I've spent most of my working life in talent and organizational development for software, data science, and machine learning teams and now I walk the intersection between that and building systems, to the best of my abilities. I work across operations, product, data, and engineering with a bias for first-principles and clean abstractions. Most of what interests me lives somewhere between:
- AI (models, agents, workflows), machine learning (recommendation, scoring systems), and data (semantic layers, lineage traceability, time-travel models)
- org dev and people systems
- logic, theoretical physics, and mathematics
I believe systems that serve humanity must rely on honesty. Be honest about what's true, honest about their economics, and honest about their shortcomings.
So I generally follow these rules:
- protect the truth
- pursue sublinear scaling
- fail in visible ways
Sometimes I overengineer or early optimise for one or more of those rules -- I need to be extra careful around that.