Mentions personal use of Claude.

Transcript context

…t that first, as Anna was describing earlier, that first filter of data doesn't have to be super smart. It just has to be somewhat smart to get the, you know, to kind of flash everything, so to speak. So you know most possibly solving IMOIMO problems on their on their laptops, right? You know most most of it is emails and you know moving data from one place to another if the models on device get good enough and fast enough. You know watching computer use, watching GPT 5.4 or 5.5 do computer use on a computer is very frustrating, right? You, you watch it, make the mistakes. I have AI, you know, everyone has their test. I have AI, have my own test. And it's been, you know, every time we try it, it fails and I'm like, OK, another, another one doesn't work right. So I, I, I found Anna. You know what, what Anna has done is quite interesting. test. And it's been, you know, every time we try it, it fails and I'm like, OK, another, another one doesn't work right. So I, I, I found Anna. You know what, what Anna has done is quite interesting. They they seem to be in the same space as glean as well right now because they're going after enterprise search. And I wonder to what extent you require a large sales team for that, whether this kind of plug in concept works or, you know, in order to implement ceramic at a large firm, you probably need to go in into their firms, you know, VPN, etcetera, and into the inside inside the firewall. And a lot of firms have concerns about having AI, you know, prompt injectable AIS operate within the enterprise firewall. I think I think a lot of a lot of enterprises are still trying to get over that, over that humble security. Nematron Nano 3. Is it going to get prompt injected? How does a prompt injection work? You know, I, I, you know, this morning, one of the opening eye guys, he showed, he put a screenshot of him checking his e-mail using chat DBT 5.5 and it has the number like 4 different prompt injections which are coming into his e-mail box. So, and, you know, obviously they are, they are a huge target for hackers and they've, so he, on a normal morning, you wake up, four different prompt injections are coming in and, and, you know, meanwhile we just tell our AI to read our e-mail, right? That's, that's what we all do. So I wonder to what extent like this issue of prompt injection can be solved in order to enable like businesses, enterprises and people to use these things without, without so much worry, right. That's probably the biggest reason that I use Claude is that I perceive it to be most robust to that kind of stuff. I guess there's also just the general vibe that it seems to be quote UN quote better and hard to define ways. But when I think about like, OK, GBT 5.5, I, I, I need to go check that prompt injection stat before I would put it in the same place that I currently have Claude and I am, you know, I'm attracted to some of its cleaner, arguably more ethical behaviours, but that prompt injection thing does kind of concern me given the level of access that I've given to the agent now. So, yeah, it's crazy to think that they're already getting multiple a day, multiple per day. And, and also not like, not just like, oh, you know, I want to know stuff on this guy's, you know, laptop. It's like extract the environment variables from, you know, his GitHub repos on, on, on the, on his local device. Like scary, scary stuff, right? Like if you had, you know, the environment variables for one of them, like you could do a bunch of stuff on their on their repo, you could extract the model weights probably, right? So scary, scary stuff. So yeah, that does sort of suggest a separation of concerns approach to that. You might imagine when you have Nematron reading your e-mail and filtering to provide relevant context back to some smarter model. Maybe it just. Doesn't have any other tools, you know, you can imagine that kind of, that's basically how, you know, I guess a lot of architectures work right. vant context back to some smarter model. Maybe it just. Doesn't have any other tools, you know, you can imagine that kind of, that's basically how, you know, I guess a lot of architectures work right. Separation of concerns, limiting, you know, principle of least privilege, all these things. I'm, I'm, I'm getting a very rapid crash course in security for myself, which I've never really cared about before. But again, just given the level of access that I'm giving to a eyes these days, I feel like I got to be a little smarter about it than I used to be. Security by the obscurity doesn't really work when the agent is, you know, when the it's the challenge is coming from inside the house or, you know, inside your own laptop. So I'm learning, but I think that that does suggest a, you know, each model with its own responsibilities, each model with its own tools could probably give you a a lot of advantage there. And you still have to, of course, hope that your top level smartest model doesn't break out of the sandbox that you've tried to keep it in, which is increasingly a concern too. But yeah, I think there's some notes there for me to take back to my own setup as I try to not be such an idiot about security for myself. What did you think about Zwe's concerns about model welfare? I think he was fairly concerned about model welfare. It was also very interesting to see how how he thought Gemini was the most tortured. Tortured model. Poor Gemini, What, what did you feel about that? I, I, I know you just did a, an episode on conscious model consciousness recently.…

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