What is vibe coding?

In one sentence: vibe coding is programming where you steer by results instead of by reading code. You say what you want in plain language, an AI model writes the code, you run it, and you react to what you see. If something breaks, you paste the error back and ask for a fix. The code is still there, but you treat it as something the AI looks after.

That last part is what separates vibe coding from the broader idea of coding with AI. The developer and writer Simon Willison put the line clearly in March 2025: in his words, vibe coding means building software with an LLM without reviewing the code it writes. If you read, test and understand every change, you are doing AI-assisted programming, which is a different practice even when it uses the same tools.

Neither is better in every case. Vibe coding is fast and fun for prototypes, personal tools and learning. Careful AI-assisted programming is what you want once software holds real data or other people depend on it. Most people move between the two, and the skill is knowing which mode a task needs.

Andrej Karpathy, an OpenAI co-founder and former director of AI at Tesla, coined the phrase in a post on X on February 2, 2025. He wrote that there was “a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” He described talking to an AI editor by voice, accepting its changes without reading the diffs, and pasting error messages back in until things worked. He framed it as fine for throwaway weekend projects.

The name stuck quickly. Merriam-Webster listed “vibe coding” as a slang and trending term in March 2025, and Collins English Dictionary named it its Word of the Year for 2025. By 2026 the phrase is used loosely for almost any building with AI, from one-prompt landing pages to agents working across large codebases.

Most vibe coding sessions follow the same loop, whatever the tool:

• Describe. You write or say what you want: “a habit tracker with a weekly chart and dark mode.”
• Generate. The AI writes or edits files, sometimes many at once, and may install packages or run commands.
• Run. You open the preview or run the app and look at what happened.
• React. You ask for changes in plain words, or paste in an error and ask for a fix.

The loop repeats until the result feels right. Each turn is cheap, so you try ideas you would never have hand-coded. The catch is that every turn also adds code nobody has fully read.

Two styles of vibe coding tool

Prompt-to-app builders start from a chat box and give you a running web app, often with hosting built in. They suit people who want a result more than a codebase.

AI code editors and coding agents work inside a real project on your machine or in the cloud. They read your files, make multi-file edits, run tests and terminal commands, and show diffs you can accept or reject. They suit developers and existing codebases.

The well-known names in 2026 include Cursor, Windsurf, Replit, Lovable, Bolt, v0, Claude Code, GitHub Copilot and Zed. They differ in who they are for, how they charge, and whether more than one person can work in the same project. We compare them honestly, including where each one falls short, in our roundup of the best vibe coding tools in 2026.

If privacy is a concern, you can also vibe code with a model running on your own computer. Our guide to using Ollama in your code editor walks through it step by step.

• Prototypes and demos. You can test an idea with something clickable in an afternoon instead of a slide deck.
• Personal tools. Scripts, dashboards and small apps that only you use are where unreviewed code costs the least.
• Learning. Seeing working code for an idea, then asking the AI why it works, is a fast way to pick up a new language or framework.
• Unblocking non-developers. Designers, founders and analysts can build the first version themselves and bring engineers in with something concrete.
• Boring glue work. Boilerplate, config files and one-off migrations are tedious to write and easy to check.

The risks are not hypothetical. They come straight from the definition: if nobody reads the code, nobody catches what is wrong with it.

Security

AI-generated code can hard-code API keys, skip authorization checks, trust user input, or leave a database open to anyone with the URL. In May 2025, a researcher’s scan of apps built with one popular AI app builder reported that 170 of 1,645 had a flaw exposing personal data. The pattern is common: the app looks finished, and the missing access rules are invisible from the front end.

Agents with too much power

Coding agents can run commands, not only write text. In July 2025, a widely reported incident saw an AI agent delete a company’s production database during a code freeze, despite instructions not to change anything. Give agents the least access they need, and keep production credentials away from experiments.

Maintainability

Code generated turn by turn tends to pile up: duplicate helpers, three ways of doing the same thing, and fixes stacked on fixes. It works until someone has to change it. If no human understands the structure, every later change becomes another round of guessing.

Review and ownership

On a team, the hardest question is “who checked this?” Unreviewed AI output that lands in a shared codebase moves the cost of understanding it onto whoever touches it next. Licences, dependencies and data handling still need a human who owns them.

• Decide the mode up front. Throwaway prototype? Vibe freely. Anything with users, payments or personal data? Review every change.
• Keep secrets out of prompts and out of code. Use environment variables, and rotate anything that leaks.
• Ask the AI to write tests, then read the tests. Tests are shorter than the code and tell you what it actually promises.
• Commit small and often, so you can roll back a bad turn instead of untangling it.
• Limit what agents can do: no production credentials, and confirm before destructive commands.
• Before launch, have a person review authentication, authorization, input validation and data storage.

Most vibe coding tools are built for one person and one AI. That works for a solo prototype, but teams hit friction fast: two people prompt agents that edit the same file, nobody knows which changes came from which agent, and review happens after the fact in a pull request.

Team vibe coding goes better when the workspace makes three things visible:

• Who is working where, including AI agents, so two people do not steer agents into the same file at once.
• What changed, while it changes, so review is a conversation instead of an archaeology project.
• How the pieces connect, so everyone shares the same picture of the codebase before generating more of it.

We wrote a practical playbook for this in vibe coding with friends, and a guide to online pair programming if you work in pairs.

Code Crux is a collaborative, AI-native code editor built for the team version of vibe coding. It is pre-launch, with a waitlist open now. Here is what it does today:

• Real-time multiplayer editing with live cursors and presence. People and AI agents both show up on the files they are working in.
• A spatial code canvas that lays files out as cards wired by the connections in your code.
• A built-in AI agent panel, docked to the side or floating, plus quick ask for fast questions.
• A model picker that includes local models through Ollama, so code can stay on your machine.
• An integrated terminal and file explorer, all in the browser, with CRDT-based (Yjs) real-time sync under the hood.

Crux will not stop anyone from shipping unreviewed code. What it changes is visibility: when your teammates and their agents are all in the same live workspace, review happens while the work happens. Compare it with other editors on our comparison page, or read Crux vs Cursor for one head-to-head. The docs cover the IDE controls, and pricing lists the plans.

Who coined the term vibe coding?

Andrej Karpathy, an OpenAI co-founder and former director of AI at Tesla, coined it in a post on X on February 2, 2025. He described giving in to the vibes and forgetting that the code even exists.

Is vibe coding the same as AI-assisted programming?

Not quite. Vibe coding usually means accepting AI-written code without reading it closely. AI-assisted programming covers any use of AI while coding, including careful work where a developer reviews, tests and understands every change.

Do you need to know how to code to vibe code?

No, you can build small apps by describing them in plain language. But knowing some code helps you spot bad output, debug problems, and decide when a prototype is ready to become real software.

Is vibe coding safe for production apps?

Only with review. Unreviewed AI code can ship security holes, leaked secrets and fragile logic. Treat vibe-coded output like a pull request from a fast new teammate: read it, test it, and scan it before it handles real users or data.

What are the best tools for vibe coding?

It depends on what you build. Prompt-to-app builders such as Lovable, Bolt, v0 and Replit suit quick web apps. AI editors and agents such as Cursor, Windsurf, Claude Code, GitHub Copilot and Zed suit existing codebases. Code Crux focuses on vibe coding together in a shared workspace.

Can a team vibe code together?

Yes. Teams get the most out of it when everyone can see what the AI agents are changing, review edits in the same place, and agree who owns each part of the code before generating it.