The systems I build and operate: healthcare integration tooling, Kubernetes platform engineering, and the AI development infrastructure behind both. Everything public links to source.
Format-agnostic pipeline that parses legacy healthcare formats (HL7v2, X12 EDI, CSV) into semantic events like patient_admit and lab_result, then routes them through configurable workflows. Integration logic reads in workflow terms instead of PID.3.1.
Includes a mapping-studio UI for building transformations without code, staged parsing with Source Profiles, FHIR R4 output, and CI with coverage gates.
Single static binary that gates outbound files in CI or a send script: X12 envelope integrity with control-number and segment-count recounts, invisible and lookalike character detection, terminator consistency, declared-versus-actual trailer counts, and fixed-width layout drift.
48 rules across X12, HL7v2, EDIFACT, delimited, and fixed-width files. Zero dependencies, JSON and SARIF output, CI-friendly exit codes, tested on a three-OS matrix. Built with agentic engineering under adversarial review, and the review is in the commit history.
Automatic hardware discovery and labeling, benchmark-driven scheduling via CRDs and a scheduler extender, multi-backend model serving (vLLM, Ollama, llama.cpp), and dynamic LoRA adapters. Runs in production on my own cluster.
A Go daemon plus VS Code and Zed extensions that manage Model Context Protocol server lifecycles, sync configuration across five AI coding tools, and provide persistent agent memory across sessions. This is the tooling I use to build everything else on this page.
Self-hosted system of record for vendor-integration work: projects and vendors, artifact capture with SHA-256 evidence hashing and data-classification levels, decisions, risks, milestones, Gantt/Kanban/status views, hybrid FTS5 plus semantic search, an MCP server surface for AI agents, Prometheus metrics, and a formal threat model gating what data may enter. I use it daily to run real multi-vendor integration programs.
Code private (operational data). Architecture walkthrough and synthetic-data demo on request.
Roughly 300 routes handling multi-source ingestion (Gmail classification pipeline, feeds, calendars), five storage tiers (Postgres, Neo4j graph, Qdrant vectors, Redis, MinIO), LangGraph agent workflows for document composition, and ARQ background jobs. About 140K lines.
Private (personal data). Happy to walk the architecture.
Everything above ships through a homelab I operate as production: K3s on Harvester, Flux CD GitOps, Longhorn storage, Harbor registry, self-hosted GitLab with CI runners, and a Prometheus/Grafana/Loki observability stack.
My day job is payment-integrity and payments vendor integrations at a Medicare Advantage payer. Those systems belong to my employer; the numbers and stories are mine to tell in interviews. The code above is what you can inspect today.