About
The person, the platform, and the engineering behind it.
About Me
What I Do
I design and build AI operations infrastructure — the orchestration layer between AI models and the systems that use them. Multi-model routing, capability-based dispatch, fault tolerance, quorum consensus, workflow orchestration. Not just calling APIs — architecting the platform that manages AI as a distributed workforce.
My current project, robonet, is an AI agent orchestration system that provides unified provider abstraction across 8+ AI platforms, capability-based task routing across a mesh of worker nodes, and quorum consensus for high-stakes AI validation.
Three Layers, Not One
Operator
Multi-model user — Claude, GPT, Grok, Gemini. Cross-validates outputs across models. Tracks error rates per model. Treats AI as peer, not oracle.
Infrastructure Builder
Builds the system AI runs inside. Provider abstraction, capability dispatch, quorum consensus, workflow orchestration. AI agents as distributed workers.
Internals Practitioner
Built an artificial neural network (ANN — artificial neural network) from scratch (~2011, pre-framework). Forward prop, backprop, gradient math, weight initialization, convergence. The internals, not just the API.
Technical Domains
10 technical domains at practitioner depth or deeper. Not 10 separate specializations — one cognitive operation (pattern decomposition) applied across domains.
- Distributed Systems Architecture
- Network Protocols & Transport
- Cryptography & Security
- AI / Machine Learning
- Enterprise Infrastructure & Cloud
- DevOps & Build Systems
- IoT / Embedded Systems
- ERP (enterprise resource planning) / Business Systems
- Web Application Development
- Systems Architecture (meta-domain)
Interests & Adjacent Domains
Domains outside IT where the same pattern-decomposition applies. Not professional claims — real depth from sustained engagement.
- Theology (pastor-level depth, covenantal focus)
- Multi-Dimensional Theoretical Physics
- Linguistics (10 human languages)
- Psychology / Cognitive Architecture
- Medicine (diagnostic pattern recognition)
Enterprise Background
Wells Fargo — PMG (Performance Management Group)
Performance Management Group — 40 engineers selected from 1,000+ IT staff. C-level escalation oversight for failing projects. SOX (Sarbanes-Oxley) / PCI (Payment Card Industry) regulated, multi-datacenter, zero-downtime environment.
Diagnosed a 2-3 year unresolved performance problem in approximately 3 days — with no prior Apache experience. Tens of millions in losses stopped.
US Bank — Application Consultant 4 (AC4)
Official title: C# developer. Actual role: the person people bring unsorted problems to. Replaced vendor product security with bank security on 2 projects. Trained senior staff across specialties.
Same "go-to" pattern emerged independently at two separate Fortune 500 financial institutions.
Background
Started coding BASIC on a Commodore 64 in the early 1980s. Ran a BBS (bulletin board system) and served as FidoNet regional coordinator in high school. Campus network admin and CS lab assistant at UW-La Crosse (Sendmail MTA (mail transfer agent), NeXTSTEP maintenance). IBM test school for Java (pre-1.0) and Eiffel. Adjunct instructor at a Wisconsin public technical college.
Currently building robonet and the xsubi platform independently — AI agent orchestration, virtualization infrastructure, build systems, and ERP modules across 6 interconnected projects.
Why I Published the Research
I spent most of my career editing myself down — simplifying so the room could follow, leaving out the parts that would raise eyebrows, fitting into whatever box the title said I was. After the stroke, I stopped doing that. The research section is what it looks like when I just put the work up instead of deciding for other people whether they're ready to see it.
The Site
Mission
xsubi provides VM (virtual machine) hosting and game server hosting on hardware we own and operate directly. KVM (kernel-based virtual machine) on Ubuntu Linux (libvirt) and Hyper-V on Windows Server 2022 DC — no cloud intermediary, no vendor lock-in, no opaque infrastructure between you and your machines.
The differentiator is control. You get full transparency into the stack your workload runs on, predictable pricing without surprise egress fees, and an operator who actually owns the hardware rather than reselling capacity from AWS (Amazon Web Services), Azure, or GCP (Google Cloud Platform).
Ecosystem
The xsubi platform spans multiple services — each purpose-built, each cross-linked.
Infrastructure Transparency
Not the cloud — your machines. Real hardware, real stack, visible at every layer.
Timeline
Engineering
Headline Numbers
AI Model Orchestration
Each model routed to its strength — strategy separated from execution
- Architecture decisions
- Use case authoring
- Tradeoff analysis
- Code review
- Task routing
- Quality gates
- Queue management
- PR automation
- Code generation
- File scaffolding
- Test writing
- Implementation
- Feature branches
- Pull requests
- Passing tests
- Merged to develop
Technical Coverage
Projects & Services
Project Breakdown
| Project | Domain | Commits | Source Lines | Tests | Stack | Progress |
|---|---|---|---|---|---|---|
| xsubi-docs | Architecture / UCs | 102 | 17,808 | — | Markdown | 60%
Active |
| qforge | Dev Tools / MCP | 37 | 18,326 | 463 | PythonMarkdownJavaScript | 65%
Active |
| xsubi-infra | IaC / DevOps | 30 | 4,395 | — | YAMLBashMarkdown | 50%
Active |
| robonet | Distributed Systems | 27 | 147,794 | 2,990 | PythonMarkdownJavaScriptBash | 85%
Alpha |
| home-lab-operations | Lab Ops | 10 | 4,362 | — | C#MarkdownYAMLBash | 0%
Planned |
| xsubi-learning | Learning Platform | 8 | 3,581 | — | C#JavaScriptMarkdown | 30%
Early |
| xsubi-resume | AI / Automation | 8 | 17,391 | — | PythonMarkdownBash | 50%
Active |
| xsubi-host | Infrastructure / API | 6 | 327 | — | Markdown | 40%
Early |
| xsubi-website | Web / Platform | 5 | 64,078 | — | C#TypeScriptMarkdownJavaScriptPython | 55%
Active |
| xsubi-games | Game Servers | 4 | 8,412 | 186 | TypeScriptMarkdown | 35%
Early |
| odoo-campground-bensons-resort | ERP / Demo | 3 | 1,264 | — | PythonMarkdownJavaScript | 0%
Planned |
| xmark | Version Registry | 2 | 2,204 | 27 | PythonBashMarkdown | 80%
Alpha |
| xsubi-bench | Benchmarking | 1 | 104 | — | Markdown | 20%
Planned |
| xsubi-drones | Drone Hardware | 1 | 291 | — | Markdown | 15%
Planned |
| xsubi-farm | Server Farm | 1 | 76 | — | Markdown | 15%
Planned |
| xsubi-support | Support / Ops | 1 | 52 | — | Markdown | 20%
Planned |
| xsubi-turrets | Turret Systems | 1 | 212 | — | Markdown | 15%
Planned |
| xsubi-vtt | Virtual Tabletop | 1 | 74 | — | Markdown | 20%
Planned |
Consulting Services
AI Systems Architecture
Multi-model orchestration, provider abstraction, agent pipelines, and capability-based dispatch. Architecture for systems where AI is a distributed workforce — not just an API call.
- Multi-model routing and fallback design
- Quorum consensus for high-stakes AI validation
- Agent lifecycle management and task graphs
- Provider abstraction across Anthropic, OpenAI, Google, xAI, Ollama
Infrastructure Design
KVM and Hyper-V virtualization, Kubernetes cluster design, CI/CD (continuous integration / continuous deployment) pipeline architecture, monitoring stack integration, and self-hosted platform engineering.
- Hypervisor fleet design (KVM / Hyper-V)
- Kubernetes and container orchestration
- Jenkins CI/CD pipeline architecture
- Prometheus / Grafana / Loki observability
Performance Engineering
Diagnosis of complex production performance problems. Root-cause analysis across the full stack — network, application, database, OS (operating system), and infrastructure layers.
- Production incident root-cause analysis
- Cross-layer performance diagnosis
- Long-standing problem triage (days, not quarters)
- Capacity planning and bottleneck identification
Security Architecture
TLS (transport layer security) PKI (public key infrastructure) design, authentication system architecture, OWASP (Open Web Application Security Project) compliance review, and challenge-response security frameworks for distributed systems.
- TLS PKI and certificate lifecycle
- Auth system design (OAuth, OIDC, Identity)
- OWASP Top 10 review and remediation
- Security architecture for distributed AI systems
Engagement
Consulting engagements are managed through Upwork for mutual protection — milestone tracking, escrow, and dispute resolution are handled by the platform.
All deliverables are gated behind payment confirmation. Scope, timeline, and acceptance criteria are defined in writing before work begins. No surprises in either direction.
Availability is limited. Complex architecture engagements are taken selectively. If your problem is genuinely hard, that's a qualifier, not a disqualifier.