Nathan Labenz: belief

Transcript context

…here that is supported by a bunch of reference files. And this is all maintained automatically in the background by a team agent. And so, hopefully, this answers your question about how do you do it because some some channels are public, some channels are private, some documents are public, some documents are private. The way it works is that there is this memory agent. We call it napping, not sleeping. So, like, it runs every something, like, fifteen minutes. Like, why why would you need to sleep every twenty four hours? So it just runs continuously in the background, and anything that is public, it updates the the team file system and the team memory with. And anything that is personal and that is private, it just updates each person's file system with. So and and in the end, it collates all of that, and the agent we talked to in the teammate uses all of that context and crawls that entire file system. So there's a lot of technical challenges we had to solve in order to get there because the the context that it accumulates is, like, many millions of tokens that you can't add all of that at every turn. So that was that was one thing we had to figure out. Yeah. Interesting. I'll share a little bit about what I stumbled into and you tell me what you have learned that might be even better. First of all, just have to connect to all the tools and get the exports. I found that there were interesting edge cases that I was constantly running into in my own personal export heuristic that I tried to write. One time I had a heuristic that the longer emails I sent are probably more substantive, and I'll definitely make sure I capture those. But then it turned out that at the very top of those of that power ranking was often something that I'd copied out of an LLM and was sending to somebody with usually a a message at the top. Here's what I got from Claude or what have you. But it was, like, weird now that according to that heuristic, this Claude text is one of the more weighty pieces of my writing. So I've bumped into a lot of these things over time and had this special case. Yeah. I guess maybe one question just like, when you step into a new organization blind and they and we logging is another right. In in our Slack channels at my company, Waymar, we've had various, you know, logs moved into Slack over and over and over again. How do you deal how do you identify these idiosyncratic special case things that could overwhelm or flood or mislead them get to the the real good stuff? So that's an excellent question. The I think the the main way that we've solved this is the fact that the memory is maintained by an agent itself. I think this is why I'm ultimately quite bearish on the rag as an approach, and I'm very bullish on on on this, like, agentic management approach because you have an actual agent which has its own memory. So you have a sort of meta memory and and and and understands what it is that it's looking at. And by virtue of accumulating that knowledge little by little about the organization gets smarter and smarter about what actually matters. It's actually sort of similar to training a model because, like, you know, if you were to insert poisoned data into the model dataset, like, hey. You know? Like, I don't know. Darth Vader was a woman. You know? Like, it it it would get the data, but it would be drowned by all the correct data. You know? So here, it's the same. It's like if you have enough memories and if you feed that to a system that understands it, if the system ends up understanding and so the very concrete example you are using is actually an emergent behavior we have seen the memory agent adopt because the memory agent has its own memory. So it's a sort of meta memory about how do I manage my memory, what sources of information matter, like, you know, all of that, and which ones are trustworthy and all of that. And we have noticed that the first time the memory agent crawls the Slack it finds, some of those channels, like, organizations have them, like, those, like, log channels, and it learns to ignore them. It's like, I'm not gonna I'm not gonna keep spending time on those channels. There's nothing for me to learn there. Saying that's been critical for us has been building meetings as as a first class citizen in this system. Like, I really do believe that meetings are are very underrated as a source of information. They all they all were like 90% of of the most up to date data leaves about the company. Like, everything that matters inside the company has a meeting around it. Every relationship, every project, every initiative, everything has a meeting around it. And so I think, like, those multiplayer identity systems cannot really get meetings like Winona whatever. Like, I think you have to really incorporate it in your system as a. And so what we did is that, you know, we we built that first class citizen. We have meetings. Now it's a first class citizen in Linde. And and most importantly, it's like again, it's not just a Granoa, you know, oh, it's it's recording your meetings and and and so about them. You can do all of that stuff, but it it goes beyond that, and it it feeds these meetings to your memory agent. And now if a meeting was public, we can set up we can set up meeting folders, which automatically add meetings. It's a meeting folders, which automatically add meetings to to themselves and share them with the entire team.…

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