Case studies
No resume language, no buzzwords. Each project below is a real system I designed, built, and operate — problem, build, result.
JARVIS — Private Voice AI Assistant
Problem I wanted a voice AI assistant that could control my homelab, manage schedules, and respond hands-free — but every SaaS solution required monthly fees, sent data to third parties, and couldn't integrate with local infrastructure.
What I built A full-stack voice AI pipeline running entirely on self-hosted hardware — Whisper speech recognition on an RTX 5060 Ti, Gemma 12B via Ollama for local LLM reasoning, Kokoro TTS for natural voice output — all routed through a Telegram gateway with smart-home integration. The companion Flutter app is on TestFlight, wrapping the backend in a clean iOS interface with voice-first UX.
Result A 24/7 voice AI assistant with sub-second response times, zero monthly SaaS costs, full homelab integration, and a Flutter app ready for App Store distribution.
Automated Futures Trading Pipeline
Problem Manual futures trading is emotionally exhausting, requires constant screen time, and reacts too slowly to market micro-moves. I needed a system that could execute strategies without me in front of the terminal.
What I built An automated MES Micro E-Mini S&P 500 trading pipeline pairing Sierra Chart's professional-grade charting and order routing with custom Python strategy scripts — all running on dedicated Proxmox VMs with redundant connectivity and risk-management guardrails.
Result A fully autonomous trading system that executes predefined strategies nearly 24 hours a day, five days a week, managed remotely through secure VPN access, with zero manual intervention required.
Homelab AI Superstack
Problem Cloud GPU instances are expensive, data leaves your network, and you can't iterate fast when every experiment costs per-minute compute. I needed local AI infrastructure that could handle speech-to-text, LLM inference, TTS, and image generation at production quality.
What I built A multi-node Proxmox cluster anchored by an RTX 5060 Ti-powered Ubuntu server running Ollama, OpenWebUI, Whisper, Kokoro TTS, ComfyUI, and SearXNG — all connected through VLAN-segmented networking with ZFS-based storage, Wazuh SIEM security monitoring, and a 4-tier backup strategy.
Result Production-grade local AI infrastructure supporting voice pipelines, image generation, code assistance, and web search — all running on self-built hardware with full data sovereignty.
