AI infrastructure for systems that have to survive real operating constraints.

I trust precise uncertainty more than polished confidence. An inconclusive result is often more useful than a dashboard showing everything in green, because it makes the limits of what is known visible.

That principle guides how I build and evaluate systems, from model migrations and agent actions to release decisions, publishing workflows, selling assistants, and monitoring tools. I keep asking the same questions: What was authorized? What did we observe? What changed? What is still not established?

I would rather ship a focused system with clear limits than a broad system that makes claims it cannot support. For me, good engineering means testable claims, understandable refusals, traceable effects, and clear responsibilities.