What should stay private?
Professional lessons have value, but useful abstraction matters when the original context carries confidentiality risk.
Now
This page keeps the site honest about what is being explored now, what is still forming, and what deserves more attention next.
Current focus
Turning real project experience into useful public lessons without exposing private implementation details.
Testing when richer domain values and small generators remove friction instead of adding ceremony.
Studying how AI and software tools change attention, comparison, memory, and learning.
Field notes
These are working questions, not finished positions.
Professional lessons have value, but useful abstraction matters when the original context carries confidentiality risk.
A technique should justify the weight it adds. That applies to source generators, package boundaries, and process changes.
Tools can accelerate work, but the deeper question is whether they improve the human decision being made.