“Vibe coding” gets thrown around like it means typing a wish into a chatbot and receiving a finished app. That is not what it is, and believing that is the fastest way to ship something broken. Here is the honest version, based on how I actually build and ship apps as a team of one.

A working definition

Vibe coding is describing what you want in plain language and letting an AI write most of the code, while you stay the architect, reviewer, and tester. You are still in charge. You are just not typing every line.

The real shift is from author to editor. For most of software history, your value was in producing code: remembering syntax, wiring up boilerplate, looking up the API for the hundredth time. Vibe coding takes that production cost close to zero and moves your value to judgment: deciding what should exist, recognizing whether the output is correct, and steering when it drifts. You spend less time typing and more time thinking about the product.

That sounds small. It is not. It is the difference between writing a paragraph and editing one. An editor with taste and a fast writer can produce a book quickly. An editor with no taste and a fast writer produces a fast pile of nonsense. The writer got faster either way. Whether that is good depends entirely on the editor.

A concrete example of the loop

Say I want a “save draft” feature. The old way: I open the file, remember the state management pattern, write the handler, wire the button, add storage, debug the typos. Twenty minutes if I am lucky.

The vibe-coding way: I tell the assistant, “Let users save a draft and reopen it later. Match how the settings screen persists data.” It produces the handler, the storage call, and the button in seconds. Then my real work starts. I read the diff. Does it handle the empty draft? Does it collide with an existing key? Is the storage call the one we already use elsewhere, or did it invent a second pattern? I fix what is off, run the app, confirm it works, and commit. Total time: maybe five minutes, and the thinking happened where it mattered.

Where it genuinely shines

  • First drafts of anything. A new screen, an API endpoint, a config file. The AI gets you to 80 percent in seconds, and 80 percent of something is far easier to finish than a blank file.
  • Unfamiliar territory. New framework, new language, a library you have never touched. The model already knows the idioms you would otherwise spend an hour googling, so the learning curve flattens.
  • Boilerplate and glue. CRUD, forms, parsers, scripts, tests, type definitions. The boring 60 percent of every project that you have written a hundred times before.
  • Refactors you have been avoiding. “Extract this into a service, add error handling, keep the behavior identical.” Tedious by hand, quick to supervise.

This is the reason a solo builder can now ship what used to take a small team. The production bottleneck is gone, so you are limited by how fast you can decide and verify, not how fast you can type.

Where it bites

  • Confident wrong answers. The AI will invent an API that does not exist and write it like gospel. If you cannot tell, you will ship it. Confidence is not correctness.
  • Silent drift. Each prompt nudges the code. Twenty prompts later the architecture is mush, with duplicated logic and three ways to do one thing, if you were not watching.
  • The 20 percent that matters most. Auth, payments, data integrity, security. The parts where a subtle bug is expensive are exactly where you must slow down and read every line.

I learned the drift one the hard way. Early on I let an assistant add feature after feature to an app without stepping back, and a week later I had three different ways of storing the same data and a bug that touched all three. The fix took longer than building it properly the first time would have. Now I steer the structure deliberately instead of letting it accrete.

The rule I live by: you are responsible for every line you ship, whether you typed it or not. “The AI wrote it” is not a defense to your users when their data is gone.

The loop I actually use

  1. Describe the outcome, not the implementation. “Users can save a draft and come back to it” beats “add a useState and a button.” Let the model propose the how.
  2. Work in small slices. One feature, one screen, one function at a time. Small diffs are reviewable. Thousand-line dumps are not.
  3. Read the diff every time. Non-negotiable. If I do not understand a change, I ask the AI to explain it or I rewrite it until I do.
  4. Run it immediately. Vibe coding without running the app constantly is just generating hope. The feedback loop is the work.
  5. Commit when it works. Frequent commits give me a safe point to roll back to when the next prompt makes things worse, which it regularly does.
  6. Keep a project context file. A short doc of stack, conventions, and “do not do X” that I feed the assistant so it stops reintroducing the same mistakes.

What actually changes about the job

The fear is that vibe coding makes engineering skill worthless. The opposite is true. When anyone can generate code, the scarce skills become the ones the AI does not have: knowing what to build, recognizing a bad solution, understanding the system well enough to catch the subtle break, and having the taste to keep things simple. Those are senior skills, and vibe coding rewards them more, not less. The junior who only ever copied the generated answer never develops them. The builder who reads, questions, and steers gets sharper with every project.

The mindset that makes it work

Treat the AI like a fast, eager junior developer with no memory and no judgment about your product. It produces a lot, quickly, and it will confidently do the wrong thing if you let it. Your job did not disappear. It moved up a level, to direction, taste, and verification.

That is the trade. You give up typing, you keep the thinking. Done right, you ship in a weekend what used to take a month. Done lazily, you ship bugs at the same impressive speed. The entire difference is how carefully you stay in the loop. For the specific habits that keep it on the rails, see how to prompt an AI coding assistant and 9 vibe coding mistakes to avoid.