Daniel Miessler: evaluation

Transcript context

…are obviously key toward consistently customizing the AI's behavior toward what you want. Cloud can do, cloud code can do a lot of that. Where is the line? How is the line moving? What do you think are the most important things that you are bringing to Cloud Code that it itself doesn't have yet? Yeah. So what Cloud Code doesn't have right now is it doesn't start by saying, who are you and what are you about? It doesn't encourage you to bring over your work and your personal goals and your main workflows that you perform in life and for your career. It's not onboarding you to have cloud code be your assistant. Okay. It's still its primary identity, which started as a coding agent. And that's still what it does the best. And it's the best at it because they just have the best approach to this. But what I'm building towards this thing called, what is it called? P-A-I-M-M, personal AI maturity model. And it goes from chatbots at three levels, agents at three levels, and then assistants at three levels. And I think right now we're at like agents level two. And when you start getting into assistance, the world is completely different. So like I'm sitting in front of these screens right now. What should be happening is my AI system should be able to control any of this tech. It should see all these screens. It should hear everything that's happening. And I should just be interacting with it. One thing I love to do, I stole this idea at least partially when I was at Apple. They stole the idea from Amazon, but it's start in the future that you want and work backwards. It's called a PR in Amazon and Apple terminology. So what we're actually looking for is like her and tars. So you start with what you actually want, which is an AI that can see and hear and interact with anything you are interacting with. When you say, play the perfect song for this moment, first of all, you shouldn't have to say that. It should just play it. But when you say that, it should be able to. I got this idea riding in Coyote Hills with my friend Mark on mountain bikes. Wouldn't it be cool? Because we both grew up very close to these mountains for it to play the perfect song. How is it going to know what the perfect song is? It has to know who Mark is, his relationship to you. What was happening in the 80s when we grew up? What were the perfect songs? And how does that associate with mountain biking in the wilderness? All of that is context. That's why the scaffolding is so important is because the context engineering is what makes the AI powerful. It's not the models themselves. So Pi starts with this concept of what are you trying to do? It starts with deep personalization. Your AI has a particular voice. It interacts with you in a certain way. It knows what your What capabilities your capabilities are. It has full access to all your skills. So it's more like. more like you're interacting with a DA, a digital assistant, as opposed to interacting with an AI model that has capabilities. And that distinction seems small, but it's actually massive. It's absolutely massive. So it's about, if I try to echo that back to you in different terms, it's really about putting you, the person, at the center in a persistent way, as opposed to with like cloud code off the shelf, we have a project-level focus. And then, of course, we go to the chat itself, we have a task or a conversation-level focus.…

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