Systems and low-level
A from-scratch Rust operating system with capability security and deterministic replay, and an intent runtime whose formal proofs run in microseconds.
A programming language designed for AI authors.
Released Programming language
LMLang is token-minimal, model-agnostic, content-addressed and contract-verified. Humans read the contracts and capability headers; models read and write everything else.
The premise is that a language written for a model to author should optimise for different things than one written for a person to type. So: minimal token cost per definition, contracts the compiler can check rather than comments it ignores, content addressing so identical work is deduplicated, and capability headers that make what a function can reach explicit.
The CLI parses, typechecks, capability-checks and executes. the verify command runs a ten-thousand-case contract gauntlet, or proves the contract outright with an SMT solver rather than sampling it. the resolve command fills declared intent holes from the store or by verified synthesis. Builds target an exact-closure artifact or WebAssembly — a Fibonacci module comes out at 91 bytes.
The reference implementation is Rust: 18 crates, 867 tests, and three complete applications written in LMLang itself.
Language and compiler researchers, and anyone building systems where a model writes code that has to be checkable.
Open source.
Words this page uses in a specific sense.
| Field | Value |
|---|---|
| Name | LMLang |
| Status | Released |
| Category | Programming language |
| Platforms | Windows, Linux, macOS, WebAssembly |
| Pricing | Open source. |
| Source | Closed source |
| Publisher | Cognitive Industries |
| Published from | Brisbane, Queensland, Australia |
A from-scratch Rust operating system with capability security and deterministic replay, and an intent runtime whose formal proofs run in microseconds.
Models trained from scratch, self-improving reasoning engines, evolutionary architecture search, plus the shipped products that lean on them.
A provider-agnostic intermediate representation that plans each piece of work onto CPU, GPU or quantum hardware with cost estimation across real QPU providers.
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