u/Sharp_Variation7003

a returns-abuse detection app where stores share anonymised return events (hashed customer info, return reason, amount) and in exchange get risk scores on customers trying to return at their store. the more stores in the network, the better the detection, a customer abusing 8 other stores gets flagged before you approve their refund.

the core insight is that serial returners don't hit one store, they hit many. but every store today only sees their own return history, so the same fraudster keeps winning. signifyd and riskified do this for payment fraud but nobody's doing it for returns abuse specifically.

free to install, contribute your return data, get the network back. paid tier kicks in once you cross a return volume threshold.

thoughts?

every dtc brand is building internal ai agents (forecasting, pricing, creative testing) but each one only sees its own data. what if there was a clean room where brands pooled anonymised signals so the agents had category context? example: a returns-abuse agent that catches serial returners hitting brand a, b, and c, not just yours.

every dtc brand is building internal ai agents (forecasting, pricing, creative testing) but each one only sees its own data. what if there was a clean room where brands pooled anonymised signals so the agents had category context? example: a returns-abuse agent that catches serial returners hitting brand a, b, and c, not just yours.

every dtc brand is building internal ai agents (forecasting, pricing, creative testing) but each one only sees its own data. what if there was a clean room where brands pooled anonymised signals so the agents had category context? example: a returns-abuse agent that catches serial returners hitting brand a, b, and c, not just yours.

aschenbrenner’s q1 26 13f: added iren, skipped nbis, trimmed bloom, exited intel. and a big new put book on ai infra: nvda, avgo, orcl, amd, tsm, smh. the agi bull is hedging the build-out.

why isn't there a neutral red-team-as-a-service that runs a standardized battery of reward-hack probes, verifier-fidelity tests, and contamination scans against RL environments before frontier labs buy them, saving labs engineer weeks of manual procurement review and giving env vendors a credible third-party artifact to sell against?

I don't see why market is undermining this stock even though it has record growth, new catalyst such as ad-revenue and streaming tv and no ai-related risks

Just updating this post for anyone who thinks I don’t put my money where my mouth is

https://preview.redd.it/scfg8q7aan0h1.jpg?width=1179&format=pjpg&auto=webp&s=15a912c5387853a2f3237f06b33bea25b3a4c91b

once models can actually adapt and update their behavior during inference, true continual learning, not just retrieval-based memory, what new business models do you think emerge? curious what people here imagine beyond the obvious personalized assistant pitch

once the models are able to adopt and change their behavior during inference, what sort of new business models would emerge?

every other day I see people talking about voice agents and automations for restaurants, but rarely anyone is building the same for spas, salons, barbershops, and similar service businesses. they need all the same automations as restaurants, plus serious discovery marketing, which ~50% of them don't even do. is there a reason no one's focused on these domains?

people sell their grocery receipts to Fetch and other apps btw why hasn't anyone made the same thing for chatgpt and claude chats yet? feels like that data would be gold right now with everyone freaking out about ai marketing. thoughts?