Safety Kernel Insights

Practical architectures, proofs, and design decisions from building deterministic safety kernels.

This section offers technical perspectives drawn from building deterministic safety kernels and related systems. Articles focus on architectures, formal methods, safety certification, and the engineering principles that matter when reliability, reproducibility, and verifiable behaviour are essential.

C Fundamentals April 15, 2026

The Type Promotion Trap: C's Silent Integer Conversion Bugs

How implicit type promotion rules turn correct-looking comparisons into logic errors in C

16 min read →
Deterministic Computing January 26, 2026 21:30

When Fixed-Point Beats Floating-Point (And When It Doesn't)

An honest analysis of when Q16.16 is the right choice and when floating-point is acceptable

6 min read →
Systems Architecture January 26, 2026 20:05

Init-Update-Status-Reset: O(1) Safety Guarantees

A four-function interface that enables static analysis, bounded resources, and compositional verification

12 min read →
Formal Methods January 26, 2026 19:00

Contracts as Documentation: Why Comments Lie

How preconditions, postconditions, and invariants become living documentation

9 min read →
Safety-Critical Programming January 26, 2026 18:30

Why 'Hello World' Fails Safety-Critical Engineers

Traditional C tutorials teach habits that certification auditors reject

10 min read →
Announcements January 24, 2026 22:31

C From Scratch: Learning Safety-Critical C

Why proving code correct before writing it changes everything

9 min read →
Deterministic Computing January 20, 2026 22:00

Stochastic Rounding Without the Stochastic

How PRNG-controlled rounding can provide regularisation benefits deterministically

7 min read →
Deterministic Computing January 20, 2026 20:45

Cross-Platform Bit-Identity: Theory to Practice

The practical journey of verifying deterministic ML across platforms

8 min read →
Deterministic Computing January 20, 2026 20:24

Feistel Shuffle: Deterministic Data Ordering

How cycle-walking Feistel networks can provide reproducible shuffling for ML training

7 min read →

10 of 36 articles