Systems engineer and administrator building infrastructure, automation, and knowledge systems to understand how complex systems fit together.

build · document · observe · iterate

I work across systems administration and engineering, with a focus on infrastructure, identity, automation, monitoring, and knowledge systems.

I like projects that make abstract architecture tangible. That usually means building the thing, operating it, documenting it, breaking it, fixing it, and learning what actually matters in the process.

I build things to understand them.

A documented private infrastructure environment used as a systems engineering laboratory.

Proxmox Linux Docker Networking TLS Tailscale Checkmk Prometheus Grafana Backups

Current work centers on service architecture, observability, secure access, reverse proxy design, recovery, and infrastructure documentation.

A private-first personal knowledge assistant built to understand retrieval-augmented generation from the ground up.

Python FastAPI PostgreSQL pgvector Docker Obsidian

The project connects two interests I keep returning to: systems engineering and knowledge management.

Practical SQL analysis focused on answering operational data questions with reproducible queries and documentation.

SQL Analytics Data

A public outside-in diagnostic tool for checking DNS, TLS, HTTPS, redirects, and security headers from a single hostname.

Python FastAPI DNS TLS HTTP Security

Current work focuses on safe target validation, SSRF-resistant network checks, and turning low-level infrastructure results into clear operational guidance.

Vector	Status
Linux
Python
PowerShell
Cloud
Identity
RAG
Automation
Knowledge management

Linux · Proxmox · Docker · Nginx · Tailscale

Checkmk · Prometheus · Grafana

PostgreSQL · pgvector · SQL

Python · PowerShell · Git · FastAPI

IAM · SSO · Google Workspace · Entra ID

Obsidian · RAG · Documentation

→ deeper Linux administration
→ Python for systems engineering
→ PowerShell beyond task automation
→ cloud and identity architecture
→ retrieval-augmented generation
→ infrastructure automation

	Principle
	Prefer evidence over assumptions.
	Automate repeatable work, document the edge cases.
	Monitoring should explain a system, not merely report that it exists.
	Good infrastructure is understandable infrastructure.
	The fastest way to learn a system is often to own its failure modes.

When I am not working on infrastructure or automation, I keep circling back to astronomy, mathematics, and writing.

I also enjoy taking my dog, Marvel, on adventures at nearby parks, trails, and lakes.

If you are here because of one of the projects above, open an issue in the relevant repository. I am always interested in comparing implementation choices, operational lessons, and better ways to design the system.

_{casual-loops // systems · knowledge · iteration}