Systems-first builder
I think about software as runtime behavior, deployment constraints, observability, and operator trust, not only features on a screen.
Paul Hogan / high school systems engineer
Building AI infrastructure, security systems, and robotics-oriented autonomy.
AI Systems Engineer & Robotics Developer
I work on the unglamorous parts of advanced systems: orchestration, telemetry, deployment, operator workflows, and the reliability problems that show up when software has to coordinate with the real world.
Systems
Agent runtimes, orchestration, Dockerized infrastructure
Security
Anti-cheat telemetry, detection pipelines, review systems
Autonomy
Robotics, reinforcement learning, perception, control
About
Self-taught systems engineering with a robotics and autonomy trajectory.
I am a high school engineer focused on the kinds of problems usually found in startups, labs, and real infrastructure: agents, security systems, orchestration, and deployment.
Distributed AI infrastructure
My strongest work lives where agents, workers, data, tools, and orchestration boundaries need to coordinate reliably.
Autonomy direction
Long-term, I want to work on advanced robotics and autonomous systems that combine perception, control, security, and human-machine teaming.
Selected Work
Case studies in AI infrastructure, security, hosting, and systems engineering.
The projects below are presented as engineering systems: what they coordinate, where they run, what constraints they deal with, and why the architecture matters.
case file / Security systems / anti-cheat infrastructure
SentinelAC
AI-assisted anti-cheat and detection infrastructure for multiplayer game environments.
SentinelAC combines telemetry collection, heuristic detection, backend enforcement, and machine-learning experimentation through AI Internal-Cheat Detection (AIICD). The work spans client/server security assumptions, event pipelines, false-positive tradeoffs, and operator-facing review workflows.
TypeScriptNode.jsPythonLuaMongoDBDockerML pipelines
Open case studyarchitecture sketch
Node 01
Game telemetry
Node 02
Detection services
Node 03
AIICD analysis
Node 04
Operator review
evidence
Distributed detection backend
AIICD machine-learning experiments
Operator review and moderation workflow
case file / Distributed AI / enterprise agent platform
CaroNet Workforce
On-prem AI agent platform with distributed orchestration, local LLM infrastructure, and Dockerized workers.
CaroNet Workforce is an agent platform designed around local deployment, orchestration, worker coordination, and practical business automation. It explores how AI systems can run near private data while remaining observable and operationally manageable.
TypeScriptNext.jsNode.jsDockerWebSocketsLocal LLMsMongoDB
Open case studyarchitecture sketch
Node 01
Admin surface
Node 02
Orchestrator
Node 03
Worker nodes
Node 04
Agent tools
evidence
On-prem worker orchestration
Dockerized agent runtime
Local-first AI infrastructure
case file / Hosting / payments / automation
CaroNet InfraHost
Hosting platform work covering active customers, game server and web hosting, Stripe payments, and automatic server setup.
InfraHost focuses on practical hosting operations: customer-facing plans, payment flows, automated provisioning, and server lifecycle work for game and web infrastructure.
Node.jsStripeLinuxDockerNetworkingWeb hostingGame servers
Open case studyarchitecture sketch
Node 01
Customer portal
Node 02
Stripe billing
Node 03
Provisioning
Node 04
Hosted workloads
evidence
Active customer operations
Stripe payment integration
Automated provisioning
case file / Systems engineering / deployment
Infrastructure / Networking Projects
A collection of orchestration, networking, Docker, and AI infrastructure experiments.
This work includes deployment automation, service coordination, networking configuration, and infrastructure patterns that support AI systems and production-style applications.
DockerLinuxNetworkingNode.jsReverse proxiesCI/CDMonitoring
Open case studyarchitecture sketch
Node 01
Network edge
Node 02
Services
Node 03
Containers
Node 04
Observability
evidence
Service orchestration
Networked deployments
AI infrastructure support
Technical Skills
A practical stack for building and operating real systems.
Languages
TypeScriptJavaScriptPythonLuaC++ fundamentalsSQL
AI / ML
AI agentsLocal LLMsPrompt/tool systemsML experimentsReinforcement learning study
Infrastructure
DockerLinux serversReverse proxiesOn-prem deploymentService orchestration
Robotics
Autonomy conceptsPerception systemsControl loopsSimulation workflowsROS interest
Backend Systems
Node.jsNext.jsWebSocketsREST APIsMongoDBAuth flows
Networking / Security
Telemetry pipelinesAnti-cheat systemsModeration toolingThreat modelingNetwork services
Cloud / DevOps
CI/CDStripe integrationHosting opsMonitoringAutomated provisioning
Leadership
Large-scale community operations with technical responsibility.
Beyond code, I have helped lead operations inside a 100,000+ member online technical and gaming community, where tooling, moderation systems, uptime, and staff coordination matter.
100,000+ member operations
Experience coordinating people, systems, procedures, and escalation paths at a scale where process quality is visible quickly.
Moderation and trust systems
Work around moderation infrastructure, abuse response, staff permissions, and community safety workflows.
Technical coordination
Bridging infrastructure, staff leadership, product decisions, and operational needs into systems that people can actually use.
Research Interests
Toward autonomous systems that can operate in complex physical environments.
My long-term interests sit at the intersection of robotics, distributed AI, security, and operator-in-the-loop autonomy.
Autonomous robotics
Military robotics
Distributed intelligence
AI agents
Reinforcement learning
Perception systems
Human-machine collaboration
Defense technology
Technical Notes
Engineering writing on AI, robotics, security, and infrastructure.
A lightweight MDX-ready notes section for technical writing and research logs.
Robotics / RL
Reinforcement Learning as Systems Engineering
Notes on treating RL as a reliability and interface problem, not only a model-training problem.
Read noteSecurity
Anti-Cheat Detection Pipelines
How telemetry, review workflows, and false-positive controls shape practical detection systems.
Read noteAI infrastructure
Local AI Orchestration
Design questions behind agent platforms that run near private data and real operators.
Read noteOperating Thesis
Engineering direction
The common thread is building systems that sense, decide, coordinate, and act under real constraints.
Make autonomy reliable enough to leave the lab.
Move AI systems closer to real operators and real infrastructure.
Treat security, observability, and deployment as core engineering surfaces.
Contact
Professional contact and project links.
For engineering schools, labs, technical recruiters, and collaborators interested in AI systems, robotics, security, or infrastructure work.