The Blog

A practical weekly loop for turning a backlog into a useful decision, a small proof, evidence, and a clearer next move.

A practical method for making an existing codebase easier to change: name one painful change, clear only its path, and let the finished change justify the cleanup.

I use AI throughout product development, but volume does not prove value, so I am measuring where it helps, where I intervene, and where work gets repeated.

AI can handle more implementation work, but people still need to choose the problem, judge the experience, manage risk, and accept the result.

Faster AI can produce changes before a team can understand them, so the delivery advantage comes from tighter tasks and stronger evidence.

Before a meaningful release goes live, decide what question it should answer, what evidence matters, and what you will do next.