"Ralph Wiggum" AI Agent Explained (& How to Use It)

0:00 Today we're breaking down the clearest explanation of Ralph Wiggins. Now, not the Simpsons character. It's the AI coding loop that everyone is freaking out about. Ralph is a simple idea with huge consequences. You give an agent a list of small tasks and it keeps picking one, implementing it, testing it, committing the code.

0:21 It's basically a way for you to have AI agents building your business, building your product overnight while you sleep. Sounds too good to be true, but it works. And it uses Claude Opus 4.5 to go and do it. So in this episode, this is the clearest explanation of how beginners can learn how to use Ralph.

0:43 You don't need to be technical to understand it. By the end of this episode, you will be capable to implement Ralph so that you too could wake up to features fully done for ideas in your head for the startup you want to build. Enjoy the episode. We finally got Ryan Carson on the pod. This is a guy who...

1:12 Ryan, I don't know if you know this, but I was a treehouse customer. I learned how to code many years ago. Many years ago, and he is one of the best communicators when it comes to learning AI, learning how to code. So we brought him on to figure out what the hell is Ralph. What is happening? Greg, it's so good to be here, man.

1:38 Like, I literally watch your show. I'm not one of those people. It's like, I watch show and don't I do. It's packed with knowledge. Show is fun to get the invite. And it's crazy, you're a treehouse student. I mean, I learned how to code getting a computer science degree, which seems hilarious now, and I decided people shouldn't need a computer science degree.

1:56 So launch treehouse, taught a million people how to code, and now people really don't need a computer science degree. That's right. So for this, for this episode, what are people going to learn and if they stick around to the end, well, you know, who are they going to be? So what I'm going to teach everybody is how to build an entire feature for your app while you sleep.

2:20 So if you watch through the end, you're going to have all of the technical knowledge, even if you're not a hardcore developer. In fact, I would say this is perfect for you if you're not a hardcore developer. You're going to have all the knowledge, all the code. I literally have a repo that you can go and download the code.

2:36 So watch to the end and you're going to have the sauce. All right, let's do it. Okay, so a friend of mine named Jeff Huntley thought up this idea called Ralph. And Jeff is super creative and he launched this a while ago. And the idea is really simple, but that's why it's so good. So yesterday, I thought, you know, I'm just going to post about this and give people a breakdown of what is this thing?

3:06 How does it work? And you know, you never quite know when the algo is going to hit on X. Sometimes it does. Sometimes it doesn't and this went bonkers, right? So you can see we're over 700,000 views. And then there's been retreats of this thing that have got over 100,000 views each. So it's just gone ballistic.

3:24 So what is it? Well, I'm going to walk you through it. I'm sure Greg will share all the links in the show notes. So you can click through and read this. But I'm actually going to walk you through a workflow of what is Ralph and how does it work? So let's start there. All right, so say that you're building an app, right?

3:45 You know that you want to add a feature to it. This is often when you do what's called a product requirement doc or a PRD. The idea is that you want to build something like, hey, I want to add this new feature that does XYZ. And you want to build that, but it's pretty complex. Like this is not a one shot, you know, make me a landing page.

4:08 Like this is a pretty complex thing you want to add to your app. Well, how do you do that? So you start off with writing a PRD. And a PRD, thankfully, is something that agents are really good at. So the way that you do that is that you actually have AMP or Cloud Code or or whoever's your agent of choice do that.

4:30 So I'm going to show you how I do that. I'm going to show you this, which is called a PRD generator. So what I do is I fire up AMP and I basically start talking. I use whisper flow, love it. And I basically say, okay, I want to build this feature. And this is all the stuff it should do. And I just talk for often like two, three minutes.

4:55 And then I tag this file, right? So this is a simple markdown file. This is a skill called PRD generator. And this isn't rocket science, right? So it's got the job that you want AMP to do or your agent to do. It's going to receive a feature description from the user. It's going to ask three to five essential questions that clarify.

5:16 It's going to ask for answers to basic questions, right? So this is a pretty standard prompt, right? So what I've done, just to remind you as if we go back to our flow, I've opened up AMP, I've clicked whisper flow and I've started talking. And then I tag this file or this skill and I say, use my PRD skill and turn this into a PRD.

5:41 And you can actually see I've got a real life example of me doing this and a file that ends up being a PRD. So let's go back here. All right, so you've got this markdown file which is a product requirement doc. And again, just in kind of normal person language, that's just a description of what you want to build.

6:03 And it often has things called user stories in there, which are specific things that you want the user to be able to do in this feature. So now you've got a markdown file, great. What do you do with that? So the next step is, this is a Ralph specific thing, is that you convert this into a JSON file. Now, I've shown sort of an example of a JSON file if you don't know what that is.

6:29 It's basically a specific format that computers like. So what you can see is that this is a user story. So the title is add priority field to a database. And then the acceptance criteria, this is the most important thing. So how does AMP or your agent know if it's done with this thing? You have to give it clear what we call acceptance criteria.

6:55 They're basically tests so that the agent by itself can build this thing and know if it was done without asking you. So the whole key of Ralph is that it's going to build this whole feature while you sleep. But how is it going to do that without you saying all the time? That's good or that's bad or this needs to be fixed.

7:14 It has to know if it passed the acceptance criteria. So this is one of the big unlocks with Ralph or any kind of flow like this, is that the agent needs to have a feedback mechanism so that it knows if what it's doing is correct. So what you're saying is the alternative is you have to be Ralph. You have to be that person to be like, yes, no, maybe.

7:36 Yeah, you have to do all the testing. And that's a pain. And the agents are smart enough now. Opus 45 is smart enough now that if you give it good acceptance criteria, it can do it. So all right, so we've got a PRD, a product requirement doc. We've used this skill. Let me take this here. Okay, so now this is called the Ralph PRD converter.

8:05 And it says it converts a PRD to a PRD.json file for the Ralph Autonomous Agent System. And it basically says, okay, take a PRD and mark down format. And then output it like this, right? So this is telling the agent exactly what you need the PRD to be turned into. And it has a couple really important things here like the story size, the number one rule.

8:30 Each story must be completeable in one Ralph iteration. So this is the other big unlock with working with agents is that they have a context limit, right? So, you know, with Opus, you're looking at about 168,000 tokens. You have to be picking chunks of work that can be fully completed within that context window.

8:52 And this is the, this is why Ralph works so well is that it runs little independent threads where it completes one user story because it's not very big. So you've got a bunch of very small user stories. Story ordering, it puts the ones that it should do first at the top. And then it says acceptance criteria, they must be verifiable, right?

9:17 So this is really key. So good criteria is our add status columns to the to task table with default pending. You can filter drop down, has options, all active completed. And again, these are things that AMP wrote for me and I verified, but they're important that it's in there. So let's go back. All right, so we've created our product requirement doc. Great. We've used the Ralph skill to turn it into a JSON file. Great. Now we run a script. So this is a little different. So most of us are, who have used agents like an AMP or a cloud code or a cursor, you're used to opening up that program, right? So you open up cursor or you open up AMP and you start talking to it, right?

10:06 This is actually a bash script, right? So this is a file that your actual local computer runs. So I'm going to show you actually what this looks like really quick. And for people who don't know it, bash is, it's just a way for... It's just a type of... So when you open up your command, your terminal, which is basically a text input box for your computer, you can type commands on it and you can also run scripts on it.

10:38 So that's what a bash script is. It's a type of file that the computer can run from the command line. So I'll show you what this looks like. And again, this is open source and we're going to have all these links in the notes. And so you can literally take this Ralph public repo, I have download it and use it. So let's go and look what this actually looks like. Now this looks kind of scary, but let's look at what it's actually doing. So it's saying, okay, how many times can this script run?

11:08 The default is 10. It has a script directory. It has the PRD file, right? Like we talked about this, so it's called PRD.json. It's got a progress file, which we're going to talk about. And then the idea is that it archives the script when it's done. And then basically it does a loop. And I'll explain how that loops works visually because it looks nicer than bash. But this is the actual file that runs. So once again, we'll quickly recap. We've got a PRD we wrote. We converted it into a PRD.json, which is just a list of user stories. Every user story is very small, right? It has very clear acceptance criteria. And then you go to the command line and you run the Ralph script, which is you type a couple characters and you hit enter. Now then what happens and

12:03 why is this so exciting? So all right. So then what happens is the agent, you know, I use AMP, it picks one of the stories. Usually it picks the first one. And it's looking for any user story that has passes false, right? So you can see here has has this user story been does it pass or not? No, this one doesn't. So let's grab it, right? And this is the other really cool thing about Ralph.

12:30 So for decades, right? Teams of engineers have been working this way. You have a list of usually sticky notes or now, now that's come on boards, right? And you have a user story, you pull it off and you say, I'm going to do that. You go to your desk, you start coding, right? And then you complete it, you commit it and you merge it, right? And then you come back to the board and you grab another sticky note, right? This is the way humans have been coding forever. And the reason why is because it works, right? You have a unit of work that you can understand that you can test and you can complete independently, right? And this is exactly what Ralph is doing. So it's picking a story, grabbing off the board and it's tackling it. So then, you know, it starts running, it starts working,

13:17 right? This is what we're all used to if you're in cloud code or if you're an amp or if you're in cursor, you know, you say, OK, build this thing and you start seeing it working, right? So I'm actually going to show you today, I post and I said, OK, this is a real thing. I actually built an entire feature with Ralph. And these are the steps I took. I created the PRD, I created the user stories and then I started Ralph and then I'm just going to show you an example of kind of what this looks like for real. So this is a thread and amp. This is basically what you would usually look at and type into. And these are, this is the system prompt that Ralph gets, right? So it says, you're an autonomous coding agent working on my projects, your task, read the PRD, read the progress log,

14:08 and just do a bunch of stuff. And then it basically says, and then it starts working on it. To do to do pretty straightforward. And then you can see down here, it's actually cranking, right? So this is the kind of stuff you're used to seeing. So there here, it starts writing code. Now the whole time that this is happening, I am sleeping, right? Or, you know, I'm having dinner with my amazing wife or something, right? I'm paying attention to my kids, right? But Ralph's slash amp is cranking on that first user story without me having to look at it, give it feedback to anything because it has everything it needs, the context, it has the acceptance criteria, it has everything. What about tokens? Is it burning through my tokens? No, this is the beautiful thing. So

14:55 I actually looked at a couple of these. Let's actually look really quick. Yeah, so look at this. This was three bucks. I mean, $3. That's less than a latte. Totally. So the question is how valuable is your time, right? I think that, you know, for some people, $3 is going to be too much, right? So, so for instance, the typical Ralph cycle is probably 10 iterations. So you're looking at maybe 30 bucks.

15:24 But think about how expensive and how hard that would have been with your time or developers time. So it becomes, you know, pretty amazing. And I'll do one plug, which is that we're about to launch a daily free token allowance on AMP. And so you get like 10 bucks a day. So you could do this for free.

15:45 So, and so I think people are afraid of like, what if the agent runs by itself? Is it going to go off and do crazy things? And the answer is no, because you gave it a clear user story with clear acceptance criteria, right? It's going to be actually a pretty small thread. So, right. I mean, it only does a crazy thing if your criteria is crazy. Right. Yeah. So this is the whole point is you have these, these very small atomic user stories. So, all right. So AMP cranks, it actually finishes the feature, the user story. And then it commits the change, right? So it's like, okay, I did everything.

16:22 And this is really important. This is in the system instructions for Ralph. When you finish commit, because you may need to roll back, right? You may need to go back and fix things. And this allows the agent to do that if it needs it. This is pretty standard stuff for agents. And then what it does next is it updates the PRD dot Jason. And if you look at this, it's going to change this from passes false to passes true. So awesome. It's like that person. They go back to the board and they put the sticky note back on and it's crossed off, right? And then this is one of the most important things is that it logs its progress. So this here is really important. So what's going to happen is when this iteration is finished, it's going to learn things. It's going to learn things about your

17:10 code base. It's going to run into a, you know, walls and have to go around them. You don't want it learning that stuff every time, right? So what it does is a very simple prompt. It says, if you learned, if any of the files you edited have agents dot MD files in that folder and you learned something that's important, update that file. So not only are you, and this is what Karen and the team over at every absolutely crossed with their compound engineering concept is that your agent should be getting smarter every time it makes a mistake. And by updating agents dot MD, you're going to get that long term benefit. This isn't just during the situation. You're going to benefit every time from now on that using up for cloud code. And for people who don't know what agents dot

17:58 MD is and the importance of it, can you give a quick primer on it? You bet. So what it's a very simple file. So let me mark down file and think of it as notes that you would give to a new developer who had never seen your code. And the neat thing is you can have an agents dot MD file in every every folder in your entire repo if you want. And what happens is amp well, if it amp starts looking at a file in a folder and it sees an agents dot MD file in that folder, it will read it first. So it's cool. It's like sticking out everywhere, right? So it's like if you, you know, have some knowledge about this part of the code base and you really want the devs to know, hey, before you mess with stuff, read this. That's what agents dot MD does. So you can have it at the top

18:46 of your folders directory. So that gets read every time the agent works or you can put it bury it down in some subfolder so that it just reads that when it's adding that file. So that's really, really important. If you don't do this, you're going to have a bad time over and over again. And then what it does is it updates a progress dot TXT file. This is like short-term memory.

19:12 So this is like saying, hey, during, during these 10 iterations that Ralph is going to do, what are some things that we're kind of learning? What are we doing? How does all this work? I'm actually going to show you. Actually, I won't show you the example because it's a bunch of text, but the idea is for each iteration, it's going to say, okay, here was the AMP thread that we used, which is great, because then the agent can go back and read it. So say that you get iteration 2 and it wants to see, well, what I do in iteration 1, it can actually read that thread and figure it out.

19:44 It also puts a couple of notes in there like, oh yeah, I probably shouldn't do that in the future or I should think about this. It's more of a short-term memory. Whereas agents dot MD is, it's long-term memory things that it really needs to remember. So that is logging in progress dot TXT. Anything confusing so far? Crystal clear. Nice. Okay. So what happens next? Well, then Ralph says, okay, is there any more stories? So it goes back up to the little board and looks for more sticky notes.

20:18 And it says, oh, yep, there is more stories. So I just do it again. I pick a story. It usually picks the next one in the JSON file, but the nice thing about is it's like a human, it can also figure out, well, I could do that story or that story. I'll pick this one because that would be smarter. So it grabs it, then it implements it, it commits a change, it updates the prd.json file, it logs its progress updating any agents dot MD and it just does this over and over again, until it's done. So this, I'm going to show you again, this was a real example of me using Ralph today. So this morning, I built a big feature into my app. So I created the prd and then I created the user stories and then we cranked and we started doing this. Now this Ralph iteration took

21:15 14 iterations to do. And just to kind of zoom into like one of these, right? What does this actually look like? So again, same system prompt. And now this is the keys. I think people don't get it like, well, why is this different than me just like using amp or cloud code like on my computer? And what's happening is you're getting a fresh loop every time. So you're getting a brand new thread or a brand new instance of cloud code every time. So you're starting fresh with a brand new context window, starting from fresh. So these are the instructions. This is what we're doing.

21:54 Read the prd, do the stuff, progress report format, append to it. Here's how to append. So the things it's putting the progress file are the amp thread so that it can read it again. What was implemented, the files that were changed, any learnings for feature iterations, you know, patterns discovered, gotchas useful context. And then it kind of cranks through.

22:19 And then it starts down here. And that's pretty much it. I mean, it's very, very simple. And with that, you can ship massive features while you sleep. And, y'all, I've been, you know, I got my computer science degree 26 years ago, right? I've taught a million people how to code, right? I've started companies with huge engineering teams, right? This loop is basically an entire engineering team while you sleep. It's unbelievable. And this just wasn't possibly four, plus four, five. I think with opus four, five, this is absolutely the real deal.

23:01 And just wait till opus five comes out. I mean, it's so, so exciting to be alive right now. Yeah. And I think it's not just an engineering team. It's a high quality engineering team that have, you know, run by best practices. Right. For $30. For $30. So it's okay. Now, now in a perfect, the real world is, so let's look back at this thread.

23:28 At the bottom, I said, you know, after I finished, I tested it manually and did find a few edge bugs. This is normal, right? You're going to probably run into a couple things that don't quite work right or perfect. But, you know, Amp and I just worked on it and cranked out fixes like really fast. And the feature shipped this morning.

23:50 So that's what all the hubbub is about with Ralph. Again, it's exciting because it's so simple. It's a loop, right? It's grabbing a user story and doing it. It's having clear acceptance criteria. And then it's writing down what it learns. So that doesn't make the same mistakes. The last thing I will say very, very, very important, I'm going to zoom in, is this, this, these two steps, writing a PRD and converting them to user stories. This is where you should spend a huge amount of time. Like you should spend an hour on this, right? It's very, very, very important that you get your PRD right and that your user stories are small atomic and that you have clear acceptance criteria. Because if you don't take your time on that, you're going to get

24:42 10 iterations of Ralph and you're going to end up with something that's not very good. And I will say one tip, one like really good at the call face with your pick as you're working is figure out how to connect your agent to a browser, right? So I have a specific skill, which is in, let me actually show you. Let's go to Snark Tank. I'm going to show you this repo called AMP Skills.

25:19 If you can download all these, these are open source. Now, not open source, but this one is what I would use, which is your web is your browser. Okay, so dev browser use this because what this does is this allows AMP or cloud code to actually use your browser and test and your user stories that involve front end code. Remember that the agent needs to be able to test that and testing browser is hard for agents. So you have to give a specific skill like dev browser. This is a free skill that a friend of mine made. It's really good. So one small tip for you.

26:03 Cool. We'll include that in the show notes. If people want to get started today, how did they get started with Ralph? So I would probably go to this repo. So just go to get hub.com, forward slash, snark tank, forward slash Ralph. It's completely free. Look at the code, but just tell your agent to look at this code. So tell your agent to go set it up if using AMP great, you know, and it will crank it out. Get it all set up and then say, okay, I want to, you know, ship my first feature. How do I do it? And it will walk you through it.

26:39 And you believe that even if you're non-technical, you can do it? Yeah. I think you need to be curious. I think you need to have agency, but I think if you have those two things, what you probably do if you're watching the show, you can do this. Now it helps to be technical, right? There's a couple of things that are useful, but I think we're now in a world where if you want those skills, you can acquire them very, very quickly. And I'd encourage you go out there and get your hands dirty. That's the most important thing is use the thing, figure out how it works. And then if you don't get it, ask AMP, ask log code like you have this superhuman tutor to help you. So just ask that.

27:22 Ryan, thank you so much for coming on. You explain this exactly how I wanted you to crystal clear, unbelievable. I would love to have Ryan back on the pot if he would come back. But you know, would you, A, would you come back on the pot? Let's do it. I love you. Let's share as much knowledge as we can. And I want that comment section on YouTube buzzing for Ryan, because listen, it might, it might look easy. Now you might be like, oh, I get Ralph. That was super easy, but it's because he explained it in a simple way, which, which I think is the beauty about you, Ryan. So thank you so much. I'll include links for all of this to get started. Links to follow Ryan on X, you know, links to AMP, links to cloud code and Ryan, anything else you want to make mention to.

28:13 Well, thanks Greg for your content. Like it is a big unlock. I think if people want to learn these things, they can. And your content is a big piece of that. And the second thing as I would say is you do not need a computer science degree. Y'all, you can do these things. Like if you are curious and hardworking, you can now do anything. And now is your moment. So if you've got an idea, build, build, build. Yeah, cool watching us right now. Get out of here. Go cut out of here.

28:42 All right. We'll see you next time. Thanks again, Ryan.