Technology & R&D Executive · Principal Systems Architect · Applied AI Engineer
I engineer the systems that don't have a playbook yet.
Sovereign AI · Evidence-driven security · Experimental interfaces · Resilient infrastructure
I've spent more than two decades building and leading engineering efforts across applied AI, cybersecurity, defense and space operations, cloud platforms, and distributed systems. My work spans executive technical leadership and hands-on research, architecture, and delivery.
Today I focus on a harder class of problem: systems that need to reason about their own limitations, reveal their internal behavior, preserve evidence, and keep humans in control.
I use AI extensively in engineering—not as a substitute for architecture, but as an amplifier for rigorous system design, experimentation, review, and delivery.
Understand the system. Expose the failure. Engineer the missing capability. Prove it works.
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Sovereign cognitive systems A long-running, fully local cognitive architecture connecting Raspberry Pi sensing with desktop GPU inference. Memory continuity, perception, epistemic controls, and governed self-improvement—built to distinguish demonstrated capabilities from systems still in shadow or maturation. |
Software engineering as a living audiovisual world Turns real development events into generative environments and original evolving musical scores. Each project becomes its own expressive system, shaped by code activity, agents, failures, recovery, and milestones—with a local-first pipeline. |
🎯 fpsdetServer-side anti-cheat evidence Analyzes what dedicated game servers accepted against the game's rules and the information available to clients. Deterministic, auditable case files; no client spyware and no automatic bans. |
🛡️ NjordScanDeveloper-focused application security Security analysis for Next.js, React, and Vite, with actionable explanations, attack-path context, and deterministic verification. Findings are explained without treating AI-generated hypotheses as proven vulnerabilities. |
⚙️ SoloNerdsEngineering recovery and systems ownership Architecture, technical triage, security-minded remediation, and recovery for applications whose complexity has outgrown their original build process—particularly AI-assisted software. |
Operational automation A long-lived Bash management toolkit for dedicated Valheim servers: world management, backup and restoration, mods, and day-to-day administration. |
| Frontier | Engineering question |
|---|---|
| Sovereign cognition | Can personal AI maintain memory and continuity without surrendering privacy or operator control? |
| Evidence-gated autonomy | How do we prevent systems from claiming authority before performance has been demonstrated? |
| Glass-box engineering | Can source code, infrastructure, and runtime behavior become visible, explainable, and inspectable together? |
| Digital forensics | How do we preserve chain of custody and make technical findings independently verifiable? |
| Generative developer environments | What happens when code, agents, failures, and engineering progress become an immersive, living medium? |
| AI-assisted system recovery | How do we restore security, maintainability, and human ownership to rapidly generated software? |
My engineering method →
OBSERVE → Establish what actually happens
MODEL → Map boundaries, dependencies, and failure modes
INSTRUMENT → Make behavior and evidence inspectable
ENGINEER → Implement the smallest coherent capability
VALIDATE → Reproduce, test, and challenge the result
GATE → Distinguish experimental, shadow, and proven behavior
OPERATE → Make the system recoverable, secure, and understandable
A demonstration is not validation. A metric without context is not evidence. An AI prediction is not an observed fact.
AI & cognition · Local LLMs · Agents · Memory architectures · Multimodal perception · Edge AI · Governed autonomy
Software engineering · Python · TypeScript · JavaScript · SQL · Bash · Next.js · React · Node.js · Developer tooling
Systems & infrastructure · Linux · Docker · GPU computing · Raspberry Pi · AWS · Distributed systems · Observability
Security & verification · Threat modeling · Application security · Forensic provenance · Deterministic analysis · Reproducibility
Creative computation · Generative graphics · Real-time visualization · Interactive environments · Procedural audio
- Evidence before authority. Experimental capabilities remain clearly labeled.
- Privacy is architectural. Keep processing and data local when feasible.
- Humans retain the controls. No hidden promotion from suggestion to action.
- Visibility is a feature. Assumptions, provenance, and failure modes must be inspectable.
- Security is systemic. It cannot be bolted on after complexity accumulates.
- Operational reality wins. A beautiful demo is only the beginning.