AI coding

AI coding tools run on large language models trained on huge amounts of text and source code. You give the model an instruction and some context, such as the file you are in, the error you hit or a description of a feature, and it predicts the code that fits.

The useful tools add two things around the model. The first is more context: the rest of the repository, the documentation, the output of the last test run. The second is the ability to act: edit files, run commands and tests, read what failed and try again. The more of both a tool has, the larger the task you can hand it.

• Suggestions in your editor. The tool proposes code as you type and answers questions about it, and you review and apply each suggestion. GitHub Copilot's code completion works this way.
• AI-first editors. The editor is built around the model, so one request can read the project and change several files. Cursor is the best-known example.
• Coding agents. You hand over a task and the agent plans it, edits files across the codebase, runs commands and reports back or opens a pull request. Claude Code works this way in the terminal, an IDE, a desktop app or the browser, and Copilot's agent tasks end in a pull request for you to review.
• AI app builders. The agent also runs and hosts what it writes, so the result is a working app at a URL instead of a diff. You describe the app, not the code. See how the builders compare.

AI coding is now ordinary professional practice. In Stack Overflow's 2025 Developer Survey, 84% of respondents said they use or plan to use AI tools in their development process, up from 76% the year before, and 51% of professional developers use them every day.

Trust has not kept pace with use. In the same survey more developers distrust the accuracy of AI output (46%) than trust it (33%). That gap is the reason the checks around the model matter as much as the model.

Vibe coding is one style of AI coding: you accept the code without reading it and judge it by running it. Most professional AI coding is the opposite, with the model writing and a person reviewing. Either way, the same checks catch most problems:

• type-checking and tests on every change, so a broken build never ships;
• a security review of anything that touches data or sign-in, such as the checklist for securing a vibe-coded app;
• a look at every new dependency the model adds, because a package you didn't choose is code you didn't review.

For the tools themselves, our list of the best vibe coding tools compares builders and editors side by side.

Vibely is an AI coding tool of the fourth kind: an AI app builder. You describe a web app or a native iOS and Android app, and the agent writes React and TypeScript code (an Expo app for mobile), type-checks and builds every change before it finishes, and publishes the result to a live URL. If you already work with a coding agent, the Vibely MCP server lets Claude Code, Cursor, Codex and VS Code build, edit and publish Vibely apps directly. The code is yours: sync it to GitHub or download it, and keep working on it with any other AI coding tool.