i think personalisation is about to become the biggest differentiator in llms, and not the kind most people think of. most people mean an assistant that remembers your name and what youve typed in the box previously. i mean one that already knows what you did and what you like before you ask it anything. @myrad_hq's already built that part. it skips the box entirely, it just reads what you did. > ask it what to eat tonight and it skips past japanese, since you've already ordered it four times this week, and puts mexican in front of you instead > ask for a gym routine and it already has your strava, three runs after work tuesday wednesday friday, two rides the other days, so it builds strength around the days you're already spent instead turning your legs to jelly before tomorrows run. > ask for a spending audit and it already knows your uber rides spiked the same week your food app orders doubled, so it flags the actual pattern instead of making you dig through two apps most chatbots guess from what you say. the myrad approach is reading what you did and never hallucinating based on inputs 3 context windows ago. your data mode is walled off, wallet gated, nobody else's data touches yours. the verification underneath runs on zero knowledge proofs, it confirms you actually watched something, rode somewhere, ordered something, without myrad ever holding your netflix password or logging into the account itself. proof of the activity, never custody of the account. this is what personalisation actually evolves into, not a memory feature bolted onto a chatbot, the actual next layer underneath every ai product built after this one. now picture that same verified context sitting inside a humanoid assistant instead of a chat window, one that already knows your routine and your habits before it opens its mouth to ask you anything. myrad is building for the future base:0x693bad964f815f32fabe0b9d4911865bffc30172