making AI adoptable.
engineer and founder between worlds — I figure out which AI fits a problem, then build the thing that ships.
fit over hype
the hard part isn't that AI exists — it's knowing which technique actually turns into leverage for a given problem. range is how you judge fit.
teach as you build
the best tools do more than automate — they build understanding, not dependence.
ship it, don't theorize it
impact comes from systems that run under real constraints — data, evaluation, trust, usability — not from decks.
one sentence.
I write more about how I think about applied AI, range as judgment, and shipping under real constraints in the journal.
lower the cost of turning AI into useful work.
questions that drive me.
- 01what should AI feel like for non-technical users?
- 02how do we evaluate AI systems before scaling them?
- 03how do tools teach users while helping them build?
- 04how do institutions adopt AI without losing trust?
let's build this future.
if you're a builder, investor, operator, researcher, or public-sector leader working on applied AI, learning, or public-sector innovation — I'd love to connect.