Lab collection
Make filtering decisions inspectable.
A dependable filtering pipeline needs a clear contract between evidence, classification, delivery, and enforcement. These fieldnotes explore API formats, versioned updates, validation, rollback, and the meaning of an AI score. They are design guidance for people building integrations, with illustrative examples rather than live endpoints or advertised models. Start by defining the consumer’s expectations and the consequences of a wrong decision. Then make the update and correction paths as explicit as the normal success path.
Read the long-form fieldnotes below, then use the linked topic guides to apply the distinctions to your own browsing, integration, or publishing workflow. Each article includes a directly relevant primary reference and practical questions you can carry into a test.
An AI score becomes useful when its target, evidence, and uncertainty are visible. Learn how to evaluate domain classifications and choose thresholds that reflect the cost of mistakes.
6 min readPlan an ad block list API around explicit rule semantics, reproducible releases, and recoverable updates. This practical architecture guide follows a list from its source record to the client.
6 min read
