Introduction

I'm Ryan, a systems architect and full stack developer based in Canada. I've been building software professionally since 2009. Most of that time has been on client/server systems for enterprise and consumer products. Platform migrations, greenfield builds, SaaS conversions, manufacturing system integrations. The kind of work where the architecture has to survive real users, real traffic, and real teams maintaining it years after you wrote it.

My first decade was spent at a small shop migrating a legacy product from PowerBuilder to .NET, then building an enterprise version from scratch and converting the whole thing from packaged software to SaaS. The company was acquired on the strength of that work. After that I moved into agricultural technology, connecting manufacturing systems and optimizing planning workflows. Now I'm building production systems for the livestock industry. Different domains, same core problem: how do you build software that holds together when the requirements are complex and the stakes are real.

About three and a half years ago I started experimenting with AI development tools. It started as curiosity. I wanted to understand what these models could actually do, so I built a benchmark, ran it on every new model as it came out, and kept track of what worked and what didn't. That experimentation grew into a serious research interest. I've used most of the major tools and services, burned through more API credits than I'd like to admit, and built dozens of projects specifically to stress test how AI assisted workflows interact with real architectural decisions.

What I've found is that the tools are genuinely useful when someone with experience is steering. They replace typing, not judgment. An agent can produce plausible code fast, but it can't tell you when the structure underneath needs to change. That's still a human skill, and it's one that takes years to develop. The hype around AI development consistently runs ahead of the reality, and there aren't enough people with deep building experience writing honestly about the gap.

That's what this site is for. I write about where AI tools actually help in professional development, where they create debt, and what guardrails make the difference. I also write about the broader industry shifts: the commoditization of certain categories of software, the workforce impact, and the disconnect between investment and revenue in the AI space. If you're a working developer trying to figure out what's signal and what's noise, that's who I'm writing for.