Curiosity, persistence, and a low tolerance for systems that don’t make sense.

A few years ago, at a paint party, I picked up a brush and somehow finished the project before everyone else had finished the first step. That is usually how I approach things: I dig in, ask questions, test, break, rebuild, and keep going until the thing makes sense.

My path has never been perfectly linear. Music degree. Cruise ships. Horses. Rock and roll bands. Computers since the mid-’90s, when I got my first one and immediately started breaking things so I could understand them.

I’ve been learning ever since. I just try to break things a little less often now.

I did not come to technology through a traditional corporate ladder. I came to it through years of building, maintaining, troubleshooting, documenting, and fixing real systems that people depend on.

That background shapes how I approach AI. I am less interested in flashy demos and more interested in what survives contact with real work: messy data, busy people, limited budgets, security concerns, and decisions that have to make sense next week, not someday.

AI is powerful, but it is not magic. The useful part is learning where it fits, where it fails, and how to build workflows that make people’s work easier instead of more complicated.

When someone asks me to explain AI, I try to skip the jargon and reach for a metaphor that makes the idea easier to hold.

Think of AI like a librarian with access to an enormous amount of information. Helpful, fast, and surprisingly capable, but still inside the library.

An AI agent is what happens when that assistant can leave the building, follow instructions, use tools, and take limited action on your behalf.

Same idea, longer leash.

I’m currently consulting on web security, AI workflows, content systems, and practical digital strategy for small organizations and individuals.

I also write and teach about practical AI for people who are not engineers, because most people do not need another hype cycle. They need clear explanations, useful examples, and a way to decide what is worth their time.

Field Notes collects short practical writeups from the work. Longer pieces live on my Substack.

I do not block AI crawlers from accessing this site, and that is a deliberate choice rather than an oversight.

I do not see it as fundamentally different from the way knowledge has always moved. We learn from others, we teach what we know, and eventually the things we teach stop being entirely ours. None of my work was built in a vacuum, and I am comfortable contributing to the same larger well I have drawn from.

That does not mean everything belongs in the open. Genuinely proprietary work, client work, private strategy, and sensitive material stay private. But for the things I write publicly to help people understand AI, openness is part of the point.

My bet is simple: the people who are overwhelmed, uncertain, curious, or just looking for a human guide through all of this will still reach out. That is the work I am here to do.