“I’m incapable of doing my job without AI”: How this top PM uses Claude + ChatGPT as his second brain

[00:00] You've actually set up these independent, fully loaded brains in Claude and chat GPT, and then use those to hold the context for you and be more efficient with your job. So show us what one of these brains actually looks like. Yeah, let's dive right into it. So here's an example of a project. You can see here that I have a bunch of them. And in it, we have my files and my instructions. Right now there's 20 files. [00:30] its brain, this is what all the projects look like. They all just have a lot of files, instructions, and you can see my endless amount of threads that I have with it. Okay, so what do you think are important files for a product manager to put in? So I usually kick it off with a ping pong, so I give it any data that I have. If there is anything that references as to what exactly is going on to start things off, then it helps me and the thread that I'm having here in projects get an understanding as to what I'm looking to achieve and more or less like what we're talking [01:02] Welcome back to How I AI. I'm Claire Veaux, product leader and AI obsessive here on a mission to help you build better with these new tools. Today I have AI PM Amir Quine who works at monday.com and is in the top [01:15] five AI-powered PMs [01:17] on his team he's going to show us how he uses ai to build scrapers to get customer feedback into his custom gpt's [01:25] Use those custom GPTs to not only make his product sense better, but improve feedback that he's gotten from his boss. And finally, he is the first guest that will demo voice mode live for us on air to show how he prepares for product interviews.

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[03:12] Hey, Amir, happy to have you here. Thank you, Claire. Happy to be here. So one of the reasons why I'm excited to have you here is you don't just like using AI. We were just talking about it. You are now [03:24] highly dependent [03:26] and maybe incapable of doing her job without AI. So tell me how you got to this point where [03:32] you're really depending on AI to do your job as an AI PM. Yeah, I think there's like the whole context switching is something that's really difficult for everyone. And as a PM, when you're going back to back meetings and jumping between different initiatives and different [03:47] requirements and asks from people, you have so many things to juggle. [03:51] Is that it? [03:52] I discovered with the help of everything that's out there online, that you can silo like brains of conversations and threads in GPT and cloud, that it made me super dependent on it in order to just do my work. And I'm doing it really well, I think I'm doing really efficiently. So yeah, [04:11] That's how I got to where I am today. OK, so what you're saying is you, like all PMs, have to context switch and hold a million different threads in your mind. [04:21] at one time, and instead of burdening your poor human brain with all that contact switching, you've actually set up these like independent, fully loaded [04:31] brains in Claude and ChatGPT and then use those to hold the context for you and be more efficient with your job. [04:40] Absolutely. Okay. [04:42] So show us what one of these brains actually looks like. What are some of the use cases for my collection, my file folder of brains?

[04:51] Yeah, let's dive right into it. So I'm going to, I guess, dive into what... [04:57] a, I guess, folder looks like my projects look like. [05:01] So here's an example of a project you can see here that I have a bunch of them. And in it, we have like my files and my instructions. So right now there's 20 files, it started with [05:13] two or three or four and then just kept growing and growing in terms of its brain and i'll dive into like some of the files that are in here and kind of like what i'm trying to achieve to help you understand like how i put this into action [05:25] But just this is what all the projects look like. They all just have a lot of files, instructions, and you can see my endless amount of threads that I have with it. [05:35] Like nonstop. [05:36] Okay, so what do you think are important files for a product manager to put in? Or what are some unique sources of, you know, context for these GPT projects that you have found particularly useful or unique? [05:50] It varies, obviously, on what you're working on. But I always start with some sort of like reference that it can understand whatever it is that I'm starting with. So I usually kick it off with a ping pong. So I give it any data that I have any data whatsoever. So if there's a company kickoff deck, if there's a team kickoff deck, if there is another reference to a PRD, if there is [06:09] anything that references as to what exactly is going on, [06:14] to start things off, then it helps [06:17] me and the thread that I'm having here in projects get an understanding as to what I'm looking to achieve and

[06:23] moralistic what we're talking about. [06:25] Great. And then what do you want these second brains or these GPT's to actually do for you? So what is the [06:32] you know, what does this one actually do for you? [06:34] I'll start this one kind of like where it began. [06:39] started at my company at monday.com, leading like the AI agents, uh, [06:44] team, the whole initiative there. And I'm sure anyone who's on AI today can allude to the fact that AI agents [06:53] probably like one of the most [06:55] fantasize thing that is in sass. Everybody wants it. Everybody wants it to work. Everybody has like this [07:00] vision of what it can do and you can do everything and whatever you hear outside is also what you hear inside and there's a thousand different voices and different opinions of where they want it to go and what they believe it will be and i was seeking to [07:13] some voice of reason that was unbiased. I was looking for conversations that people were having [07:21] that [07:21] weren't connected to my company. [07:23] that didn't have any specific [07:25] narrative they want to go down i just want to know what people are interested in what they expect and what they want [07:31] which is what led me to Reddit. [07:34] Okay, so you were a PM. You were given the great and wonderful task of building out agents. No one has agreed on what the definition of an agent is. No one has agreed what the agent should do. No one really understands if customers actually even want agents. Technically, there's a lot of complexity and aspirationally, there's a lot of goals around these. So this is a very common product manager problem where you're given a big, meaty, interesting, technically complex problem

[08:04] with a lot of hopes and dreams in it, [08:06] And then it's your job to go make it reality. And so what I'm hearing from you is you went and sought out these different resources that were external resources. [08:14] that could give you more of an unbiased filter on which to... [08:19] make some product decisions and maybe even go kind of back to your leadership partners or people with big hopes and dreams and say, this is what people actually want or this is what's actually possible. And so you mentioned Reddit. Yeah. [08:31] What did you actually pull from Reddit that you think was so useful here? So I'm going to walk you through like what I did here in order to scrape conversations on Reddit with the help of Claude. [08:41] I had this idea, I had this vision that I'm going to be [08:44] able to get this file or many files containing thousands and thousands of conversations that people have about anything that I wanted. In this case, I wanted about [08:54] agents, AI, Monday.com, all in relation with one another. So I went into [08:59] I went into Cloud and I said here, [09:02] Help me scrape conversations. I started my conversation here with just like, I want to find everything that's written about money.com, whether it be on Reddit, [09:10] Twitter, LinkedIn, anything that's online. [09:14] And I wanted like a sort of an automated freeway to do this. So it went into like a bunch of different options. [09:20] LinkedIn and Twitter have more limited APIs. Some of them are paid. Eventually, we got down to what I needed, how to get it, and how I can do more automated. So I asked, "Okay, great. We know Reddit is the place I need to be. Now give me a step-by-step guide of what I need to do."

[09:34] I'm not not technical. I am a little bit. I have a little bit of experience with things. But I want to make sure I'm not skipping any steps. I want to make sure that everything I'm doing here is going to be exactly what needs to be done, because I had a vision of what I'm getting out of this here. [09:48] So it literally walks me through here, like open Safari. [09:50] Go to brew. [09:51] write a command, open your terminal, write, like literally step by step of what I need to do as if I'm [09:57] five years old and I don't know how to program or code or anything, which was fantastic. It tells me to go into Reddit, go into this, make a developer account. Literally followed it step by step. I didn't even try changing any name. I limited it as it asked. And then we get to, you know, [10:13] doing brew install, it's getting stuck, whatever. Eventually, we get to the good stuff, where I have everything set up. [10:19] It's finished, now what do I do? [10:21] I had the environment set up and I had all the packages that I needed to set up. [10:24] And now it starts giving me like all these commands to run. [10:27] So install different packages and libraries that I need in order to run the script that I write for me. Here's a script that I'm going to help you write. [10:35] And let's get started. So basically, it writes me up this script we can see over here. [10:41] where [10:43] And... [10:44] I put in my client ID would ask me to take out from Reddit, my client secret, and my username. And then it scrapes everything. [10:50] That has to do with Monday, with things that I'm looking for, with AI and whatever it be. And lo and behold, came out a file with 34,000 rows. [11:01] of conversations. [11:03] Okay, so you have this, again, a very classic product manager problem of...

[11:08] I know people are talking about this product. Like, I know. I know there's information out there, and I can manually call through and... [11:15] you know, do Reddit searches and search through Twitter, but I want to do this in a programmatic way. I'm technical, but not that technical. And dear God, I don't want to write a bunch of Python. And so what you did is you went to Claude and you said, I have this general problem. I just want to know what's out there. You actually said the prompt is really interesting. It says, I want to know. [11:31] everything. [11:33] out there. Everything everyone is saying. That's a very broad. Yeah, very ambitious flow. And then [11:40] You know, Claude narrowed you into, well, here are the actual accessible sources of data for you. [11:46] And then you wrote a script to go pull all that Reddit data and then [11:51] What did you [11:52] do with that, you said 34,000 or something, 3400 line spreadsheet? What did you do with that data? [12:01] So I'll show you. Basically, first of all, I want to show you what the file looks like, just so you get a glimpse as to what this monstrous file is. So this is what 34,000... [12:12] rows of threads looks like in relation to the conversations that I wanted, the theme that I was looking for. So this is great. [12:20] And it really helps me understand it. It has a lot of conversations. I also went crazy with it. I said, like, give me different files, show me how it looks like in terms of our competitors, show me conversations that are happening, like in relation to our competitors, like comparing us to [12:32] any other competitor and their AI and give me some sort of idea. [12:35] When I had all that information, I went back into Claude.

[12:39] And I said, OK, now be my analyst and start breaking this down. I'm like really into data analysis. [12:46] I have like a little bit of a data background. So I came into it and I said, here, I'm giving you this file. This is one of the files that was pulled out with 30,000 rows of conversations. And I said, I want you to summarize it in a table where you put like the frequency. So how often it comes up at what percentage I need weights, because I need numbers to go back to my team and for myself to know what to prioritize, see what the biggest, hottest topics are that people are discussing and some of the key discussion points. And out comes this table. [13:16] which is really nice and really helpful because I have, okay, here's, here's, here's some sort of idea. I know like what are the top, [13:22] Five. [13:23] areas that people are [13:25] wishing and hoping and having conversations about around this, their expectation and their dreams around what AI or an agent would do. [13:32] And did you spot check this at all? Did you just trust it? Did you feel like this was relatively accurate based on what you saw in that spreadsheet? [13:40] Yeah, it's a really good question. I did check it. Obviously, I'm not checking 34,000 rows of conversations. But I did do keyword searches. And I looked at different threads and conversations that I put up. Also, what I typically do with-- [13:53] whenever I ask GPT or Claude to analyze something, I say reference, give me at least one or two quotes as to where this came up from. And [14:00] I have tons of conversations where it does that. So then I fact check it. I copy that. I go back into... [14:05] document and make sure that I was actually there. [14:07] Awesome. And so you built this scraper, you took the data, you analyzed it. Did you then feed this analysis back into that GPT project we saw before? Kind of how does this become part of your corpus process?

[14:21] of one of your secondary brains. Yeah, fantastic. So I'll show you how that looks like back in the project itself. So here I am in the project. You can see that I have [14:32] like the thread it like the AI threads on Reddit and in general, like these are different like Reddit files that I had. And this is how I started things off. I had my kickoff that I had. [14:43] that my manager did. And I had a couple of pages that like, [14:46] I took from our site of like how Monday works and I just, [14:51] command pd, so it's a PDF, and put in another classic example of everything is a text. And then it so it has an idea and knows what Monday is and knows what in more or less like what our goal is as a team. And here are some conversations that are happening. So you have some sort of idea as to [15:04] what [15:05] people's dreams and hopes are when it comes to AI. [15:09] Yeah, I just want to call out that little trick for folks that might have missed it because I do the exact same thing, which is I actually just use our marketing website. [15:16] or our support website and I just print things as PDFs and download them and then upload them to GPT projects or whatever kind of store because then I know it's scoped to that specific piece of content. [15:27] I've done it a lot with like support pages. I've done it with the homepage, obviously, because that's usually like the source of truth of your positioning. And then I do it a lot with like pricing pages. So I'll go like print a bunch of competitor pricing pages and drop them in a GPT. So I think that's a really great source of information for a GPT. [15:44] No, for sure. I think like once you start, like when I started with this and I had the CSV file from [15:50] from Reddit

[15:52] my mind was like, okay, literally everything is text. So like, even you may not necessarily think of like a slide, it was a slide from like Google Slides as like text. [16:03] But it is. [16:04] So you just download as a PDF and you have just like [16:08] whatever it is, however many slides here as a reference. And [16:12] like the example that you said as well, like the list is endless of what is text. So I have it also like instructions more or less of like, [16:21] what I want to do, make sure that [16:23] is a professional and product management expert and all this stuff that has a good product strategy mindset and product sense mindset um i also give it like a lot of like instructions on you know you know how to give feedback to me you know i did candid feedback and you're not going to be like too nice to me you know to challenge it's because i hate it when the ai will be like super supportive of every idea that i come up with like push back on things you have the knowledge so feel free to challenge me on things i come up with and then the ping pong begins and then we start going back and [16:53] of conversations here. [16:54] And so are you using this as a thought partner? Are you using this to prep for meetings with your boss? Like what are the most common use cases for this GPT for you? All my projects, I start with a scope. I want to get to some sort of like outline overview that'll help me with PPRD with a [17:11] product review, something that I can deliver to my team, so narrative. So it always starts off at the ping pong. [17:17] always and then once I've gone to some sort of document and I have like this [17:21] overview doc report it is. [17:22] I,

[17:23] played around with it. [17:24] I've downloaded it and I put it back into its knowledge. And now it has that. [17:28] as a basis. And then it goes from there. The more work I do with it, the more knowledge I add into it, [17:33] into its files. And then everything that comes after that is just super... [17:38] intuitive and fluid. I'll give you examples, like if I need to write something [17:42] how often I get asked questions by like marketing and with the communications theme of like, okay, you're releasing something. Can you give me like a two line sentence is what it is? [17:51] Those are classic examples of like catch me in the middle of the day on Slack and ask me then. I don't know. I don't have the capacity to think about that. [17:58] here is something that has a brain for this. I ask that question and usually does a pretty good job. And then [18:03] Lo and behold, here we go. [18:05] Great. And then, you know, speaking of documents, I think one of the common complaints that [18:10] you know, people have about AI generated documents is the writing maybe isn't great. And at the very least, maybe it's good thinking, but it's quite long. And I know you've also built a custom GPT to make sure that your writing is concise. So can you show us a little bit of what that looks like? [18:28] Yeah, there's also a background to that. So if any of my past managers and colleagues see this, they will be able to confirm this. But I've been giving feedback multiple times and my writing is good, but it can be really long. So my Slack messages on updates and channels are really long and [18:46] It's hard for you to get through. And Lenny released, I think there was a newsletter where he had West Cowell say,

[18:53] more or less like her recommendations on [18:57] how to write concisely, how to deliver a message well. [18:59] And again, example here of everything is written. [19:03] I took these newsletters, I command feed it, downloaded them, and I put it into a custom GPT as my guidelines to how to write. [19:10] And I'll show you what this custom GPU looks like. - You've seen the doom and gloom headlines. AI is coming for your job, but the reality is a little bit brighter. In Miro's latest survey, 76% of people say AI can boost their work. [19:26] It's just that 54% still don't know when to use it. As a product leader and a solo founder, I live or die by how fast I can turn fuzzy ideas into crisp value propositions, roadmaps, and launch plans. That's why I love Miro's innovation workspace. It drops an AI co-pilot inside the canvas. So stickies, screenshots, and brainstorm bullets can become usable diagrams, [19:51] product briefs, and even prototypes in minutes. Your team can dive in, riff and iterate. And because the board feels like a digital playground, everyone has fun while you cut cycle time by a third. Miro lets humans and AI play to their strengths so that great ideas ship faster and happier. [20:11] Help your teams get great done with Miro. Check out Miro.com to find out how. [20:18] That's M-I-R-O. [20:21] dot com. [20:22] So while you're pulling that up, I have to ask,

[20:25] Did you try LennyBot yet? Because I know he has his own bot with all of these newsletters and some content loaded in, but you wanted something really precise, right? So you created your own. [20:37] I had something really specific because I know what it is that I needed. So [20:44] I took in [20:44] her newsletters because he hosted her on on his podcast. She also referenced a bunch of books. I looked for those books online and put them in here. I never read them. [20:55] But I just put them in here. I was like, OK, you must know then how writing and delivering is. [20:59] And then you have this here, this instructions that like this [21:03] GPT rewrites Slack messages to be more concise, to be more clear, to be readable, maintains the natural voice of the user. Because I want anyone in my company, anyone in general to use GPT so it doesn't make it super AI. Like I said here as well, like, you know, avoid it. [21:17] too much dashes, don't give so much bullet points, all that stuff like that. And then I come into here so often, all right. [21:24] a message out on Slack to like myself. [21:27] I'll copy it, paste it in here, and it does a really good job of making it super concise, sticking to these guidelines of like, [21:34] delivering to the point, having a really good follow-up and lead-up as to what the next section will be. [21:38] It is a lifesaver. People respond to my Slack messages more now. [21:41] So I know that you didn't read the books, but I do have to recommend as somebody with a liberal arts degree, those are exceptional books on writing. So I highly recommend reading them. We'll link to them and the newsletter and Wes's website in the show notes. But what I love about the approach here is...

[21:59] A lot of times people get sort of professional development feedback like you got. Your writing's great. [22:06] It just needs to be shorter. Like you just write too much. And a lot of people don't really build for themselves a way to coach into improving those skills, right? They're like, thanks for the feedback, but I'm not going to sit with my boss for an hour and have them like edit my Slack messages and edit my PRDs. Maybe I'll read a book or maybe I can do some research, but it's a hard thing to put into practice into your day to day. And so what I like about this is you took some coaching feedback that you got and you're like, oh. [22:33] cool, I'm going to create myself a coach for this very specific [22:38] development piece that I need to work on and I'm going to rely on experts that I think are great. [22:44] And then I'm going to use it day to day to actually make things better. And then what I like to, again, about what you said is. [22:51] And then I share it with my colleagues because I'm sure I'm not the only one that has this problem. So I think that's a really nice process. [23:00] And I think it's really cool that you mentioned like all that, like I'm not going to now send this and have like somebody rewrite it, like go, [23:06] pre GPT era and my dependency on AI, I did that. I got this feedback. I'm all for feedback. I love feedback. And I love improving anything on stuff like that. I would literally do that. I would write out a message to myself, send it to my manager, [23:19] and say, hey, tell me how this can be improved. And he wouldn't respond. He came back a couple days later. By then, like, I lost momentum. That's what I wanted to say. And then this is... [23:27] a game changer for me. And yeah, I definitely think other people probably should as well.

[23:32] Well, and it makes me think of another idea that maybe folks would find useful, which is, you know, people get performance reviews on maybe an annual or twice annual basis. Like, what do you do with that? And. [23:43] you know, maybe what you do is you load up your performance review and you load up your peer feedback into a GPT. [23:49] and you reference it and you say, hey, I just made this doc, but based on the feedback that I've gotten in the past, is there any like, [23:54] blind spots that I might have. I think it's a really great method for self-reflection and self-improvement if you're, you know, if you're open to feedback, like it seems like you are. [24:04] Yeah, absolutely. [24:06] It's limitless what you can do. [24:09] Okay, so for our last use case, you know, you seem like a person that likes a lot of feedback and likes to prep. You actually use GPT voice for mock interviews, and we are going to push the bounds of the technical... [24:24] capabilities of this podcast and see if we can demo this. So while we're getting set up, can you tell us a little bit about the problem you were trying to solve and why you kind of went down this path? [24:33] Yeah. Okay. So basically anyone who's prepping for interviews for, for product interviews knows that there is, [24:40] a lot of content out there. YouTube is loaded with mock interviews. Exponent is a fantastic channel. [24:46] There's a lot of different people I could shout out, but I'm not going to say off the top of my head. I haven't been interviewed for a job in a while. But... [24:53] Basically, interview prep, what I would do, and I think a bunch of people would do, [24:57] leading up to product interviews was watch these videos over and over again. I'm obsessive. I would watch the same three videos over and over and over again. Remember any nuance and stuff like that, that when I walked into an interview,

[25:09] It was a copy paste of what I just listened to and [25:13] I would feel super awkward to be like, let me practice on something. I tried in front of a mirror. It didn't work. My mirror is not talking back to me. I tried with my wife. I was like, hey, these are the type of things to ask me. She didn't know what [25:25] I wanted from her. So... [25:27] when GBT Voice came out, [25:29] I'm like, I'm going to do this. I will be that lunatic in my living room talking to AI as if we're in the movie Her. [25:35] And it was like the most exciting thing ever. Like I forgot that I'm talking to AI. I feel like I have this like, [25:41] This-- [25:42] interview coach who's with me at all times, who's constantly challenging me and pushing me and finding the right things, coming up with fantastic questions. And I'm like, this is what I needed. And I walked into every interview after that. [25:53] Super prepared. It was the best feeling ever. [25:57] literally can't recommend it more anytime anybody asks me like, how to prepare for a product interview, I said just [26:03] OpenGPT Voice and just go for it. [26:06] Okay, so I have to ask, because you have this very... [26:09] structured setup from a writing GPT perspective and a product GPT perspective. But it sounds like you're just opening up GPT voice and going for it. Are there any tricks to getting... [26:21] GPT voice into... [26:23] the right mindset. And then once you tell us, let's try it, let's try it live. Let's see if we can make it happen. I'm going to make you feel so awkward. We could definitely do it. Um, [26:34] I'm going to be super rusty. First of all, there's two different approaches. Yes, I am organized and structured, but I do love to

[26:42] freestyle it [26:43] I did make a custom GPT for something like this. I can show it to you so you have an idea of what it is, but basically like [26:51] So similar to the other one, it has like instructions on like, [26:55] what product sense questions like you need to look out for and what product execution questions you need to look out for and like [27:02] For instance, [27:04] The interviewer [27:05] The interviewer needs to clarify the company's mission and the product goals and identify specific target users and pinpoint their real pain points and then design a solution that solves those pain points and then [27:15] They all have a sequence to it. So I want to make sure that it understood like, [27:19] what exactly you need to look out for and what type of things you need to ask. And then obviously I put in some knowledge base to it too. I have my own product manager interview prep document. I took Ben Harris's posts. He's fantastic on product coaching, on product sense and product execution. And now it has this whole background. You can pop into this GPT and have a conversation with it. And it's super guided on what a product sense interview looks like, what a product execution interview looks like. [27:46] So that's like, [27:48] more structured, you can use the voice mode and go for it. Freestyling, we can try to freestyle. Let's do that. I'll open my phone and freestyle. One other thing that I would add, you can also plop in your CV and a link to the role. [28:01] that you're wanting for. So there's more context as to who you are and what they're looking for in the role itself. And then there's more of like guidance towards things to pick on. But [28:09] you can freestyle it. And also, if you have back-to-back interviews with GBT boys,

[28:16] it can learn from like what you've made mistakes on beforehand. Like I was really bad at user segments. I always struggled with it. And then like, I'll be like, okay, let's talk through this and figure it out. And then the next two interviews... [28:26] And they go, you're. [28:27] your user segment actually has improved. You really figured out how to narrow down different user segments that are super different. [28:34] So anyways-- Great. [28:35] Let's get started. Okay. Let's try. [28:38] For those that are listening, not watching, I've made Amir very uncomfortable by making him talk to AI. [28:44] I have it open here. What's that? Can you, can you hear me? [28:48] It's like everything's working fine on my end. How can I help you today? So I want you to interview me for a Product Sense interview. [28:58] I'm interviewing for a role at Monday.com as an AI product manager. [29:02] And I need you to give me a product sense question. It's really important that you don't guide me in anything, that you are super candid, that you let me take the reins on [29:13] where I take this interview and in terms of like the direction. Don't lead me to anything. Don't be specific or give me any hints. And at the end of it, give me some candid feedback. [29:25] Let me know if that makes sense to you. [29:27] That makes perfect sense. I'm happy to help you print for your interview. So let's dive right in. Here's a product sense question for you. Imagine you are tasked with improving the user experience for one of Monday.com's AI powered features. How would you go about identifying areas of improvement? And what steps would you take to implement those improvements? Feel free to take it in any dry can you like.

[29:52] Claire, I'm going to stop there. I'm not going to do the interview with it. Yeah, that's great. You're like, I don't want people to see if I'm a good or bad P. Also, we should be doing product sense all the time, but... [30:02] No, yeah. If you could have caught me like a couple of years ago, I probably would have been ace any of you right now. No, this one's great. I mean, what I think I want to call out for folks. So thank you for being a good sport. That was really, really good for folks listening at home. Give a small round of applause for Amir. So the thing that I want to call out that I think is really great in your setup is... [30:21] Again, you gave it a job like you're going to help me prep for interviews. [30:25] And then we're all so frustrated with how nice AI is. It's so nice and it wants you to succeed. And it'll sort of craft you into questions that like, [30:36] lead you to the right answer. And I think your caveats that you called out in prepping the voice agent [30:43] are really important, right? Like, don't lead me to anything. Be super candid. Kind of be objective. Give me a real interview. And then finalize with some feedback. And I think that's really great feedback. The other thing that I want to call out is, [30:57] How natural. It doesn't feel natural to do GBT voice mode on a podcast live that, you know, thousands of people are going to watch. But it does feel natural to speak. [31:08] and practice interviewing with voice. It's not the same to sit there and type out. [31:15] an answer to a question. And in fact, like your written communication skills are just very different [31:21] than your verbal communication skills. And so

[31:24] I think the process, if folks were listening... [31:27] of just how the intonation of the voice, how they ask the questions, how you can interact sort of live, I do think simulates the interview process a lot better than sort of like a text-based GBT would. [31:39] No, for sure. I would never do that text based. Absolutely. It's super natural. It's oddly natural. So 100%. [31:49] And then I'm going to call out a couple other, you know, maybe adjacent use cases that people can be inspired by. [31:54] My my darling husband is one of the people that can prep for public speaking by like [32:00] going to the bathroom and speaking in the mirror. [32:04] I can't I can't do it. I hate I hate looking at myself too much, which is odd to say for for podcasters. But, you know, just thinking about maybe doing kind of like speech and public speaking prep via voice mode, like where did I do well? Where did I not do well? And then I'm even thinking for my kids, we're doing a really we're trying to do a consistent job of getting them to read out loud. [32:27] at night and think about their... [32:30] punctuation and emphasis and all that. And I'm like, oh my gosh, my kids should do oral dictation to GPT voice mode and get a little feedback. Although I love our bedtime story time. Yes. [32:44] No, totally. The world of AI with kids is unreal. [32:48] It's unreal. It's just, you know, we can go that for hours as well. The stuff that I do with my daughter is so much fun. I think at some point we're going to do a roundtable parents in AI episode where we get like half a dozen of us on to talk about all of our tips and tricks. OK, Amir, this is so great. So just going back to the beginning, what do we cover? Product GBTs.

[33:09] Using Quad to help you discover and technically implement ways to access access [33:15] customer and market feedback data, do data analysis. And then as you say, create these GPT's as a secondary brain that you can ping pong with that you can go back and forth with. [33:25] and have a thought partner on. [33:27] You taught us how to take feedback well, which is AI aside, a really good trait. But take feedback and then put that feedback from your boss or your peers into a custom GPT that then can coach you and improve something you need to prove on for you. It was using concise writing tactics. And then the third piece, again, a very feedback-oriented person. You prepare for interviews. You want to do a great job. There's a lot of resources out there to prepare for interviews well. [33:57] Thank you. [33:58] And so you use GPT Voice to do mock interviews and not only do mock interviews, but get ongoing feedback about how you're improving on product sense and products execution. [34:09] interviews. So you really are dependent or at least a power user of AI. I love the power user. I love the power user. I think there's there is in like my company, they really try to reinforce AI to everybody. [34:23] And they did this top 10-- [34:26] AI. [34:27] powered use you like pms using ai um since january 2025 my son was born in february so i was on pat leave for two months so i was active at the company for four months of this six month range

[34:41] There is 90 PMs there. I was number four. [34:44] of power users. [34:46] And I have a two-month [34:48] delay on people. So I'm considering myself number one or number two. [34:53] And I also want to add like a bonus thing, Claire, if that's alright, that your episodes inspired me a lot, that I vibe coded a scraper [35:02] that can do what I did for anyone [35:05] It's in the works. I wish that I finish it up until this point. [35:08] But basically, like, [35:09] The concept is what I wanted. Find any discussion anywhere. [35:13] where users can kind of just write in their own natural language what they want to scrape from any platform. So let's say, for instance, I have like, I want every discussion that relates to AI with the money.com. [35:22] I have hard mentions that like with all the keywords, it'll always reference that towards whatever it is I'm searching the platform that I want the output. And then I want to search for it and give me the file. So it's in the works. It's not done yet. I have like the results are OK. [35:39] But when it's out there, I'll share it. Well, if it's live by the time we... [35:45] put this episode out. We will link to that in the show notes or we'll do a follow-up and I'm happy to be... [35:50] a beta user because that looks super useful all right we're gonna wrap up with two lightning round questions and get you back to spending time with voice mode my first question is for pms that are not on the top of that leaderboard [36:07] What do you think the number one thing they're missing out on is?

[36:11] honestly it's the ability to be at so many places simultaneously [36:17] That's really what it is. [36:18] I can be asked [36:20] on the spot from pretty much anyone in the company. [36:23] about something, and I can give you an answer really quickly. [36:26] I couldn't have done that. [36:28] pre-AI. [36:29] Amazing. I think it's such a great, I mean, it's, [36:32] you know, as a as a leader, [36:35] It's our favorite trait of our favorite PMs is know everything, are everywhere at all times. It's really tough as a PM to do that, though, because of, you know, just physical limitations, space, time, mental capacity, all that kind of stuff. And so. [36:48] I do recommend these tools because they just give you a lot of horsepower in terms of answering questions, getting stuff done right away. Yeah. [36:56] and holding all that context without totally, totally breaking your brain. Okay, last and final question. I ask everybody when AI is not listening. [37:05] and it's giving you bad results or analyzing your data incorrectly, what is your tactic? [37:13] for getting it back on track. Do you bribe it with money? [37:16] I just yell. I just yell. I'm going to voice someone anyways, right? Can you not hear my tone? [37:24] You're the first person that's admitted to getting angry with AI on this podcast. So pre-voice it was caps. I'll be like, this is not what I asked for. And now voice, you can get upset. No, but mostly what I do is I'll copy the response and I'll say, this isn't what I was looking for. This isn't what I was looking for. That's usually what I do to just have some sort of

[37:50] benchmark reflection on it. [37:52] amazing all right well thank you so much for sharing your workflows with us [37:56] Where can we find you and how can we be helpful? [37:58] I'm like not too active on social media. I like pop into LinkedIn from time to time. [38:02] So-- [38:03] That's always good. And [38:05] Honestly, anyone who's posting anything cool and what they do with AI is super, super helpful. [38:11] the list of things that I do here, a majority of it is just [38:13] inspiration from what I've seen online. So keep it up. [38:17] Great. Well, you keep it up as well. Thanks for being here. [38:19] Of course, Claire. Thank you for having me. Thanks so much for watching. If you enjoyed the show, please like and subscribe here on YouTube or even better, leave us a comment with your thoughts. [38:29] You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiaipod.com. [38:46] See you next time.