Transcript
A (0:00)
So you want to use Claude code, you want to get the most of it, but you don't know exactly how. This is a crash course how to master Claude code and we explain it in the most simple way. There are thousands, literally thousands of other cloud code tutorials on the Internet, but there are none as simple as this. I brought on Professor Ross Mike. He comes on and he shares it in the simplest way so that anyone could create jaw dropping startups and software using cloud code. We're going to give you the exact steps to, for how you can set it up, thinking about the beginner, how to think about the terminal, how to think about prompting. But if you stick around to the end of this episode, there's a tips and tricks section which I think is super valuable. And I can't wait to see what.
B (0:45)
You build the startup by this podcast. It's tipping time, baby.
A (0:54)
We got Ross Mike on the pod. By the end of this episode, what are people going to learn?
B (1:00)
Hopefully you're going to not feel overwhelmed with cloud code. I know the terminal is scary and it's a big boogeyman, but I'm going to give you the blueprint how to use it. I'm also going to share, consider this the ultimate crash course on how to use Claude code or any agent effectively.
A (1:15)
Okay, let's, let's get into it.
B (1:17)
So, I mean, the best way to start these episodes is with sharing our screen. So when we think of building applications using AI, using some sort of agent like cloud code or open code or Codex, whatever it is, there's a couple of things that you always have to keep in mind. You know, the principles never really change. One thing that it's important for us to understand is however good your inputs are will dictate how good your output is. Right? We're getting to a point where the models are so freakishly good that if you are producing quote, unquote slop, it's because you've given it slop, right? There was a time where the models weren't good enough. There was a time where, you know, we had serious qualms and issues with the quality of code the models gave us. But now we're starting to get to a point where even myself, like, I'm reviewing a lot more code than I write and I never thought I'd be able to say that in the early months of 2026. So very important for us to understand our inputs, how good they are, how precise they are, how articulate they are, are just as good as our outputs and will dictate just how good our outputs will be. And the way I want people to think about this is, Greg, is like imagine you were communicating this to a human, to a human engineer, right? If you give them sparse instructions and if anyone is in like client work, you realize that most clients, they, they, they tell you one thing, but you have to sort of extract the deeper thoughts of what it is they want it. Same way when we work with these agents, when we work with Claude code, we need to be really, really precise with how we build our inputs. Now what do I mean by inputs? What I mean is our PRDs or our to do list or our plans, right? Like there's, you know, people are giving you different names, it doesn't really matter. It's all the same thing, right? And when we think of a PRD or when we think of a to do list or when we think of a plan, I want us to think in such a way as this. Let's say I'm trying to build this product, right? Let's say I don't know. Greg, any product ideas that me have?
