Python made data accessible. ThinkingLanguage makes it fast, safe, and intelligent, in one compiled language. Tables, streams, tensors, models, and agents are native types, not libraries.
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Data enters as native types, the compiler checks it like code, and one toolchain ships it as a native binary or a WASM module.
A source is typed. A transform is a function over a table<T>. Training a model is a keyword, not an import. The pipe |> moves ownership down the pipeline, so the compiler catches data bugs before anything runs.
source users = postgres("db").table("users") -> User transform active_users(src: table<User>) -> table<User> { src |> filter(is_active == true) |> clean(nulls: { name: "unknown" }) |> with { tenure = today() - signup_date } } model churn = train xgboost { data: active_users(users) target: "is_active" features: [tenure, monthly_spend] }
TL is opinionated about the things data and AI work actually needs.
Tables, streams, and tensors are first-class types in the language, not library objects.
ETL and ELT flows are first-class constructs you compose, not scripts you glue together.
train, predict, embed, and agent are keywords, not imports.
Automatic partitioning across cores. Concurrency you get for free, not fight for.
Built-in error handling for the unreliable data sources you actually deal with.
Python-like readability with Rust-like static guarantees the compiler enforces.
Native code with lazy evaluation. Performance is the default, not an optimization pass.
Five native types power the language, on a DataFusion engine, with the full developer toolchain in the box.
We are onboarding a small group of design partners: teams who ship real workloads on TL, meet with the founders monthly, and get lifetime free access to ThinkingLanguage Cloud. Limited seats.
Apache-2.0, compiled to native or WASM. Open the web workspace and write your first pipeline, or clone the repo.