A collection of technical projects demonstrating hands-on experience with infrastructure, automation, and modern DevOps practices.


Self-Hosted AI Inference Stack & Agent Team

Production-grade, local-first AI platform combining MLX on Apple Silicon, Ollama on Linux, and Claude as cloud fallback — all routed through LiteLLM with Redis caching. Powers a team of six specialized AI agents handling research, monitoring, backend, frontend, and creative work.

Technologies: MLX, Ollama, LiteLLM, Redis, Anthropic Claude, OpenClaw, Apple Silicon


Home Lab

Self-hosted infrastructure running on Proxmox and Raspberry Pi. Includes monitoring, DNS, dashboards, and containerized services managed via Dockhand.

Technologies: Proxmox, Docker, Pi-hole, Grafana, Prometheus, Tailscale


GitOps Infrastructure

Infrastructure as Code repository for managing all homelab Docker configurations. Version-controlled deployments with secrets management and remote stack control via Hawser.

Technologies: Git, Docker Compose, Dockhand, GitHub


Docker Security Review

Comprehensive security audit of homelab Docker infrastructure. Identified critical vulnerabilities including privileged containers and unprotected Docker socket mounts, then applied hardening fixes following CIS Docker Benchmark guidelines.

Technologies: Docker, Security Auditing, Linux Capabilities, Container Hardening


Container Resource Management

Implemented comprehensive memory limits and fixed Prometheus alerting across 20 Docker containers. Fixed broken alerts showing +Inf%, applied resource limits based on usage analysis, and improved monitoring accuracy.

Technologies: Docker, Prometheus, Grafana, PromQL, GitOps


Claude Code Memory System

Structured context system for AI-assisted infrastructure development. Converts markdown-based memory files to XML format for better hierarchy and queryability across development sessions.

Technologies: Claude Code, XML, Bash, Git


Semantic Memory: Claude Memory System 3.0

Evolution from file-based context to semantic vector search using ChromaDB. On-demand retrieval via MCP integration replaces pre-loaded context — AI memory that scales with your infrastructure.

Technologies: ChromaDB, MCP, Python, Vector Embeddings, Claude Code