AI Code Generator

Often, for small and well-defined pieces. The dependable cases are the ones written thousands of times already — a debounce, a date parser, a retry loop, a join across two tables — where correct and conventional amount to the same thing. Reliability drops as the problem gets more specific to you: your data's odd shape, your dependency versions, the rule that applies only to accounts created before a certain date. Nothing here executes anything, so nothing is tested before you see it. Compile it, then run it with a realistic input and a deliberately awkward one.

Describe a task, not a topic. Name the inputs and their types, say what comes back, say what should happen when the input is empty or malformed, and state any constraint that is not obvious — a runtime version, no third-party packages, must not block the main thread. Those details matter more than length. If the result has to match an existing function signature, write the signature out and it will be built around. Ask for one thing per run, too: a parser, a command-line wrapper and tests together spread a fixed output budget thinly.

Assume it leaves your machine, because it does. The request carries two values — your description and the language you selected — to a route on this site and on to Anthropic's API, which is the thing that writes the code. Most tools here work inside your browser; this one cannot. Nothing reads your files, your clipboard or your repository, so what is sent is precisely what you typed, which leaves you in control of it. Keep credentials, customer data, internal hostnames and unreleased product names out of the description, and check your employer's policy on third-party services first.

Because the text comes from patterns in how code is usually written, not from reading the library you have installed. A method that fits the shape of the surrounding code is a likely continuation whether or not the package defines it, so you get convincing names: a flag that was never added, an argument borrowed from a rival framework, a helper from a newer major version. The remedy is dull and effective — check the documentation for the exact version in your lock file. Missing imports fail loudly; real-but-wrong signatures are the ones that slip through.

Treat it as unreviewed. Generated code mirrors what is common in published material, and a great deal of published example code is written for clarity rather than safety — SQL built by string concatenation, a hashing choice that aged badly, a certificate check disabled to make a tutorial run. It also has no view of where your input arrives from or who can reach the endpoint, which is what most vulnerabilities turn on. Read anything touching authentication, file paths, deserialisation or a database yourself, keep query parameters bound rather than interpolated, and run a scanner over it.

Nothing on this side claims a licence over what comes back, and the route stores no copy of it. Ownership of machine-generated work is genuinely unsettled, though, and the answer varies by country: several jurisdictions require human authorship for copyright, which can leave a generated fragment in a murkier position than something you wrote. In practice, short utility functions look like the code nobody claims anyway, while long distinctive blocks are worth writing yourself. Where a project carries compliance obligations, that is a question for your legal team rather than a tool page.

Volume decides it. JavaScript, TypeScript, Python and SQL have the most public code behind them, and results there are usually idiomatic and usually right. Go, Java, C# and PHP handle ordinary work well. Rust is the interesting one: the syntax arrives fine and the borrow checker does not, so expect a compile-and-fix loop rather than a clean first draft. Shell is riskier than it looks, because a script that runs on your machine breaks on the first filename containing a space. The dropdown is a fixed list of eleven, with no free-text field.

Read it in the order it runs, not top to bottom, and give the dull lines the attention you would give a colleague's pull request — the failure modes hide there, not in the clever part. Check three things first: what happens on empty or malformed input, whether every error path really returns or raises, and whether anything opened is closed. Then run it, once with a real input and once with something hostile. A line you cannot explain is a line to delete: code nobody can explain is code nobody can fix at two in the morning.