Currently

I'm building real-world projects using applied AI — moving past tutorials and small experiments into things that actually work end-to-end. A big part of that is getting comfortable with cloud infrastructure, mainly AWS, so I can deploy and scale what I build instead of just running it locally.

Alongside that, I'm building custom LLMs for personal projects — not fine-tuning for the sake of it, but shaping models around whatever a specific project actually needs, whether that's a smaller footprint, a particular use case, or better performance for the task at hand.

It's less about chasing one big idea and more about staying hands-on — building, breaking, and rebuilding things until they actually hold up.

Most days that means switching between writing code, reading docs, and testing things that don't work the first time. I'd rather spend time on a handful of projects I actually understand end-to-end than collect a long list of things I've only touched briefly.

If you're curious about the specifics — what I'm building, what stack I'm using, or just want to see the code — the projects and skills pages have the details. This page is more about where my attention is right now.

Last updated — September 2026