Work

How I think, design, and build.

I turn abstract ideas about trustworthy AI into small, reviewable artifacts and applied research. Public repositories show the method; the research below shows the range. Most production work is private — these are the clean-room versions that show how I work without exposing private systems.

How I work

The same loop runs on every project — the difference between a claim and something you can trust is whether each step actually happened.

01 · THINK

Follow the pattern

Start from a real problem and pull structure across disciplines — ethics, cryptography, cognitive science, systems design — instead of staying inside one field.

02 · DESIGN

Make it bounded

Turn the idea into an architecture with explicit boundaries: what it may do, what it must refuse, who stays in the loop, and how a decision is recorded.

03 · BUILD

Ship the small version

Build the smallest real thing that runs under real constraints — a scorer, a schema, a runner — rather than a slide about what it could do.

04 · GOVERN

Test & record

Attack it, gate it, and leave a reconstructable record. If it can't survive its own review, it doesn't ship.

05 · PUBLISH

Publish honestly

State the claim, show the evidence, name the limitation. Restraint is the credibility.

Applied research

Longer-form work on how governed, sovereign AI systems actually get built. Selected papers — the strongest are on Writing.

White paper · 2026

Sovereign AI

Distributed cognition on consumer hardware with a governed boundary that keeps local models out of the reasoning path — cloud-grade capability with zero data egress. Honest about its own limits.

Read the paper →
Formal methods · in prep

Verifiable Self-Governance

A formal approach to safety in adaptive autonomous systems: specifying and checking governance properties so a system's constraints can be verified, not just asserted.

Systems · in prep

Explainability, Auditability & Data Sovereignty

A framework for advanced AI systems that keeps decisions explainable, records reconstructable, and data under the owner's control.

Explorations

Where I follow a pattern into new territory — bounded, honestly caveated, and useful for showing how I think.

Cognition
Python · CC BY 4.0

Active Inference Primer

A minimal educational primer on prediction, uncertainty, and agency for anticipatory cognition in autonomous agents.

View repository →
Alignment
CC BY 4.0

Eudaimonic Alignment

Research notes exploring AI wellbeing as an alignment strategy, drawing on Aristotelian philosophy and mapping knowledge traditions to modern governance.

View repository →
Distributed cognition · in prep

UNA Distributed Cognition

How reasoning, memory, and perception split across nodes while a governed boundary keeps the reasoning path clean — the architecture behind UNA.

Edge AI · in prep

GeoAIF — Edge Seismic Intelligence

A design for real-time seismic detection and early warning over mesh sensor networks and edge AI, without centralized infrastructure.

Multi-agent · in prep

THRUM — Swarm Coordination

Exploratory work on coordination and learning under constraints in multi-agent, swarm-like environments.

Exploration
Simulator-first · caveated

Quantum + AI Experiments

Simulator-first experiments at the quantum/AI boundary, with explicit claim limits — clearly labeled non-production.

View repository →

The discipline behind all of this comes from UNA — the deterministic, governed AI system I build and run. Its governed change pipeline, capability boundaries, and auditable receipts are the private counterpart to the public work above. See how UNA's governance works →

Want the full research corpus or a specific paper? Get in touch — several papers are in preparation and available on request.