Cross-language type safety is a problem most multi-language teams accept as unsolvable. Your TypeScript front-end and Rust back-end define the same data structures independently, drift apart over time, and break at runtime in ways that static analysis should have caught. Genotype is a new programming language that transpiles to idiomatic TypeScript, Rust, and Python types, giving teams a single source of truth for shared data structures without imposing a transport format or runtime dependency.
Why Shared Types Keep Breaking
Teams working across multiple languages face a recurring pattern. A Rust back-end defines a struct. A TypeScript front-end defines an interface. A Python service defines a Pydantic model. All three represent the same concept. None of them stay in sync. A field gets added in Rust, forgotten in TypeScript, and the API returns data the client cannot deserialize. The bug is not in the logic. It is in the type definition.
Protocol Buffers address this by defining types in a schema and generating code for each target language. But Protobuf imposes its own transport format and serialization layer. For teams building web applications, API clients, or services that communicate over HTTP with JSON, the Protobuf runtime and wire format add complexity that does not match the ecosystem. TypeScript developers end up with generated types that do not look like TypeScript. Python developers get Protobuf message classes instead of Pydantic models. The interop works, but the code does not feel native.
Genotype takes a different approach. It defines types in its own language and generates code that looks like the developer wrote it by hand. The TypeScript output uses idiomatic naming and follows project conventions. The Rust output uses Serde. The Python output uses Pydantic. There is no Genotype runtime. The generated code is just types.
A Language You Can Learn in 15 Minutes
Genotype is small. The syntax is designed to be approachable for developers who already know a statically typed language. Generics work as expected. Recursive data types are supported. Target-specific behavior is controlled through annotations, so a single type definition can produce different output for TypeScript than for Rust when the ecosystems demand it.
The language is not opinionated about what you generate. Need Zod schemas instead of plain TypeScript types? The generator supports it. Want a ready-to-ship npm package instead of loose type files? Possible. Need to generate code directly into existing modules rather than a separate output directory? Also supported. Legacy Python versions with older Pydantic? Handled.
This flexibility matters because real projects do not have uniform toolchains. A team might use Zod for runtime validation on the front-end, Serde for serialization in Rust, and Pydantic for data validation in Python. Genotype produces the right output for each context from a single definition.
How It Differs From Protobuf
The distinction is practical, not theoretical. Protobuf is optimized for data transfer performance. It defines a binary wire format, requires runtime libraries for serialization and deserialization, and imposes a specific encoding. This is excellent for high-throughput internal services communicating over gRPC.
Genotype focuses on producing clean types with minimal to no runtime. TypeScript output has zero runtime. Rust uses standard Serde. Python uses Pydantic. There is no Genotype-specific serialization format. The generated types work with whatever transport layer the project already uses, whether that is JSON over HTTP, MessagePack, or something else.
The type system is also more expressive than Protobuf in several areas. Genotype supports more complex data shapes that Protobuf cannot express directly. For API clients and web application client-server interop, where the wire format is JSON and the priority is type safety rather than serialization speed, Genotype fits more naturally into existing workflows.
Practical Use Cases
The primary use case is teams with a TypeScript front-end and a Rust or Python back-end. Genotype defines the shared data structures once and generates types for each language. When the API changes, the developer updates one definition and regenerates. Both sides stay in sync without manual coordination.
Building API clients is another common scenario. Genotype can generate a full client package with types, request handlers, and response validation. The output follows the conventions of the target ecosystem, so it drops into an existing project without refactoring.
File format and standard definitions benefit as well. When a project needs to read or write a specific file format across multiple languages, Genotype provides a single definition that produces compatible types everywhere. Gradual language migration is supported too: as a team moves a service from Python to Rust, Genotype types can target both languages during the transition.
AI Agent Readiness
Genotype includes infrastructure to help LLMs use it correctly. An llms.txt file describes the language and its conventions. Code comment breadcrumbs in the generated output guide AI assistants toward correct usage patterns. A skill definition allows AI coding agents to understand Genotype's syntax and generate valid definitions.
This is a practical consideration for teams using AI-assisted development. When an LLM needs to create or modify shared type definitions, having structured guidance in the output reduces errors and hallucinated syntax.
No Lock-In, Easy Migration
Genotype generates plain types with no runtime dependency. Removing it from a project means keeping the generated output and deleting the Genotype definitions. There is no framework to uninstall, no runtime to remove, and no serialization format to migrate away from. The generated code stands on its own.
Installation is a single command. On Unix systems, a curl script handles it. On Windows, a PowerShell one-liner does the same. The language itself compiles quickly and the generation step is fast enough to run in a pre-commit hook or CI pipeline.
For teams tired of maintaining parallel type definitions across languages, Genotype offers a concrete path to a single source of truth without abandoning the ecosystems they already use. The generated code looks like their code. That is the point.