Field notes / Engineering judgment
Our public thinking focuses on the decisions that separate a compelling demonstration from a system worth operating.
Original notes · no generic AI trend summariesAccuracy can rise while the exception queue gets worse. Forecast error can fall while the planning horizon remains unusable. Evaluation starts by naming the action that changes when the output is right or wrong.
A smaller pretrained model with constrained inputs and explicit review can outperform a larger dependency once production cost, failure containment, and data boundaries are included.
Prototype code may survive, but that is not the success condition. A useful prototype produces evidence, exposes missing data and integrations, and creates a defensible go, change, or stop decision.