What is vibe coding?
Vibe coding is building software by describing what you want to an AI tool — Lovable, Bolt, Replit, Cursor — and accepting the generated code largely without reading it. The term was coined by Andrej Karpathy in February 2025, and it now covers everything from weekend demos to AI-built apps with real, paying users.
That last part is where the definition gets expensive. Roughly 45% of AI-generated code introduces known vulnerabilities, according to Veracode's 2025 GenAI Code Security Report, which tested more than 100 large language models on real coding tasks. Doomity, a software development firm serving clients in the US, UK, Spain and Portugal, hardens vibe-coded apps for production — this glossary explains what the term means and where its limits are.
Updated: July 2026
What does vibe coding actually mean in practice?
In practice, vibe coding means the human describes outcomes and the AI writes the implementation, with nobody reading the diff. You prompt, the tool generates, you click through the result, and if it looks right you keep going. The code itself becomes something you have, not something you know.
The vocabulary is still settling — vibe-coded app, AI-built app, AI-generated prototype all describe the same artifact. Doomity uses the terms interchangeably on purpose: what matters for risk is not what you call the app, but whether anyone qualified has read what the AI produced before real users depend on it. The tools themselves — and what each one leaves out — are compared in vibe coding tools.
What is vibe coding genuinely good at?
Validation speed, and it is genuinely good at it. A founder with no engineering team can put a working product in front of real users in days, watch what they do, and learn whether anyone wants the thing at all. That used to cost months and a hired team.
The parts a vibe-coded prototype gets right are real assets. The screens, the flows and the business rules survived contact with actual users — which is why Doomity's position is to keep what the prototype validated and rebuild only what the AI faked, not to throw the work away.
Where does vibe-coded software break when real users arrive?
It breaks in the places a demo never exercises. Five failure points show up so consistently across tools that Doomity checks them first in every audit:
- Placeholder authentication. A login screen that convinces, in front of checks that enforce nothing.
- Open database rules. The second user can read the first user's data — the classic vibe-coded leak.
- Secrets in the frontend. API keys shipped to every browser, waiting to be copied and billed.
- No rollback. One bad deploy and there is no way back, because deploys were never designed, only repeated.
- No tests. Every change is a gamble, and the AI that wrote the code cannot tell you what it breaks.
None of these appear in a demo, which is exactly why demos keep convincing everyone right up until production disagrees.
Is vibe coding safe enough for production use?
Not by default, and the people closest to the tools say so themselves. Stack Overflow's 2025 developer survey shows growing distrust of unreviewed AI-generated code among the developers who use these tools daily — the practitioners trust the output least precisely because they read it most.
Doomity's position, stated bluntly on its own service pages: most vibe-coded prototypes should not go to production as they are, and some should not go at all. This is not an argument against the tools. It is an argument against launching their output unread, with real users' data behind it.
How does vibe coding compare with professional development?
The honest comparison is not vibe coding versus hand-written code — it is validated-fast versus safe-at-scale, and the winning strategy usually sequences them.
| Dimension | Vibe coding | Professional development |
|---|---|---|
| Time to first demo | Days — the strongest argument for the approach | Weeks to months, depending on scope |
| Cost of the first version | Tool subscription plus founder time | A real budget line from day one |
| Security and data handling | Unknown until audited; ~45% of AI code carries vulnerabilities (Veracode 2025) | Designed, reviewed and tested as part of the work |
| When it wins | Validating an idea before committing money to it | Real users, real data, real payments — anything with consequences |
Do you have to abandon a vibe-coded app to go professional?
No — and a vendor who says otherwise before reading your code is selling comfort, not engineering. In most audits, more of the prototype survives than the owner expects: the validated screens, flows and business logic stay, while authentication, data rules and infrastructure get rebuilt where the AI faked them. The full comparison lives in fixing AI-generated code vs rebuilding from scratch.
Doomity runs this as a fixed-scope engagement: a five-day audit of what holds and what does not, followed by a hardening plan. Its engineers come from teams that have built for Holcim, Canon España and Indra, and its own delivery process is AI-integrated — up to 40% faster, with every finding checked by a senior engineer.
FAQ
For validation, yes — it is the fastest route from idea to something real users can touch. The problem starts when validation quietly becomes production: the same unread code that was fine for a demo now holds real users' data and real payments. Doomity treats vibe coding as a legitimate first chapter, and treats shipping its output unread as the actual mistake.
Only after the parts the AI faked are made real. The recurring gaps are authentication that enforces nothing, database rules that let users read each other's data, secrets shipped in the frontend, no rollback and no tests. An audit tells you which of the five your app has. Doomity's Production Readiness Sprint answers that in five working days, with a hardening plan sequenced by risk.
Assume it is not until someone qualified has read the code — Veracode's 2025 report found roughly 45% of AI-generated code introduces known vulnerabilities. For a first signal, Doomity publishes a free production scorecard at /tools/production-scorecard: ten questions, three minutes, and you leave knowing whether the honest next step is launch or hardening.
Expect them to find out, because their technical advisor will read the repository. AI-built code is not a red flag by itself; unread AI code running in production is. If a funding round is on your roadmap, the sequence that works is hardening first, then due diligence preparation — walking in with your own audit beats explaining someone else's findings.
If your vibe-coded app has outgrown the demo and real users are arriving, the next step is a fixed-scope audit that tells you what holds and what needs hardening — before your users find out for you.