MACHINE INTELLIGENCE NATIVE DESIGN
Intelligence, compiled.
Cross-substrate bit-identity with embedded provenance. MIND is a deterministic compiler whose output is bit-identical across CPU and ARM substrates (verified on x86_64 (AVX2) + ARM64 (NEON), 2026-07-05), with an emitted evidence chain embedded in the artifact itself (cryptographic post-quantum signing (ML-DSA, FIPS-204) is the next milestone) — compile-time tensor safety, compile-time autodiff, and auditable builds for ML systems that must be inspectable.
1fn main() {2 // 2x2 input tensor (dense literal, concrete types)3 let x: Tensor[f32; 2, 2] = [[1.0, 2.0], [3.0, 4.0]];45 // Elementwise operations work; inter-fn tensor args are Phase 116 let y = x .* 2.0;78 print(y);9}
Shapes and dtypes are known at compile time, so invalid tensor math never reaches production. Dense tensor literals, elementwise ops, and tensor-returning functions are Phase 11 Tier 1 (shipping v0.10.0); inter-function tensor arguments and deterministic intrinsics (matmul/softmax/randn) are Phase 11 roadmap.
The problems we solve
Today's AI stacks are fragmented: Python for research, C++/CUDA for performance, separate runtimes for cloud and edge. Models fail in production with runtime shape mismatches, training loops carry per-iteration autodiff overhead, and regulated industries can't get reproducible builds.
Runtime shape bugs
Tensor shape and dtype errors surface in production, not during development. MIND catches these at compile time with static tensor types.
Fragmented toolchains
Python for prototypes, C++ for production, glue code everywhere. MIND gives you one language from research to deployment.
Non-deterministic builds
Can't reproduce training runs or audit model provenance for compliance. MIND delivers bit-identical reproducible builds (verified on x86_64 + ARM64) and deterministic execution mode.
What MIND does
A programming language and compiler stack built specifically for AI and numerical computing — tensor-native types, static shape checks, automatic differentiation, and a native-ELF backend with MLIR as the downstream-interchange path for specialty targets — all in one toolchain.
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.
Compile-time autodiff (scalars + core ops)
Gradients computed once during compilation for scalar and core tensor ops. No runtime tape overhead. Full tensor-parametrized AD is Phase 12 roadmap.
Deterministic execution & auditable builds
Bit-identical reproducible builds verified via SHA-256 cryptographic hashing on supported substrate targets (x86_64 (AVX2) + ARM64 (NEON), verified 2026-07-05). Critical for regulated ML and model certification where determinism is required.
How it works
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 (shipped); GPU execution on the commercial roadmap
Compiler & runtime
MIND IR lowers to native x86-64/ELF directly in the normative self-host path (deterministic by construction), with MLIR as the downstream-interchange backend for specialty and exotic-chip targets via a pluggable backend trait.
- Native-ELF backend: front-end self-host fixed point closed (byte-identical bootstrap)
- MLIR interchange for specialty targets; LLVM for hardware-specific codegen in that path
- Lean runtime modules for AOT, JIT, and embedded targets
Performance That Matters
MIND optimizes both compilation and runtime — fast iteration during development AND production performance when it matters.
Fast Compilation
Frontend processes ML programs in the microsecond range (parse + typecheck + IR). Instant feedback enables tight iteration during development.
Deterministic Mode
Bit-identical builds verified via SHA-256 cryptographic hashing on supported substrate targets (x86_64 (AVX2) + ARM64 (NEON), verified 2026-07-05). Every compilation of the same source produces identical output — essential for regulated industries and model certification.
Determinant: a byte-exact, bit-reproducible deterministic BLAS →
Low-Overhead Autodiff
Gradients computed once during compilation, not on every training iteration. No runtime tape or graph construction overhead.
Who is MIND for?
Regulated ML & audit trails
Healthcare, finance, autonomous systems — industries where model provenance and reproducibility aren't optional. MIND's deterministic builds deliver auditable ML.
Platform teams scaling ML infrastructure
Standardize on one language for research and production. Eliminate glue code between notebooks, services, and accelerators.
Edge & embedded deployment
Compile to lean, deterministic binaries that fit into constrained environments where interpreters and heavy runtimes are not an option.
Open core + enterprise
Community Edition (Apache 2.0): The compiler and language are open source and ready for experimentation.
Commercial runtime + hosted offerings from STARGA, Inc.: Deterministic execution mode, audit logs, compliance tooling, and hosted control plane with SLA-backed support.