In Development
Runway
A menu-bar app that tracks Claude usage in real time, surfacing burn rate against rate limits before you hit them. Built to scratch my own itch — then built properly because the itch wasn’t just mine.
Twelve years leading product, from early-stage to scale. I lead by staying close to the work — writing specs, sketching flows, sometimes shipping code. That habit became more useful, not less, once AI started reshaping how products actually get built.
The shape of product leadership has changed a lot in twenty years — more strategy meetings, more dashboards, more layers between the executive and what actually ships. There are good reasons for most of it. But for me, the work gets sharper when I stay close to it.
I write specs. I sketch flows. I prototype features. I’ll ship code when the situation calls for it. Not because I don’t trust my team — because that’s how I stay calibrated to what they need from me, and how I notice the small things that turn into big things later.
The AI shift is making this matter in a new way. The teams figuring out where AI actually fits aren’t doing it from reports — they’re doing it by using the tools, hitting the limits, and forming opinions from the inside. When the executive shares that experience, decisions get sharper. When they don’t, strategy starts drifting from how the work is actually changing.
That’s the model I bring: a player-coach who builds and leads. Senior enough to set direction. Hands-on enough to keep that direction grounded.
A menu-bar app that tracks Claude usage in real time, surfacing burn rate against rate limits before you hit them. Built to scratch my own itch — then built properly because the itch wasn’t just mine.
DBA thesis exploring what actually makes an organization ready to work with AI — not at the tools layer, but at the level of how decisions get made and who has authority to change them. The question I’m trying to answer for myself before I have to answer it for anyone else.
Writing on what changes for product leaders when the work itself is changing — and what doesn’t. Mostly notes from the thesis, in shorter form.
Slots reserved for what comes next. A few small products in early stages. The pattern matters more than any single one — consistent shipping is the only portfolio that compounds.
How do organizations actually become AI-ready — not at the tools layer, but at the level of operating model, decision rights, and how executives relate to the work itself?