MACHINE INTELLIGENCE NATIVE DESIGN
Intelligence, compiled.
MIND is a programming language and compiler stack built specifically for AI and numerical computing — tensor-native types, static shape checks, automatic differentiation, and MLIR-powered code generation, all in one toolchain.
fn main() {
// 2x2 input tensor
let x: Tensor<f32, 2, 2> = [[1.0, 2.0], [3.0, 4.0]];
// Parameter tensor with compile-time shape
let w: Tensor<f32, 2, 2> = randn();
// Autodiff-ready computation
let y = relu(x @ w);
print(y);
}Shapes and dtypes are known at compile time, so invalid tensor math never reaches production.
Why MIND?
Today’s AI stacks are fragmented: Python for research, C++/CUDA for performance, separate runtimes for cloud and edge. MIND collapses that into a single language and compiler pipeline.
One language from prototype to production
Author models, training loops, and serving code in the same language. No “Python version” and “C++ version” to keep in sync.
Tensor-native and statically checked
Shapes, dtypes, and device semantics live in the type system, catching whole classes of bugs at compile time instead of at runtime.
Compiler-grade performance
The compiler lowers through MLIR into LLVM, giving you highly optimized CPU and accelerator code without hand-written kernels.
How the stack fits together
Language & type system
A Rust-inspired language with first-class tensors, deterministic memory management, and built-in automatic differentiation.
- Shape- and dtype-aware tensors
- Differentiable functions with compiler-generated gradients
- Device annotations for CPU, GPU, and future accelerators
Compiler & runtime
Source code is lowered into a custom MLIR dialect and then into LLVM IR, producing optimized binaries and modular runtimes for CPU and accelerators.
- MLIR-based IR for tensor and graph optimizations
- LLVM for hardware-specific code generation
- Lean runtime modules for AOT, JIT, and embedded targets
Who is MIND for?
AI platform teams
Standardize on one language for research and production. Eliminate glue code between notebooks, services, and accelerators.
Applied ML engineers
Express models in high-level syntax with compiler-checked shapes and gradients. Spend time on modeling, not on fighting build systems.
Edge & embedded builders
Compile to lean, deterministic binaries that fit into constrained environments where interpreters and heavy runtimes are not an option.
Ready to explore the language?
Start with the language spec, then dive into the core implementation. MIND is open-core: the compiler and language are MIT-licensed and ready for experimentation.