Last month, curl paused security reports for a stretch because the queue had filled with AI-generated slop — plausible-looking bug reports that were wrong. It's the same complaint the Stack Overflow survey put a number on: the top frustration with AI output isn't that it's useless, it's that it's almost right. Confidently, expensively, almost right.
I ran into a smaller version of this problem a while back, building a pipeline that pulls financial news from a stream, decides which items actually matter, and forwards only those. The failure mode that mattered wasn't missing a story. It was forwarding a wrong one. A false positive here isn't a minor annoyance — it's a signal someone might act on.
So I built the whole thing around one goal: prefer silence over slop. Here's how, and — more importantly — the eval harness that keeps it honest, because a filter you can't measure is just a vibe.
Why false positives cost more than false negatives
Most classification tutorials optimize for balanced accuracy, or F1, treating a miss and a false alarm as roughly equal mistakes. For a lot of problems that's fine. For this one it's exactly wrong.
