Memory & Personalization

Generic AI is increasingly unbearable. You don't want a chatbot that treats you like everyone else. Personalization is where memory gets interesting because it's not just about storing facts, it's about learning patterns specific to you. Memory-driven personalization works by analyzing what you repeatedly ask, what you care about, how you like to be communicated with, and what's worked before. An AI that knows you prefers brevity can compress its responses. One that knows you like technical details can drill deeper. This is where Vity shines for individual users. It learns your communication patterns across multiple AI platforms. Use it with ChatGPT? Vity remembers you clarified a concept three times, so next time in Claude, the AI gets that context and explains differently. It's genuinely personal memory infrastructure. The mechanics involve behavior clustering, embedding your interaction patterns, and comparing incoming requests against your historical preferences. Some systems build explicit preference models (ratings, explicit settings). Others infer preferences from behavioral signals (you copy-pasted this output, so maybe you like this format). The tricky part is avoiding echo chambers. Personalization that's too strict leaves you stuck in your own preferences, missing out on better approaches. The best systems balance personal history with external recommendations, letting you discover new styles while respecting your established patterns. There's also the temporal dimension: your preferences change, so effective systems decay old preference signals and weight recent behavior more heavily.