before i wrote a line of code, i studied how institutions work — universities, companies, the informal rules that hold an organization together when nobody’s watching. that turned out to be the right training for what i do now.

for almost ten years i’ve built software professionally. the shape of that work has changed more than once — and every shift taught me something the last one couldn’t.

that decade taught me the thing a lot of ai-first engineers skip: taste. knowing when a pattern is right for the people using it, not just correct on paper. now the tools have changed again — i build with ai assistance the way i once learned to build with frameworks, and the shift feels familiar rather than threatening, because i’ve already lived through one generational rewrite of how this work gets done.

that’s also where the mentoring comes from. i’ve watched this field reinvent its own tooling every few years, and the people who stay useful are the ones who can translate — between old code and new, between what a model suggests and what actually belongs in production, between an engineering team and the humans who have to live with what it builds.

you get someone who shipped production frontends for a decade, designed before he built, and spent the last few years turning ai from a novelty into infrastructure a real organization can trust — not three different people you’d have to hire separately.