I'm a senior software engineer who gets most excited when the answer isn't obvious yet.

What fascinates me about AI isn't just what the latest models can do.

It's what we get to build with them.

When a new AI capability becomes practical, my instinct is to get hands-on with it: explore the possibilities, build a prototype, find out what works, and turn the promising ideas into something real.

Explore → Prototype → Learn → Build → Hand Off

I'm especially interested in:

• Applied AI exploration — discovering useful applications for emerging AI capabilities by building with them
• Agentic software development — exploring how AI agents can become reliable participants in serious engineering workflows
• AI developer tooling — building the tools, context, orchestration, and evaluation around AI-assisted development
• Local-first AI — using models that run locally to create private, responsive, offline-capable experiences
• Zero-to-one engineering — taking ambiguous ideas from "could this work?" to something concrete enough to find out

I enjoy working at the point where a new technical capability opens up a design space that didn't quite exist before.

Sometimes the prototype becomes something useful.

Sometimes it proves the idea was wrong.

Both are progress.

Loqa is a local-first voice assistant built around privacy, control, and offline AI.

The question behind it is more interesting to me than the technology list:

What can we build when speech recognition, language intelligence, and automation can happen privately on hardware you control?

Loqa explores that question with:

• 🎙️ Wake word detection and local speech recognition with Whisper
• 🧠 Local language models via Ollama
• 🔌 A plugin-based skill system with Home Assistant integration
• ⚡ Event-driven services using Go, gRPC, and NATS
• 📊 A Vue frontend for understanding and debugging interactions
• 🔒 An architecture designed to work without depending on cloud AI

Stack: Go • Python • Whisper.cpp • Ollama • NATS • gRPC • Docker • Vue.js • SQLite

I've spent my career building real software across the stack, with particular depth in backend, distributed, and event-driven systems.

Today, I'm applying that engineering foundation to AI systems and experimentation.

• Languages: Go, Python, TypeScript, Rust
• AI: LLM integration, agents, RAG, orchestration, local models, voice AI
• Systems: APIs, distributed systems, event-driven architecture, gRPC, NATS
• Product: Full-stack development, software architecture, rapid prototyping
• Infrastructure: AWS, GCP, Docker, Kubernetes

I enjoy technical leadership, but I never want to get so far from the code that I stop building things.

I'm interested in the expanding space between:

"AI can do this now."

and

"Here's something useful we built because of it."

That includes agentic systems, developer tools, local-first applications, multimodal interfaces, intelligent devices, accessibility, IoT, and whatever new capability arrives next and makes me think:

"Huh. I wonder what I could build with that."

📍 Dallas–Fort Worth, Texas
💼 LinkedIn
🔊 Loqa

Code is how I contribute. Kindness is how I collaborate. Curiosity is how I grow.