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 β€” 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 β€” the difference between a claim and something you can trust is whether each step actually happened.

01 Β· THINK

Follow the pattern

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

02 Β· 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 Β· BUILD

Ship the small version

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

04 Β· 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 Β· 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 β€” the strongest are on Writing.

White paper Β· 2026

Sovereign AI

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

Read the paper β†’
Formal methods Β· 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 Β· 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 β€” bounded, honestly caveated, and useful for showing how I think.

Cognition
Python Β· CC BY 4.0

Active Inference Primer

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

View repository β†’
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 β†’
Distributed cognition Β· in prep

UNA Distributed Cognition

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

Edge AI Β· in prep

GeoAIF β€” 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 Β· in prep

THRUM β€” Swarm Coordination

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

Exploration
Simulator-first Β· caveated

Quantum + AI Experiments

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

View repository β†’

The discipline behind all of this comes from UNA β€” 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 β†’

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