Hi! I’d like to suggest a new way to handle AI memory that goes beyond just daily summaries or short-term recall.
Right now, ChatGPT stores user memory in a kind of “external” layer—it references it when needed, but this is distant from the model’s core knowledge. This feels like short-term memory: helpful, but limited and not deeply integrated.
My idea is to introduce a new layer of “long-term personal memory”—a middle ground between core training and short-term recall. Each night, the AI could:
Review the user’s chats,
Consolidate important takeaways,
And store them in a format that’s accessed as naturally as built-in knowledge, like how the model knows facts (e.g., speed of light) without needing an external search.
This would make personalized knowledge feel “native” to the AI, not just appended. It would:
Improve personalization dramatically,
Reduce reliance on inefficient memory lookups,
And allow the AI to “grow with the user” in a meaningful way.
Thanks for considering this approach—I truly believe it could bridge the gap between static knowledge and dynamic personal memory.