AI Code Generator — TypeScript, Python, Go, Rust and More

Code that looks right is not the same as code that is right

This generates working code in TypeScript, JavaScript, Python, Go, Rust, Java, C++, PHP, Ruby and SQL from a description of what you want, using a system prompt tuned to produce runnable output rather than pseudo-code — which is why the result differs from a bare chatbot.

Never ship it unread. Generated code is frequently idiomatic, plausible and subtly wrong, and the fluency is the problem: nothing about it signals which parts to distrust.

The characteristic failures

• Invented APIs. A method that does not exist on that library, or exists with different arguments, or existed two major versions ago. This is the most common failure by a distance, and it arrives with complete confidence.
• Version drift. Code written against an older release of a framework, because training data is necessarily older than the package versions you have installed.
• Missing error handling. The happy path is usually correct; the failure path is often simply absent.
• Edge cases. Empty input, zero, null, unicode, timezone boundaries and concurrency — these break generated code because your request rarely mentioned them, and the model optimises for the case you described.

Security deserves separate attention

There are specific, recurring patterns worth watching for: concatenating user input into SQL rather than parameterising it, skipping input validation entirely, interpolating untrusted values into shell commands or HTML, and embedding credentials inline as literals.

Treat anything touching authentication, authorisation, cryptography, payments or user input as needing careful review regardless of how reasonable it looks. "It looks fine" is not a security review — and generated code is specifically good at looking fine.

Where it genuinely earns its place

The rule that makes this tool useful rather than risky: use it where verifying the answer is cheaper than writing it.

• Boilerplate you have written many times before.
• Translating an approach you already understand into an unfamiliar language.
• Regular expressions and shell invocations you would otherwise go and look up.
• Test scaffolding.
• Explaining unfamiliar code you already have in front of you.

In each of those, checking the result is fast. Where verifying correctness is as hard as writing the code yourself, the tool has saved you nothing and may have cost you time you will spend debugging something you did not write.

Privacy and licensing

Your input is sent to this site's server and on to a third-party model provider. It does not run in your browser and it is not private. Do not paste secrets, API keys, credentials, customer data or anything under NDA.

On licensing: generated code can resemble code it was trained on, so anything you intend to redistribute deserves the same scrutiny you would apply to code copied from any other source.

See also the regex tester for checking generated patterns, and the JSON formatter.