Vibe Coding Explained: What It Is and Why Developers Are Doing It

:::note[TL;DR]

Vibe coding means prompting AI to write code without reading every line — steer via plain language toward working output
It works well for prototypes, internal tools, and boilerplate; risky for production systems handling user data or money
Main risks: invisible security holes, accumulating bad patterns, codebases nobody fully understands
You can do it responsibly — read the output, prompt for security reviews, commit often, keep sessions focused
The skill floor drops but the ceiling doesn’t — experienced engineers produce better vibe-coded software :::

Vibe coding is a term coined by Andrej Karpathy in early 2025. The idea: instead of writing code yourself, you describe what you want to an AI, accept whatever it generates, and keep prompting until it works. You are essentially vibing with the AI — not reading every line, not really understanding the details, just steering toward an outcome.

It’s not a formal methodology. It’s a real shift in how a growing number of developers (and non-developers) are building software in 2026.

What does vibe coding actually look like?

You open Claude Code, Cursor, or GitHub Copilot. You type something like:

“Build me a REST API for a todo list app using Node and Express. Include create, read, update, delete endpoints. Add basic input validation.”

The AI spits out 200 lines of code. You run it. It mostly works. You prompt again to fix the error on line 47. You don’t read the fix — you just copy it in and run again. When it works, you move on.

That’s vibe coding. The code is AI-generated. Your job is to describe, test, and direct — not to write every line.

Why is it suddenly everywhere?

Because it works well enough for a surprisingly wide range of tasks:

Prototypes and MVPs nobody will maintain
Internal tooling where reliability matters less than speed
Learning projects where the goal is to see something work
Repetitive boilerplate nobody wants to write manually

In 2025–2026, the models got good enough that a motivated non-programmer can ship a working web app in an afternoon. That’s a real change from 2023, when AI code was mostly useful for snippets.

Where does it fall apart?

Vibe coding has real failure modes.

You don’t catch bad patterns early. When you’re not reading the code, subtle bugs — off-by-one errors, missing null checks, wrong assumptions about async behavior — stack up. The AI fixes the symptom you report, not the root cause.

Security is invisible. SQL injection, exposed secrets, missing auth checks — the model won’t always flag these unless you specifically ask. If you’re vibe coding an internal tool, maybe fine. If it touches user data or money, not fine.

:::warning If you’re vibe coding a client dashboard handling user data without verifying API ownership checks, you expose sensitive information to unauthorized access. Always review generated code for authentication and authorization logic — the AI won’t flag what it doesn’t know to look for. :::

The codebase becomes unownable. After 10 AI-generated sessions, the codebase is a jumble of different styles, patterns, and abstractions that nobody fully understands. Debugging novel problems gets painful because there’s no mental model of how it works.

Context limits hit. As the project grows, the AI loses track of earlier decisions. You start getting suggestions that contradict what’s already there.

The scenario: You’re building a quick client dashboard over the weekend because the client is asking for something by Monday. You vibe code the whole thing — forms, API calls, charts. It looks great in the demo. Three weeks later the client reports that any user can see any other user’s data. You realize you never checked that the API actually validates ownership. The AI never mentioned it because you never asked.

Vibe coding vs traditional coding

	Vibe Coding	Traditional Coding
Speed	Fast early	Slower early, faster later
Code ownership	Low	High
Debugging complex bugs	Hard	Easier
Good for prototypes	Yes	Overkill
Good for production systems	Risky	Better
Security	Requires explicit attention	Built into review

Is vibe coding real programming?

This debate is loud and mostly pointless. What’s true:

The cognitive work shifts from syntax to problem decomposition and testing
You still need to understand what you’re asking for and whether the output is correct
Bad prompts produce bad code just as reliably as bad thinking produces bad hand-written code

The skill floor drops. The ceiling doesn’t. Someone who understands software architecture, security, and data modeling will produce better vibe-coded software than someone who doesn’t — because their prompts are better and they catch problems earlier.

How to vibe code without getting burned

Read the code before shipping it. At minimum, skim for obvious issues. You don’t have to understand every line, but you should understand every section.
Prompt for security explicitly. “Are there any security issues with this code?” is a valid follow-up prompt. Do it every session.

:::tip Before shipping vibe-coded features, ask the AI: “Are there any security issues with this code?” This one follow-up catches SQL injection risks, exposed secrets, and auth gaps that silent vibe coding misses entirely. ::: 3. Keep sessions focused. One feature per session. The more context you pack in, the more the model starts to drift. 4. Write tests, even if the AI writes them. Ask the AI to write unit tests for what it just built. Run them. They catch regressions when you iterate. 5. Commit often. Before each new prompt that changes working code, commit. Vibe coding sessions can go sideways fast.

The tools people actually use

Claude Code — best for complex, multi-file tasks and agentic workflows
Cursor — best IDE integration for inline vibe coding
GitHub Copilot — decent for autocomplete-style vibe coding in existing codebases
v0 (Vercel) — specialized for UI component generation from descriptions

:::note v0 by Vercel is a UI-only tool — it generates components from descriptions, not backend logic. For full-stack vibe coding (backend, database, deployment), use Claude Code or Cursor instead. :::

Related: Cursor vs Claude Code in 2026

Summary

Vibe coding means steering AI to write code via plain-language prompts rather than writing every line yourself
It works well for prototypes, internal tools, and repetitive boilerplate — not for production systems that need full ownership
The main risks are invisible security holes, accumulating bad patterns, and a codebase nobody fully understands
You can vibe code responsibly by reading the output, prompting for security reviews, committing often, and keeping sessions focused
The skill floor drops but the ceiling doesn’t — strong engineers produce better vibe-coded software

Frequently Asked Questions

Is vibe coding just a fancy name for using GitHub Copilot?

Not quite. Copilot is autocomplete — it suggests the next line as you type. Vibe coding is a broader pattern where you describe features or changes in plain text and the AI writes entire functions, files, or workflows. You review and accept rather than type and autocomplete.

Can a non-programmer vibe code a real app?

Yes, for certain scopes. Simple CRUD apps, landing pages, scripts, and internal tools are achievable. Production apps handling payments, sensitive data, or complex business logic are risky without some programming knowledge to catch what the AI gets wrong.

Does vibe coding produce worse code than hand-written code?

Not inherently. A skilled developer vibe coding with careful review and good prompts can produce clean, well-structured code. An unskilled developer vibe coding without reading the output produces garbage. The tool doesn’t determine quality — the operator does.