I have been redesigning the way connections work and are made inside Aevron. Most knowledge tools and RAG systems work on a basic principle that lets find similarity and connect the things. So for example, If I search for something about AI and critical thinking, AI could weaken critical thinking because it removes too much cognitive effort. A similarity-based system might surface: another note about AI dependency a paper about cognitive offloading something I wrote about critical thinking another observation about LLM usage But now lets say i capture/search something else on another instance, Calculators made us better at mathematics because they removed unnecessary cognitive work. This capture is nit directly similar to the 1st search/capture but it's the more useful connection. Because now i have: Why do I think removing cognitive effort helps in one case but hurts in another? What kind of cognitive effort actually matters? Where's the boundary? That connection doesn't just give me more information. It forces the original idea to develop. This is what i am trying to develop within Aevron. So instead of just the connection, Aevron is going to focus on why this connection matters, test, assumption, contradictions. But I'm increasingly convinced that semantic similarity should be the start of retrieval, not the final ranking of importance. A thinking system also needs some understanding of: what you've already resolved, what you're still uncertain about, where your own ideas disagree, which questions are still alive. I am more interested in the challenge rather than just retrieval.   submitted by   /u/mercurias98 [link]   [comments]