Flo Crivello: belief

Transcript context

…Ish, and it actually increases the performance of the agents. Like agents apparently see it was just surprising because there was so much JSON in the training set of the models, but they're they're operating more comfortable with tunes than with JSON. JSON does have a lot of noise associated with it for sure. Yeah. I'm more comfortable with YAML than I am with JSON. That's so that's something. The AIs, they're just like us. That's listen. One of my biggest surprises as a brief aside, I'm actually working on new I don't if this will be a podcast or a thread or whatever, but just taking stock and, you know, of course, agents can help make this a quick project where it would be a very tedious project otherwise. Just going back in history and identifying things that I've been wrong about prediction wise and some things I've been right about prediction wise. I mean, one of the things that I used to say quite confidently that has aged maybe the poorest is that we shouldn't be anthropomorphizing models or that will lead us astray. And I've just been shocked over and over again by how productive it can actually be to anthropomorphize the model. It still feels a little dangerous kind of, but it's hard to argue with the results. I 100% agree. I do think so that sounds like a a happy medium of of superposition, and I think, like, that's that's a little thing that's very top of mind for me right now is to what extent does superposition stop being true in particular open the curtain to AI employees? So for example, the way I've changed my mind and I haven't changed my mind completely, but I've changed my mind a little bit, relatively speaking, is about multi agent systems. I actually have come to to believe that most of the time, as much as possible, you want to consider that as much as possible and do a single agent, not multi agents. That's not always possible, and there's there's still good reasons to use multi agents. Like, this is not, an absolute statement, but most of the time, that's the case. And I actually think that humans, like, intuitively find themselves biased towards very, very multi agent system, like far more multi agents than is optimal because the the entrepreneur facing a little bit too much, and they're comparing a little bit too much to human organizations. Say, I have a data scientist. I have an engineer. I have a designer. I have a PM, and so I'm gonna create one agent for each of these things. And, actually, the reason why human organizations do that is because every human has only twenty four hours a day and can only contain that much context, plus agents obviously have none of those constraints. They can fork. They can duplicates. They can they can do as much as they want all day. And so I find that division of labor is not a good reason to have multiple agents, which is which is a major major fact. Now you, again, there are still multiple there's still different reasons, but the division of labor is not one of them. So I agree. eason to have multiple agents, which is which is a major major fact. Now you, again, there are still multiple there's still different reasons, but the division of labor is not one of them. So I agree. I think by and large, I think people are right to transform morphized models because they will, after all, trained on human tokens and always, you know, human RL and all of that stuff. But I also think that AI organizations shouldn't be overly anthropomorphized. Yeah. That's interesting. Is it as you talk about a single agent, obviously, they can run multiple copies of we can run multiple copies of it of single agents in parallel. Have you reached the point yet where that starts to create database style problems where if multiple agents are updating the same segment of history at the same time, we have all these sort of, you know Yeah. Transaction level guarantees in traditional databases for that reason. Does that start to be an issue as you have 10 windy teammates running over the same kind of file system corpus? Yes.…

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