Hello my 406 twitter followers. Hopping on to share two things: 1. I’ve started a CS PhD at Stanford! For the love of the game ofc. 2. I’m releasing impersona-env, a proof of concept to train personalized assistants by building an RL environment out of user models. Your assistant learns to help you by repeated asking a simulated version of you questions, probing its internal latent thinking and feedback, and updating its priors accordingly. I think this unlocks a new dimension to personalization beyond discrete fact recall. My personal assistant model now understands nuances of my preferences that even I didn’t know about. Full blog: It’s still a proof of concept and there’s still a ton of things to figure out in this space. It’s unclear to me how to build user model evals that generalize across users and downstream applications, how to best represent longitudinal personal data at training time // inference time, the interplay between population level and individual level preferences, the immense questions surrounding privacy and hillclimbing in decentralized data access regime… if you want to chat on anything in this area please dm me!