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altmanaltman: Did you check if this leads to any actual benefits? If so, how did you benchmark it?
endymi0n: I've experimented quite a bit with mem0 (which is similar in design) for my OpenClaw and stopped using it very soon. My impression is that "facts" are an incredibly dull and far too rigid tool for any actual job at hand and for me were a step back instead of forward in daily use. In the end, the extracted "facts database" was a complete mess of largely incomplete, invalid, inefficient and unhelpful sentences that didn't help any of my conversations, and after the third injected wrong fact I went back to QMD and prose / summarization. Sometimes it's slightly worse at updating stuck facts, but I'll take a 1000% better big picture and usefulness over working with "facts".The failure modes were multiple: - Facts rarely exist in a vacuum but have lots of subtlety - Inferring facts from conversation has a gazillion failure modes, especially irony and sarcasm lead to hilarious outcomes (joking about a sixpack with a fat buddy -> "XYZ is interested in achieving an athletic form"), but even things as simple as extracting a concrete date too often go wrong - Facts are almost never as binary as they seem. "ABC has the flights booked for the Paris trip". Now I decided afterwards to continue to New York to visit a friend instead of going home and completely stumped the agent.
hazelnut: Congrats, looking promising. How does it compare to supermemory.ai?
aleksiy123: Anyone have experience with these and competitors? im curious if you saw a difference.also any open source local or self hosted options?
SkyPuncher: How can you decide if something is a contradiction without having the context?I'm incredibly interested in this as a product, but I think it makes too many assumptions about how to prune information. Sure, this looks amazing on an extremely simple facts, but most information is not reducible to simple facts."CEO is Alice" and "CEO is Bob" may or may not actually be contradictions and you simply cannot tell without understanding the broader context. How does your system account for that context?Example: Alice and Bob can both be CEO in any of these cases:* The company has two CEOs. Rare and would likely be called "co-CEO"* The company has sub-organizations with CEOs. Matt Garman is the CEO of AWS. Andy Jassy is the CEO of Amazon. Amazon has multiple people named "CEO".* Alice and Bob are CEOs of different companies (perhaps, this is only implicit)* Alice is the current CEO. Bob is the previous CEO. Both statements are temporally true.This is what I run into every time I try to do conflict detection and resolution. Pruning things down to facts doesn't provide sufficient context understand how/why that statement was made?
