Code Generators

From sample payload to typed models in seconds

Hand-writing model classes for an API response is the most mechanical work in programming: read a field, declare a property, guess the type, repeat for forty fields across six nested objects. These generators do it from a single pasted sample — names, nesting, arrays, optionality and types inferred — for nineteen target languages including Java, Python, TypeScript, C#, Go, Rust, Kotlin, Swift, and Dart, powered by quicktype, the same engine behind the most popular IDE plugins.

Both JSON and XML samples work as input (XML is parsed to a data model first, attributes included), and the output follows each language's idioms: data classes in Kotlin, structs with serde derives in Rust, dataclasses in Python, interfaces plus runtime converters in TypeScript. Generated code includes the serialization helpers, so the round trip from wire format to typed object ships with the models.

Getting the best output from one sample

Generation quality follows sample quality. Use a response where optional fields are present at least once, arrays contain at least one element, and numeric fields show their real range (a 1 could be an int; 1.5 settles it). If a field can be null, include a null occurrence so the generator marks it optional. Thirty seconds spent enriching the sample saves the manual type-fixing that generators are supposed to eliminate.

Which language family should I use?

The generator hub covers nineteen output languages, but they cluster into a handful of practical families. JVM shops reach for Java or Kotlin; .NET and Unity teams want C#; frontend engineers pull TypeScript interfaces, JavaScript PropTypes, or Flow types straight from an API response; Python and Ruby cover scripts, data pipelines, and Rails models; Go and Rust generate structs for high-performance services and CLIs; Swift, Objective-C, and C++ target native iOS, macOS, and embedded code; Dart feeds Flutter apps; and Elm, Crystal, Pike, and Haskell serve smaller, more particular ecosystems. A nineteenth option, JSON Schema, is not a programming language at all — it is a contract other tools validate against, and two more targets generate runtime VALIDATORS rather than plain types: JSON to Zod for TypeScript projects that want a schema and a type from one artifact, and JSON to Pydantic for Python projects (FastAPI especially) that want validation on construction rather than a plain class. Each family shares the same input step (paste a JSON or XML sample) and the same quicktype engine underneath, so switching target languages mid-project costs nothing but a dropdown click.