About
I am a software developer based in Ottawa. I have been developing on the web and getting paid for it since 1999, and I have been thinking carefully, in one way or another, about what makes that work go well ever since.
The trajectory has not been a straight line. The early post-schooling years were IT and Tech Support, which turned into Salesforce platform development, beginning in 2008, which has remained a recurring lineage. Between 2014 and 2017 I was an Application Engineer at Twitter during the company’s growth period — a multi-year stint that taught me what engineering at scale actually looks like, and what it does not. From there I moved into a venture-firm consultancy, where I wrote the harness behind that firm’s first cryptocurrency index fund and, later, built most of the data-and-execution services that supported an ML-driven high-frequency trading system. When that fund wound down in 2020, I returned to consulting in the Salesforce space before I stepped back from full-time development to focus on my mental health and to spend more time teaching, doing research, and on prose writing. 2026 has marked a desire to return to more active development work, which I have so far satisfied with developing and contributing to open-source projects.
A few things, I think, are worth highlighting from that arc. My machine-learning experience was earned in production rather than through a course; what I know about it I learned by building. The teaching has been real, including week-long classroom courses on JavaScript and modern development with Web Components, and it changed how I think about the difference between knowing a thing and being able to convey it. The time away from full-time development, deliberate as it was, has left me with a sharper sense of what the practice asks of a person and of how to keep at it sustainably.
Considering the current developer ecosystem, my focus is the discipline that emerges when human practitioners work with agentic coding tools, rather than around them or in spite of them. The first long-form piece on this site, Five Categories of Human Excellence in the Age of AI, lays out the working map I have arrived at: five categories of human effort — Constraints, Context, Curation, Conceptualization, Creativity — where attentive practice most concentrates the value the practitioner brings. The argument, briefly, is one of structural asymmetry: the categories where the human surpasses the tool are the categories the training distribution cannot supply. The site is the home for further thinking along that line.
Alongside the writing is a structured daily reading practice that is a continuation of a life-long fascination with Liberal Arts and the concept of a Classical Education. In an approach similar to Dr. Charles W. Eliot’s Harvard Classics Reading Plan, each week takes a single question — What Is Knowledge?, What Is Real?, and so on — and works through five primary-source excerpts paired with ~1000-word responses, feeding into a weekend synthesis essay. This practice builds on the study I did formally in Liberal Arts before I transitioned to working in the tech industry in the early 00s. The daily entries are published, lightly edited, in the Reading section of this site.
Outside of those two threads, the long-running interests include programming language theory, applied mathematics, the philosophy of mind, the history and method of liberal education, learning science as a discipline, and the relationship between cognition and the tools we build to support it. I read more than I write, and I write to think.
On the subject of tooling: I use AI extensively, and I hold myself to specific standards before anything AI-assisted goes out under my name. That practice is described on the How I Use AI page.