I should be charging $999 for this Claude Code Tutorial

1. 0:00 – 1:46

   Intro

   1. AGAakash Gupta0:00

      Claude Code hit 500 million ARR in four months with just two product managers, no advertising, no marketing, massive growth that is replacing tools like Cursor, Lovable, Replit, and Bolt.

   2. CVCarl Vellotti0:11

      It's funny how quickly expectations of how good these things should be can change.

   3. AGAakash Gupta0:15

      Carl Vellotti, who runs the largest PM Instagram account in the world and is an expert with this tool. While some of us are vibe coding, he's building tons of agents to coordinate his life, and this episode is gonna break it down. If somebody with zero technical experience is listening right now, why should they care about learning Claude Code?

   4. CVCarl Vellotti0:32

      It takes you out of the interface of just a chatbot, and it lets you build new workflows.

   5. AGAakash Gupta0:37

      This is the power, right, of Claude Code that you were saying-

   6. CVCarl Vellotti0:40

      The last thing that makes this really cool and much more powerful than an LLM-

   7. AGAakash Gupta0:44

      This is actually crazy. Why has Claude Code taken off so fast? How do you think it's reached 500 million ARR in a few months alone?

   8. CVCarl Vellotti0:51

      This is my secret weapon for helping me create-

   9. AGAakash Gupta0:54

      The last question you want a interviewer to say, "Did you use ChatGPT to do this?" [laughs] Really quickly, I think a crazy stat is that more than 50% of you listening are not subscribed. If you can subscribe on YouTube, follow on Apple or Spotify podcasts, my commitment to you is that we'll continue to make this content better and better. And now on to today's episode. In today's tutorial, we're gonna take you from beginner to Claude Code hero, so that you can create a copilot to make you more productive. And today's guest is Carl Vellotti. He runs the largest product management Instagram account out there. He has been a senior PM for many years. Carl, welcome to the podcast.

   10. CVCarl Vellotti1:35

       Hey, thanks for having me. I'm super excited to be here. Uh, and I'm really excited to talk about Claude Code. It's one of my favorite features that I've found in sort of the new AI era, and I have a lot of really good stuff to show you guys.

2. 1:46 – 4:42

   Why PMs should care about Claude Code

   1. AGAakash Gupta1:46

      If somebody with zero technical experience is listening right now, why should they care about learning Claude Code?

   2. CVCarl Vellotti1:51

      Yeah. So Claude Code, uh, as you start to, you know, do more product management work, and you start to see all these different use cases for LLMs, you sort of start to feel a l- feel a little bit limited. Uh, you- or you're in the chat interface, and you're prompting and then now there's projects, and you can bring in files, and you can manually tell the AI, "Hey, please reference this file," or, "Hey, I found this prompt online. I'm gonna copy and paste it in here." But then you wanna reference that prompt in the future, and it's not really easy to get to. And Claude Code really just does all of that. It's just, it takes you out of the interface of just a chatbot. It puts you into a file system where you can just bring in and dynamically use any of these things really however you want, and it's super flexible, so you can really easily adapt it to sort of any kind of workflow that you already have, and it lets you build new workflows as you get better and better at using it.

   3. AGAakash Gupta2:42

      How is Claude Code different from GitHub Copilot or Cursor or Lovable or Replit? We keep hearing it's better if we're on Twitter, but what makes Claude Code better?

   4. CVCarl Vellotti2:52

      Yeah. So Claude Code, really it is just an awesome way to use an LLM for all types of things. So you can use it for code, of course. It, it's in the name. But Cursor and these other IDE-based, uh, systems, they're really meant really just for coding. So if you wanna do stuff like research, you wanna do stuff like writing a PRD, you can prompt them to do it, but they're not really built out of the box for it. And the really nice thing about Claude Code, and one thing that just makes it sort of nice by default, is that Claude is really, in my opinion, is the best writing agent or is the best writing LLM.

   5. AGAakash Gupta3:27

      For sure.

   6. CVCarl Vellotti3:27

      You can use it for, you know, ra- writing documents and creating-- uh, doing research in ways that ChatGPT, while they're good models for coding, they're just not as good at writing.

   7. AGAakash Gupta3:38

      Everyone can recognize ChatGPT's writing. Claude is the writing partner of choice. How does Claude Code compare to other CLIs out there?

   8. CVCarl Vellotti3:46

      Yeah. So the really amazing thing about Claude Code is that it r- is a completely new interface, an interface that I think people wouldn't even really expect a revolutionary new product to go to, which is the terminal. So just purely text-based. It basically doesn't even have its own interface at all. And there are a couple other products like this. Uh, Google has come out with one since Claude, c- their Gemini CLI. Um, OpenAI has their Codex. I would say because Claude was first, it's just the most polished of any of the models or any of these options out there. Uh, its tool use is basically perfect. Its use of agents and sub-agents is basically perfect. So, uh, you know, the meta around this changes really fast, and I've heard good things about OpenAI's Codex. But I think right now, Claude Code is just-- it's the first one, and they just really have it the most polished and sort of the best vision for this early on. And that's why of these sort of options of the CLIs, it's still the best.

3. 4:42 – 13:23

   Claude Code vs GitHub Copilot, Cursor, Lovable

   1. AGAakash Gupta4:42

      And people, this is not $200 a month. Whenever I talk to people, they're like, "I don't wanna shell out the money for Claude Code." You can use it on the $17 per month Pro plan. So I'm really excited to get into this. Can you show us in action how this looks, Carl?

   2. CVCarl Vellotti4:55

      Yeah, for sure. Uh, one thing I'll say on the, on the pricing is we'll talk about it a little bit today. I'm gonna be using the Max plan, but I'll show you some sort of hacks and things that you can do so that you can do basically everything we're demonstrating today just on the Pro plan.

   3. AGAakash Gupta5:09

      Awesome.

   4. CVCarl Vellotti5:10

      Okay, let's get into it. So to start off, we'll just go ahead and, uh, get it working. Yeah. So in order to get us started here, we're just gonna go to Anthropic's quick start guide. As you can imagine, it makes this very quick to get going. They have this NPM install method, which was the original one, but they have a even newer way that just makes it really fast and basically only one command you have to do. So if you scroll down here to this Native Install section, I'm on a Mac, but this works on Windows or really, uh, m- it's the exact identical process for Windows. But for the Mac, you just copy this command. So now what we're gonna do is we're gonna take this command, and we're gonna do something that's gonna seem a lot of scary to anyone who's n- not an engineer. We're gonna open the terminal. And so what a terminal is in general is it's just sort of like a command line interface for you to input commands, and they will just talk directly to the files on your computer. So for, to starting, we're not really in a folder or anything. We're just at base, so we're just on my computer. And all you do is you just take that command that we copied from the quick start guide, you paste it, and then you hit EnterAnd what this will do is it kind of runs the command, it's going to this website, and it's grabbing the code it needs to set this up. Setting up Claude Code, and now it's installed. So that's it, literally one command, you put it in, and now we're here. And then to get Claude going, you just type Claude. And now we are in the terminal, and we have launched Claude Code. So to start, it-- this is gonna be very familiar if you've ever chatted with an LLM. It's that interface, but just with no images or no text or anything. So we'll just say, "Hello, Claude." And now, we'll, we'll kinda talk about the UI here, that it does have a little bit more. But, um, now we are just talking to Claude, and we are, we are in Claude Code. So there's not too many commands or anything you really need to know. You can basically just work with it exactly like you would talk to anyone or talk to an LLM. The commands that are good to know, uh, we're gonna use one a lot called Clear, which will just basically take whatever conversation we're, we're having, and it will reset it. Uh, that's just to sort of manage context so that as you're going, it doesn't pull information from the past conversations. And we'll, we'll talk about the other commands as we go. But really, at this point, all Claude Code is, is even though we're in a command line interface, we're just using natural language to talk to it.

   5. AGAakash Gupta7:34

      Awesome. How does this differ from, like, a full IDE?

   6. CVCarl Vellotti7:38

      Yeah. So we actually are gonna jump into a- an IDE in a, in a little bit. Um, the main thing that's different here is it's, uh-- we don't have a good way of sort of, you know, referencing files from this because we can't see what they are and, like, drag and drop them. Um, so as I mentioned, when we launch this, we're in just the base sort of level of my entire computer. What we actually wanna do, and the way that you'll use Claude Code, is you're gonna open it up in, like, a specific folder. And so what I'm gonna do here is I'm gonna go ahead and close this terminal. And I have a prepared file for us today. And what I'm gonna do is you just right-click, and then you're gonna create a new terminal at that folder. And just to kinda show you what's in this folder, this is sort of-- You can imagine this as a, a demo, uh, internal wiki that you might have at your company, where you have a bunch of sort of data that you've uploaded from, uh, you know, that you're already working with at your company. And so we have, we have data, like some customer interviews in here. We have, we have docs from business meetings, and then we have some code. So we'll, we'll be using these files sort of throughout this whole demo. Uh, and just kind of to call it out, you can either create these files as you're working with Claude Code, and it will do that, or, um, if you're, you know, using this at your company or using this for any personal use, it's really easy to pull in files from Notion or Google Drive, and we'll get into that later. But for now, just sort of assume that we have this information about our company in here, and we'll work with that for the demo.

   7. AGAakash Gupta9:03

      Sweet.

   8. CVCarl Vellotti9:03

      So, uh, back to the terminal. So now we are in this specific file, uh, for Claude Code. So again, we'll launch Claude by typing Claude.

   9. AGAakash Gupta9:15

      Mm-hmm.

   10. CVCarl Vellotti9:15

       Now we are, we are in this. So we can ask questions about sort of anything that's in here. And so one thing that we'll do is, for example, we have, uh, these customer interviews. So we can now ask Claude, um, "How many customer interviews have we done?" And this will just go through that, that project, and it will search for us anything that's in there. So right now it's searching. You can see it's hustling. Okay. So it found those three interviews, and then you can ask questions about, you know, any of those interviews. So, um, I'm gonna use-- I'm using a tool here called Whisperflow to help me just dictate commands because it's a little bit faster, which I highly recommend.

   11. AGAakash Gupta9:55

       Mm-hmm.

   12. CVCarl Vellotti9:55

       Uh, can you summarize the top takeaways from the interview with Jessica? And so it has its, all of its little, um... It's smooshing. So what it's doing is it's reading that file, and it is using these tokens, which you can see here, and is basically gonna read that file and just respond to us like it would, like any LLM would. And so it's kind of summarized here those points. And then you can ask it, you know, "Can you summarize the differences between the interview with Je- uh, with Jessica and Marcus to tell me what's different between healthcare needs and retail needs?" And the sort of demo project here is based on a, a fictional company that I called Streamline AI, so it's like a automation builder, so that the answers will be sort of based on that. You can kind of imagine it. I think we'll talk about this product later, um, N8n. It'd be kind of a version like that where you can build workflows. So here it's telling us it's done this analysis. So it's taken our... These are just raw customer interviews that we had in this file, and it's looking at them between healthcare and retail, and it's given us, uh, this summary. So-

   13. AGAakash Gupta11:01

       This is the power, right, of Claude Code that you were saying, where it's a better interface than chat. You've-- It's automatically reading the folders. It's figuring out how many files are in there. It's going in there and reading them. It is the ability to context engineer much faster.

   14. CVCarl Vellotti11:18

       Yes, exactly. That's a really good way of thinking about it, is, uh, you know, as we've moved from prompt engineering into, you know, uh, we've gone into context engineering, what you can give the LLM to work with is so key in what it's able to actually do. And that's a through line that we're gonna see through everything that Claude Code is good at, is just this, a really fast, simple way to give it the right kind of context for any of these different kinds of things you wanna do. Um, okay. So we've seen it search files and kind of analyze files here. Um, it can also search the web. So as I mentioned, we're gonna use Clear a lot just to keep the context clear. We've seen it use files and read files and, um, analyze files. Let's go ahead and show how Claude can search. So we're gonna go ahead and have it compare the, uh, do a quick search on the iPhone that just came out this week, date this little, date this video a little bit. Um, but what it's doing here is it's running the search. It's, um, looking for anything that came out September 2025, and, um, it usually seems like it checks 10 sources. So we'll see what it comes back with

   15. AGAakash Gupta12:21

       I love it. Combobulating

   16. CVCarl Vellotti12:23

       Yeah, combobulating. We can see the time. Uh, we can see the tokens, which is, uh, a pretty interesting aspect of the Claude Code interface. When you're using ChatGPT or you're using Claude, they never actually tell you the amount of tokens they're using. So I think when you see the pricing is, you know, $2.50 for, um, a million tokens or whatever, whatever it is, um, you never really know what that means. So this shows, so it's counting up these tokens really fast. Um, okay, great. So here it's... It got a little confused about my exact request, so it's something we could probably make better, but it did grab the iPhone Air key specs. Um, it just didn't do the direct comparison, so I think that's probably something just with the prompt. But, um-

   17. AGAakash Gupta13:07

       Mm

   18. CVCarl Vellotti13:07

       ... yeah, so just interesting. You can, you can ask it to sort of do any sort of search, and it's, it's very good at search. Uh, I know early on there's sort of people thinking you had to, like, implement Perplexity in order to have it do the search, but overall, Claude Code is, is quite good at doing the search on its own.

   19. AGAakash Gupta13:22

       Nice.

4. 13:23 – 14:31

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   1. AGAakash Gupta13:23

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5. 14:31 – 19:36

   Getting started: Installation and setup

   1. CVCarl Vellotti14:31

      Okay, and then the last thing I want to show before we kind of get into, like, more complicated stuff is just that even though this is a, uh, completely text-based interface, you can, uh, bring in images. So you can either drag them in, so this is a, this is a graphic that I prepared and posted on LinkedIn recently. So I can say, "Can you please analyze this image and give me feedback?" Um, I didn't clear the context, which normally that would be the best thing to do. In this case, I think it will be okay. And so when you give it an image, it just sort of says, like, Image one. Um, you can of course do-- You can post in screenshots or, or anything like that, um, and it will use it. So that's very helpful for, like, debugging. So in this case, you can kind of see it. It was able to, to see the image and then give us, give me some feedback here.

   2. AGAakash Gupta15:17

      Nice.

   3. CVCarl Vellotti15:18

      Yeah.

   4. AGAakash Gupta15:19

      Search images, context from files.

   5. CVCarl Vellotti15:24

      And the, the, the last thing that makes this really cool and much more powerful than, uh, an LLM, for example, or, uh, one, like a normal interface that you're using, is it can actually run code. We're gonna ask it to do something that you could never really ask ChatGPT to do, which is I'm gonna give it the, the repo for, um, a YouTube transcript API, and I'm gonna say, "Please get the transcript from this video," and then the video I'm giving is a past product growth one, really good one on building agents, and then put that output in an MD file. And what we're gonna see is that all by itself, without really us needing to know exactly what the code is or really looking at it, but just knowing that this API exists, we can give Claude Code this information, and it will just run it. Um, and now we're also seeing the first, like, really, really, like, unique feature of Claude Code, which is its ability to create plans for itself. So do you see this to-do file or this to-do here?

   6. AGAakash Gupta16:19

      Yeah.

   7. CVCarl Vellotti16:19

      So what it did is it, instead of just sort of immediately starting, it actually created a task list for itself. So it says, "I need to install the YouTube API, I need to create a script that will use that API, and then I need to extract the, the video, and then I need to put it into a file." And so what, what that basically means and what it really helps, uh, Claude Code be much more agentic is it's creating a task list for itself, and then it will go through those one by one, and it will check itself, and it won't move forward onto the next task until it actually has, has done those things. And so here we can see that it wrote the, it wrote the script, uh, it ran it, it was able to pull in the file, it put it into this... It was able to pull in that information, and then it put in into this new file, and it's just sort of confirming here that it did all that. So very, you know, very cool, and you can already kind of see, like, you can bring in any code that you find on GitHub or that you're, that you see people posting, and Claude Code can just basically use it right away.

   8. AGAakash Gupta17:22

      I think a lot of people shy away from those GitHub repos and stuff, but this is a great way that you can actually interact with it, even if you're pretty non-technical, 'cause we're just talking into WhisperFlow here.

   9. CVCarl Vellotti17:32

      Yeah, exactly. I think for me, since I've started vibe coding, that's been one thing where I remember used to see cool stuff in a, in a GitHub, and anytime it's a GitHub link, you know, you know, you know you can't really use it or you know it's gonna be hard to get set up. But now you just... Now those are perfect for just giving an LLM, and they can work with it right away. So okay. So we created this file. We created youtube-transcript.md, and if we go into, like, our overall sort of file, uh, that we're working in, we'll see it here. But now the question is, like, how do we actually work with these files? Um, how can we actually view them? How can we see what's going on? So what we're gonna do now is we're gonna go ahead, and we're gonna exit this terminal, and we're gonna go ahead, and we're gonna enter an actual IDE. So, um, you can use any IDE for this. My preference, just because it's, like, sort of the one that I've been using, otherwise it's Cursor. And so this is kind of meta because we're using Claude Code in Cursor, but I'll show you why we're doing that

   10. AGAakash Gupta18:28

       And it seems like just about every tool has released this functionality where you can use Claude Code in it now-

   11. CVCarl Vellotti18:33

       Yeah, anything with a terminal, and that's gonna be basically like any IDE. Um, so here we're in, uh, Cursor, and there's really... The main feature that makes Cursor Cursor is this sidebar, um, where you can, you know, you can prompt here, and then it will do the coding. We're gonna not use that at all today. Instead, we're just gonna-- I'm gonna close this, and what we're gonna do is if you hit Control, um, s-the squiggle, I'm not sure, back, backtick, Control + Backtick, it will open up the terminal down here.

   12. AGAakash Gupta19:03

       Mm-hmm.

   13. CVCarl Vellotti19:03

       And again, we're still in that same folder, and then to get start-- get Claude started, we go ahead and type Claude.

   14. AGAakash Gupta19:08

       Okay.

   15. CVCarl Vellotti19:08

       And so now it's the exact same thing that we just saw, but instead of being in like an isolated terminal window, we're just in the terminal in Cursor.

   16. AGAakash Gupta19:16

       So now we can-

   17. CVCarl Vellotti19:16

       This is a good point and this is a reason where being in Cursor, one thing that's really nice is we saw those plans from earlier. There was the Pro and the Max and the Pro Max. Uh, if the Pro plan can really do almost everything that you're gonna want Claude to be able to do, uh, Sonnet is still a really good model for researching and for writing documents

6. 19:36 – 24:19

   Terminal basics and first commands

   1. CVCarl Vellotti19:36

      and just writing in general. Where you might want the Max or the higher level plans is for coding because then you get access to Opus and those models. But honestly, if you're in Cursor, and Cursor is only $20 a month separately, you get access to that model plus all the other really good models. And so you can pretty easily use Claude Pro for the Claude Code things that you wanna do, and anytime you actually really wanna do some heavier coding, if you just get Cursor and then you use-- you can use the absolute best models for quite a lot cheaper. That's a good sort of hybrid way to use Claude Code for non-coding things, and if it gets too complicated, just go into Cursor and use the better models.

   2. AGAakash Gupta20:13

      Nice. So $37 a month.

   3. CVCarl Vellotti20:16

      Yeah. And, um, I would say very high ROI for most product managers on that. Um, okay. So we are now here in Cursor, and we can go ahead and we can -—the transcript that we just pulled in, we, we can look at that here. And, um, now here it is. So easy way to visualize it, and then in Cursor, you can, um, you can always preview, which will basically take that markdown, which is just sort of a... Here it's just raw. Um, but if you can actually format it and see it how, like, it would be published online, uh, if you just preview it. So here we can see, like the bold is nice and everything here, and then if there's anything you wanna edit, you can double-click and that will take you, like, into the file and you can edit it here. Um, but so this is just basically showing the rest of the demo we're gonna be in here just because it's a faster way for us to kind of see what's going on and, and pull in files to give the LLM to use.

   4. AGAakash Gupta21:08

      Got it. Uh.

   5. CVCarl Vellotti21:14

      Okay. So the first thing that you're gonna generally wanna do, uh, when you start using Claude Code and you open up any project is it has a command called init, which is just the initia-initialization command. What this will do is it will basically just take whatever sort of Claude just imagines it gets dropped into this file structure, and now it has to figure out what's going on. And that's kind of what it's like as a human too. You know, e-even if you know what a project is supposed to do or the files that are supposed to exist, you still have to look around and kind of try to figure it out. Claude will just go through and analyze everything, and it will-- what it will do is it puts together what's called this CLAUDE file, all caps CLAUDE, and it basically saying, "Okay, here's what I found. Here's the structure of this file. Here's like the core components of how it works, and, um, here's how a person can get it working." And so in this case, because I already had one, but now, uh, it's-- I've run it again, it will update this. And so on one hand, uh, this is just like a helpful sort of thing for you to see how the whole, uh, sort of repo is structured. But what's, uh, really powerful about this is that every single sort of session that you have with Claude, it will reference this CLAUDE file. And so you can imagine this is very helpful for it when you ask it a question, it doesn't have to newly search or newly find that context every time. It just already kind of knows how your ch-- uh, project is built. And this is also where you can give it rules like, "Never commit to GitHub without asking me first," or, "I always wanna use this style of writing," or, "When we're talking... When we're doing a research or when you're doing a web search, make sure that you give me the results back in this format." And so it will always have this, and it's different than even like a, a prompt because in a prompt, let's say you're having a conversation and, you know, you say something, you say, "Never commit without asking me first," and then, you know, five or ten like messages later, it might start doing it again. That's because it, it sort of, it's getting further back in its context and its context window is only so large. But everything that's in this CLAUDE file is in its sort of memory all the time.

   6. AGAakash Gupta23:21

      Got it.

   7. CVCarl Vellotti23:22

      And, uh, the quickest way, whenever you're-- let's say you, you realize Claude did something that you didn't want or you wanna-- you're like, "Oh, this would be a good thing that I should add to the CLAUDE file," if you just do sort of number sign and then, you know, "Always ask before committing anything to GitHub," then that will just automatically add it to the project memory and it will be there. And, uh, we won't go into it too much today, but the other sort of main use of this CLAUDE, uh, types of files is you can put them into sort of subfolders, and then anytime it's doing something in those subfolders, it will follow those rules. So let's say you have a, you know, a, a folder that's really all about helping you write PRDs. If there's specific rules you always wanted to keep in mind for those, you can create that file there.

   8. AGAakash Gupta24:05

      Okay. Got it.

   9. CVCarl Vellotti24:07

      Yeah. Okay. So speaking of creating PRDs, now we're gonna go ahead and we're gonna look at how does all this sort of easy context engineering within Claude Code really make it powerful for, you know, doing things that product

7. 24:19 – 30:26

   Searching the web with Claude Code

   1. CVCarl Vellotti24:19

      managers do all the time. So in this file, and this is something that would be really helpful to have for your own self, is, uh, we have... this business info. So this is basically like information around just how does this business work? So as I mentioned, it's a fictional company here called Streamline AI. It's a automation builder. So you can have Claude Code anytime you're doing something where you want it to give a, a very specific response that makes sense for your business rather than just, you know, a general LLM response or, you know, you just, instead of you just typing, you know, one sentence about what your business does, you can have this document that it can reference at any time that tells it what your business does. Uh, and as another example, you can also have writing styles. So let's say you have a, you have an internal-- like, whenever you're writing something for an internal audience, you want it to have one style. If it's a more technical document, document, you want it to have another style. And so what we can do is you can sort of combine all of these things really quickly. Okay, so now what we'll do is we will show off, we, you know, we've looked at how Claude Code can use, can read files and write files and can reference some of those dynamically. What we're gonna do here is we're gonna use all those capabilities in sort of one prompt to show you why is Claude Code so awesome. And so what I, I have here is I have this prompt. It, uh, basically what you wanna have as part of your, your project is you want to just keep information that you might wanna reference often. So in this case, in this project, we have, uh, we have some writing styles. So we have like an internal audience style, uh, a technical style, user-friendly style, and then we also have business info. So this is a great one for whatever you're, you're trying to build, um, or whatever you're doing. If there's information that you want the Claude Code to actually really know, instead of just saying, "Oh, I have a business that does this," if you put together a document that says, you know, "Here's my business, here's all the different things that it does, here's like key information," then anytime you need to do something l- like write a PRD, you can reference that information altogether without having to, you know, go copy and paste it in, which is easy to bring it in here. And so what we h- and then the other thing that's also helpful to have, LLMs are always much better if you give them examples. You can, instead of even just providing like individual examples, you can build a folder that has a bunch of examples of things that you like. So in this case, I have this example PRD. So let's say I'm working at this company, uh, a bunch of killer PMs who are writing some really great PRDs. You can use those as examples for your own PRDs. And so what we have here is we have this sort of super prompt, um, that is taking in saying, "Build this feature, uh, with GPT real-time," which is OpenAI's new speech-to-speech model. Uh, you can, uh, use this business context from this document and then use these example PRDs with this writing style. And so just by having that stuff already pre-created, we can run this prompt, and it will do all of those things altogether without us having to go, like, try to find all that information and create this prompt.

   2. AGAakash Gupta27:17

      I love how the folder structure is coming in clutch again here.

   3. CVCarl Vellotti27:23

      Yeah, exactly. That, that folder, just keeping this, uh, organized and keeping, um, all of your information sort of where you, in a, in a way that you can quickly give it to Claude makes all this so fast. So what we're seeing here is, um, it's come up with another one of those plans for itself. So it, it recognizes that it doesn't know what GPT real-time is. It just came out like two months ago. Um, so it's researching that, and then it's gonna read the business info, and it will, um, create the PRD, and then it will make sure it goes with this writing style. So it's taking all of that information that we just gave it, and it made a plan for itself. And we can kind of see it running through it here. It's, um, reading business info. It's, um, figuring out what that technical style is. And, uh, it's, right now it's mainly it's, uh, doing this web search. So it finished those things, and then the tokens are counting up again. So it finished those tasks, and now it's gonna actually write the PRD, um, for us.

   4. AGAakash Gupta28:15

      It's fun to watch it just cross off to-do list items.

   5. CVCarl Vellotti28:18

      Yeah. There's like a, there's a little bit of like a little dopamine burst as you, as you see it work through it, almost like when you cr- complete your own to-dos.

   6. AGAakash Gupta28:26

      Yeah.

   7. CVCarl Vellotti28:27

      Okay. So it did all the research, and now it's putting it together into a PRD. Yeah, we see these tokens. At first, they-- you can see every individual one, and then it just becomes a decimal point. Uh, tokens definitely get used up very, uh, fast. Luckily, on the-- luckily for Sonnet, uh, which is sort of the, the main, the main model and then now their best model, which is Opus, you get a lot of usage on both the Pro plan, which is the $17 one, and the, the Max plan. With Opus, which is the better model, which is helpful for things like coding, th- those, these tokens, as you can see, they're counting up very fast. Uh, they get used up quickly. So that was kind of my, my recommendation for using Sonnet for, you know, this type of stuff that we're doing here, but then maybe coding in another less expensive tool might be the way to go. So now we can see that it created the PRD, and sometimes the first time it does something when in a session, it will ask to make sure it's allowed to make that change in the code base. Uh, here it's just saying, "Okay, I made this. Can I add it?" And then what we're gonna do is we'll go ahead and just say yes, and you don't have to ask again. Um, one command I'll, I'll probably demonstrate later is there is like a YOLO mode where you can just have it do whatever it wants, and it never has to ask.

   8. AGAakash Gupta29:45

      [chuckles]

   9. CVCarl Vellotti29:46

      Um, it's a-- We're literally having it, like, you know, really work with the files on your computer. So in this case, we're in a contained environment, so it probably couldn't do too much damage. But you have to be very careful, uh, especially if you're doing code-based stuff with, with that permission.

   10. AGAakash Gupta29:59

       Yeah.

   11. CVCarl Vellotti30:00

       Okay. So I said, "Yes, that's fine," and then it created our PRD. So let's take a look at this quickly. So it-

   12. AGAakash Gupta30:10

       What was the command to make it look better here?

   13. CVCarl Vellotti30:13

       Oh, yeah, sorry. Um, we're just looking here, and then what you can do is you can right-click, and then you just go ahead and go Open Preview.

   14. AGAakash Gupta30:20

       Oh, nice.

   15. CVCarl Vellotti30:20

       Um, and then I just did the Shift+Command+V.

   16. AGAakash Gupta30:22

       Nice.

   17. CVCarl Vellotti30:25

       Okay, now we're,

8. 30:26 – 37:24

   Running code and using GitHub APIs

   1. CVCarl Vellotti30:26

      we're seeing this. So it has our, our format that we wanted. This is modeled after the Akash recommendedRecommended PRD, um, has the problem statement and the, and the goals. And of course, you'd still wanna go through this and, and make sure it's good. But it's pretty, looking like pretty thorough given the, the information that we gave it. And we asked it to use a more technical voice, and definitely it looks like it really thought through those technical constraints. Um, and it even linked to a bunch of documents for us to use here. So this is a really good first draft of a PRD that you'd, that you'd wanna go work on from there.

   2. AGAakash Gupta30:58

      And that prompt was so simple.

   3. CVCarl Vellotti31:00

      Mm-hmm.

   4. AGAakash Gupta31:00

      That, it's really the power of the searching online to figure out what the real-time API is and then connect it back, so that's so cool.

   5. CVCarl Vellotti31:07

      Yeah. And, and ways that you could definitely... This is sort of still very hypothetical. I didn't give it a lot of guidance on the user experience we're expecting from this. But you can imagine that you might have met with your designer, you have a meeting transcript. You've met with your engineers, you have meeting transcripts. You have all this information from other things that you've sort of put together, and then now you can give it all of that, and it will, it will really use it, um, correctly in, in these files. So that's just a, a good example of how it can put together all this information and then output something really awesome without you having to do a ton of extra work.

   6. AGAakash Gupta31:40

      And you just mentioned correctly. I think that there was this paper which was basically like, as you give things, these LLMs more context, sometimes, like, there's a dip in the quality if you just... I think it's, like, token degradation or something like that is the context they were talking about there. Do you see that, where you can overload it with too much context?

   7. CVCarl Vellotti32:01

      I think the nice thing about Claude Code is that it makes it pretty, like, easy to not do that in the sense of like, okay, we finished this task. Now we can clear. And I think it makes you a lot more cognizant of the fact that you are using context, 'cause it's telling you the tokens, and it makes it really easy to start new sessions. Where I think in, like, a chatbot, you know, system or, or something, it's, uh, it's very easy to just keep chatting, keep chatting, keep chatting, and that's where I think it starts to get a lot worse. I think if you're able to just give it all of that information up, upfront, then it really actually does use it, um, better than if you're kind of putting it together over time.

   8. AGAakash Gupta32:38

      Makes sense.

   9. CVCarl Vellotti32:39

      Yeah. And, uh, the context windows for these things are still pretty huge. Uh, you know, like, if it's a million tokens or if it's, you know, 200,000 tokens, even if we were giving it, like, a lot of information, we're not uploading, like, books into this. So I think it, it's still usually, like, well within the context window of what it's able to, to handle.

   10. AGAakash Gupta32:58

       Got it.

   11. CVCarl Vellotti32:59

       Yeah. Um, and then, yeah, so that, we just did kind of like a, a pretty beast, beast mode example. Um, one other sort of quick use case I'll show, uh, is, let's say we had a folder with these meeting transcripts. You can also just have it say, um, "Hey, please, please take these transcripts," and then just add a summary with action items to the bottom. So it doesn't always have to be super beast mode. It's also useful for just, like, smaller things as well. So here it's gonna just go into our folder. Let's say, for example, you were, you know, all day you just were in meetings back to back, which I'm sure product managers can relate to, and you haven't had time to go back through everything. Here you can just say, "Hey, all of, everything is in this document or in this folder. Can you help me figure out what all the action items were?" And, um, this one, it's very fast, right? The last one took some time because it had to search, but this one, it's, um, looking at the directory, it's reading the transcripts, and now it's adding the summary.

   12. AGAakash Gupta33:52

       And it looks like it's going in and changing your files right then and there, which is s- not anything you're gonna be able to do with a chatbot.

   13. CVCarl Vellotti33:59

       Exactly. Um, so here we're, we're opening up this document, and then we scroll down to the bottom, and, uh, we're seeing the meeting summary and then the action items, and it, it, it... In this case, so, and what we're gonna look at next is how you can kind of get more structured outputs. I didn't tell it really, like, what types of information I was looking for, for, um, these outputs, but, you know, it's still, Claude is, like, still, like we were saying, it's good at writing and still pretty smart. So it figured out that, you know, the person that it needs to assign these to, exactly what they're gonna do, and then, like, a due date. Um, so let's say that you did have a much more specific format that you wanted these action items in. Uh, now I'll kind of introduce this concept of, of, uh, you can build your own commands. So, uh, we looked at, there's a bunch of sort of pre, prebuilt commands that Claude has, but you can also create your own commands, and you can think of these as basically stored prompts. So let's say, for example, that we had, uh, a specific format that we always wanted notes to be structured in.

   14. AGAakash Gupta34:59

       Mm-hmm.

   15. CVCarl Vellotti34:59

       What we can do is we can build a command for that. So I have, I have this command that I've created here called meeting notes. And let's say here it's like, extract key information, I want it in this format, and then some things that the one that we just prompted without telling it anything that we wanted. We can say, "Make sure that you list the action items, um, uh, the important metrics of data that were mentioned in the meeting, and then specifically next steps and risks." And so what you can do is you can do something similar to what we just did, um, but you can run a command. So you can say meeting notes, and then you can just ex- for example, one nice thing for Cursor, you could just even bring it in here. Um, and now it will use that command with this basically the saved prompt for that file. And so you can kind of think, like, when you're on Twitter, you're on LinkedIn, people like to share these big prompts all the time. But, you know, it's kinda like, what am I exactly gonna do with it, or when am I really gonna use it? If you start to figure out which ones are really useful to you, you can save those into your file so that when the right time comes, you can just e- easily trigger it rather than, you know, having to go into your bookmarks in, in Twitter that you probably never use, um, and find it and then copy and paste the image.

   16. AGAakash Gupta36:08

       Well, yeah. Or go into your outdated prompt library that you set up two months ago and be like, "Oh, shoot, I didn't even put this prompt in there." [laughs]

   17. CVCarl Vellotti36:16

       Yeah, exactly. Uh, so this is pretty similar to what we just saw, but we asked it to do that on this folder. And then, um, yeah, for this one I, I asked it to put it at the top. So it's kind of, these are maybe much better, uh, summaries from, summary from the meeting rather than the one that we just sort of prompted it to do.

   18. AGAakash Gupta36:34

       Yeah.

   19. CVCarl Vellotti36:34

       So again, you can create all these commands, and you're really starting to see how you can mix and match and put these things together in, like, almost any flexible way.

   20. AGAakash Gupta36:42

       This is actually crazy [chuckles] because I think one of the big challenges if you're using, let's say, ChatGPT for meeting notes or whatever out of the box, is making things in your own style, right? Putting it in your own voice. You-- The last question you want is your coworker to say, "Did you use ChatGPT to do this?" Because that usually means that they didn't think it was very good, right? And so here you're inflecting your own style through these simple markdown files that you then reference in the prompt and create commands around.

   21. CVCarl Vellotti37:12

       Yeah, exactly. And then you can kind of do-- I, I don't have this demo prepared, but I just thought of it. You could do something like, let's say you had a, a file or something for, like, your writing style. Like, I have this internal audience style. You could do

9. 37:24 – 40:35

   The init command and CLAUDE file

   1. CVCarl Vellotti37:24

      something like, uh, please write... Uh, okay, let's do this. Please write a Slack message for me to send to Sarah asking her the status update on her items. It's January 28th, and she hasn't completed them yet, so I just wanna double-check. So here we're saying, okay, we have this, we have, um, this file that says the things that Sarah was supposed to complete, and so we're gonna give it that file, and then please use the tone in this file. And then we can say, "Please use this tone for my internal audience." And so this is sort of, now it's gonna-- it has the context of what happened in this meeting. It knows the action items that Sarah's supposed to be completed, and it knows the style that you wanna write in. And so it can help you put together the Slack message really quickly. Um-

   2. AGAakash Gupta38:12

      And could you connect this actually to your Slack?

   3. CVCarl Vellotti38:15

      Yeah, you could. Uh, we will talk about MCPs a little bit later, but, uh, Slack is pretty easy to connect, um, into Claude. I think in that case though, I don't think it would look like it was sent from you. Um, but what you could do is you could have it send it, like, to yourself as a message from the Slack MCP, and then you could copy and paste it.

   4. AGAakash Gupta38:33

      Awesome.

   5. CVCarl Vellotti38:34

      And so this is, like, a pretty good message. Like, "Hey, Sarah, a quick check-in on the items, uh, that were due yesterday from the meeting that we had last week." And then it's saying, uh, "The roadmap proposals, like, I know these directly impact our objectives," and it's-- and now it's, it's r- it's kind of a good message. Like, this might be even more information than you would include, and you could decide. But, like, it even knows, like, what was talked about in that meeting in relation to the business. And so it's saying, uh, "I know these directly impact our Q1 strategic objectives and the ARR recovery plan that we have. Can you please make sure that we, like, actually get this done? Because we have dependencies here." So this is, is an example where you can do pretty, like, interesting stuff, even as, like, a product manager that would be hard to do otherwise, um, all together with, with Claude Code.

   6. AGAakash Gupta39:19

      Very cool.

   7. CVCarl Vellotti39:20

      Yeah. Um, okay. So we just covered slash commands, and what we're gonna move on to next is we will move on to, uh, planning mode. So, so far, all what we've done is we've seen Claude Code create plans for itself. But sometimes if you're gonna be doing something a lot more complex, then you wanna actually sort of plan with the L- LLM first. If, if you've ever done any coding with LLMs, you know that you'll say, "What would it take to, you know, change this so it operates this way?" And then LLMs, you know, they're just built to be helpful, and so it will just start doing it. It will just say, "Okay, I will change that file." And then you realize that pretty much as soon as it gets started, that wasn't exactly what you wanted it to do, or you d- you know, it didn't really have the requirements that it was supposed to have before it got started. And so one thing you can do is you can always say, you know, "Don't code. Tell me, talk to me first." But even then, it won't really give it in, like, a consistent format. And for Claude Code's planning, there's just a much better way to do it, which is we've been on this auto-accept mode, but what we're gonna do is we're gonna go into this plan mode. So you just do Shift+Tab, and now we're in plan mode. And this basically does what I just explained, where it, it, it-- now it can't edit files. It can still search, and it can still read files, but it can't edit anything. And so this is where you can kind of take

10. 40:35 – 52:18

    Creating PRDs with context engineering

    1. CVCarl Vellotti40:35

       a step back, and you can work with Claude to sort of come up with a plan. And what it will do is it will actually pre-create that checklist that we saw before, and you can look at that, and you can make sure that the steps there make sense and that it's not forgetting anything. And then you can sort of let it go wild.

    2. AGAakash Gupta40:50

       Nice.

    3. CVCarl Vellotti40:51

       So in this case, I have, like, a-- We're gonna do something more complex that's gonna combine a lot of the things that we've seen so far. So let's say we, we pulled in those YouTube transcripts earlier.

    4. AGAakash Gupta41:02

       Mm-hmm.

    5. CVCarl Vellotti41:02

       Let's say that we are building a tool where we wanted to build, like, a YouTube transcript summarizer, and we're building an, uh-- and we want to use an LLM to summarize those transcripts for our, for our product that we're building. There's a couple things that you have to figure out there. One is, what is the prompt that you wanna use to summarize the transcripts? And then let's say that we're-- you can use any model you want, and so you wanna know, is Gemini the best? Is ChatGPT the best? Is Groq the best? And so you want to create multiple prompts, and you wanna test against multiple models. And that would be pretty hard to do on your own, because you'd have to take the prompt, and you'd have to take the transcript, and then you'd have to copy and paste it sort of into each chatbot, and then you'd have to copy and paste. You'd have to do all this comparison. But what we can do with, with, with Claude is we can literally have it write those prompts, and then we can have it, uh, run those prompts against the LLMs using code, and then we can have it put together the files for us in order to review.

    6. AGAakash Gupta41:57

       Mm.

    7. CVCarl Vellotti41:57

       So I have this prompt sort of already made, but just saying, "I'm making this tool that analyzes YouTube transcripts. I wanna test these prompts. So I want you to come up with three unique prompts, one for short, one for medium, and one for long. And then I want you to run the models that I have in this project against those prompts." And then here I've already uploaded the, the keys for it to be able to run that code, and then, um, use this transcript as an example. So we're still in plan mode, so let's see what happens. So we're asking it to do a lot, so we wanna make sure that it's gonna get it right when it does actually go execute on this. 'Cause there's some things here that, you know, th-this is a lot to ask the LLM to do at once. So saying, "I'll help you do this." So right now it's gonna basically explore the, the code base and figure out, uh, what, what we, what we can do here. So it's seeing the code that we added earlier, which was the YouTube transcript API. So it's looking at that. It's looking to see if we already have any prompts, and then it will come up to us, and it will tell us a, a plan. So it says, "Perfect. I have a comprehensive plan or understanding of your project."Uh, but I, I understand that you already have this transcripts tool, but, um, now I understand what you're trying to do. So what it's done here is it's come up with this plan. And so it's saying, okay, the current state is you have this, and then I'm gonna create these three prompts. So you wanna look at these. One is gonna be an insights prompt, one is gonna be a, an educational breakdown, and one's gonna be a critical analysis. So that's the short, medium, and detailed. So that looks pretty good. And then we're gonna look at these steps. But then let's say I j- I just realized, oh, I don't really understand... Um, it's gonna put these new files as .txt, but for everything that we're doing, we actually want those as Markdown files. So what we can do is we can say, "No, keep planning," and, um, I actually want these to be in Markdown files. And then another thing that we can se- kind of see with the output is that maybe it's not gonna put those in the right type, right types of files. Um, so please give me these files, uh, one file per prompt, and for each prompt, show me the three different responses from the three LLMs. So now we've basically modified the plan, and if we had just had it run through that first time, then I think what it would've given us is basically, like, nine files for all the different prompt outputs, which isn't what we would want. And that's sort of a small example of a correction, but sometimes it'll really get, like, much bigger pieces trying to infer what you want wrong, and this lets you catch those before it happens. And then now this looks pretty good, and now we can say, "Okay, go ahead and run it."

    8. AGAakash Gupta44:30

       Mm.

    9. CVCarl Vellotti44:30

       And now it's gonna go ahead and do all, all of those things that we just said all at once without us having to watch it at all. So this kind of gives us an opportunity to do something, um, kind of fun, which is while this is running, we can actually just launch Claude Code again. So we're gonna go into another terminal-

    10. AGAakash Gupta44:52

        Mm-hmm

    11. CVCarl Vellotti44:52

        ... and we're just gonna launch Claude again. And so this is a completely new, fresh instance of Claude. Uh, while this other one, it's doing the work for us. It's created this, like, long to-do plan. But now what we can do is we can go into Claude here, and we can do something different. And so you can kind of imagine, and I've heard that there are, you know, engineers who are really good at this. They'll have, like, six instances of Claude Code all running at the same time.

    12. AGAakash Gupta45:16

        Nice. Yeah, I sometimes have, like, three or four Claude chat windows, but this is, like, even better. [chuckles]

    13. CVCarl Vellotti45:21

        Yeah. Yeah. It's kind of like, um, I feel like maybe I'll do it with, like, a deep research in ChatGPT where that'll be running in one place and then go somewhere else and use, use ChatGPT in another place. But yeah, this is where you can just really have it doing, like, all this work at the same time. Um, this is kind of a good opportunity to start talking about agents. So what we've looked at so far is we ha- we've seen sort of some, like, agentic capabilities where, uh, it comes up with this... Yeah, it's, it's figuring itself out over here. Where it comes up with this plan, and then it will go through that, and that's kind of agentic. But one-- another thing that you can do that's really cool is you can, like, just like we right now have two versions of-- two, like, separate instances of Claude that are running, Claude can actually make itself have multiple versions of itself that are running.

    14. AGAakash Gupta46:08

        Mm.

    15. CVCarl Vellotti46:08

        And that's where the, the agentic sort of capability of Claude Code starts to become really cool. So as an example, let's say that we, uh, you, you can, like, parallelize tasks. So right now we have, uh, these three customer interviews, the ones that we referenced earlier. So let's say that we wanted to get summaries from, uh, like, key insights from all three of those interviews. We could say, "Hey, Claude, use this folder, and then go through each one and tell me the insights." But what we can also do is instead of doing that, we can actually say, "I want you to go through those interviews, and I want you to analyze them all in parallel." And what that will do is it will create three instances of, like, a UXR agent, and then it will, for each one, it will give it one, one transcript, and then they'll just work completely in parallel. So I'm gonna start this.

    16. AGAakash Gupta47:01

        Is the UXR agent defined in your files, or it'll figure out what that is?

    17. CVCarl Vellotti47:05

        Yeah, great question. So in this, in this case, this is a- an agent that is not defined, so it will actually just create, like, a new-- It'll just sort of spin it up on its own. It'll figure out what that should be. But, um, it's kind of like what we looked at earlier where you can ask it to do something, or you can create a command that has that thing structured in a way that you actually want it to have happen. Um, so here i- it looked at the files to, of those interviews, and now we have three tasks all running at the same time.

    18. AGAakash Gupta47:34

        Very cool. Can't do that in regular ChatGPT or Claude.

    19. CVCarl Vellotti47:40

        Yeah, exactly. And so, you know, this is a very basic example of, uh... These are just, you know, sort of an input, you know, analyze this thing and then append your insights to each one. But, um, for some of the things that we've looked at that were much longer tasks, it could also do that. Uh, it could also, you know, run much longer tasks in parallel. So let's say you had some sort of prompt around wanting to do competitive research where you wanted to check these different files and use these different tools and, uh, do a bunch of dif- competitive research, and then you could have it do that. You could even have it figure out who your competitors are and then have it research all of those competitors in parallel. So something that might take, you know, you, it would take, like, a whole week. If you're using an LLM, uh, regularly, it might take a whole day. But this can get those types of tasks down to, like, a single hour-

    20. AGAakash Gupta48:28

        Yeah

    21. CVCarl Vellotti48:28

        ... because it can run all those things in parallel.

    22. AGAakash Gupta48:30

        Crazy. I'm sure burn a lot of tokens too. [chuckles]

    23. CVCarl Vellotti48:33

        Yeah, exactly. Um, okay. So it's, it's just appending those insights onto these files. Um, so now if we click into them, we can see that it's, it's added key insights to the top of each of these. Okay. Let's go ahead and go ahead and check back on that other one. Okay. So it's, it's still working, uh, on the-- It's still creating those prompts, and it's still running them. So that's just happening in the background, and it is, uh-Yeah, still working. So another thing that you can do with agents, so what we just looked at is we just looked at using one agent or one type of agent multiple times at, at the same time. What you can also do is you can also have, uh, the same... Take one task and have it be approached by like multiple different types of agents at the same time. So an example of where that might be useful, let's say you have a slide in a deck and you want some feedback, and you can just say, "Hey, Claude, give me feedback on this." But you might actually want a different perspective on how would an exec look at this? How would an engineer look at this? How would a designer look at this? So you can get multiple different types of, of feedback. And so kind of to the point that you mentioned earlier, before with this UXR, this was not like a real agent that we defined. It was just, it made its own version of that agent. You can actually build your own agents. And so here, what we're gonna look at is, uh, a couple different agents that we'll use to do this review. And what these are, they're kind of just sets of conditions of when this agent is called up by Claude, how does it work? And they're really powerful because they are basically, as soon as Claude will give this agent that task, that agent is kind of spun up into like a, a new, a new universe. It doesn't inherit the context that it w-was in the rest of the chat. It doesn't really take any, any other information. It's just this agent with, as you can define it here, with the task that it's given, and then it will output, and then it will go back kind of to the, the original main Claude agent. And, um, there's a, there's a lot of these. So here, what I have is I have three sort of review agents. Once we go through these, I'll show you a bunch of other examples of the types of agents you might wanna use in a Claude Code project.

    24. AGAakash Gupta50:45

        Okay.

    25. CVCarl Vellotti50:46

        So here, um... Okay. So to show this in action, what we'll do, so I'm gonna go ahead and clear this. And then what we're doing is we're gonna say, "Please review," let's say, this PRD that we had written earlier, um, from the perspective of a designer, from the perspective of an engineer, and from the perspective of an executive, and then put it all into a new file. And so what it will do is it won't just create like new agents. It'll use those ones that we have defined here. And, um, one thing that's kind of cool is with these agents that you've defined, you can give them a color. And so, for example, if we go into the Designer at the very top, the, the color is pink. So we actually see that in UI. I've seen one thing that people-- It's very common on Reddit, um, and I should have done it here. Uh, people will add like text faces to the beginning.

    26. AGAakash Gupta51:38

        Mm-hmm.

    27. CVCarl Vellotti51:38

        And so you can, you know, you can really give these kind of like personalities and, uh, their own ways of, of approaching work. And so you can have like a, a skeptic, or you can have a, a more like a more enthusiastic or more like a yes, and type of agent. Just all these different sort of ways of approaching the work from these different agents that can really tackle work in different ways. And it's, it's interesting because the way that you define the agent, it really will sort of like approach tasks differently and will give you types of outputs that you will never really get if you're just using the main Claude Code agent. Um, and one thing while this is running that I can show, um, there are a lot of these, these agents, they're just basically

11. 52:18 – 56:53

    Building the knowledge base structure

    1. CVCarl Vellotti52:18

       like text files, right? Uh, if we look at this Engineer one, like this is just a text file. There are some really cool databases of, of these. Uh, one, uh, that I like is this, uh, subagents.cc. And so there's just these big databases of like all these different types of agents that will-- can do work in different ways. And so, uh, in this, in this particular database, there's like a few for product, and these are really easy to bring in. So let's say that we-- a legal advisor. Of course, if you're working in a company, you're gonna wanna actually talk to legal. But maybe before you talk to them, you want to, uh, like get, just get an idea of like, what are the big things that you might be missing. You can just come in here. You can either copy this file or, um, you can just get this command, and then you can come back to, to Cursor, wherever it is, and then you can run that command. And now it just pulled it in. So, uh, just like that, we now have a legal advisor in our project-

    2. AGAakash Gupta53:22

       Oh, wow

    3. CVCarl Vellotti53:23

       ... that we could use to, uh, you know, review a PRD. Like I think it would be helpful in, in this particular instance for saying like, you might-- I think it's easy as a PM to completely forget to consider like what are the different types of regulations that might exist. So you can have an idea of what those are before you actually go to talk to legal, and they tell you that you missed these, these big things.

    4. AGAakash Gupta53:43

       Yeah. I imagine like this is really gold for experienced PMs. As you have like a year or two of knowledge, you can put in, "Here's all the other things we've encountered with legal issues. These are the main laws that end up coming up. For a particular executive, this is what he said in past product reviews." So make it that executive's agent and just really load it up on real-life context.

    5. CVCarl Vellotti54:05

       Yeah. That's a great example. That, that, that's an id-- that's like a, a really good example of where for your specific product and your specific industry, if you build up that, that knowledge and that file in here, then it's really easy to reference later, just like you're saying. Okay, so we had two tasks running in parallel. We had the first one, which was creating those prompts and then running them against the different types of LLMs. Then we had the other one, which was reviewing those, that file from the perspective of all those, uh, different agents. So let's go ahead and see how that first one turned out. Okay, great. Perfect. I've successfully completed all tasks. Let me provide a quick summary of what was accomplished. Um, I created three unique YouTube prompts and ran them, um, and then you can see them in these files. So now we can go into these files, and we can see... Okay, so this was the-- This was like, uh, this is the actual insight. So this was the, the one where it's supposed to just give the answers really short. And what we can see here-Is that it ran-- It's basically saying: What was the prompt? And then here is the Gemini response, here's the ChatGPT response, here's the Groq response. And then you can easily kind of go through and decide, okay, well, I think this is the best prompt in general, and now which model responded to that the best. And again, this is the type of thing where doing this manually would take a long time, and you also now, you sort of have all, everything already built that you could do this, you know, again, with you could give a different example transcript, or you could, uh, you know, try completely different types of prompts, or you could have it use different versions of these models. So it's just example of where you can do a lot of work in Claude Code without having to manually do it all on your own.

    6. AGAakash Gupta55:47

       And where did we get the tokens for these ChatGPT and Groq API queries?

    7. CVCarl Vellotti55:51

       Yeah, good question. So in this case, I, uh, I already added the environment keys. So, um, so these are-- I have accounts on, uh, for these other LLMs, and it's just using those. So Claude Code doesn't natively have those. Yeah.

    8. AGAakash Gupta56:05

       Yeah. It's not some free way to get access. [chuckles]

    9. CVCarl Vellotti56:09

       Yeah. Um, okay, and then this other one. Uh, okay, so it's still working on... Looks like the Engineer review is still happening, but the other two are done. So we can come back to this one in a bit, see what happens with this, with this engineering one. One thing you can always do as well, so, um, it's sort of showing us, it just says that the Designer is done, the Executive is done. Um, it also tells us that if we do C-Control+R, that we can see more information around, like, what is actually happening in the, in the agent. Although in this case, that didn't work. Oh, no. Okay, so now we've covered, we've covered a

12. 56:53 – 58:11

    Custom slash commands

    1. CVCarl Vellotti56:53

       lot of ground here so far. So we've looked at all the different ways that Claude can, uh, research files, research the web, put that all together in pretty complicated ways, and we've started to look at agents. Kind of the last main things to look at are, um, right now we've just been giving Claude the base tools that it already has access to. You can really power up, uh, uh, any sort of LLM, especially Claude Code, by giving it access to more tools through MCPs. Um, have you, have you used any MCPs, or are there any that you found that you think are really interesting?

    2. AGAakash Gupta57:25

       Well, I guess let's do the Slack MCP since we mentioned that earlier.

    3. CVCarl Vellotti57:28

       Yeah. So, in this case... Okay, well, we're gonna-- Usually helpful. Give me one second to try to find the... Let me see if there's an official one. Okay, we'll, we'll ask Claude. Okay.

13. 58:11 – 59:12

    Ad

    1. AGAakash Gupta58:11

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14. 59:12 – 1:15:22

    Plan Mode for complex tasks

    1. CVCarl Vellotti59:12

       So, so far we've really covered basically all of the, like, main c-functionality of, you know, what they might call vanilla Claude Code, which is all of the things that Claude Code comes with on its own, which, which is quite a lot. It can search files, read files, put it all together. You can add-- It can create its own subagents. You can add subagents. The last thing to sort of really cover to sort of show the full power of Claude Code is that all of the tools that we've shown are just the tools that Claude Code comes with. But you can also add tools. The main ways that you can do that is you can either give it APIs to work with, which is kind of what we saw with giving it the ability to pull in transcripts from YouTube. We gave it the API, and now it has this new superpower, and it can go use that. The other way, which is sort of a, a API built just for LLMs, um, are called, uh, MCPs. And so these are... They're, they're a lot like A-A, uh, APIs, but they're just specifically built for LLMs to be able to use in, like, use in all these different types of workflows. And so, um, here what I have is, is just sort of a, a simple one for, uh, Reddit. So Reddit in general makes it a little bit hard to get information off their platform if you are an LLM, if you wanna do any sort of, like, scraping or anything like that. But if you use the, the, the MCP for Reddit, then it's actually quite good at, at getting information straight from Reddit. And so as an example, um, let's say what we could do, and we'll show, like, sort of a, a bigger example after this. But what you, what you could do is you can just say, "Hey, I found this thread on automation. We have, like, this automation product. Can you go through and can you extract all the information and find the pain points?" And so it's gonna use the Reddit MCP, because if it tried to do that on its own, it wouldn't really be able to do it.

    2. AGAakash Gupta1:00:54

       Mm.

    3. CVCarl Vellotti1:00:54

       And now we can see that right away using that tool, it was able to pull in that information and get all, all of that. Um-

    4. AGAakash Gupta1:01:01

       Oh

    5. CVCarl Vellotti1:01:01

       ... and you could sort of imagine how you could build some agents that use these, this MCP that are really powerful, where you could have it run every day like, uh-"Summarize the top posts on r/productmanagement today and tell me what the top things that people are writing about." You know, if you're maybe trying to create content or something. Or, uh, you know, tell me, like maybe competitors have their own channels. You could have that run every week and say, "What were the main pain points that people brought up this week?" And by giving it this tool, you give it access to that information that it wouldn't be able to get on its own. So here, we see that it went through that automation thread, and now it's summarized these. And you could have it put these in a file, or you could have it, uh, sort of even what you could do at the very last thing we'll demo is we could even have it sort of build a prototype that gets at these pain points, and then we could go show that to our boss, and they'd be really impressed 'cause we have, like, real users that we're, that we're demoing it or real, real feedback from the real world that we're basing our prototypes on.

    6. AGAakash Gupta1:01:59

       Yeah. That's amazing.

    7. CVCarl Vellotti1:02:01

       And then, um, yeah, just the last thing for these MCPs, as we kind of saw, they do take some setup to get going, but there are some, like, really awesome, um, registries of these. For example, there's like a, a couple that people have put on GitHub where they're just like these huge lists of like all the different tools that you can give, uh, to, to LLM. So there's like a Google Drive one, which, you know, in this example, I had all these files sort of that I added already. But if you're working at a company or you're doing personal work, you could use a Google Drive MCP to pull that information in really easily.

    8. AGAakash Gupta1:02:36

       Mm-hmm.

    9. CVCarl Vellotti1:02:36

       Um, and there's just, there's tons of these. One thing I think is kind of an interesting thing maybe for product managers to think about is, as we've seen, Claude Code is really good at a lot of stuff outside of code. But just, I think, based on who the users are and, like, what the use cases are so far, most of this is really, really coding. And this AI agents, uh, base that I built or that I-- I didn't build this, that I showed off earlier, um, is it's like really if you look at it, it's mostly coding. Uh, it's like, uh, we see a lot of architecture and integration and engineering and, and things like that, and not a lot in the other use cases. So while there are a lot of tools today, and it makes it really good for coding, I think there are for sure, as these tools become more widely available and used by the more general public, a lot of opportunities to build these types of agents and tools for things that aren't just for coding.

    10. AGAakash Gupta1:03:29

        Mm-hmm.

    11. CVCarl Vellotti1:03:31

        Uh, yeah. Okay. So that kind of takes us through all of, like, the absolute sort of core abilities of Claude Code. You can kind of imagine how as you go, you get better at mixing and matching all these things. The one thing we didn't really demo, which I think would be good just to show off, is, um, while I was kind of recommending that you, you might wanna use Cursor for, you know, more code-based things, Sonnet is still a really good model, uh, that you get with the base level of, uh, of Claude Co-- of Claude Pro.

    12. AGAakash Gupta1:04:01

        Pro.

    13. CVCarl Vellotti1:04:02

        So it'd be good just to sort of quickly show how it is pretty good at prototyping. So in this case, I created a spec earlier, um, that just sort of has like a workflow that we might want it to use. And so it doesn't have it already built though. It just has like a, a quick little spec. We'll just show off the kind of capability of it to actually do code. So for example, if you did wanna build a prototype from, you know, you just talked to, you just did a user interview and you summarized those pain points and now you have an idea, you don't necessarily have to jump into another tool. You can just ask Claude to code up a quick prototype for you to look at. And so as we see here, this is a good one where in the real world, we'd probably wanna do Plan Mode first, but here I'm, I'm just sort of trusting Claude to, to get it right. And so it's created its, its little to-dos here, and it is gonna check those off and, and we'll see what it comes up with.

    14. AGAakash Gupta1:04:50

        And can we take a look at what the workflow builder spec was?

    15. CVCarl Vellotti1:04:53

        Uh, yeah. So, uh, basically, the, the spec here for the workflow builder is just let's say that we want like a very, um, starting point where we just wanna say, I wanna be able to sort of do a classic workflow builder where you can add nodes and then connect them together. Because if you're gonna, you know, building, starting with a prototype, you need sort of like somewhere to start. And so it's still, there's still some, like, real sophisticated aspects here where it needs to be able to manage adding nodes onto this, uh, this canvas, and then it has to be able to connect them. So we're gonna see sort of how it does with that. And I have a feeling it'll probably do pretty good.

    16. AGAakash Gupta1:05:38

        Mm-hmm.

    17. CVCarl Vellotti1:05:38

        Okay. All right, it's working through it. Um, any other things we should, we could try or you wanna see demoed while it's working?

    18. AGAakash Gupta1:05:54

        One of the things that I think a lot of people have on their minds is, like, building out evals. Is Claude a good tool for doing that?

    19. CVCarl Vellotti1:06:02

        Yeah. So that's a good question. I think Claude Code could definitely be used for building out evals. Uh, it's a good way to, to sort of just make sure that the prompts, like, like we kind of showed earlier, where we had it write the prompts and then test those prompts against different models. You could definitely imagine a workflow like that where it's also helping you figure out what are the evals, or you could have a bunch of data that you're pulling in that shows like what good responses are. Uh, I think Claude Code would actually be really good at, at helping you integrate evals into your overall workflow.

    20. AGAakash Gupta1:06:32

        Mm-hmm. And then, uh, a lot of PMs are thinking about what if I, like, have, like, a really easy front-end change and I just wanna, like, vibe code that front-end change? Would Claude Code be a good tool for that?

    21. CVCarl Vellotti1:06:48

        Yeah. So I would definitely say Claude Code is, it's, it's very good at front-end. Uh, and the, for example, what it's building right now is mostly front-end. Claude Code is very good at front-end, and for those simpler types of things that don't necessarily require, like, a deep understanding of how the back end is architected, it could definitely do that. And even within these demos that we've done today, it's done some, like, pretty complicated stuff where, you know, we gave it... the YouTube API without really any instructions on how to use it, and it got it right basically on its first try. So it is definitely a good model for that type of work.

    22. AGAakash Gupta1:07:21

        So if you can get read access to your code base-

    23. CVCarl Vellotti1:07:24

        Yeah.

    24. AGAakash Gupta1:07:24

        You could potentially use Claude Code, and it could just pick up the existing code base, design system, tech stack, et cetera.

    25. CVCarl Vellotti1:07:32

        Yeah, exactly. Uh, you just have to be really nice to your engineers.

    26. AGAakash Gupta1:07:37

        [laughs] And get yourself a very safe testing environment.

    27. CVCarl Vellotti1:07:42

        Yeah, a testing environment. And I think the, the main thing with all of that type of stuff, um, 'cause I think there's, like, a little bit of... You see it on social media. There are some engineers who I think are very, um, I don't know if they're exactly anti-vibe coding, but I think they, they do sort of really want to reiterate that it's, like, not real engineering, which I think is, is true. So a- as long as you're, you know, talking to your engineers or you're working with them and you're, you're coding these things up, I think as long as you don't present it as something that is really production ready, but is more in terms of, "I'm showing you an idea, and, like, of course, we need to figure out how we would actually build it. But here's a, a way for me to, like, think through what it would look like in a very visual way, in a way that kind of helps you figure out, like, what are those edge cases that you would forget about if you're just thinking in a Figma." So I think this is a little bit outside the scope of your question, but I think that type of stuff is, is awesome, and it-- you can definitely do those types of tweaks and explorations. But I think if you want it to be production ready, I wouldn't, I wouldn't use any LLM right now, and I wouldn't use Claude Code for it.

    28. AGAakash Gupta1:08:43

        Okay. [laughs]

    29. CVCarl Vellotti1:08:44

        Yeah.

    30. AGAakash Gupta1:08:45

        'Cause yeah, these experimentation tools are building it in now, whether it's Optimizely or Amplitude or Chameleon, where you can just prompt, like, a front-end change, and they're trying to push, like, you can run the experiment. [laughs]
15. 1:15:22 – 1:24:33

    Where Claude is Best

    1. CVCarl Vellotti1:15:22

       Code.

    2. AGAakash Gupta1:15:22

       Okay. So just to summarize one more time for folks, anything to do with writing where you need to load it up with context, Claude Code is a much better way to interact with stuff. Anything you need to do with research, simple prototyping, and I think the fourth thing people are really interested in is what about AI agents? When should I be building my AI agents in Lindy or n8n or Claude Code?

    3. CVCarl Vellotti1:15:45

       Yeah, it's a good question. So I think this is where the term, uh, agent, I think, is getting a little bit overloaded in general, like, in the over-- in the, in the space. So Claude Code as an agent is a little bit more, uh, it's a little bit more tactical. So it's more you say, "Hey, here's a task that I'm giving you right now. Like, I'm sitting here, and I'm giving you this task, and I want you to go do these specific things." And then it will go out, and it will come up with a plan for itself, and it will work through that plan, and it, and it, and it will do it. And it's very good at that. And I think that's where it's sort of an agent or agentic, where it can think for itself and come up with a plan. Whereas, you know, n8n or I think Lindy, they're more of-- they're more based on, like, an automation, where you have an LLM brain inside of it that helps guide the automation. But they're more of something that's a recurring task that you feed it information, and then it will sort of figure out how to navigate that and give you an output. But it's not as tactical as Claude Code. So I think they're just sort of different types of, of products, and I think if you, if you want to build an agent that helps you, like, accomplish, you know, new types of tasks or things that you can't automate, then that's where y- Claude Code agents are really useful. But if it's something that's a, a recurring thing that you're gonna be doing, you know, over and over, and you just need an LLM integrated, that's where platforms like, uh, n8n or, or Lindy are really good.

    4. AGAakash Gupta1:17:07

       So you're way deeper in n8n and Claude Code than 99.9% of people listening. So having used those tools so deeply, what are the top AI agents that a PM should be building, and which tool should they be using for each of those agents?

    5. CVCarl Vellotti1:17:21

       Yeah. So I would say if you are-- Any-- Like, in general, I think, like, the, the guiding principle for when you should create an agent is when you find yourself doing something that's sort of a, uh, the same type of task frequently and it's something that an LLM is actually good at. So let's, like, talk at bo- about both of those things. First, anything that you're doing frequently. So if you are-- If you need to get competitor-- If you need to, like, really closely watch competitors 'cause you're in that kind of industry, and you find yourself going to the same blogs or doing the same type of searches every week, that's where you should build, you know, an n8n automation that can do that search and then have a LLM summarize it for you and then put it in a database for you. That's, like, a great i- that's, like, a great example of something where an LLM can really help you just do that thing that you're doing all the time. Or if, you know, you need to structure your meeting notes in a specific way, you know, you build an, you build an agent in Claude Code where you just say, "Hey, here's all my meetings from the week. Can you help me get the-- summarize the main takeaways and then write some Slack messages for me to ask people for status updates next week?" Like, any type of, like, re-recurring tasks like that. And then you'll kind of notice that those examples that I gave, those are, like, text-based, not too much creative work type tasks, which is where LLMs are really good. They're amazing at, at those types of, like, you already have existing information, and now you need to summarize it or put it in a new format. I think where it can be sort of like a trap is trying to create too many agents for, you know, too many things. Um, there are certain types of work that, you know, LLMs just really, really aren't that good at. So as an example, if you need to... Like, if you need to come up with feature ideas based on user research, then summarizing that user research and getting key insights from it, that is something that LLMs are good at. But actually figuring out what to do with that information, that's probably, like, I would say that an LLM agent that comes up with feature ideas that you're supposed to be able to implement or would, like, actually solve user problems, I would be very skeptical of those types of things. So thinking about what is the rote sort of work that you're doing that is text-based and LLMs are good at-And then give that to the LLMs. And, and the, the principle I like there is also, uh, have LLMs do the work that you hate, not the work that you love. So you don't, like, probably love, you know, doing the Google searches. You probably like, you know, trying to get the insights from the information. So I think that's how you should think about when you should use each one.

    6. AGAakash Gupta1:19:56

       So there's all this hype around multi-agent systems. When do you use a single agent versus a multi-agent system?

    7. CVCarl Vellotti1:20:02

       Yeah, it's a good question. I think it's, uh, I think it's another type of example where it sort of depends, like, what the, the use case of the, the agent is. But, um, the more complex the thing that you're doing, the-- it's like you can always add, uh, basically more layers. So, like, let's say that you have a system where you both, first you need to get a bunch of information from online, and then you need to come up with some key insights, and then you... Let's just, you know, even though I just gave a counterexample. Or let's say that you, um, you have, you're giving it ideas of things that you want to prototype, and then you want it to go build those. Like, the more layers that you have, the more, like, multi-agents you can start to add. But I think the thing to keep in mind is that the more that you add, and we're already starting to see this, and I have, like, some, sort of some engineering friends who I talk about this who are, like, very into trying to get LLMs to code, is these models aren't perfect, and there are some hallucinations in there, and there are just decisions that it will make that, like, the less the human is in the loop and the more sort of creative work that you try to give it, and the more agents that you do involve, the more risk of the output just not being very good or getting stuck somewhere that, um, it sort of can't get past goes up. So I still think that for the most part, where you are, like, heavily in the loop using agents in ways that you exactly understand, rather than having a whole chain of agents that are supposed to do tons of different things, is where we're at now. I think as the models improve, multi-agent things will be more powerful, but right now we're still in this, in this mode where there still needs to be pretty heavy human supervision over these things, where you can't go too many layers without it kind of starting to get really ridiculous.

    8. AGAakash Gupta1:21:49

       So if you're trying to build out your AI tool stack, it sounds like you do want n8n, and you do want Claude Code in your tool stack. Do you also want an AI prototyping tool? Like, is it better to have a v0 that's a lot faster, or should you just allow Claude Code to do all of that for you?

    9. CVCarl Vellotti1:22:07

       Yeah, it's a good question. I think, uh, for me, like, I, my main sort of tools are I use Claude Code for all the sort of like text-based awesome interface for using the LLM. I use n8n for those types of, um, automations that we talked about. And then for me, my prototyping tool is really just Cursor, where I'm using the better models, um, directly. I think that if, for, like, the specific prototyping tools, like Lovable or v0, I think they are still helpful because if you're not as comfortable with, like, the heavier duty tools, they can still get you that, like, quick interface, and they can connect to a back end really quickly in ways that, uh, Claude Code can. But unless you're, like, really comfortable working in, like, a more step-by-step way that's closer to, it's, it sort of gets further away from vibe coding, and it gets closer to AI-assisted development. I think if you are not that comfortable with doing stuff, like, more in the architecture and in the code, then it's still useful to actually have a prototyping tool.

    10. AGAakash Gupta1:23:08

        Mm. And I think you referenced this a little bit, but the meme out there is that Claude Code is just spending a bunch of time manifesting. What are the right ways to avoid kind of manifestation hell?

    11. CVCarl Vellotti1:23:20

        Yeah. The, the best thing you can do is you can just spend time in Plan Mode, which is one of those things where I think a lot of times with all these LLM tasks, if you go slower up front, then you can go much further and much faster overall. And so I think where if you just give it too general of a task, then it will, it will do its best. No matter what you give an LLM, they will try to do the task in any way that they can think of. But if you can really make sure that it knows, like, what you're actually looking for and, like, the type of output, and you can h-have it tell you its plan so you can see mistakes that it's gonna make just from, you know, just being a human who has more intelligence than these models, then I think that's a way that you can really keep it from getting stuck. Uh, and the other thing, and this is sort of what I was mentioning earlier, it is helpful to just pay a little bit of attention. I think there's the, the, the hope that you can, you know, have all these m- all these agents doing all your work at the same time and that you don't need to supervise it, but we're not really there, and so it's easy for them to get stuck in loops. Um, so I'd say it's still sort of using them very tactically in a way that you're still paying attention to what they're doing can, uh, make them like your partner rather than something you can just delegate too much of your work to.

    12. AGAakash Gupta1:24:32

        Amazing.

16. 1:24:33 – 1:29:24

    How Carl Growing on Instagram

    1. AGAakash Gupta1:24:34

       That about covers it for our really deep tutorial on Claude Code. I wanna shift gears a little bit and talk about you, right? You actually lo- run the largest Instagram product management account. I think I had my Instagram account posting for, like, two years, and I acquired, like, 360 followers or something like that, and I acquired, you know, maybe like 100K lifetime impressions. Then I reposted, like, three of your memes, and I got, like, 200K [chuckles] impressions right away. So you have mastered Instagram for product management. Talk us through your story with Instagram. How did you even discover this meme space? How have you grown to become the king of PM memes on Instagram?

    2. CVCarl Vellotti1:25:15

       Yeah. You know, it's, it's sort of, it wasn't really intentional. Uh, I started out with, uh... It's funny. I started out on Twitter. Uh, it was of course where, like, where I started posting product management content and-As soon as I posted I, I, like, my first good meme, it got a lot of engagement. And, you know, it's, it's sort of easy when you're posting on social media to start optimizing for things that maybe you shouldn't optimize for just because they're getting the most likes or the most engagement. And so I realized, you know, I can't have my Twitter, 'cause I'm still trying to create real content on there. I can't have it only be memes, even though there's... Even to this day, there's still some, some temptation to do that.

    3. AGAakash Gupta1:25:51

       [laughs]

    4. CVCarl Vellotti1:25:51

       Um, so I was like, "Okay, well, what is a platform I don't necessarily care about, you know, as much and is a much better place for memes?" And, and right around this time is also when Threads was, was coming out, and so that was when, you know, Twitter was in its early Elon days. People were saying that, you know, maybe Twitter was gonna completely die as a platform. That was the only place I was posting. So I started posting my memes there. And then, uh, I don't know, I just like making memes. I think that was what it was early on. And the nice thing, which I s- I kinda look back on it in a little bit fondly, is this was, like, sort of pre-LLM or when LLMs were still pretty bad. So they were all just, like, genuinely, like, I was just coming up with these memes, and it was just this challenge of, like, "I have to think of these completely on my own." Uh, and so just really being consistent, like, posting images, like, like two times a week. Sorry, two times a day, um, every weekday. And then, uh, I started posting Reels, and then Instagram just loves, loves, loves Reels. And that was where it got a little bit harder, because, uh, with memes, with, like, image memes, there's a bunch of examples. You know, for any given popular meme format of an image, you can find, like, tons of examples, and then you just kinda think, like, "Okay, well, what's a product manager version of this?" Uh, and with Reels, you have to take videos where you can't really find, like, a large database of where that video's been used for a meme before. And so I would say the, the challenge of writing good jokes with those video-based memes went up, but then just, like, the rewards from Instagram also went way up. Y- And then as you go, you kinda just start to find common themes. So one of the, like, most common themes that... There's two, there's two common themes I'll just say that, like, if you're making PM memes, they're-- they just always work the best. One is the relationship between, uh, engineers and product managers.

    5. AGAakash Gupta1:27:33

       [laughs]

    6. CVCarl Vellotti1:27:34

       I think everyone just thinks it's so, so funny. And the key of really having, like, good Instagram or, like, things that go viral on Instagram is that it's a little bit less... Like, on Twitter, it's like how witty and, like, how insightful is your meme that's, like, really the main thing that will make it go big or almost, like, how, like, irreverent. Whereas on Instagram, it's how shareable is it. And so if you, if you post something like the, about the product manager/engineering relationship where, you know, the, the very common trope of, of the engineer being sort of the, the grumpy, smart one who does all the work and the eng- and the product manager being, like, the peppy, sort of dumber, but, like, people person one who just takes all the credit. That, that type of format I think is just so shareable, 'cause I think PMs relate to it and share it with their other PM friends. I think engineers relate to it and share it with their engineer friends. So I think just optimizing for what is, like, relatable and shareable is what I've realized is, like, the absolute sort of key to Instagram. And, um, so there's the product manager/engineering relationship, and then there's just in general the PM not doing any work. [laughs] So, like, any time you have a meme where, like, you know, there's, like, a whole construction crew and there's one guy who, like, looks like he's working, but he's just walking around. That, tho- those types of memes, um, just, like, always, always do well. So-

    7. AGAakash Gupta1:28:54

       Yeah, those memes-

    8. CVCarl Vellotti1:28:55

       And then besides that-

    9. AGAakash Gupta1:28:56

       Right

    10. CVCarl Vellotti1:28:56

        ... it's just consistency. Like, I think I've posted, uh, two memes every weekday for, like, two and a half years now.

    11. AGAakash Gupta1:29:01

        Wow.

    12. CVCarl Vellotti1:29:01

        And that just compounds over time, yeah.

    13. AGAakash Gupta1:29:03

        And a story, right?

    14. CVCarl Vellotti1:29:06

        Uh, yeah. I post them all to stories as well. That's, um, yeah, that is, like, a, it's less of, like, a growth tactic, but it's, like, a good way for people to be able to, like, uh, respond to you, um-

    15. AGAakash Gupta1:29:16

        Oh

    16. CVCarl Vellotti1:29:16

        ... when you post. And any time I get a DM, I respond to, like, every single DM, which I think also helps kind of keep it in people's feeds.

    17. AGAakash Gupta1:29:23

        Yeah, for

17. 1:29:24 – 1:36:16

    Agents Carl Using to Grow his Instagram

    1. AGAakash Gupta1:29:24

       sure. And are you using any AI agents or anything for your Instagram?

    2. CVCarl Vellotti1:29:27

       Oh, yeah. Good, good question. So, uh, I have built... So I, I mentioned that as, like, pre-LLM, 'cause now it's, like, it's funny how many things these days I feel like it's hard to do them [laughs] without, like, at least trying to use an LLM. Um, so I have built something. Uh, I, I don't really use an agent, but I have, like, a, like an autom- uh, I, I don't really have an agent, but I have a, a s- a thing that I built. I can... I guess we're not doing the screen share, but I could screen share it. Okay, so this is sort of, uh, my top secret weapon. So what I have is, I call it Meme Mage. This is something... I do eventually wanna turn it into something that, like, uh, is a real usable tool, but for right now this is just, um, just mine. It's not quite-- It's very optimized just for myself, but it's still... This is my first vibe coding project ever, so it has some, some things in here that are, I don't think are the best user interface. So the making jokes with LLMs is, like, a really interesting kind of context engineering challenge, because, you know, when you're... So just to show the or illustrate the, the type of meme that I m- mainly post on my account are these, like, uh, you know, image or video that's happening in the middle with a caption on the top. And so very simple. So you can kind of, like, imagine how this type of joke is or this type of format is, uh, is common. But, uh, if you just say it, if you just tell an LLM, like, "Hey, like, here's some screenshots of this video. Can you help me make a meme out of it?" Then they won't really understand. And so what I have built is I've built this little database of, like, templates for these different types of memes, and what it does is it uses, uh... Gemini has a model that can watch videos, and it c- and basically I will give it a meme, and it will try to understand, like, what the joke of the meme is. So in this video that we're showing here, there's, like, a train that just... And usually the, the joke will be, like, you know, when the weekend finally comes, and then it rushes by super fast.

    3. AGAakash Gupta1:31:27

       [laughs]

    4. CVCarl Vellotti1:31:28

       So what I have here is I have, like, this template that sort of-... explains like everything about how this joke works. It's like, what is happening in this video, and then what, how exactly does this joke work, and then what are examples?

    5. AGAakash Gupta1:31:41

       Mm-hmm.

    6. CVCarl Vellotti1:31:41

       And then I have this small little database of like, uh, personas. So I have like a product management persona. Um, at one point I was trying to make a bunch of these different accounts, so I was testing this. But um, it's like, okay, product managers are this role, and then it's like examples of the types of jokes that that person, that, that likes. So then you kind of have like the right context that you need for the LLM, where you have who is the person and what types of jokes do they find funny, and then what is the template and what types of jokes can you make with that template? And then this is my sort of interface here, where it will go and it will... In this, in this version, it just randomly selects templates and then matches them against that persona. And so now it's, you know, basically sends off all that information to the LLM, and now it's, it's writing captions.

    7. AGAakash Gupta1:32:31

       Very cool. And I assume you just go, you edit these, 'cause it's an LLM of course, but it's giving you so much fodder to like-

    8. CVCarl Vellotti1:32:39

       Yeah, so-

    9. AGAakash Gupta1:32:39

       ... re-ask and improve.

    10. CVCarl Vellotti1:32:39

        Exactly. So now it has the videos, and then it shows like, you know, product manager realizing they accidentally shipped the staging, staging environment to prod. And like, what I... This, I'm, this is still pretty experimental, but I have like a bunch of different options. And it's one of those things where because it's an LLM, like one of these 10 caption- or one of these 20 captions that it makes for the videos will be like 90% of the way there, and then you still have to like kind of workshop the actual text. Um-

    11. AGAakash Gupta1:33:08

        Yeah

    12. CVCarl Vellotti1:33:08

        ... usually, usually they're like a few words too many, or there's like, "Oh, that's an interesting idea, but it needs to be worded a little bit different," uh, is how you like really, really get these to be good. But yeah, that's a... This is my, sort of my secret weapon for being able to, helping me, uh, create memes with LLMs.

    13. AGAakash Gupta1:33:26

        Amazing. That's the secret weapon behind two and a half years of consistency, guys. You're not gonna grow to... What is the latest Instagram follower number?

    14. CVCarl Vellotti1:33:35

        I think we're at about 55K.

    15. AGAakash Gupta1:33:37

        Wow. 55K for PMNiche is insane. They're like... I think that's higher than Lenny. That's, that's insanely high, so good for you. What is this all turning into?

    16. CVCarl Vellotti1:33:49

        Yeah, so-

    17. AGAakash Gupta1:33:49

        What is the business of Car- Carl looking like these days?

    18. CVCarl Vellotti1:33:53

        Yeah. So I, uh, I left my job earlier this year. Uh, I've been a product manager, senior product manager for about eight years. And uh, you know, just based on that thing that we just looked at, which I, my Meme Mage, I realized I had been writing... I'd been creating product management content for a couple years, and about two years ago, I had a newsletter that I called The Future Proof PM, and I was writing about AI use cases for product managers. And it was just a little bit too early, I think, because after writing that for about, like, 20 weeks, there just wasn't a lot of new stuff because it was mostly like AI wrappers that weren't, or LLM wrappers that weren't that good, or it was, you know, "Here's a new prompt. Here's a new prompt." And so I sort of stopped that newsletter, and then I, I really sort of stopped content creation for almost a whole year. I moved, um, to a new city, and then I started my new job, and uh, I sort of just wasn't creating any content for most of 2024. And then at the beginning of 2025, I like finally, finally decided to start Cursor, 'cause vibe coding was really going viral as a, as a concept, and I was blown away. I could not believe, like, how much you could do. So that whole thing, that whole Meme Mage thing that I, I just showed, that was like my first project, and I was like, "This is incredible." And then I just got really, really deep into it, and I realized that the space had just evolved. The models had just gotten to a point, and the tooling had gotten to a point where so much more is possible. And so I've always wanted to start my own business, but I didn't know exactly what it should be. And then, you know, I'm still, you know, I still love product management and creating product management content, and now there's just all these new capabilities, that I left my job earlier this year, and then I started a, a new newsletter called, uh, the, The Fullstack PM, and I'm building that now. And so, you know, I, I... Creating content on, on Instagram is a, a piece of it, 'cause that's like a, a, like a, a good place to sort of like interact with my audience. But now sort of building out this, this, you know, community and newsletter specifically for like product management builders is, uh, is what I'm working on now.

    19. AGAakash Gupta1:35:53

        So you're monetizing yet?

    20. CVCarl Vellotti1:35:55

        So far I'm, I'm early. Like no monetization, just, uh, just sort of trying to grow it and build a community.

    21. AGAakash Gupta1:36:00

        Wow. One of those people who just dared out, ventured into the unknown, no monetization. Really excited to see the journey. We'll have to check back in a couple months. Carl, thanks so much for being on the podcast.

    22. CVCarl Vellotti1:36:13

        Yeah. It was great. Thank you for having me.

    23. AGAakash Gupta1:36:15

        See you later, everyone.

18. 1:36:16 – 1:36:54

    Outro

    1. AGAakash Gupta1:36:16

       So if you wanna learn more about how to shift to this way of working, check out our full conversation on Apple or Spotify Podcasts. And if you want the actual documents that we showed, the tools and frameworks and public links, be sure to check out my newsletter post with all of the details. Finally, thank you so much for watching. It would really mean a lot if you could make sure you are subscribed on YouTube, following on Apple or Spotify Podcasts, and leave us a review on those platforms. That really helps grow the podcast and support our work so that we can do bigger and better productions. I'll see you in the next one.

Episode duration: 1:37:04