AI Code Generator

It is a language model asked to produce source code from a description in words. You say what the code should do and in which language; the model writes an implementation with comments. It is not a compiler or a search engine: nothing is looked up or executed, the code is composed from patterns learned in training, and it has to be read before it is trusted.

The selector offers nine: JavaScript, Python, PHP, HTML/CSS, Java, C#, TypeScript, SQL and Bash. Your choice is prepended to your description as a line reading 'Programming language:', so it steers the model directly. A language outside the list can be requested in the description itself, but the download extension will still follow the selector.

It is free and there is no account: no sign-up, no API key, no export paywall. The only limit is ten generations a minute from the same connection, which exists to stop scripted abuse. Reach it and the panel shows a network error message until the minute rolls over.

Python is one of the nine options and the model handles it well for self-contained functions. A request for a function filtering a list of dictionaries by price and sorting the result came back documented, with an example call, in under six seconds. Multi-file projects and library-specific APIs are where accuracy drops.

No. There is no sandbox and nothing is executed, imported, compiled or type-checked. The code is text, produced in one pass, and it can reference a function signature that does not exist or an argument that was renamed years ago. Run it yourself in your own environment before trusting it, and write the test the model did not.

Not in one go. It is single-shot with no conversation and no project context, so it is at its best on one function, one query or one component. For a site, generate the pieces one at a time and assemble them yourself; asking for an entire application gives you a plausible-looking skeleton with gaps you will spend longer finding than writing.

The output is yours to use, including in commercial projects and public repositories. Review it first the way you would review a colleague's patch: check licences of anything it suggests importing, and be aware that a model can reproduce a well-known snippet closely enough to matter if the original was under a restrictive licence.

Because the instruction behind the page asks for comments and explanations in the language of the page you are on, while the selector controls the programming language. On the Spanish page you get Spanish comments around the same Python. Switch to the English URL if you want English comments.

The model predicts the next token of a plausible answer, one piece at a time, conditioned on your description and on the fixed instruction this page adds. It has no model of your program's behaviour, so it produces code that looks like code that solved similar problems in its training data. That is why it is fluent on common tasks and unreliable on rare APIs.

That depends on how you read the output. Taking a snippet without understanding it teaches nothing; comparing it with your own attempt, asking why it chose a particular structure, and rewriting it yourself is closer to reading someone else's code, which programmers have always learned from. The tool cannot tell the difference, so the discipline has to come from you.