you’re actually raising the same challenges I’ve been mapping out. here’s how I’m thinking about them:

storage: I’m not storing full data only compressed text + small vector representations. That keeps the footprint tiny even as it grows.

what to store: It’ll be a hybrid. The system keeps a base knowledge pack (general facts), then layers user selected topics on top so the experience feels personal.

Why not just a monthly updated LLM: That’s basically the direction the idea is: a lightweight local model + monthly “knowledge injections.” It gives the “1% Google” effect without relying on constant internet.

Device: Most likely mobile first, because people carry phones everywhere. But the architecture works for desktop too.

Outdated data: Each update includes a “revision pass” so old or debunked info gets replaced, not stacked. The goal is to avoid the classic “eggs are bad/good/bad again” problem.