Deterministic computing for safety-critical systems.
Open source tools and libraries for building systems where reproducibility and correctness are non-negotiable. Fixed-point arithmetic, deterministic ML inference, and safety kernel patterns — all designed to support certification under DO-178C, ISO 26262, and IEC 62304.
Floating-point non-determinism, hidden race conditions, and opaque ML models don't belong in systems that control aircraft, medical devices, or vehicles. These projects demonstrate that deterministic alternatives exist — and can be practical.
Certifiable ML Pipeline
- certifiable-data — Deterministic data pipelines
- certifiable-training — Reproducible ML training with Merkle audit trails
- certifiable-quant — Model quantization with error certificates
- certifiable-inference — Fixed-point neural network inference
- certifiable-deploy — Cryptographic model packaging
- certifiable-monitor — Runtime drift detection
- certifiable-bench — Performance benchmarking
Education
- c-from-scratch — Learn C using Math → Structs → Code
- fixed-point-fundamentals — Fixed-point arithmetic from first principles
Tools
- C-Sentinel — Semantic security monitoring
Each repository includes documentation in docs/ with requirements, build instructions, and examples. Clone, build, run the tests.
See CONTRIBUTING.md. Issues and pull requests welcome.
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