MIND Language Documentation
Welcome to the MIND documentation. MIND is a tensor-native, Rust-inspired language and compiler that unifies modeling, compilation, and deployment of intelligent systems.
Quick Start →
Get up and running with MIND in minutes. Write your first tensor computation.
Installation →
Install the MIND compiler and runtime on your system.
Shapes & Broadcasting →
Learn how MIND handles tensor shapes and broadcasting semantics.
Source Code →
Browse the MIND compiler source code on GitHub.
Core Concepts
Tensor-native types
Tensors are first-class citizens with shapes and dtypes encoded in the type system, enabling powerful compile-time guarantees.
Static shape checking
Shape mismatches are caught at compile time, not runtime, preventing a whole class of common deep learning errors.
Built-in autodiff
Automatic differentiation is a first-class language feature, not an add-on library, allowing for efficient gradient computation.
MLIR + LLVM
The compiler leverages MLIR for high-level tensor optimizations and LLVM for highly efficient machine code generation.
Full-Stack AI
MIND is evolving into a complete platform for building, deploying, and scaling AI systems.
Distributed Execution
Scale models across clusters with data parallelism, model parallelism, and automatic gradient synchronization.
Deployment
Deploy to cloud, edge, or on-premise with one command. Built-in serving, auto-scaling, and monitoring.
Language Specification
The formal language specification is the authoritative source for MIND syntax and semantics. It is automatically synced from the mind-spec repository.
Browse Specification