From idea to execution.
The model is one part of the system. The craft is making the whole thing work.
01 / Start with the work
A useful AI product starts with an actual workflow: the inputs people already have, the decisions they need to make, and the output they can use. I connect generation to an editable process instead of leaving it as a one-off prompt.
02 / Own the execution
Native applications need more than an interface around a model. Queues, local runtimes, progress, cancellation, file formats and recovery are part of the product. I build across that boundary.
03 / Make systems inspectable
Receipts, explicit states and reproducible checks help explain what ran and what failed. I distinguish implemented features from work still in progress in every project case study.
04 / Keep people in control
Local data and portable projects give users room to experiment. External services and model runtimes should have clear boundaries, with deliberate choices about what leaves the machine.