I want to build an AI/LLM app that preserves privacy. Please roast my idea :)

overdrawn98901: > The true silver bullet is E2EE, where the two ends are the user and the model, server is blind to the operations. The perfect world scenario is FHE, fully homomorphic encryption, but that’s a holy grail. Tokenizing on the client + GPU servers with a TEE (Trusted Execution Environment)? If you trust TEE tech then that’s E2EE with no ability to introspect the content server-side. (The “tiny” problem of getting ML libraries to work inside a confidential compute environment is non-trivial, but I will assume it’s solvable until proven otherwise.) Downside is that GPUs with TEE are rather pricey. I’d guess users would pay 50-100 USD/mo for competitive models if you run as a non-profit … until there is enough scale to buy physical hardware and then the entire dynamic shifts. Would that be too crazy an ask for fully private conversations with a model that’s Opus 4.5 / GPT 5.2 competitive? Also is encryption/security all that matters for privacy? (Feels too simple.) > That’s exactly what some users (myself included) are achieving with Qubes OS. Interesting stuff @ls.skuggi It’s a solution for isolating a local app from the rest of your data, right? Or am I missing a piece here.