Field notes on data, AI, infrastructure, and systems that have to survive reality.

I write about data engineering, AI-native tools, local AI, product architecture, and the messy parts of building software: drifting schemas, brittle pipelines, half-useful AI, and systems that looked simple until they met real users.

This site is where I write those things down. Not as polished thought leadership. Not as hype. Mostly as working notes from the field.

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New here? These carry the ideas the rest of the site leans on.

What I write about

Why this site exists

I use writing to think. Some posts are technical deep dives, some are product notes, some are rough explorations, and some are just me trying to understand where a tool, pattern, or idea breaks.

The common thread is simple: can this system stay useful, understandable, and maintainable after the demo is over? That question shows up everywhere — and it is the backbone of how I think about systems. I've written that up on the philosophy page.

A note on how these are written: I work with AI in the loop — to think through ideas, fact-check specifics, and tidy the prose — then rewrite everything in my own voice. The arguments, the judgment, and the mistakes are mine. Many posts are field notes from real work; where that touches someone else's business, I change the identifying details and keep the shape of what actually happened.