I’ve been thinking a lot about how limited AI feels when every conversation starts from zero. Over time I’ve collected a ton of useful stuff from work: customer interviews, sales notes, old project docs, competitor research, meeting notes, PDFs, random observations I thought were important enough to save. The problem isn’t really storing it. The problem is that most AI tools only know what I put in front of them right now. I noticed this when I was looking at customer feedback. I could give AI a batch of interviews and ask what people were unhappy about, and the answer was usually decent. A few weeks later I’d do the same thing with newer feedback and get another decent answer. What I was missing was the connection between the two. I didn’t just want to know what customers were complaining about today. I wanted to know what had changed, what kept coming up, and whether something that looked minor a few months ago was becoming more common. That’s the setup I’ve been experimenting with in CREAO. I’ve been feeding in older and newer material together and using a workflow that compares them instead of treating each batch like a fresh question. The useful part isn’t really the summary. It’s the fact that the answer has some history behind it. That made me rethink the whole “AI second brain” idea a bit. I don’t think I need AI to remember every file I’ve ever touched. I’d rather it keep enough relevant context to help me notice changes over time. Curious how people here think about this. Is long term context actually becoming useful yet, or are we still mostly using AI one session at a time? submitted by /u/whatingadzooks
Originally posted by u/whatingadzooks on r/ArtificialInteligence