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Where the day-to-day pioneering happens: bringing AI into DevOps and developer workflows — the parts that measurably move delivery, not the demos.

Product engineer who ships. Now pioneering AI in DevOps to build what’s next — adopting what measurably moves delivery, and dropping what doesn’t.
Before it was AI, it was product. I’ve spent my career turning ideas into software that ships and holds up in production — designing systems, leading engineers, and owning the path from first commit to what real users depend on.
Deep technical work paired with product judgment: what to build, what to cut, and how to make delivery boring in the best way — automated, self-serve, and reliable enough that teams stop thinking about it.
New tech earns its place by evidence, not by being new. This is how I run the frontier — not AI for AI’s sake.
Give agents a real harness, not guesswork
MCPs that feed coding agents compliant CI/CD pipelines and searchable platform & SDLC knowledge — so they build on the org’s real conventions, not plausible guesses. promoted
Make the design system AI-ready the same way
An MCP that hands agents proven, accessible components instead of generating UI from scratch. verified
Chasing every new model on its leaderboard
I run Claude Code, opencode, and Pi on real work and keep the setup that ships — not the one that tops a benchmark. rejected
Swapping the stack for hype
I test frontier models hands-on, including open ones — most “SOTA” gains vanish outside the benchmark. rejected
— verified in production at


Where the day-to-day pioneering happens: bringing AI into DevOps and developer workflows — the parts that measurably move delivery, not the demos.
My studio for launching products — the vehicle for turning what proves out on the frontier into things people can actually use.
Building a product, or bringing AI into how your teams deliver? Start a conversation — no pitch, usually a quick reply.