Essays & working notes
Ideas tested against real products, written down once they've survived contact with one. Newest first.
Every AI initiative I've seen fail in financial services failed on trust, data, or adoption — never on model quality. What that means for how you sequence the work.
Most model documentation is written for auditors who will never read it. The parts that get read — and the parts that get you in trouble — are predictable.
If the producers don't use it, the model doesn't exist. Why adoption belongs on the dashboard next to precision and recall, and how to design for it from day one.