is often a simplified model that works for the demo and breaks for the real use case. A product where the AI assumed one subscription per user, one team per project, or one role per user will have schema migrations in its future that touch every table built on those assumptions.

The rescue plan

The rescue plan I follow when working on a vibe-coded codebase has four phases:

Phase 1: Security audit. Before any new features are added, the security vulnerabilities are identified and fixed. The audit covers: authentication and authorization logic (are the right resources protected? can users access each other's data?), input validation (is server-side validation present on all endpoints that accept user input?), and dependency vulnerabilities (are any packages in the dependency list known to have vulnerabilities?). Security findings are prioritized by severity and fixed before the rescue moves to Phase 2.

Phase 2: Observability. Add structured logging and error tracking (Sentry is the standard) before making significant code changes. Observability serves two purposes in the rescue: it reveals what is failing in production before the rescue begins (establishing a baseline), and it provides feedback on whether the rescue changes are improving or degrading reliability. A rescue without observability is flying blind.

Phase 3: Data model stabilization. Review the schema against the actual domain and identify the assumptions that will need to change. For each, plan the migration path. Not all migrations need to happen immediately -- the goal of Phase 3 is to know what migrations are coming and in what order, so the codebase is not surprised by schema changes later.

Phase 4: Incremental replacement. The fragile parts of the codebase -- the code with no error handling, no tests, and inconsistent patterns -- are replaced incrementally as features are added or bugs are fixed. The rule: every bug fix adds a test, every new feature follows the team's established conventions. Over three to six months, the proportion of reviewed, tested code grows and the proportion of raw AI-generated code shrinks.

Implementing the engineering process

The vibe-coded MVP has no engineering process. There is no code review, no CI pipeline, no staging environment, and no deployment procedure. Adding this infrastructure is not optional for a product with real users and real data.

The minimum viable engineering process for a rescued vibe-coded MVP: a branch protection rule requiring at least one code review before merging, a CI pipeline that runs the existing tests (and any tests added during the rescue), a staging environment that mirrors production for testing changes before they are live, and a deployment procedure that is documented and followed consistently. This infrastructure can be set up in a day and maintained with minimal ongoing time.

Common mistakes in the vibe-coded MVP rescue

1. Starting the rescue with a rewrite. The rewrite discards the working code along with the problematic code. The incremental rescue preserves what is working while replacing what is not.
2. Fixing cosmetic problems before security problems. Code style inconsistencies are low-risk. Authentication gaps are high-risk. Fix the high-risk problems first.
3. Not establishing the engineering process before growing the team. A second developer added to a codebase without a review process produces two unreviewed developers' worth of code. Set up the process before adding the team.
4. Not measuring the rescue's progress. The percentage of code with test coverage, the number of open security findings, and the error rate in production are the metrics that demonstrate the rescue is working. Without measurement, the rescue has no endpoint.
5. Doing the rescue alone. A founder who brought in an engineer specifically to rescue the codebase benefits most if that engineer can work autonomously on the rescue while the founder continues the product and business work. The rescue should not consume the founder's attention.

Where to start: a 3-step rescue initiation

Step 1: Get a security audit of the authentication and authorization logic within the first week. This is the highest-risk category. An engineer with security experience can review the relevant code in a day and identify the critical findings. The findings that require immediate attention should be fixed before the next user-facing change is shipped.

Step 2: Set up Sentry (or equivalent error tracking) and review the first week of error data. The error data from the first week reveals what is failing in production that the founder has not been able to see. This data informs the priority of the Phase 3 and 4 work.

Step 3: Set up the branch protection rule and the CI pipeline. From this point forward, every change to the codebase goes through a pull request and a CI check. This single change to the engineering process prevents the further accumulation of unreviewed changes and is the foundation the rest of the rescue builds on.

The Codebase Worth Saving

Yashveer Singh. Founder of Yashveer Labs. I have done several vibe-coded MVP rescues for founders who built something real and needed to make it trustworthy enough to grow. The work is specific and bounded -- it is not a rewrite and it is not an indefinite refactoring project. The codebase that is secured, instrumented, and brought under engineering process is a codebase that the team can build on confidently. That outcome is achievable in three to six months of focused work, and it preserves the market position and user learning that the vibe-coded MVP created.

Related reading

• The Founder Who Tried to Hire AI Out of a Hole
• The First Project Test: How to Trial a Developer Without Risking Everything
• The Technical Debt That Is Worth Paying
• The Engineering Migration Patterns That Work