I run my whole day out of cloud code and cursor. These are my AI superpowers and I'm encouraging my entire team to use them. >> This is Rachel Woolen, the chief product officer at Webflow, the $4 billion web giant used by companies like TED Talks, SoundCloud, and even Reddit. >> So, it's almost like having a data scientist in my pocket. >> And this is the art of building amazing AI native product. gets rid of a lot of the junk in my inbox first and then it will actually create drafts for a few people that it thinks I need to actually send emails to. And I think that's the one thing I would say about a lot of like building an agent is trying it out and then going and adjusting what you want the agent to do. >> This is the road map to becoming a great product leader. There are tons of tutorials about claude code and cursor for ICPMs. But what about product leaders? Today's episode is a master. What is this concept and how can CPOS effectively do IC work? >> To me, ICPO means as a leader you are able to get your own answers to practically any question. >> We've just showed all these amazing workflows. How do you set up your organization to work this way? >> I think one of the things I would first assume is that 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. >> Welcome to the podcast. >> Thanks, Akash. Great to be here. >> What are we going to do today? >> We are going to go through some of my workflows, my agentic chief of staff, um, and a bunch of different ways that I use cloud code and cursor. And then I'm going to walk you through what it's like to actually build AI native products in the wild. And we are in the middle of getting a product, a new codegen product ready to go. And so I will walk you through the good, the bad, and the ugly of trying to get a new AI native product out into the wild and into our customers hands. >> Awesome. So as you alluded to there, there's really two sides to AI product leadership. There's being a productive AI product leader and there's shipping AI native features. So in the productivity bucket, let's start here at this concept of ICPO. What is this concept and how can CPOS effectively do IC work? >> Yeah. So to me, ICCPO means that as a leader, you are able to get your own answers to practically any question. And if you stated that as a goal, then you kind of have to work backwards and look at how do I make sure my data is in shape where I as well as anybody else on my team could go and self-s serve answers and that is a large task to undertake. I can tell you that from experience and we're still in the middle of that. Um I think the second is making sure your team has the right tools for what they're trying to accomplish and then can even stair step their way up. And then I think the third is really um you know figuring out how and when to model for your team not because you expect them to copy your workflows but you want them to be inspired. I think that part of you know being a great leader today is also being a great I I see and getting your hands as dirty as you can um carving out time to experiment and showing your team that it's okay to experiment and for sometimes it works sometimes it doesn't but that's part of you know like building today. >> Amazing. So let's start with Claude and cursor. Can you walk us through how you're doing some of this ICCPO activity through them? >> Yeah, absolutely. So what I've done is I've built out what I call my agent chief of staff. And this is a combination of a set of cloud code agents rather as well as an app that I'm building that I use on a day-to-day basis. So first I'll kind of walk you through how I use cloud code. Um and this is by the way in cursor. You could do this in any IDE. I also, depending on what my task is, sometimes I will use cursor and the cursor agent. A lot of times if I'm like trying to start something a project from scratch, I will install from cursor. I will also sometimes use uh codecs, especially if it's like a complex um type of task where I'm trying to understand content from our monor repo. And so, you know, I I basically am running out of terminal for a bunch of different tasks. And so what I've done is I've created a set of agents. Um, and I'm I'm kind of like constantly adding to those agents. So I'll just show you what like an agent looks like. And this was generated by Claude. And we'll we'll go through like how to actually generate an agent, but this is this one is like understanding the priority of a calendar event >> and trying to decide if is truly important. Um, and so what I'll show you is like I I actually was running this earlier today and you know you're trying to decide is this part of my priorities and then trying to filter out noise and then looking for different types of meetings that are very important. Um, and then also really like this is all generated by cloud by the way. Um, and then what I'm doing so I ran this earlier today. Let's see. Hopefully it doesn't have anything too crazy in here. And what I you know I think I looked at this before I I got in here. And so what I did was first like I asked it uh earlier today I ran it because it takes a little bit of time. I I asked to look the last two weeks and I said can you analyze my calendar uh like how I how I spent my time. Um so analyze my calendar for the week. How did I spend my time? Where could I have been more effective at delegating? Right? So, this is something that I do usually like once a week, but I also will run this once a day as well. Um, and then f first it kind of gave me like here are delegation opportunities. These are actually right. I ended up not attending this meeting because I needed to get ready for this podcast. Funny enough, um, this is a meeting that usually I do send one of my directs to. That is our our growth lead. Like, this is spot on, right? Um, this is a demo lab that we run um called Alpha Arcade. and I usually don't end up attending that one. Um, merge council is something that I have somebody on my direct team. So, this this is all like completely correct. And then it also identified like rag of flags like where I'm like double and triple booked. Um, you know, it like said, hey, you're not like your context switching too much. I mean, this is this is correct, right? And this is something that I give to my EA and I'm telling her, hey, like this is this is kind of what we're seeing and we'll we'll start with this at the beginning of the week. Um, and then, you know, it also I also like to look forward at the following week and be like, hey, what can we do to improve things? And you know, a lot of it is like what what do you recommend I should cut for next week? Um, I'm not sure every I agree with everything in there, but a lot of this is like a first pass and it's organizing it in a way that, you know, makes a ton of sense to me. And so, and this is just from basically running the agent and giving it that one line. >> That's cool. So, you've got one of your chief of staff agents. What else are you building around the chief of staff space? >> Yeah. So, I do the same thing in email. So, it has complete access to my email triage and and this is spot on. So, basically, I asked it um to triage my email, and I I think I ran it a couple of times accidentally. What it does is it gets rid of a lot of the junk in my inbox first, but it will first go through and run it. It runs a triage first, and then I tell it what to archive. So, I don't want like calendar notifications or marketing or systems messages. Then, it like pins the messages that are kept, and then it will actually create drafts for a few people that it thinks I need to actually send emails to, right? I don't want it sending emails on my behalf. That's not the point. But I do think that there are like opportunities to where it's like an email that's been sitting in my inbox and sees me. It kind of is actually like watching the behavior in my inbox. Um, and then, you know, ultimately I'll get into a much much healthier state. The other thing that was like funny, I ran it this morning and I had a a meeting with someone that it didn't have a meeting link and I called it out. So, it's it's like kind of little things like that where maybe there are mistakes. Um, and it's not typically acting on my behalf. It's just running the triage um for me. So, it it recommends it would archive 40 emails, right? It would keep it in the inbox and then I basically say yes or no to go and and run those actions. >> This is epic. So, how would somebody set this up? How do they connect their email and calendar to claude code and give it access? >> Yeah. So, I basically set up a token in Google. Um, I'm not going to show you my exact end uh env set up, but I I store I basically generated a token on Google Cloud and then I store that in my ENV file. Um, which is I'm not going to show you the actual file because it has all of my token and regenerate them. Um, but basically I have like an env file and then that the reason why it has a this is basically ignored by cursor, right? This is also ignored by git. So I have this as like a GitHub repo that I maintain but it doesn't this this particular file does not get synced and so there are variables that are in that file and and cloud code like generates it for you. So it's not I basically tell it hey generate a variable for Gmail right and it'll say okay it's in this file and then it will go then I go and I generate it on uh Google in in the console. >> Awesome. So, we got the highle overview. You're getting your full chief of staff agents. You also have an analytics agent. Can you show us that? >> I do. And this one's really fun. Um, so I figured I would show one thing that was more fun. So, I my wife has a a company called Shirts. It is an AI uh t-shirt design company called Shirts.com. This is uh my wife's company. It's called Shirts.com. It is an AI t-shirt generator. Uh, you know, I generated a t-shirt. We're talking about answer engines. This is, you know, a fun t-shirt I generated for my uh for my team because we just launched an answer engine uh optimization product. And what I'll show you in I assume you can see my cursor here. >> Yep. >> Great. >> The way that I run this is I can actually query Snowflake out of Claude. This is obviously a workspace where we have our website running out of web flow. Then we've got a number of different sites that you can see and I can ask it questions about those sites and when was the last time that you know how many HTML blocks like what uh when was the last time it was published what features is it using and this is really useful for me you can imagine if I like go into a customer meeting and I want to know what they were using on web flow >> that is sometimes like not something I want to go and bother a data scientist with >> they have lots more important problems than this to go and and tackle but it is useful And it's something that I want to enable anybody in the team to do. So one of the things that I was talking about um previously was that I think a big like my vision for the our our insights team was to be able to self-s serve any insight um that is kind of like a an insight where maybe it has a yes or no answer or it has like a very specific piece of data that you're trying to collect, right? Like in this case, I'm trying to collect information about the websites in this particular workspace for shirts, right? And so like I said, shirts is a AI design generator, but I'm like, oh, this is interesting that we're not actually using very much, you know, like we're it's not a very complex site yet. It shows a very simple vanilla JavaScript web implementation. This is then uh informed by this directory. So we've actually gone and we've done a bunch of analysis of all of our models and then we've started to document our models. You kind of have to do this in order to be able to get good outputs from your Snowflake when you're sending natural language queries. The other thing that I've done here is I've basically set up MCP servers for Snowflake uh for Tableau. Um Snowflake and Tableau I believe are not officially supported uh repos. And I basically the way that I set it up was I I just fed the repo to Cloud Code. And I'll I'll I can we can put those in the show notes. And then I said, "Hey, I want you to use this MCP server." And then all it does is it authenticates with your credentials. So it uses my SSO credentials and so I'm not like sharing any data that I don't already have access to >> and this is all being done locally and it's being run through a work uh you know basically through through our work um anthropic account. >> So you know I think that's like a big thing to really be thinking about. What do you have access to? are you trying to give too much information to the you know to the model and so a lot of this I think is also like a good exercise in understanding privacy um and really trying to think about uh you know how like when you're building software even what what information do you want to give to the model and are you comfortable with >> yep you see that right there that's linear today's sponsor if you haven't heard of linear yet check it out it is the task management platform that is dethroning Jira teams like OpenAI, Versel, Perplexity, they all use linear. Why? I had Nan Yu, the head of product on the podcast and he explained that those teams value speed. Linear is built for speed. If you have engineers who want to be operating at that next level of speed, you need linear. And even if you're a product manager, you need linear because linear has linear agents where you can call Devon, call Cloud Code, call Cursor to complete a task for you. Imagine you have a ticket, you ask an agent to do it, the on call engineer reviews it, and it's live in production. PMs are getting closer to the bare metal of the product with Linear, today's sponsor. So, check it out at linear.com/ashos. So, how would you use the analytics agent? >> Yeah. Um, so the analytics agent is really like a way for me, you know, let's say I want to understand what how many um how many sites does shirts have in its workspace. So I just ask it like a natural language question and then it's basically going to go and it's going to write a query. So it's using tokens to write that query and then I've already authenticated so it shouldn't go it might the way I've set it up is that my authentication. Yeah. So it's already authenticated and it just basically spits back this is how many sites you have. So it's very much like it can act like an agent. if I asked it a more complex question like help me understand the signup behavior over you know the last week and a half. Um again that's like proprietary data so I probably won't ask that question on this podcast but it will pass back to me and say oh well this is how many visitors you have and if this is your week overweek behavior. So it's almost like having a a data scientist in my pocket. Um, and the way that I set this up is I actually have a analyst that I've set up that is like a a snowflake uh, sorry, I have like a basically set up a agent that is a snowflake agent that monitors uh, different trends for me. And so then it then it can basically go out and this agent can go and like report meaningful changes and discrepancies. And so that is feeding into another agent that I've set up that allows me to go and analyze what is actually in my in my snow snowflake repo. >> So how are you invoking these different agents and what is the right way to organize these? >> Yeah. So one of the reasons why I like to keep these in separate windows is a lot of times like for example I I'll go into the podcast prep because this was kind of a fun one that I did for your you know for this. So what I did was you can basically pull your agent in. So that's like one way that you can invoke it. Usually it picks it up what I wanted to do, right? So let's say I want you to prep me for uh Akash product growth podcast. Which agent are you using? And let's see if it like actually picks it up correctly. It should pick up this podcast prep researcher agent. >> Nice. So basically just by having the context of the agent markdown file in your agents folder in the folder that you've opened up cursor in. >> Exactly. >> It's using this. It's picking this up now. Right. >> Love it. So it just invokes the agent just by having that markdown file there and you just keep them in an agents folder. It sounds like there's not really much else to it. >> There's not much else. And that's where Claude generates it. >> Y >> um so like for example, we'll maybe go and generate a LinkedIn post generator. But what I wanted to show you that I thought was pretty cool. So, what I've also done beyond just I like this trick-or-treating. That's fun. It's Halloween. Um, what I want to show you here is this is actually the output. So, I run this as like a app. Um, so I know that I am in the middle of a podcast with you and so I've kind of built this out as my calendar. Um, I also have like different agents that have outputs. This is this is the markdown file. So, it's reading the markdown file and then this is what it actually generated. And this was like the prep work that the agent did for me for this podcast, right? >> Pretty epic. I mean, it's pretty industrious. It's not just doing a little bit. It's going the next level. This episode is brought to you by Linear. You know what's broken about product development right now? It's not the coding. We've got AI helping with that. It's everything else. The planning, the feedback synthesis, the endless context switching between tools. You're drowning in PM busy work while the actual product work suffers. Linear started as that issue tracker engineers actually wanted to use, which let's be honest is pretty rare, but it's evolved into something way more powerful. Your entire product development hub. Their new AI features are a gamecher for product managers. Product intelligence automatically surfaces insights from customer feedback and support tickets. No more manually combing through hundreds of reports. And their AI agents help draft PRDs, scope projects, and handle all those status updates nobody wants to write. Product teams from OpenAI to Verscell use linear to build complex products at speed. There's a reason they call it magical. So visit linear.app/partners/ aos. That's linear.app/partners/ a kh. So I have a see I have a video transcriber. So this is an agent where I will like pass several of your previous YouTube videos and it will transcribe those videos and it will pull out context for the for our podcast session. So I look I find it like not easy necessarily to read the markdown file which is why I created the app for myself. Yeah. Um and the app I'm like kind of constantly tweaking. Uh I'll show you like one other version. And I went to dinner last night and I prepped myself for dinner and literally all I did for that dinner was I gave it the I I gave it who I gave it like my calendar invite of who was going and then it like generated this amazing like this is who is going to be there and you know what they what they have been talking about that went into their LinkedIn and you know so to me this is like what an epic chief of staff would do. Um, and I literally like all I did was I went into my dinner guest research and I invoked the agent and it generated that. >> All right. So, you just walked us through the analytics agent. The next thing is can you help show us from scratch how we would build an agent together? >> That sounds great. Now, I'm going to create a LinkedIn post generator. Okay. >> All right. >> I have to create a lot of LinkedIn posts. I have a custom GPT, but I do think that this is like a new agent where it would be a lot easier if I just fed it a bunch of content. Um, and so I'll show you the way that I think about doing this. So all you do is go into agents, you manage your agent configurations, and then I'm going to create a new agent. So this is literally just walking me through this. And then I'm going to give it access to the whole project. You can give it less access, um, but I'm just going to give it access to the whole project. And then I'm going to use claw to generate that agent. And I wanted to write a linked in post and generate a meme image using open AI uh image gen model. Um >> maybe we should throw in there not to use an M dash so it doesn't give you away. >> There you go. Don't use an M d. Is that how you Yeah, probably. Close enough. Is it m dash or is it m- dash? I'm not sure. Um, and then let's let's also reference the materials I'm going to give you for what makes a great LinkedIn post. I'm also going to give you my best performing posts. >> Nice. Okay. So, that's what I give it. And then what it's going to do is generate the agent. And then I'm going to point it to I I kind of like grab some data to begin with. We'll get there in a second. It takes It takes its time, >> but at least it has fun verbs to let you watch along. >> I know. Doesn't it make you feel so good? I like that it has personality. I I feel more connected to it. >> And if you guys really hate it, just use codeex. >> Well, I think Codeex has like a a time and a place. Um, okay. So I'm going to create that and give it I've been using sonnet for these types of tasks. I think it does pretty well. And then we will make it let's make it blue because LinkedIn's blue. And then so this is basically what it generated. You are an expert in LinkedIn content strategy social media blah blah blah. And then I'll show you like the full version of this. So it just created this file. So it created this file that is a markdown file. It's telling you what to do. You want to analyze reference materials. So you're going to see I'm going to point it at those reference materials. craft a compelling post and then it's gonna generate a complimentary meme image. Let's see if it works. >> Can I show everybody something cool which I don't know if you know about yet? So, if you two-finger click on the markdown file in the left, you can actually open preview. So, like kind of like right click >> okay >> on the markdown file name in the in the left bar. >> This one? >> Uh yeah. If you go all the way to the left like where it appears in the file. >> Yeah. And then two finger click or right click on it. >> Uhhuh. And then open preview. >> Oh, I didn't know about this. This is exciting. >> It's exciting, eh? >> I love that. >> Yeah. >> Cool. This is a lot easier to read, isn't it? >> Yeah. It's a great way to work with Markdown in cursor. >> Yeah. Thank you for showing me that. That was like a life-changing thing you just showed me. >> And we just got a life-changing agent that you showed us. So >> cool. So, let's see if it works. Should we try this? >> Yeah. Let's test it. >> Okay. So now I'm going to close this. We're going to go back to our agent. Um, and the other thing that I'm going to do is basically like escape out of this. I am going to upload a directory of stuff. So I have like a few different posts that I've that I pulled down. Um, and then I also pulled I like Tom Orbach and his content about how to write viral LinkedIn posts. Um, and then I had like a couple of other posts that like did really well like like south I like writing southwest and uh I wrote a post about that, you know. And so that's more or less what I'm going to feed it. I'm just going to upload all these and I'm going to tell it let's see add these to my reference materials and point this agent at these materials. So then I'm gonna like reference this agent, update the agent. It probably will do that anyways, but >> and for people who that went by too fast. You literally just drag files into >> I literally just dragged files. >> It's so easy. >> It's super easy. And I think that's part people don't understand with terminal is that it is like a very rich interface, you know. Then I think what I'm also enjoying is like the improvements to the IDE as well. Like what you just showed me with cursor, I'm like, "Oh, I'm going to use that all the time." Um, and you know, now I can like go and preview that. I'm like, "How cool is that?" So I think that there's so much innovation that's happening in the terminal uh and with the the CLIs with the you know the command line interface and then there's so much innovation that's happening just in the I may have uh gotten too too ambitious there. >> That's okay. >> Let's see. I think it I think it actually did update it. Here we go. LinkedIn reference. So successfully created it not updated. Um, here I'm gonna try re-uploading those because I think it only I see it. I see what happens. It did not. I'm going to do one more thing here. I'm going to pull them in one by one because I think that's what happened because I didn't like zip it up. >> Yeah, I think it prefers that sometimes. I was actually surprised that it took all of them. >> Yeah, I was surprised too and then I it didn't. All right, let's see. You know, that's the thing. I feel like it's very you just have to sort of be paying attention and be like, did that work? Nope. So, let's pretend you just pulled those in. And so, you can just say, "All right, so I've just pulled the files in and we'll go from there." >> Yeah. Okay. Give me one second. We'll let it. Okay, there we go. So, I've just pulled in those files and you can see that they're all accessible here. And now what we're going to do is check and see, did you update the A2 reference? So, now I'm going to allow edits during that to reference the linked in reference files. Just to double check. So I think it's updated right here. So now I'm making the edit. Yeah. So now I can see that that post creator is critical. It's actually analyzing. It's analyzing and using all those reference materials that I created. Right. >> So looks like that happened. So I'm going to go. Should we should we try creating a post now? >> Moment of truth. >> Moment of truth. Uh what's what's something snappy? Let's see. I think >> maybe I should do something about being a working parent on Halloween. >> I like that it is Halloween. Uh, sure. Let's just give it something really simple. Something fun Halloween. Let's see what it does. >> Is it going to be smart enough to re-engineer what made your Southwest post go viral? Is the test here. >> That's that's the big question. All right. So, it did pick up that it wants me I wanted to create a LinkedIn post, right? I didn't tell it. Actually, it it had enough context from this previous thread, but it is using that agent, which is cool. Okay, so let's see. This is not a bad post. Lower your standards. >> That's funny. >> I think this is a good post. >> This is a pretty good post. >> This clearly is learning something from the context we provided. >> Yeah, >> it's not just if you had typed that into a regular claude, you would get that answer. >> Yeah. So, I think that that's And then let's see. Did it actually like Yes. So let's generate that meme image. >> And so now here we told it to go through the chat GPT image model. So just to enable it for that some at some point you've probably given claude your open AI API key. >> Exactly. I gave it my open AI key and let's see if it's actually good at generating images. So we spend a lot of time uh my wife and I thinking about how to generate images for her company shirts. I think it's like chat GBT is very good at generate images without a reference image. Let's see. use chat GBT with DO and I think that's the the one thing I would say about let's see so basically I could a lot of like building an agent is trying it out and then going and adjusting what you want the agent to do and usually after using it like three or four times you can kind of have it dialed in like I've used the podcast prep one three or four times and now I've got it dialed in and how I want to do prep for me >> yeah the mistake is to assume that this first version is the final version. Just assume that's like step one of four. You're going to have to dial in most of these agents, especially if you're going to have drafting your emails or something very critical like we showed earlier. >> Exactly. Like with the email agent as well as the calendar agent and analytics, I have kind of gone through and continually updated those agents as I've seen it make mistakes and now I have it working in a way that, you know, is to my liking, but I'm still constantly kind of tweaking it and when a new model comes out, I can see that is able to do different things. So the analytics one is a perfect example. Previously I number one did not have all the context from dbt so I wasn't able to pass that in. I've been using the analytics agent for several months and then once we got dbt stood up and our our models really well documented I was able to go and actually start quering snowflake much more effectively. But then when Sonnet 45 came out, it was able to actually it it kind of managed to call much better and then it also is able to run a much longer agentic task. So you kind of and I would say maybe that's like a good um segue into talking about like building products uh that are AI native as well. And right before we get into that, the last thing I want to ask you, we've just showed all these amazing workflows. As you talked about, as a leader, we have to motivate others and inspire others. How do you set up your organization to work this way? Obviously, you need to get everybody a cloud code license. You need to allow people to access the MCPs for Snowflake and whatever else that might be needed. But beyond that, how do you really build a product organization that is at the bleeding edge of AI? This is a a great question. So I think one of the things I would first assume is that your organization is going to be like every other adoption curve known to man. So you will have people in your organization that are the early adopters. You'll have the early majority, but you will also have the late adopters and the lagards and then kind of everyone in the middle. And you want to really figure out how to cater to all of those different people in your organization so that they can start to ascend the ladder themselves. So whereas I might be I would probably put myself more in early majority at least if it's hardware but maybe with software I'm more in the you know the early adopter I think that I have people in my team where I'm like hey I only want to see prototypes for example when you're going to have meetings with me and that's kind of created a dynamic where we spend a lot of time looking at the prototype and they spend a lot of time investing in what that prototype like what that experience is like. doesn't mean we don't have a PRD but we've kind of like shifted away from a PRD in some in a lot of cases and maybe it's like a more evolving you know document right and so then you know when I kind of think about how do we train our team >> we have everybody has like access to Figma make for example >> um as a very like a much easier tool to sort of learn and we've taken our design system and made that accessible in Figma make we also have a repo That is our design system that is accessible through cursor. We just did a cursor training. We did a Figma make training and we've done a couple of these builder days where we have people that are like the champions on our team who are maybe at the bleeding edge, but they are really there to like help walk people through getting over the technical hurdles. And so we're going to do a builder day where like everybody has to demo something. Um, and it can be in any of those tools, but you do have to go a little bit outside of what you're comfortable with right now. And ideally, you know, what we saw when we uh went through the that exercise was the first time we did it, we just did in design. And we basically went from like nobody in design using cursor to about 30% of the team using cursor weekly. Um, now that's like kind of crept up a little bit because once you have like a base of people using in an organization, you start to see more and more people leveling up. And then we're about to go do a second builder day and it's going to be for product design and insights which is like user research and data science. Data science is a different set of use cases for cursor than product managers than design. And so a lot of this is like trying to both have people who are champions that are like kind of bottoms up showing things off and then also saying here are some of the behavior changes that we expect. We are actually rewriting our um career ladder to incorporate this as like an expectation. We're thinking about you know so it's like you want people to be supported but also you want to create the right incentives inside of your team. Um, and then you also want to make sure that you're thinking through like well are you just inserting AI for AI sake? Are you going to get at the end of the day you want to get to a better outcome. So like I'll give an example that happened to me yesterday. I was in a meeting. This is hilarious where this designer had put together an amazing prototype. It was awesome. It was like really like very future forward. It incorporated like a lot of new thing new elements um around our answer engine optimization workflow. Uh this is kind of the new AEO is the new SEO and what we but somebody another like one of the directors of design in my team was like hey I was in this design review where somebody else had a prototype looked a lot you know like some of the workflows you are building and I want you two to go and like harmonize your two prototypes. This is a lot easier to do now than like being like you're you know so far down the the product development life cycle and you're building something and then you're like oh crap these you know two workflows like don't work with each other. So I think it's it's it's really productive but it it's definitely like a different way of building. >> So many insights packed in there. If I were to synthesize for everybody at the base of the pyramid start with access. So we talked about cursor access, cloud code access, Figma make access then giving the MCP access for those tools. The second layer supporting your team whether that's training builder days bringing the people who are at the bleeding edge and helping others and then we talked about getting the incentive structures right. So even changing your career ladder that's how you actually create these AI native product organizations. So once we conquered the productivity side, the other side is shipping AI native features and I think you have a really interesting story about eval. So can you tell us that? >> Yeah, so one of the things that I think is really fun about building AI native products is so much is changing. So what you're seeing here is web flow. Web flow is a website experience platform that is AI native. And you know we this is a website that I've built out for a u a event planning company called party parrots and what you'll see here is a set of components um and variables uh that are for this particular site. Now we decided to build out an appgen product that uses those variables uses those whole design system as well as our CMS. That's that's really like how we thought about differentiating. Now, what was funny is I was getting ready for this podcast. This product broke >> and I know exactly. And uh so obviously the product works like I've generated full apps. Um but you know we're we're getting we're about two weeks away from launching this product uh into the world which is exciting and also we decided to go and change out the model. And so I was like patient zero going and like generating apps for this podcast. And what I realized I I kind of kept telling the team like hey this is this the the agent keeps dying. Why is it dying? And we were trying to figure it out and there were some other variables that we had like changed in the experience and what we realized was we had changed the underlying model and our eval didn't have enough coverage to fail when we changed the model. Mhm. >> And I think that is one of the that is a new skill set for a lot of people is is building evals which are effectively test cases um for a model and a lot of times you want a test case that is going to fail inherently. That's really hard. But you also want test cases that you think will pass. And you know, so each time you go and change out the model, you want to see how the model does, the new model does against your like what I call like dream evals. And so in this case, like we didn't, we actually lacked the coverage. Um, and so we've been really trying to think through how do we instrument product leaders? How do we help product leaders? Again, this is one of those new tools that's part of the the AI product manager um toolkit. So, how do we teach PMs how to write emails? How do we teach them to have enough coverage? Um and so we've been working with our vendor uh we we use brain trust and are trying to understand well what are the best practices across all the other teams that are out there and you know where should we use for example synthetic emails where we're like generating it using uh another model right and so I think that's been like a really interesting process for especially for the we have we have a number of products that are AI native products that we're building here and but then it's also been very interesting to see I mean when you're building a codegen product, you know, like for us, we wanted it to be so simple that like literally all you did was give components and CMS collections and like a very very simple prompt and we wanted you to get something >> simple. Very powerful. >> Very powerful. I mean, this is a chat kit app that actually works. >> So to me that that was like the you know, how do you build something that's differentiated in a AI native space where it's maybe even noisy? We still think that's very possible and I think that's like been a very interesting process for us to to go through. >> So the next lesson is you need to choose features that are actually related to your strengths. How did you guys decide and how do teams decide what AI features to be putting on the road maps? >> I think that's a you know part of it is like number one if you see a trend in market do you think that trend is applicable to your customers? So I'll I'll share like another app that and just kind of like walk through that. So in our case, our strengths are really helping our customers bring visitors to their front door, right? And so we think of our CMS or uh which is a which is basically like a collection of database items that then get rendered for search engines and answer engines. An answer engine is like chatbt to discover. So this is our core competency. What we saw was like, hey, we think there's a lot of people who, for example, don't necessarily want to have an app off to the side generated by, you know, Lovable or VZero. That's more like a prototype. We want we people want to generate like production apps. And so, what does it mean to have a production grade app? And so, I'm like, well, it looks like your brand. >> That I think that was like one of the the first principles. Um, it uses your, you know, it uses like your your CMS. So this actually uses your CMS to generate these and then it can integrate into your workflows. And so we've really focused on how is this natively integrated with everything else and still simple enough for our customers. We we have kind of a wide variety of different types of users. Everything from a designer who maybe might be technical but not a developer. We have developers on our platform and we have marketers that are, you know, like content marketers or even um performance marketers that are not very technical. And so we wanted a product that could actually cover that gamut and like use our production grade hosting, benefit from all the security capabilities we've built into Web Flow Cloud, etc. And so a lot of that is like we've built a lot of the scaffolding around this and we're like okay we're not just bringing a uh you know a coding agent to market. We're bringing a way for you to prompt an app to production and that we believe is like quite a differentiated experience than what you would get out of the box from another coding agent. >> Yeah, I think this is extremely powerful. As a product leader, you need to be leveraging the latest technology. you can't see a trend like all the stuff we just demoed with cloud code and not think about how could this be brought to my product and I love the thinking framework you've given us here of well what are my strengths in web flow's case you know building production apps having an incredibly large enterprise backing of users being able to do it on your hosting being integrated with the CMS for people who don't understand CMS content management system this is like where you're actually publishing all your blogs and your pages and the content on them and then leveraging that to build a product I think this is the art of building amazing AI native products. The final lesson is you need to think about distribution. How do you build products with a distribution first mindset? >> Yeah, it's a great question. So I think that if you think about the different waves of innovation that have happened um you know each of those waves has had a different distribution mechanism that product managers um have had to learn. And so if you think about the first wave that was really the internet and then you needed a way to find websites. So, people learned how to optimize search engines. Search engine optimization. There's a lot of dark art to optimizing your website for keywords and keyword stuffing back in the day. >> It still works. >> It still it still works sometimes. Uh, and so that was kind of like phase one. Phase two was you were like, "Okay, I also want to launch a mobile app." And mobile apps also have app engine optimization, right? So people did had very similar tactics to get to the top of the iOS store, right? And so that again was a very specific set of tactics to get distribution for your product. Wave three was social. So you were building for virality. You're trying to get people to discover your product, to come back to your product, to share your product, and on and on and on. That, you know, worked on it still works on many different platforms. And so these are all still viable distribution paths for some segment. But the next wave is really going to be around how do you get listed in answer engines? How do you get your brand recognized in answer engines? How do you get your, you know, if you make a product change, it's like a major product moment. How do you get that answer engine knowledge to swap out? And so a big part of that is going to come through your website. understanding that you need to feed, you know, a FAQ to your website and get that updated and that is something that needs to be part of your product process in order to get that information updated. Um, or you can use a platform like Web Flow that does it automatically. uh you need to really think about how are you going to create apps that showcase or maybe highlight part of your experience as you know chat GBT apps start getting adopted and inevitably there's going to be all kinds of agentic experiences around these agentic browsers uh that will also want to interact with your app or with your website or with your e-commerce store etc. So really thinking about I mean and that's going to be a huge opportunity for growth right that's going to be an unseeding of how people discover things and so the more you can understand how people's discovery is shifting the more you can actually drive growth to your product. Yes, I think that this is the final thing that so many PMs they maybe outsource to product marketing or outsource to their execs, but it's actually worth thinking about from the very beginning so that you make it a part of all your plans. So, that concludes our master class in AI product leadership. We started with AI productivity. We walked you through how to be an ICCPO, how to use cloud code and cursor, how to build agents, what agents Rachel is building, including her amazing chief of staff team of agents, and how to set up your organization. Then we talked you through the three most important lessons on shipping AI native features. Having great eval in place, continually iterating on them, playing off your strength, and thinking about distribution first. This is the road map to becoming a great product leader, a top 1% product manager. You have to embrace these tools. Rachel, thank you so much for dropping all the sauce. >> Thank you so much, Akash. It was great to be to come on and just really appreciate it. Thanks for letting me share. And if you guys didn't know, I write a paid newsletter, of which Rachel has been a subscriber for a really long time, which is really, really special. I only found that out in our pre-all recording. Check that out as well if you want. Check out Web Flow for their amazing new app builder, and we'll see you in the next one. Bye. So, if you want to 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.