Introduction
Welcome. I'm an experienced software engineer with a passion for building thoughtful, high-quality systems.
I created this page to replace an older static site that was more of a portfolio. Why am I telling you this? So glad you asked!
I hail from the earliest days of the internet, back when computing power was expensive and "e-" was a popular prefix. "E-generated" text was laugable, so each "home page" felt like meeting someone for the first time.
I've tried to recapture that experience a bit here--without making it *too* boring. ( i hope :)
Philosophy
Good software is clear, maintainable, and purposeful. One should understand problems deeply before reaching for solutions, and work to manage complexity wherever possible. Where not possible, one must at least understand unknowns and document complexity.
Software is rarely prefect. As systems grow, the surface area for failure expands nonlinearly. Assumptions calcify, interface expand in scope, and the understanding of any given implementation fades into memory without proper documentation. With it, maintenance and updates can become expensive.
Managing the technical product — its architecture, its constraints, its long-term health — is not overhead; it needs to be a primary area of focus. Organizations that treat it otherwise often pay the difference, with interest! Part of that is the successful application of new technologies like AI, but more of it comes down to having the right team and the right mindset.
Don't get me wrong--AI techniques carry genuine promise. They accelerate prototyping, enable automation of common patterns, and help augment human capacity to explore domains we would otherwise not have the data to tackle alone. Where the cost of error is manageable, it's especially useful.
Everything comes down to context -- in more than one way! The right question is not whether AI *can* be applied, but whether a tool's capabilities are well-matched to the problem's structure, what stakes are involved, and whether the situation demands more reliability. Applied with rigor and with clarity, AI is a powerful collaborator. Applied without, it's output is worse than noise--it's a mess masquerading as a solution.