A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest “twin” examples, neighborhood tightness, mixed-vs-homogeneous evidence, and “no reliable twins” abstention. Argues similarity and evidence packets beat probability scores for trust and safety.

A governed system for translating applied AI research into auditable, decision-ready artifacts.

"Advanced ternary logic framework for processing uncertainty in computational decision systems—solving the fundamental limitation of binary logic in real-world applications."

A design framework for decision-centered automation, governance, and early misalignment detection.

Audit-grade governance artifacts documenting non-overridable behavioral constraints. Schemas and redacted audit examples showing how certain failures are made structurally impossible before discretion or hindsight.

Full-stack freelance defense toolkit featuring rule-based client risk scoring, contract generation, invoice tracking, and red-flag detection.

Offline sourdough starter manager with diagnostics, stability tracking, corrective and conversion logic.