About
I'm a tinkerer. As a kid that meant caring about the Game Boy as much as whatever was in it, and taking apart anything with screws. That hasn't really changed — I still can't use something well made without wanting to know how it was put together.
So the history is a generalist's. I went to university for computer science and hated it, switched to statistics, and found my way into machine learning through the back door. Then a family business sold and I spent two years running money across public markets, private equity, venture and property. Now I build software end to end.
How I got here
Four stages, and the thing at the end of each one is what pushed me into the next.
StatisticsI went to university for computer science and hated it.Toronto · 2016–2020
What I hated was the coding. What I loved was the math underneath it, which nobody tells you is the same subject wearing different clothes.
So I switched to statistics, and then found the half-space between the two in statistical learning. That was the door into ML and I went through it.
Machine learningI started out making graphs for the business team.Fundmore · 2021–2023
I ended up building a new ML product, Fundmore IQ, doing document extraction for mortgage origination.
The three things I actually learned there had nothing to do with modelling. How to use git properly. How much of “data work” is just manual work. And that explaining a technical thing to a non-technical person is its own craft — one I was bad at, and then less bad at.
A break from techThen I left, because my family needed me more than a codebase did.2023–2024
A business had just sold and somebody had to build whatever came after it. So I did: entity structure, mandate, allocation, across public markets, private equity, venture and property. Some of that year I spent on a construction site with a nail gun.
It's a strange line to have in your history and it's the most useful thing I've done. I learned more about finance than a course would have taught me, and I learned what an operator's day actually looks like, which is the reason I now build for small operators instead of at them.
ProductAnd then Claude Code showed up.2024–now
I build end to end now. I finally don't need to be an expert in React to give my models a frontend.
Which means the question I actually care about — does this work for the person using it — stopped depending on which half of the stack I happened to be better at.
What came out of all that is a preference for systems that refuse rather than guess. Confidence per field, provenance on every number, and a rule that sends the uncertain cases to a person. It shows up in almost everything I build, because the failure I find hardest to forgive is software that's confidently wrong.
I also build things from scratch to understand them. A GPT by hand in MLX instead of importing one. An eval harness because I couldn't find a straight answer to a question I had. Most of it isn't novel and that's fine — the point is that afterwards I know how it works.
Outside of that: soccer once a week, hackathons for the deadline rather than the prize, reading widely and taking terrible notes on purpose, and a knowledge vault my agents read and write to.
You can see what I've built on Projects, what I'm thinking about in my writing, or reach me at [email protected].