Vibe coding is a term that gets thrown around a lot lately, sometimes to mean anything involving AI and code, sometimes as a dismissive slang for "let the AI write it." Neither definition is useful to a developer who wants to know whether this belongs in their actual work or only in Twitter demos. This post is a plain explanation, no decoration.
The short answer
Vibe coding is a workflow where you describe your intent to a tool — what the app should do, which entities exist, how users interact with it — and the tool generates and edits whole code to realize that intent. You don't write the syntax. You decompose the problem, describe the parts, and critique what the tool produces. The "vibe" is the direction of intent, not the mood.
This is different from AI autocomplete. Autocomplete suggests the next line as you type. Vibe coding moves the unit of work from "line" to "feature." You don't operate at the character level; you operate at the requirement level. That shift is what makes it a platform for building whole apps, and not just a smarter code assistant.
How it actually works
The workflow is almost always the same loop: intent description, code generation, iteration. You describe what you want — in natural language, or by editing a short, structured spec. The tool reads the project's context, plans which files to touch, generates them, and shows you the result. You review, give feedback — "that's not what I meant, here's why" — and the tool edits. The loop repeats until the feature is right.
The difference between tools lives in two points: how wide a description can be before the tool breaks, and how much of the project's context the tool keeps between sessions. A tool that can accept "build an order-management system with customers, products, and orders, user auth, and CSV export" in one shot is a different tool from one that needs each endpoint explained separately. And a tool that remembers yesterday's API contract is a different tool from one that starts from zero every session.
Where it helps
Vibe coding wins when the build-it part is faster than the think-about-it part. Prototyping is the obvious case: you want to know if an idea works, and you don't care that the code isn't a work of art. But the real cases are more interesting than that:
- Internal tools — admin dashboards, ops panels, back-office apps. These are apps the organization needs but never gets a full dev cycle for, and the boilerplate-to-logic ratio is high.
- Full-stack apps with a standard data model — CRUD with auth, permissions, integrations, reports. Most line-of-business apps are this shape, and it's exactly what a tool learns to generate well.
- MVPs that need to be real — not a screen mockup, but a deployed app users can sign into, that can go to production when it's ready. When the tool is production-grade, the MVP is also v1.
In all of these, the velocity value is high and the teardown-rebuild cost is low. You're not giving up control; you're giving up writing code that someone else — including an AI — can produce faster than you.
Where it breaks
Vibe coding has known failure modes, and they aren't stylistic — they're architectural. If you can recognize them, you know what to demand of a tool before you trust it.
- Context loss — as the project grows, the tool forgets what was built and starts guessing or rewriting things that worked. This is an architectural problem rooted in rescanning context every session; without persistent context, it gets worse over time. <a href='blog-context-loss.html'>More in the post on context loss</a>.
- The frontend-only trap — the tool generates a pretty interface wired to external backend-as-a-service. It feels fast until you need real server logic, control over your data, or to leave the platform. The app isn't really yours.
- Cost opacity — when the AI bill at month's end is a surprise, you usually can't see where the money burned. Expensive retry loops slip by unnoticed, and pricing becomes a problem precisely when the app succeeds and grows.
- Lock-in — you can't take your code out of the tool. The app lives in the platform; the tool owns what you built. If the tool changes pricing, shuts down, or just stops fitting, you're stuck.
What to look for in a vibe-coding tool
The practical question isn't "does vibe coding work" — it works. The question is which tool gives you the value without those failure modes. A short checklist:
- Real backend — not an external managed service, but a server you run and control. Real server logic, control over your data, ability to leave.
- Persistent context — the tool keeps a durable model of the project's structure, entities, and API contract, so understanding doesn't erode as the project grows.
- Real staging — a separate environment where the tool can run, be reviewed, and be accepted before touching production. Not live editing on an app that users are using.
- Code ownership — the code is yours, sits in your repo, and doesn't only live inside the tool. You can edit it by hand, hand it to a team, or move it to another tool.
- Cost transparency — you can see in real time how much each iteration costs, not only at month's end. Cost should be a measurement, not a surprise.
The bottom line
Vibe coding is real. It's not a gimmick and not a fad. It moves the unit of work from line to feature, and that is a genuine change in how apps get built. But it doesn't come with the virtues for free — and the tool's architecture is what decides whether you get a toy fast or a production app. A tool that remembers, that has a real backend, that gives you the code, and that is transparent on cost — that's infrastructure for real work. A tool that forgets, is frontend-only, holds your code, and loads you with hidden costs — that's a pretty demo that breaks when the project gets serious.