From Full-Stack to AI: Why Engineering Roots Still Matter

I didn’t start in AI. I started in code.

Over the past 15+ years, I’ve shipped platforms for schools, nonprofits, agencies, and scrappy founders—from rebuilding legacy PHP apps to designing scalable SaaS systems in TypeScript. That experience taught me something foundational: good engineering is about clarity, reliability, and knowing how to debug your way out of trouble at 2 a.m.

Now I bring that same mindset to AI.

Because while it’s tempting to treat AI as magic, I treat it like any other tool—one that needs guardrails, observability, and real business use cases to justify its weight. Whether I’m wiring up a private LLM, building a workflow copilot, or automating repetitive tasks inside a school system, my engineering background keeps things grounded.

I’m not here to experiment for experimentation’s sake. I’m here to solve problems—with real, testable outcomes.

And I believe that’s what makes AI truly useful: not hype, but horsepower. Not demos, but delivery.

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