The "it's AI but it tricked me" samples are honestly the most useful feedback we get. Worth another look if you've got a moment, the latest builds ship with our new OmniAID model, which is a meaningful step up on the cases that were tripping the old classifier. The remaining weak spots are mostly heavily edited photos and a handful of Stable Diffusion fine-tunes, and if you can email a screenshot or even a hash to support@slopornot.ai we'll add similar examples to our evaluation set so the next pass narrows the gap further. Thanks for sticking with it.

Thanks for the careful test. The flat-colour and low-entropy failure you described is real, and we've been chipping away at it. The classifier in v1.0.6 and later was calibrated against more solid-colour reference plates and handles single-fill regions differently, so a flat-colour screenshot shouldn't come back as 99% AI any more. On the Britishizer, that one's there on purpose. The whole point is to show how easy it is to dodge AI-detection tools, so people see where the limits sit. If you ever want to revisit, mail support@slopornot.ai and we'll walk through your samples.

Quick note on the numbers since they get conflated a lot. Per-class accuracy on our test set and per-image confidence aren't measuring the same thing. 75% confidence on one image doesn't contradict 95% per-class accuracy. False positive risk is called out in the app itself and on the store page, and PDF or document attachment isn't on the roadmap because the iOS Share Sheet already handles those flows. If you've got a sample you want us to look at, mail support@slopornot.ai and we'll dig in.

You're right, iOS won't let an app scan things while you're inside another app. Apple's privacy rules need an explicit tap to hand content over. For now the quickest route is Share Sheet, then pick Slop Or Not. We're looking at an opt-in Photos auto-scan for a future build. Thanks for keeping the door open, please give it another go when that lands.