Field notes for AI founders
Practical reporting on the product decisions, infrastructure, and operations that turn AI prototypes into dependable businesses.
Product / Infrastructure / Operations / Revenue
An assistant that remembers everything can still get the important things wrong. Useful AI memory needs context, a record of where facts came from, and a dependable way to change its mind.
What we examine
From narrow workflow to customer value, evaluation, trust, and product judgment.
Models, data, deployment, observability, permissions, and the failure paths between them.
The systems, approvals, integrations, and controls that turn output into dependable work.
Pricing, usage, margins, billing architecture, and the economics behind the interface.
The editorial promise
Clear sources, visible affiliations, and practical questions you can take back to the build. We explain how the systems work and where their limits are.
Read our editorial standards