I came to AI research through publishing and information design rather than machine-learning engineering. For more than three decades, my work has focused on how information is structured, presented, and made usable by people. That background led me to ask what changes when AI becomes an intended reader and reasoner over the same material.

I have been developing the AI Source Analysis Framework independently, using repeated comparative testing and failure analysis to revise the architecture rather than treating the current design as fixed. I am particularly interested in source-grounded reasoning, evidentiary relationships, provenance, boundaries, uncertainty, and how small interpretive departures can propagate through later AI reasoning.

I work independently from Elko, Nevada, and am currently focused on developing, testing, and publishing this line of research.