One thing I've noticed is that people often look for an "AI governance tool" as if governance is a single category.
In practice, it usually looks more like a stack. Different controls solve different problems, and most teams end up combining several of them as AI agents move from experiments into production.
1. Model Monitoring
This is the layer that helps teams understand how AI systems behave over time. Performance drift, unusual activity, reliability issues, and changing usage patterns tend to show up here first.
Ive been dropping the most cursed photos of my friends mid conversation on snapchat and watching them freak out in group chats and wanted to gamify it and build a game.
So i built PhotoBomb. one person picks a prompt, everyone else submits their most cursed photo for it, and the prompt picker chooses the best one. bonus points if someone screenshots your photo. its free IOS only
