Chapter 1: What is the main topic discussed in this episode?
So you want to use Claude Code, you want to get the most of it, but you don't know exactly how.
Chapter 2: What are the best practices for using Claude Code?
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 Claude 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.
Chapter 3: How can you effectively use the Claude Code Plan Mode?
We're going to give you the exact steps 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 you build. We got Ross Mike on the pod.
Chapter 4: What is the Ask User Question Tool and how does it enhance planning?
By the end of this episode, what are people going to learn?
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 cloud code or any agent effectively.
Chapter 5: Why is it important to avoid starting with Ralph automation?
Okay, let's get into it.
So I mean, the best way to start these episodes is with sharing our screen.
Chapter 6: What are Ralph loops and why do they require careful planning?
So when we think of building applications using AI, using some sort of agent like Cloud Code or OpenCode or Codex, whatever it is, There's a couple of things that you always have to keep in mind.
Chapter 7: How can you track progress and ensure code quality with Claude Code?
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.
Chapter 8: What practical tips can improve your coding workflow with AI?
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.
And if anyone is in like client work, you realize that most clients, they tell you one thing, but you have to sort of extract the deeper thoughts of what it is they want. It's the same way when we work with these agents, when we work with cloud 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 PR gear, 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?
Me have product ideas?
Yeah, that's actually the best person to ask, right? Yeah. Um, let's say I go on ideal browser.com and I was just going to it.
I was just going to it. Yeah. Pick the idea of the day from idea browser says it's a diagnostic tool for appliance text, losing hundreds of repeat visits.
See, I have no idea what that means, but let's say I know what that means. Essentially, when thinking of this idea and looking to build this into a full-fledged product, generally the way you're going to think is, okay, if product X does Y, Z, A, B, and C, How I would reach that is I'm going to think of features, right?
So let's say there's four core features to this application that Greg just mentioned. And if I have these four features built out, we can safely assume that we have said product, right? The way we are to design our PRDs, to-do lists, and plans is such that we want the agent, the model, to build out all these features, right? Because all these features put together is our product.
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