The rise of the professional vibe coder (a new AI-era job)

Transcript

I'm the first official Vibe Coding engineer at Lovable. You're at the top 0.1% elite level of Vibe Coding. It's a dream job for so many people. It became a job by building in public. You don't need a company to hire you. You can hire yourself as a professional Vibe Coder first. You've never coded. You don't want to look at the code. Coding is going to be like calligraphy. People are like, "Oh my God, you wrote that code? That's so amazing." It's going to be so rare that it's going to become an art. These Venn diagrams of engineer, designer, PM used to be very separate. Now they're converging. AI, regardless of your background, is an amplifier. If you don't know what you're doing, you're just going to produce garbage faster. It feels like an emerging core skill is learning clarity in the ask of the AI. I like to use the Aladdin and the genie analogy. You rub the lamp, a genie comes out. "I'll grant you three wishes." The first wish is, "I want to be taller." The genie makes me 13 feet tall because I was not specific. AI just doesn't understand what you mean when you say, "You know what I mean?" So you need to be specific. I'm optimizing 100% of my time today on good judgment, clarity, quality, taste. Today, my guest is Lazar Yovanovich. Lazar is a professional vibe coder. He gets paid to vibe code all day and build internal and external products. This conversation is going to blow your mind in so many ways. This is not only a really interesting new career path for people to consider. If you listen to what Lazar shares, it's also a really important glimpse into where things are heading for tech roles. I found myself thinking more deeply about the future of product management and engineering and design during this chat than I have in a long time. We also spent a bunch of time on Lazar's best advice as an elite vibe coder for getting the most out of AI tools. He's got a bunch of really interesting and useful frameworks that I have not heard anyone else share that will immediately level up your success using all the latest AI tools. This conversation is going to expand your mind in so many ways. I cannot wait for you to hear it. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. And if you become an insider subscriber of my newsletter, you get over 20 incredible products for free for an entire year, including a year free of Lovable and Replit, Bold, Gamma, N8N, Linear, Devon, Postdoc, Superhuman, Descript, Whisperflow, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chapman, Demobit, and Stripe Atlas. Head on over to LennysNewsletter.com and click Product Pass. With that, I bring you Lazar Yovanovich after a short word from our sponsors. This episode is brought to you by Strela, the customer research platform built for the AI era. 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Run your next study at strela.io slash Lenny. That's S-T-R-E-L-L-A dot I-O slash Lenny. Today's episode is brought to you by Samsara. If you listen to this podcast, you know that we spend a lot of time talking about building things that sit on a screen: onboarding funnels, mobile apps, and checkout flows. Samsara is building products for the physical world: first responders racing to emergencies, truck drivers carrying critical supplies, construction workers building our cities and data centers. These are people who put everything on the line every single day, and Samsara's technology protects them. Samsara is solving complex problems at the intersection of hardware, software, and edge AI. And their AI doesn't just detect events. It reasons about intent and answers questions like, "Did that truck driver brake abruptly because they were distracted, or was that a heroic act?" If you want to ground LLMs in messy, real-world telemetry or solve edge AI constraints at a planetary scale, Samsara wants to talk to you. If you like playing with enormous data sets, moving fast, and working in small teams, come help build the technology that makes the physical world safer and more efficient. Visit samsara.com slash Lenny to learn more. That's S-A-M-S-A-R-A dot com slash Lenny. Lazar, thank you so much for being here, and welcome to the podcast. Thanks for having me, man. Okay, so I had Elena Verna on the podcast. She's head of Growth at Lovable. She mentioned that she works with a professional vibe coder, you. I had so many questions. I almost wanted to go on a tangent with her to try to understand this role. Instead, I asked you to come on the podcast. There's so much I want to talk about. I want to talk about this career path and how you got into it, how other people might get into it, and where you think this whole vibe coding thing is going. Also, I want to get into what you've learned about being successful using all these AI tools, because this is your job. First, I want to start with understanding this actual job. What is it that you do day to day? You're basically being paid a full-time job to vibe code. Incredible. What are you responsible for? What are you doing day to day? Well, as you said, it's a dream job, right? I get paid to do what I would have done anyway, right? It's the best job in the world. I get to use tools like Lovable every day to push projects to production, whether internal or external. Those could range from different templates on the marketing side, sales side, or whatever, or they can be as deep as building some internal tools with a lot of integrations and connections and whatnot, right? So the surface area that I cover is pretty wide across all departments because it's such a flexible role and it complements so many things, right? It's an ideas role. A lot of people have a lot of great ideas, but they don't know how to build them, or they just don't have the bandwidth to. And that's where I step in today, to make sure that these ideas come to life fast and with the quality and security they should have in order to be available for users in production. One thing that's really interesting here is it's both internal and external tools. A lot of companies have someone building a bunch of internal tools using AI. You ship stuff that's actually public, and it's product-level products. Yeah, definitely. Some of the stuff that I've shipped that is public is when we launched our Shopify integration. Most, if not all, of the templates that users were remixing were built by me, right? So stuff like that, or the merch store, because we wanted to obviously prove the concept that, hey, Lovable and Shopify just works. It's so simple. Anybody can do it. I vibe coded our merch store. So all the merch, including this shirt, that people were buying online, they would have bought from a store that was built by me. But then again, on the internal side, we want to track a lot of things. One of the cool things that we want to build now, for example, is a feature adoption matrix. If we build a feature, how many people are actually using it, adopting it? And that's a pretty custom build, right? We have a very custom stack. We're building custom features. There's nothing out there that I could just pick off the shelf and adopt faster than I would have built it myself. At this point, I'm at a stage where if it takes me an hour or two hours to set up a big enterprise account somewhere, I'm just going to build it myself faster. So I'm in that position of build versus buy. I'm in the build boat, so to speak. Yeah. And then who do you report to? Are you kind of this rover that helps wherever, or are you with a specific team? I'd say probably closer to the former, right? I started in growth, right? Elena brought me on early on because she has so many great ideas, and she just needed somebody with the right type of mindset and velocity and ownership to just take them away. We have a very custom stack. We're building custom features. There's nothing out there that I could just pick off the shelf and build, build, or adopt faster than I would have built it myself. At this point, I'm at a stage where if it takes me an hour or two hours to set up a big enterprise account somewhere, I'm just going to build it myself faster. So I'm in that position of build versus buy. I'm in the build boat, so to speak. Yeah. And then who do you report to? Are you this rover that helps wherever, or are you with a specific team? I'd say probably closer to the former. I've started in growth. Elena brought me on early on because she has so many great ideas, and she just needed somebody with the right type of mindset and velocity and ownership to just take them away, build them up, get them into production, whether they're based on education or anything go-to-market or whatever. But then, obviously, when you're able to ship fast, everybody needs that in an environment that we as a company are now living in, which is we're the fastest-growing startup in history. So every department needs a Lazar now or yesterday. So now I'm shifting a little bit, I guess, into some of the go-to-market roles and even building some, again, internal tools for enterprise team. But I'm working on some community tools as well right now, as we speak. So I'm a little bit all over the place, but I thrive in that environment where I'm given a rough concept, a rough idea, and I'm just tasked to bring it to life as soon as possible. Okay. I'm hoping with this chat, we create a lot more Lazars, and I want to get to the career path, how you got to this, and what it takes to actually become a full-time vibe coder. But I want to start with, because you do this full-time, you're at the top 0.1%, elite level of vibe coding. You're doing this full-time. They hired you to do this as a job. So I'm so curious what you've learned. What are some pro tips that you've developed for being successful with AI tools, Lovable, and also just more broadly? What are maybe two or three things you've learned that help you be really good at this job? The first understanding that I had very early on, even though I, just in full transparency before we begin, don't have a technical background. I never wrote a single line of code in my life. I've written a couple console logs manually, and that's about it. So I very much lean onto AI assistance. Let me actually follow that thread because that's such a good point. It's something that, when we were chatting earlier, you pointed out. Your feeling is it's actually an advantage to not have a technical background when you get into the space. Yeah. Yes. I honestly feel that it is because people like me don't know that they are not supposed to be building XYZ, and that's how we actually are able to build it. Let me give you an example. Six, seven months ago, somebody in our community was like, oh, I wish Lovable can build Chrome extensions. And then folks that are not technical were like, well, why is that not possible? And then people that are technical start explaining, well, it's React, it's a different stack, it's this. And people like me, including myself, just go into Lovable and say, build me a Chrome extension based on this app. And I was able to do that with Lovable. There were people that were able to build desktop applications on Lovable. Again, something that shouldn't be possible, it simply is. Our community manager, Whitney, at one point was building this presentation deck for something. She's like, would it be cool if this was a video? And then she just prompted her way into generating an actual video inside Lovable before that was available. Now that's a feature. Now you can prompt Lovable to do it, but back in the day when she did it, even I thought it was impossible. I never tried it. So I think that's the advantage that we have over people that are technical. We just come into this completely unbiased and very positively delusional, which I think you have to have when working with AI tools. You have to come with this delusion that absolutely everything is possible until proven wrong. And that's just the pursuit that I have in my mind that has helped me, among other things that we'll chat about today, to excel in this role that I have at Lovable. Two of the, I think, concerns, maybe traps, people that don't have a technical background fall into in theory are, one, if you get blocked, it's not obvious how to solve a problem. And two is, are you building this teetering slop that will collapse someday? Because you don't know system architecture, you don't know if this is going to scale, all these sort of things. So, coming back to what you've learned about how to be successful and build successful products, talk us through things you've done and things you've learned for how to weigh those sort of things and what you do when you get stuck, as one example. I'm happy that you mentioned those limitations. I have some other ones that I want to bring in, but let's address this one first, which is the most important one. And that is, you have to be self-aware. I didn't come into this, yes, I am delusional, as I mentioned, in the sense that I just don't want to accept something's not possible, but I'm also well aware that I need to be better in order for it to become a reality from my own point of view. So I understood very early that coding is not the problem that we're solving for here, that the problem we're solving for is clarity. The output that AI can do is much faster than human output anyway. Very early on, I started leveraging chat mode, and to this day, I can say I spend 80% of my time in planning and chatting and only 20% in executing the actual plan. I'm optimizing for the right kind of speed. Most people optimize for the wrong one. That's the first lesson that I learned literally on day two, because I came into Lovable. That was my first exposure to this. I've tested and played around with all the tools, obviously, but whether somebody's using Cursor or Claude Code, it doesn't matter where you are, the problem remains the same. You need to be clear on what you want to do, and you need to know what you're doing, because these are still just tools. Yes, AGI is coming, but it's not there yet. So until it's here, you're still steering the ship. In order for you to steer the ship, you have to know the instructions. And the best way to learn is by building, but treating these tools almost as technical co-founders and educators, and learning while doing, and religiously reading the agent output, not the code output. I don't care about the code. The syntax is not of my interest. It's what the agent tells me that matters to me. I put a lot of trust in LLMs and AI these days, and I understand that there may be some people that are not as confident as I am. I just feel that the models today are good enough for me to trust in their syntax output. However, I'm concerned about the agent output because of the two limitations that I want to tackle next. The first one being that there is a limitation when you work with LLMs so that there's a machine-level limitation and there's a human-level limitation. The first one is there's something that is known as the context memory window. For non-technical people, I like to use the Aladdin and the genie analogy when I explain. It's very simple. Everybody knows the storyline. You rub the lamp, a genie comes out and tells you, okay, I'll grant you three wishes, not 3,000 wishes, not 3 million, just three at a time. To me, when I translate it into working with AI, that simply means, hey, I can only make so many requests within a request at a time for AI to be able to listen, understand what it needs to do, scope it, do the research, read, take all the actions, all the inputs and ingredients that it needs to produce a high-quality output. So that's the first part, understanding that there's a limit, and it's denominated in tokens. Maybe that's going to be different a year from now, but today there's a token limitation. I'll take an arbitrary number of 100,000 tokens, for example. So when you make a request, right? It's very simple. Everybody knows the storyline. You rub the lamp, a genie comes out and tells you, okay, I'll grant you three wishes, not 3,000 wishes, not 3 million, just three at a time, right? To me, when I translate it into working with AI, that simply means, hey, I can only make so many requests within a request at a time for AI to be able to listen, understand what it needs to do, scope it, do the research, read, take all the actions, all the inputs and ingredients that it needs to produce a high-quality output, right? So that's the first part, understanding that there's a limit and it's denominated in tokens. Maybe that's going to be different a year from now, but today there's a token limitation. I'll take an arbitrary number of 100,000 tokens, for example. So when you make a request, a part of those tokens AI spends to read stuff, another to browse the web, another to think, and then another to execute the code, right? Then there comes the second limitation, which is you, me, and you, humans, which is, let's go back to the analogy of the genie and Aladdin. I ask the genie for the first wish and the first wish is I want to be taller, and guess what happens? The genie makes me 13 feet tall. All of a sudden, I can't sit in the car, I can't get into my house, I'm a dysfunctional human being, right? Right? Because I was not specific, right? So the part that we need to optimize for today, it's going to get better, but today it's still not there yet, is that AI just doesn't understand what do you mean when you say, you know what I mean? You do when I tell you that. We as humans, we have, I'm 36, so I have 36 years of experience living as a human to know what you mean, but AI doesn't have that, right? So you need to be specific, you need to provide references, you need to provide the right context. So what I've learned is how to combat that part, and I think, because I can't control the first part, which is the token memory window, the quality of the LLM models, you are 100% in control of the latter, and that's what I want to dive into today as well and just trying to teach people, okay, if I'm the malleable part, how do I, how do I fix that part, right? I think that's the key lesson here. This is so helpful, and I love this metaphor of the genie. This piece about clarity is such a thread I've been noticing across people that have been successful using AI tools, and it feels like an emerging core skill is learning how to be, learning clarity in the ask of the AI. AI, do you have any advice or anything you do there to help be better at being clear with what you want? Yeah. So first of all, you need to be, as you said yourself right now, you need to be good at understanding what clarity means and how to translate it. In my terms, clarity means understanding what tasteful looks like, what's good enough versus what's world-class, what's magical. And I developed that through something that I heard from you, you mentioned before, which is exposure time, right? Making sure that I'm exposing myself to content and to people and to relationships or whatever that are going to help me to level up in that domain. Again, it goes back to self-awareness. I knew, even before I joined Lovable, I was like, okay, even before I started using Lovable already, AI tools, the first thing that I knew was I don't know how to code, right? So my first thing was, oh, I can build. Wow, amazing. But a week later, it was, oh, I can build, but I'm not fast enough. So I optimized for speed. So I was like, oh, I can build and I can build so fast. And then two weeks later, my development cycle that I'm in began and it's still ongoing, which is, wait a minute, should I have, have I even built this in the first place? Because it's like, once you figure out that we solved for the how, which is AI assistant or rapid engineering, call it whatever you want. You can call it vibe coding if you want to, but we solved for that. Now we gotta solve for everything else, and everything else is what matters. Good design, good taste, good user experience. When you think about who you're building stuff for with these tools, you're building it for humans. Humans are emotional beings, and we all make our purchasing or any kind of decisions on an emotional basis, right? So I think that the core skill there to work on and develop today isn't, again, coding, although I have nothing against traditional engineering, and I'll say later why. I'm actually a big fan of it, of elite engineering, but people like me, people watching that are like, should I start learning how to code? If you haven't done it yet, I'd honestly say no. You're optimizing for the wrong skill set. We won't be rewarded in the world of AI for faster raw output. We will be rewarded for better judgment. So I think that better judgment comes with, again, to go back to your question, how are you solving for that? How are you solving for this? Well, it starts with exposure. So I'm deliberately exposing myself to people and resources that I know I need to consume to level up. And then a lot of it just comes from building as well. If we're honest, it's a muscle. Everything is a muscle. You need to practice. You need to see what's possible. And that's where some of the techniques and mindset shifts that I want to also use as an opportunity today to ingrain into people's minds later down the call may be useful. So, okay, so what I'm hearing here is because coding is now essentially a solved problem. I love that you don't look at the code. You've never coded. You don't want to look at the code. You don't care about what's happening there. Instead, you're watching this agent output. I want to actually ask about that. But what I'm hearing here is the areas you are investing in, building in yourself, is at the front end, clarity around what it is. And I want to hear how you actually do that, what you do there. You have a really cool system there. And then there's the taste and judgment of knowing, is this the thing I want? It feels like those are the two sides now that are more and more important. And on the taste judgment side, you share this concept, something Guillermo Rauch shared in our conversation, this idea of exposure time, exposure hours, being exposed to great stuff. Here's a great user experience. Here's a great onboarding flow. Here's a great, I don't know, website. So I really like that advice. It's so actionable. Okay, I'm going to spend more time with stuff that's great to inform my taste and judgment. And then on the clarity piece, let's actually talk about that. What do you, what do you do there to be clearer with Lovable and other AI tools to help it build the right thing? This is the first mindset shift that I want to put into people's minds, right? If you just have a vague idea, let that be your first version of the project. Open Cursor, Lovable, whatever it is that you're using, and just input a brain dump prompt, right? Just talk into it. Lovable specifically, I don't know about the other tools, has a really cool voice function. You click it and just dictate the hell out of it and just press send, right? Don't even wait for it to finish. Open a new window. Again, Lovable.dev, and here you're like, okay, as I was brain-dumping, I think I found a good thread, right? I think things are getting clearer. So let me start another project now with more clarity, more deliberability. I know which features I want, which pages I want, and maybe I can even find a good reference. Maybe I can go on Moven, maybe I can go on Dribbble, maybe I can go wherever, get a good screenshot, get a good animation, and attach it because most of these tools accept files as a part of the input. So you have the second project starting. Now things are even more clear. Now you expose yourself to quality, and now you're like, well, what if I, what if I found a template that actually is already out there? Why reinvent the wheel? I'm building a platform that somebody else built. Why not expose AI to what quality looks like, right? So what I'll do is I'll go to and find a library, 21st Dem or A Dot Build or whatever places allow me not to export screenshots, another project, now with more clarity, more deliberability. I know which features I want, which pages I want, and maybe I can even find a good reference. Maybe I can go on Moven, maybe I can go on Dribbble, maybe I can go wherever, get a good screenshot, get a good animation, and attach it because most of these tools accept files as a part of the input. So you have the second project starting. Now things are even more clear. Now you expose yourself to quality, and now you're like, well, what if I found a template that actually is already out there? Why reinvent the wheel? I'm building a platform that somebody else built. Why not expose AI to what quality looks like, right? So what I'll do is I'll go and find a library, 21st dem or a dot build or whatever places which allow me not to export screenshots, but export code snippets. Because guess what? Even though English is the number one programming language, Lovable and all other tools still communicate in code the best. If you want to get pixel-perfect results, just give them code. It will interpret it better than your English or Spanish or whatever language that you use in these tools. So that's the third way. You're like, okay, now I'm even more deliberate. I'm not even going as wide as giving it vague concepts. I'm giving it code snippets. I want this exact design. I want this exact type of functionality. So that's your third project. And then by the time you do all of these three, you're already at a level of clarity that you wouldn't have if you just sat with an empty piece of paper or maybe chatting just with ChatGPT, but not taking action. I think taking action is so cheap these days and free, by the way. All the tools I mentioned have free plans. Most times you would be able to do this without spending any money at all, just by starting multiple projects because guess what? That doesn't also cost anything either or doesn't incur additional cost except for builder credits. You're going to get three, four, five, six different concepts that you can compare. As you're comparing them, clarity just keeps coming, and things get better and better to understand. And you're also solving for one big problem that you mentioned. You used the term AI slop, and I like it because a lot of people, when they say AI slop, they don't refer to beautifying the code, but beautifying the design, right? This process that I just mentioned actually gives you four or five different design options and, in the long run, saves you massive amounts of credits because a lot of people obsess over the concept of, oh, when I give them this hack, they're like, oh, but doesn't that cost more? I'm like, yes, up front, it may cost a little bit more. In the long run, if you really want to finish this project, you're actually saving hundreds of credits and maybe even hundreds of dollars, not to mention the amount of days, simply because you started from a point of better clarity and a better refinement process, right? So that's the first step of solving for clarity. There are more, right, which is the second layer, but I assume you may have some questions on this one. Questions, and also just wow, this is such a great, it shows you the power of having someone come into this world without an engineering background. This advice of just build it five times in parallel. You ask AI to try all kinds of stuff. This is not how someone that has been a software engineer or a PM or designer would approach stuff. So your advice here, which is so fun, is as you're getting started with a project, just run five different approaches at it to start. One is just brain dump. Here's what I'm thinking. Here's a general idea. Use Whisperflow or use the built-in mic. And then two is, okay, now I have a general idea. Let me try to type it out, actually thinking through the prompt. Three is, let me find a mock design somewhere online. And the sites you suggested were Mobbin and Dribble. Those are the two that you go to? Yeah, most times. Okay. And then the fourth, and these are all in parallel. It's great. Is find actual code template that looks similar to the thing you want to build, download the zip file basically, and attach it. Or is it just HTML and CSS? Is that kind of what? Anything. Anything you got. Cool. Here you go. Okay. And then cool. Here's the prompt here. Make me what I want. And what I love is there's two wins here. One is just it helps you clarify the idea as you see the tool build it. Like, oh no, that's not what I mean. Let me try it again. And then two is you pointed out you can pick the right direction so that you're not locked into your first design and first architecture. To your point, if you then spend all this time trying to fine-tune design and direction, it's like all these tokens are being lost. You could have just started over. This is so great. Someone may think, okay, of course, you're just getting us to spend all these Lovable tokens. This is what a Lovable person would tell me. But what I'm feeling is this is where you could save the most money because if you get it correct in the beginning, you save so much work trying to get it back to where you want it to go. A million percent. I'm actually saving people. I'm actually going against what I should be saying. If I was thinking about Lovable, I'd be like, no, no, just try to fix it in perpetuity. But that's not, we're not in the business of doing that. We're in the business of empowering anybody to build anything that they want. And then it's my personal mission that resonates with me because if there wasn't Lovable, I would have never built anything potentially in my life. And I don't think that that would have been a fun life to live. So I guarantee people, I've tested this framework with many people, and everybody is telling me the same thing. Eye-opener. So simple, yet unintuitive, as you said, even though for me, it's kind of, I don't know, as you said, I attribute it to a non-technical background. To me, that was the first thing that I would do. I just did it. I never thought about it, like, oh, I'm developing this amazing hack. I was just like, I'm waiting all this time for these agents to finish. I might as well start another project and another one and another one. And it's also a productivity hack. That's what people ask me, like, wow, how do you ship so many things? I'm like, I never build just one project at a time. I build five or six. I have six Lovable tabs, and I just switch between them. And that's the next hack that I want to talk about, if you allow me, which is the question in return is the obvious one, which is, how do you context switch? You talk about context so much, yet you keep switching between apps. How do you manage to do it and do it in a way that's productive and not produce bad code or bad product? And that's how I solve for that LLM problem, again, the Aladdin and the magic lamp and all that, which is, if there's a limited token window, how do I make it dynamic? And what I mean by that is this. If you just go and you prompt and you prompt and you prompt and you prompt, you realize that no matter what tool you use, the memory just isn't infinite, right? By the time you reach message number 10, 15, 20, 30, 40, snippets of early messages get lost in the translation because Agent is optimizing for speed, right? If it had to read the entire conversation and the entire stream of requests that you made, developing anything viable or large would be impossible because it's just consuming a lot of time and a lot of memory and a lot of tokens. So again, something that I just figured out very early on as I was building was like, okay, if it can't remember things, my job is to provide it with reference. So let me treat Lovable or any other tool as an engineer that I'm supposed to be providing perpetual context as the project goes. And you can do that in many ways, but the most efficient way that I found was like, I would do the four parallel builds. Let's continue off of that example. Very quickly, after you've built hundreds of projects like I did, you see the winner. The winner is so obvious. It's not even a competition. You maybe do one or two more prompts to calibrate it, and when you're like, okay, the winner is here, at that point, I either ask the tool that I'm using or I'll maybe, let's say, go to ChatGPT or whatever and ask the LLM to produce a series of PRDs. What PRDs are very early on, as I was building, I was like, okay, if it can't remember things, my job is to provide it with reference. So, let me treat Lovable or any other tool as an engineer that I'm supposed to be providing perpetual context as the project goes. You can do that in many ways, but the most efficient way that I found was I would do the four parallel builds. Let's continue off of that example. Very quickly, after you've built hundreds of projects like I did, you see the winner. The winner is so obvious. It's not even a competition. You maybe do one or two more prompts to calibrate it, and when you're like, okay, the winner is here, at that point I either ask the tool that I'm using or I'll maybe, let's say, go to ChatGPT or whatever and ask the LLM to produce a series of PRDs. What PRDs are, for again, people that are not familiar with the terms, they are project requirements documents or, for me, I call them sources of truth, right? What needs to be true for this project to be successful from a couple of perspectives? I usually build something that I call a master plan. It's a compass saying, here's what we're building, right? It's like talking to a human. I really treat Lovable like a human being. So it's like, this is what we're building. Then I build an implementation plan, which is, this is how we are going to build it, and this is the sequence, right? It's very important to me, again, going back to quality, taste, human nature. I need to define, because I'm still working with a system that is not emotionally intelligent yet, how I want the app to look and feel. So another PRD that I build is design guidelines. And then finally, something that just circles it all around, which is, okay, when we know how things look and when we know how we're building it, how does the user journey look, right? The user registers, and then what? And then when they register and do that first step, what's the second step and what's the third step and whatnot. So I build at least four PRDs, right? And then when these are built, I read them. That's the planning, chatting part. That's where I'll spend a lot of time now. When I nail down that first design, I'll spend an entire day, if I need to, just planning this part out, documentation and breaking things down, because that's how I'm setting the course. Everything's going to be dependent on this particular part of the process. When I'm done doing that, I build one final document, which I call either plan.md or tasks.md, and the .md part is markdown. I'm just using markdown format because I've learned that AI likes to read markdown. What that serves is a source of truth on actual tasks and subtasks that it will need to execute to get to the finish line, right? And then there's the final, final layer, which is, depending on what tool you use, Cloud Code or Cursor have what's known as rules.md or agent.md. What you're doing with rules or agent files is you're letting the agent know how you want it to behave and what it should focus on in the long run so that you don't have to repeat yourself with every prompt, right? So in Lovable, there's a separate menu for that in your project settings where you can define project knowledge, and usually what I'll say is, hey, read all the files before you do anything. Don't do anything before you read all the PRDs, read tasks.md to see which task is next, then execute on that next set of tasks, and when you're done, tell me what you did and how I should test it. And that's where that conversation about I religiously read the agent output comes into play. I gave the agent everything, all the tools and resources that it needs to succeed. I gave it the rules, I gave it the docs, I told it what to do with them, and at that point I'm just sitting and reading. I don't prompt anymore. From that point on, I can switch as many windows as I like. My prompts have become proceed with the next task. I don't need the context. I outsource that and delegate that back to the agent. The agent needs context, and I need to make sure that it's dynamic. I need to make sure that I'm regularly updating the documents from time to time so that we shift that token window it uses and how it uses it over time, but I'm not prompting, I'm not interrupting the flow. Yes, I'll go in, test, maybe put a prompt in here or there, but that's how I can build five projects simultaneously and never lose the productivity part, which is, again, as I said, I do this today manually. Call me to talk three months from now. An agent will do this for me. I'll be out of a job pretty much. That's why I don't optimize for this skill at all. I'm using it today to bypass the shortcomings of human nature and LLMs, but I'm optimizing 100% of my time today on good judgment, clarity, quality, taste, good copy, good fonts. People don't talk about fonts at all that work with AI. They're 60% in my mind, maybe even more, in how your output is going to look. That's my obsession. I don't obsess over these things that I'm talking about today because I know what's coming. The agents are going to get better. The models are going to get better. They're not going to need me to extend the context. They're going to do it themselves. So for me, the skill that I optimize for is the one that requires better decision-making rather than better output or better alignment. Oh my God. There's so much here. This is so awesome. Okay. So essentially what's happening here is you start a project, try a bunch of stuff, pick a direction that feels most correct. And once you have a set direction, you spend essentially a day not building but working with this AI agent to plan. And then, well, I want to talk about that. And once you have the plan, then it's amazing that you can do stuff like this with what some people may feel are not sophisticated tools that can build incredibly powerful things. You can do a lot of this with tools like Lovable, have plans and rules and MD files. A lot of people may not think that, may not know that. And so the idea is, okay, spend all this time planning because, again, that'll save you a lot of time down the road. And then only once you have a plan, you get it going. And a key part of this three wishes rule is really important. The reason you're doing this, in large part, beyond just being really clear about the plan, is this idea of one task at a time keeps the agent's context window small so that it doesn't lose track of where it's at. That part seems important, right? It's like, do this thing and then, okay, cool. Now do the next thing. Right? Yes. Yes. Because, again, let's say you didn't do this. Let's talk about you ignoring this. You're like, I just want to vibe my way. Okay, great. No problem. You work, you work, you work. At one point, something breaks, right? You haven't documented anything. There are no reference points. You report a problem. You're not referencing files or architecture at all. You're just describing the issue. Here's what's going to happen. Any tool, Lovable or Cursor or whatever tool you talk about, is going to do this. It's going to be like, okay, let me start investigating. And then your code base gets bigger and bigger and bigger and bigger and bigger. When you first start, you have 20 files. It can read 20 files. But what happens when you have, I'm building a project right now that has 60, 70 edge functions, right? What happens then when I say this broke and there's no reference for which edge function does what? Guess what? Lovable is going to read all of those, and it's going to consume 80% of the token allocation on reading to get clarity, leaving only the final 20% for thinking and executing. What I'm guessing, and I can't prove this, and all the experts in the comments may say that I'm wrong, but this is my best guess as a non-educated person. These tools are very obedient and very agreeable. They're going to lie to you. They're going to tell you that they fixed the problem even though they didn't. They're just going to try to make you feel happy and say, yes, I found what the problem is and I fixed it. A lot of times when they don't, people blame the machine, and to an extent, I will say that's true. It's your fault, my friend. You did not provide any clarity or context to this tool. You just used its raw power which edge function does what? Guess what? Loddable is going to read all of those, and it's going to consume 80% of the token allocation on reading to get clarity, leaving only the final 20% for thinking and executing. What I'm guessing, and I can't prove this, and all the experts in the comments may say that I'm wrong, but this is my best guess as a non-educated person. These tools are very obedient and very agreeable. They're going to lie to you. They're going to tell you that they fixed the problem even though they didn't. They're just going to try to make you feel happy and say, yes, I found what the problem is and I fixed it. A lot of times, when they don't, people blame the machine, and to an extent, I will say that's true. It's your fault, my friend. You did not provide any clarity or context to this tool. You just used its raw power and dug a deeper hole with your spinning your wheels into the mud, right? Obviously, I think we're heading into a world where AI is more honest than obedient and says, hey, I only partially fixed this. You did not give me enough context, right? The bigger mistake that people make then is they trust the tool fixed it. They test, they see it, then they get mad at it, start cursing and yelling, as we say, and then it gets even worse because, guess what? Another bad trait of AI is it's best not to hurt your feelings and never say, you're the dumb one. It says, no, I'm the dumb one. So it focuses, in the next request, instead of focusing on reading, it spends another 30% of tokens trying to come up with an apology, right? Again, I'm not educated, but if you ever read a stream of ChatGPT's thinking in thinking models, you see exactly what I mean. When I insult it, I see that the first message is, okay, the user is mad, so I need to think of ways how to reduce their anxiety or whatever. I'm like, oh man, I just fell for the worst trick in the book. I made it spend the most scarce resource, which is those tokens, on thinking how it should address my anxiety versus focusing on the actual problem. So my advice for people is yes, vibe your way for fun and vibe your way while you're prototyping, because that's the exploration part. I love that part. But when exploration is done, please, please, please use referencing, documentation, use all the agent files that you can because that token allocation is so scarce. It's going to get expanded over time. Things are going to get cheaper, faster, but right now it's still so valuable and precious. You really need to make sure that they are allocated in the right direction. This is hilarious. I think the genie metaphor is so good here. Just thinking about this genie is you're trying to be clear about what it is you want. And if you're just vibe wishing, it'll do the wrong thing. So the advice here is give it as much context about what you want it to do as possible. And these files we'll talk about right after this. But the idea here is just laser show the point, the laser where you want it to fix the problem. Don't just assume it'll go figure it out, because it will, and it'll try really hard to, and it'll waste all your tokens. It'll fill the context window. And I remember at one point you mentioned before this recording that because it starts to run out of space in the context window, the solution ends up, it doesn't actually work that hard on figuring it out in the end because it spent all this energy on reading and thinking, and then it's like, okay, here, at the last second, here's a solution. I think it just picks the first thing it thinks is broken. Again, this is me, completely uneducated, coming into the conversation and just thinking out loud. That's just my gut feeling and the way I think logically about it, which is, hey, if it consumes most of its window and knows that it's running out of it, maybe it's aware that it's running out, maybe it isn't, but either way, I've had the experience, anecdotally, where my request is unclear. I feel it takes the easiest fix in the book, just the easiest, versus the other way around, where I'm spending so much time finding the right file, referencing that file, really putting in the effort of hand-holding it in dark, maybe giving it a flashlight, and then saying, here's the problem. I think that this is the problematic file. And then it's like, oh yeah, you're right, and now I'm going to actually fix it over and over and over. And I've seen that because, again, all I do is read the output. Agent makes me learn how to use it by... so people read, I don't know what people read, but all I read is the output. I don't read the code until later down the road because I know that it can do that much better than I can. Again, I feel, if there's a good quote I've read, I apologize to the author because I can't attribute it off the top of my head, but it's like the ceiling on the AI isn't the model intelligence, it's what the model sees before it acts, right? So that's the ceiling right now. What are you exposing your agent to? We talk about exposure time for humans. What are you exposing your agent to as well? That is as important, if not even more important, before it makes code edits. Yeah, coming back to these files, I think this is really important. So let's think about just what's the MVP for someone that wants to do this better. You listed all these files, these MD files, essentially, that you're building over the course of a day before you start actually building the thing. You had design guidelines, the user journey, tasks, agents.md, rules.md. Say you wanted to just move one step forward and be better at this stuff. What are the files you'd create, and then what do they roughly look like? What's inside these files? Yeah, so the master plan is the first one, which is a 10,000-foot overview, right? It really high-level explains the intent that I have with this app. And this is masterplan.md? Is that what you call it? Yes. Yeah, masterplan.md. And it's really just like, hey, this is why I'm doing this, this is who I'm doing it for, this is how I want them to feel. And a lot of times in the master plan, I will reference the other PRDs. I'll be like, the design needs to feel modern and slick, but for exact parameters, consult and read designguidelines.md, right? So I'm using the master plan as this high-level overview, right, to get the agent into, oh okay, yeah, we are building XYZ, right? Then there's the implementation plan because there needs to be some order. If you just dump stuff on top of each other without any order, you're never going to get to the finish line. And this is tasks.md? Is that what you call this? No, that's the implementation plan. Implementation plan, yeah. Okay. And implementation plan is kind of in service of the future tasks.md, if that... all of these files are in service of building tasks.md. When you build tasks.md, then the rest is almost irrelevant. It's just a basis for you to build tasks to execute, right? The implementation plan is kind of the first layer, which is again a higher-level overview. It doesn't go into the depth of how to get there. It just goes into the explaining of like, oh well, if we're building this, I think we should start with the backend, and we should start with tables, and then later authentication, and then after that we're going to bring in the API, and then after that we're going to do this. It's again just... think of it as having... I'm an ideas guy. I'm sitting with a technical guy. It's me and you, we're building our startup. I know you're a software engineer by background, and I'm telling you my idea. I'm giving you the master plan, and you come back to me and you're like, okay, if you want to do this, it's doable. Here's how I would order it. You don't have a roadmap. You didn't open your Linear and start writing features and RFCs and whatever. You're just high-level talking about the order of things. And then me and you again, as two co-founders, we talk and say, okay well, if we agree on this, how should this look? How should this feel, right? Let's describe it high-level, but now because I use AI, I can go a little bit deeper, and that's where I like to see Lovable or any other tool, ChatGPT is good at it. I even built custom GPT. So if people want to start somewhere before they even get into any tool, they can go to ChatGPT store for GPTs and just type Lovable Base Prompt Generator or Lovable PRD Generator and find those that I built and just brain dump in them and then get these files as output, right? So I like to see some elements of CSS in design guidelines because, with design, it's a little bit tricky. AI is sometimes over-creative, so that's where I'm doing a little bit more technical steering, right? And then finally, it's just the user journeys, just like if we know how things look, if we know how they feel, if we know what we're building high-level, like high-level, just very high-level. how should this feel? Right. Let's describe it high level, but now because I use AI, I can go a little bit deeper, and that's where I like to see Lovable or any other tool. ChatGPT is good at it. I even built my custom GPT, so if people want to start somewhere before they even get into any tool, they can go to ChatGPT Store for GPTs and just type Lovable Base Prompt Generator or Lovable PRD Generator and find those that I built, and just brain dump into them and then get these files as output, right? I like to see some elements of CSS in design guidelines because with design, it's a little bit tricky. AI is sometimes over-creative, so that's where I'm doing a little bit more technical steering, right? And then finally, it's just the user journeys. If we know how things look, if we know how they feel, if we know what we're building, high level, very high level again, how do people navigate? What are some of the features in there? And stuff like that. And then tasks, that MD gets into the nitty-gritty, like, oh, if you want these user journeys and you want the backend built first, here's a set of tasks that I need to do. It just takes that as an input. I'm just making the tool do the gritty work that humans used to spend so much time on. I feel like with these tools, we're all becoming product managers on steroids. We're leveraging AI, but good product managers, I think, are not compensated for writing good PRDs. They're compensated, again, for good judgment, right? Somebody else can do the writing. You, as somebody who directs and builds this product, product, you need to know, again, what's going to be useful, what's going to be tasteful, what's going to be something that actually moves the needle. I will say one thing, though. Just because I put so much emphasis on, oh, you need to acquire taste, that doesn't mean you shouldn't build. You get better at this by building, actually. So everybody listening to this should literally go and build something today. One, two, three, four, five projects. Test all these tools, because that's how you get to clarity, not just by reading, but also by doing as well. Here’s a puzzle for you. What do OpenAI, Cursor, Perplexity, Vercel, Plaid, and hundreds of other winning companies have in common? The answer is they’re all powered by today’s sponsor, WorkOS. If you’re building software for enterprises, you’ve probably felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by big customers. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Whether you’re a seed-stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise-ready and unlocking growth. They’re essentially Stripe for enterprise features. Visit workos.com to get started, or just hit up their Slack support, where they have real engineers in there who answer your questions super fast. WorkOS allows you to build like the best with delightful APIs, comprehensive docs, and a smooth developer experience. Go to workos.com to make your app enterprise-ready today. I’m imagining people hearing this may start to feel like this is so much work. I just have to sit here and create all these rules and figure out all these little details. In one sense it is; in another sense, this is like you spend a few hours, maybe a day, planning, and then you have AI build this thing that would have taken somebody weeks, months, right? The amount of investment to achieve this thing is absurd ROI. This also shows you just what professional vibe coding looks like. Everyone imagines vibe coding as I’m just sitting here, type stuff, go do this. Good. If you want to actually build something really great that moves the needle, as you said, that solves people’s real problems, that lasts, that scales, this is how you do it if you really want to do this as a job, and also if you want to build things that are really great. Yeah, and don’t get me wrong. There’s obviously a ton of value in prototyping. There are a lot of people maybe watching this that are like, okay, I want to use Lovable at work, but I can’t, or whatever. There are different reasons. Maybe you’re in healthcare or finance, or there’s something regulatory that just prevents you from pushing to production. Building for the sake of prototyping is one of the best use cases. Our motto for 2025 was “demo, don’t memo,” which is like instead of writing all these documents and talking and sitting in meetings with your engineers trying to get your vision as a marketer or a sales guy in the office across, go into Lovable and build the prototype in 30 minutes and just hand it over. I have a real job that I held before Lovable. That’s exactly what happened. This time last year, I needed something built, enterprise-grade, really, and Lovable and I were not there yet to build it at that point, but I had a team of engineers that I worked with. I built the prototype in four hours, and they actually were able to replicate it six to seven months later into production, connecting all the pipes and everything. But if I had to describe it, I would say it would take me at least a week or two just to get the words out there. I just sat and built it in four hours, and that’s Lovable January last year. Lovable today, January 2026, is ages ahead with functionalities. It’s so much better. It’s not even a contest, right? I think now we’re at a stage where, for instance, there’s, I’d say at least to the best of my knowledge, at least half of S&P 500 companies have people working in them that are using Lovable to some extent, right? And we have a lot of enterprise companies that are actually on enterprise plans with Lovable that are creating super meaningful projects. I’m not going to name names, but leading rideshare companies of the world, leading telecommunications companies of the world, leading companies of the world in many, many aspects, healthcare, finance, are actively, with their teams, using Lovable. And it’s always the same feedback, which is yes, we may not be able to push to prod, but our marketers are no longer waiting for engineers. People in go-to-market or sales or HR or whatever roles are now just confidently building internal stuff for us to manage our expenses or manage employee onboarding. There are so many use cases like that where you’re seeing Lovable and other tools, for that matter, being used to push things into production. To help people do this workflow that you’re describing with all these MD files, do you think you could share, after we record this, just templates, simple templates of what these files look like for people just to look at and copy? I would literally go to ChatGPT, as I said, and brain dump into it. Just type Lovable GPT, Lovable PRD Generator. You’ll see my name there, right, and that I’m the author. Go in, brain dump. It will ask you a couple of questions to get clarity and just produce four files for you, and you can just go ahead and upload those. Amazing. Cool. We’ll link to that. So it’s not just, here’s a bunch of files. Go talk to this thing. It’ll generate the right files for you, and then you plug that into Lovable or other tools. Yeah, it’s trained to think like I do. Oh, amazing. Okay, that is perfect. By the way, I want to talk about how you unblock yourself because there’s a whole other series of tips you have there, but I just want to reflect on it. It’s so interesting how, one, you’re kind of, from first principles, learning how to build product as a PM, as an engineer, as a designer, and you’re kind of figuring out a workflow where AI is helping fill in all the gaps that you don’t have as an engineer, as a PM, helping you craft PRDs and design. So I think that’s so interesting, that these functions still work and are necessary. Now it’s you and AI helping create all this, basically this triad that’s always existed: product manager, engineering, and design. And something I’ve always thought is that there’s this question of which background will be most valuable in this future. Is it a PM? Is it an engineer? Is it a designer? My mind has always been the PM function. Their job is to clarify, figure out what to build, clarify what to build, be really clear about the requirements, figure out what success looks like. It feels like that’s where the skill is most needed. There’s also a design component of make this look awesome, and I feel like the value of being really good at design and taste and judgment is only going to go up. Before we get to things you’ve learned about unblocking yourself, because a lot of times things don’t go in the right direction, there’s a bug, without being an engineer, what do you do? Before we get there, is there anything else you wanted to share around tips for being successful if we measure success in the right terms again? AI, as you pointed out, regardless of your background, is an amplifier. So if you don’t know what you’re doing, you’re just going to produce garbage faster. One thing, again, I just want to double down on is in the old world, good enough was good enough, right? Because even producing good enough was not easy, right? Ten years, 15 years ago, just producing was more than plenty, more. skill is most needed. There's also a design component of make this look awesome, and I feel like that's going to be emerging. The value of being really good at design, taste, and judgment is only going to go up. Before we get to things you've learned about unblocking yourself, because a lot of times things don't go in the right direction, there's a bug. Without being an engineer, what do you do? Before we get there, is there anything else you wanted to share around tips for being successful, if we measure success in the right terms again? AI, as you pointed out, regardless of your background, is an amplifier. If you don't know what you're doing, you're just going to produce garbage faster. One thing, again, I just want to double down on is, in the old world, good enough was good enough, right? Because even producing good enough was not easy, right? 10 years, 15 years ago, just producing was more than plenty, more than good enough. You built a SaaS, who cares how it looks? It works, it does stuff. Oh my God, I'm so much more productive today. If good enough was here, let's visualize it for people, if this was pretty bad, could be better, mediocre, good enough, world class, if this was the gap between good enough and world class, well, guess what? The gap is now this, because everybody produces good enough with AI. Absolutely everyone does it. So now learning and optimizing for how do I produce world class and magic, is that the key lesson to take away today? As you pointed out, I think PMs are the winners of AI today because they bring clarity. If I was a betting man, as they say, I'd bet that the next class that wins are designers because we're training these tools to be more clear, to be better, to make better technical decisions. I don't think we will train them just yet to make better emotional decisions, and I think design is all about emotion. That's where the level of the skill-up needs to come. That's the biggest level-up. If you asked me, what is the main thing you figured out when you joined Lovable? What's the biggest personal skill-up, let's say? It's working with Felix and Abby, all of the people that are designers. Really, what moved and shifted the needle for me, I'm like, oh, so this is how world class looks, and this is what it takes, right? You always use the analogy of, I wanted to steal one of their designs and bring it into my Lovable project, so I went into Figma and I was like, let me just take this background and just put it in there. I went in and realized that what could be interpreted as a pretty simple, or rather simple, gradient took 50 different layers to produce. So I clicked on that component. I was like, oh my God, this is not three colors, this is 50 colors. And not just 50 colors, 50 colors with different gradients of levels of opacity. So I was like, oh, okay, well, that's the big disconnect that I've had all along. Again, if I'm answering your question directly of, okay, what are some of the other tricks, what are some of the other things? Design. Just expose yourself to exquisite designs. Follow Felix from Lovable. He has an amazing newsletter. Learn how to prompt for good design. Learn about design styles. I didn't know what Bauhaus meant, or glass morphism, had no idea. So I built an app as well for that in Lovable. I needed to build an app to learn these styles, so now it's public. Anybody can see it. It's like some-ui-style.lovable.app. I don't know what it is. It has 18 different styles and prompts to replicate them. Learn what good design means, learn all the design styles, learn how to prompt to get them, is probably what I would optimize for at this stage. While we're on this topic, what's your sense of engineering as a function? Do you feel like there will be a future where software engineers are still a thing? Do you feel like that goes away based on your experience? It never goes away. We will need elite engineering more than ever. Let me tell you this: in a world where everybody builds and everybody's building everything, who's doing the maintenance, right? Maintaining code bases, scaling code bases, maintaining projects, they're still going to be a thing, definitely. And obviously AI is going to be good at this, but again, that requires a different level of skills, right? It's one skill to build something. It's a completely different set of skills to expand it, extend it, and maintain it. Not to mention that in a world where everybody's building, infrastructure suffers, right? We all know and experience Cloudflare went down two or three times in the last two or three months. The whole internet goes down. Elite engineers are the ones fixing this. Lovable experiences massive amounts of influx of new users, infrastructure there suffers. Elite engineers are the ones building the infrastructure to hold the fort, right? So I think we're going to need a lot of people with really good skills of, hey, who actually builds the world that needs to support billions of builders now? Because everybody's going to want to learn how to build stuff. How do we teach them? How do we maintain everything that they need, the hostings, the security, the email, the connectors, the APIs, the whatnot? So I think there's going to be room for it. But I'm also on the boat of people, if I had an 18-year-old brother and he asked me what should I do, I would tell him, hey, go become a plumber. Don't go and get a CS degree. Learn a good trade, because the new generation of millionaires in the US are actually electricians and plumbers and whatnot, right? So it's a balancing act, I'd say. I don't know. I do still think that good engineers with good sense of understanding where the future is going are always going to be needed and scarce. Such an interesting question. I think, to your point, there's definitely going to be people needed to keep building the machines that power all this stuff. Well, we need engineers to build the actual products. The application layer, that's the question. Is everyone going to be like you? Is every designer just going to be all we need? Everybody's going to become an engineer. Let's speak to that end. I feel like I'm a rapid engineer. I'll refer to myself as a rapid engineer in a year from now because vibe coding is just coding in 12 months from now. Even today, we spoke about this before, how many elite engineers are publicly admitting they're no longer hand coding or manually coding, whatever we're going to call it. AI writes all the code. I use the analogy here of coding is going to be like calligraphy. You writing code is going to be the equivalent of fine printing on a canvas, and people are like, oh my God, you wrote that code, that's so amazing. It's going to be so rare that it's going to become an art, right? It's going to be commoditized completely, like it already is in a sense. Most elite vibe coders rely on AI. Again, it's an amplifier, right? So I think everybody becomes an engineer in the world of the future. A designer, a PM, everybody is an AI assistant engineer, or an LLM engineer, or a vibe coder. The term is irrelevant. We're all using LLMs for raw output based on good judgment or bad judgment. Essentially, these Venn diagrams of engineer, designer, PM, they used to be very separate. Now they're converging, and people with deeper PM, engineering, design backgrounds can all do the same thing, essentially. All the roles are converging. What a time to be alive. It's so hard to predict exactly how this all goes, but it's fun to pontificate. I want to get back to when you get blocked. Speaking of elite engineers, in reality, you're still writing code using these tools. Sometimes things go wrong, bugs are introduced, there's a weird database thing, there's some network issue. What do you do when you get stuck? Do you have a workflow you go through, unblocking yourself? Yes, great question, and absolutely true. No matter how good of a plan you have in place, you're going to run into problems eventually. I have a small little framework that I call four by four. Again, analogies, right? Four by four, if you have it on your car, you're going to get yourself out of the mud much easier than the other way around. In that sense, four different ways to debug. Attempt one of each only once, and I'll explain why in the end. First one is, again, every tool is different. I'll reference Lovable's workflow, which is when something breaks, Lovable's agent is smart enough to say, hey, I made a mistake. It will label that message in orange and have this little button, usually, which is called try to fix. So the agent basically admits it made a mistake. You click on a button, and most times, when it's a smaller issue, it corrects the course, fixes it, no problem, right? Now, there are situations, obviously, when the problem is a little bit deeper than that, right? You click to try to fix, but the problem persists. And sometimes even the problem persists, but Lovable's agent is unaware that it persisted, so there's no more try to fix button. your car, you're going to get yourself out of the mud much easier than the other way around. In that sense, four different ways to debug: attempt one of each only once, and I'll explain why in the end. First one is, again, every tool is different. I'll reference Lovable's workflow, which is when something breaks, Lovable's agent is smart enough to say, "Hey, I made a mistake." It will label that message in orange and have this little button usually, which is called "Try to fix." So your agent basically admits it made a mistake, you click on a button, and most times, when it's a smaller issue, it corrects the course, fixes it, no problem. Now, there are situations, obviously, when the problem is a little bit deeper than that. You click "Try to fix," but the problem persists. Sometimes even the problem persists, but Lovable's agent is unaware that it persisted, so there's no more "Try to fix" button. Lovable thinks everything's working, but in reality it isn't. The culprit there is usually you're using a third-party integration. You did not give enough context to Lovable where, what to observe and what to see, so it can't see that the problem exists, because Lovable, Cursor, Cloud Code, you name it, all these tools are good enough today to fix any problem they're aware of. Again, awareness is the key here. When they're unaware of it, there comes the second part, which is, okay, I need to bring the awareness layer. What I do there is I go and very simply open the preview sandbox dev environment of my app, whatever, try to run the function that's broken, right-click, read the console log. Every browser allows you to just go and read the console log, and a lot of times it will record stuff. If it doesn't, you can prompt any tool and say, "Hey, I don't think you're seeing the problem, so instead of me yelling at you, let's find it together. I think it's a problem with XYZ. I want you to write console logs in relevant files so that we can monitor every step along the way. Let's just bring awareness layer into the equation." It writes the console logs, you rerun it, guess what, now you have a full history of everything that was happening. You copy that, you paste it inside your chat. Ninety-nine percent of the time, that's enough. That's already enough. It was like, "Okay, got it, found it, fixed it." But then there are situations when even that's not sufficient. It's like, okay, I need to go even deeper, and that's where code reviews and evaluations come into play. My go-to tool today for that is Codex, OpenAI. What I do is, any build that I do, I will export it to GitHub. Lovable allows you to own your code, Cursor as well. All of these tools allow you to have a copy of the code that you can export to GitHub and then import it into wherever you want to. So I use Codex since beta, import it in there, and then I'm using an external tool. So in the first try, if you remember, I use the tool and I was total vibes, I'm relying on the tool. In the second try, I use myself as the awareness facilitator. In the third one, I'm using an external tool as a facilitator, which is, I'll either connect to Codex and chat with Codex to then fix the problem in Lovable. I don't allow Codex to make code changes for me. A lot of people will say, why don't you? It's a good model. I just don't know its agent well enough. I don't want to go and use a tool that I don't know how to steer, so I use it only for diagnostic purposes. I'll also do it manually. It's an old workflow that I had before Codex and before Cloud Code, which is there's a tool called Repo Mix, which allows you to compress your entire code base into a single file. You download it, and then I upload it to Claude, just regular Claude, or ChatGPT, and I'm like, "This is what I'm building, read it, and this is the problem that I have. These are the console logs." Again, it's almost like having an external consultant at that point. You're hiring help elsewhere because your team just can't handle it. Then the fourth one is usually the best one, because one of the time when there are problems, it's my fault. No matter how your ego has been, guys that are watching this, it's your fault, trust me. You had a bad prompt, you premised your request in the wrong way. You just don't want to admit it, or you can't remember that you did, but it's your fault. So again, in Lovable and in all these other tools, you can revert back. There's version control built into Lovable, Cursor, Cloud Code. You go and say, "Okay, I tried these three things. I'm just going to take three steps back, and I'm going to think about my prompt a little bit more, take a couple breaths, go for a walk, have some coffee, come back with a clear mind, and try again." Because guess what, AI is just writing code very fast, and sometimes it stumbles on a very small rock, and it only happens then and never again. So you just have to make the same request again, and usually that just fixes the problem. It's just a snag, it's a syntax error, it's something minute. Then I do the final thing, which is this, and this is the key one, actually. When the problem gets fixed, I go into the chat mode and I ask Lolo, I say, "Okay, I needed to do four different things to fix this. How can you help me learn how to prompt you better so that next time I have a problem, we do it in one go?" Ninety-nine percent of the time, I get such a great answer that I don't have the problem of not knowing what to do next time. Again, we all need to be aware and realistic. These tools are so good at doing things the right way if they are used the right way. It's always our fault. I say 90, but honestly it's 100, our fault, because they're good enough. It's just that I'm not dynamically shifting token allocation, I didn't reference the right file, I didn't say it the right way. For me as a non-designer, I don't know any of the terminology, none of the headings and whatnot, and I still don't know it to this day. So when I struggle with prompts, a lot of times I use chat mode to help me craft a good prompt. Anybody can do this too. If you are just stuck, it's 10 p.m. and you don't know what to ask, switch to chat mode, brain dump, and be like, "Help me draft a better prompt. Help me prompt you better," and let the tool effectively prompt itself. A lot of times you're going to solve your problems by not introducing them at all with bad inputs. Oh my God, everything you share is so interesting. I want to keep digging. Just to reflect back the sequence, and then I want to follow up with another question, the sequence you go through when you get stuck, which is going to happen to everyone: one is just ask the tool to try to fix it, and oftentimes it's telling you something is wrong, can I fix it for you, and you're like, please fix it. Sometimes that'll work. Two is work on adding more debugging messages to the console log, and this advice I love, of just ask it to add more debugging lines to its own console log to help see what's going on, and then you can ask it, okay, now that you're looking, look at all the output of your console log, see if you can help find the problem. Then step three is go to Codex, which is so funny, and I hear this a lot, that Codex is the most elite engineer as an AI. Karpathy tweeted this once, that we had the head of Codex on the podcast too, by the way, that he's like, anytime I have the most gnarly bug, I just go to Codex, let it run for half an hour, and it solves it unlike any other tool out there. So it makes sense that that's where you go. So the idea here is you point Codex to your code, you show it all the console output logs, tell it what the problem is, and just have it go figure it out. Sweet. Then this final step is so great, and this is where I want to go, which you use this as a learning opportunity so that next time you solve the problem more quickly or avoid it completely. So what you do there is you ask the agent, "Okay, here's what happened. What can I do? What could I have said? How could I have prompted you better to have gotten this immediately solved?" Yeah, and then even deeper than that is once you go through this conversation, you're like, okay, let me eliminate myself again completely out of the equation, because I won't remember to prompt you better two days from now. Put this into rules. Put what we just learned into rules.md, because I'm making you read the rules every time anyways, so you might as well just record it there. So I'm not going to prompt you better, you're just going to learn that I'm stupid and you're going to prompt yourself better. Again, just eliminate yourself and move the context. You solve 99% of the problems with AI today. So the idea here is help it build its own brain and rules and way of thinking based on problems you're into. So great. Okay, so I want to come back to this point you've made a couple times, which is so interesting, this idea that you watch the output of the agent to learn what is going on. There's something I've seen other people, Ben Tossell, who I think is a factory now. conversation you're okay let me eliminate myself again completely out of the equation because I won't remember to prompt you better two days from now. Put this into rules. Put this, what we just learned, into rules.md because I'm making you read the rules every time anyway, so you might as well just record it there. I'm not going to prompt you better. You're just going to learn that I'm stupid, and you're going to prompt yourself better, right? Again, just eliminate yourself and move the context. You solve 99 of the problems with AI today. So the idea here is help it build its own brain and rules and way of thinking based on problems you're into. So great. Okay, so I want to come back to this point you've made a couple times, which is so interesting: this idea that you watch the output of the agent to learn what is going on. There's something I've seen other people, Ben Tossle, who I think is at Factory now, share recently. He's also basically vibe coding all the time. He was brilliant at no-code tools before, and now he's all about vibe coding. He shared basically that he's learning how coding works and learning how systems work by watching the agent output. This connects to something Michael Terrell, the CEO of Cursor, shared when he was on the podcast. He had this vision of Cursor becoming basically what comes after code. What's the layer that we are adding on top of code where people don't need to worry about code anymore? At that point, it was a year ago that we chatted, and it feels like this is the layer. It's the agent conversation of what it is, what it's thinking, and then what you tell it back. So essentially, it's English in a conversation. It's not even pseudocode. It's interesting, but that's where it feels like things are heading. The layer over code is just its thinking and your conversation with it. Yeah, yeah, exactly. Again, in a way, I really optimize for good judgment, and part of good judgment comes from learning how these tools work. You need to know what's possible. We talked about it, and I know I may sound contradictory sometimes, right? But it's because, as you said, it's so interesting, the world we live in, that things contradict each other. It's an advantage not to know what's possible, but then at the same time, you cannot be completely oblivious to something that's a factual thing. So let me talk about a failure of mine that came from being delusional. Back in the day, when OpenAI released image generation natively in the app, so you could go to ChatGPT and be like, generate an image of XYZ, the whole world exploded. That was the biggest thing ever. Obviously, the first thing that comes to my mind is I want to build a Lovable app. I just want to build a wrapper, and I want to build image gen with Lovable, without thinking that OpenAI did not release an API for that just yet. So I spent at least a week trying to brute-force my way into making this work instead of just waiting another week, because a week later they had an API, and I built this app in 30 seconds. The problem was that I tried to do it when it was impossible, impossible. So I think, again, it's just a matter of really learning what's possible through communicating with the agent. Replit and Lovable and all the other tools are agentic now, which means they don't just write code. They can browse the web, they can read files, they have reasoning and thinking capabilities. So that's why I'm so invested in that conversation, because a lot of times it will tell me, hey, what you're trying to do is just undoable at the moment because of XYZ. So I always use those as a learning opportunity, and I just level up most by being in chat mode for planning and learning purposes because it develops your clarity, your judgment capabilities, rather than coding capabilities. Yeah, the other point you made here that I think is really important is that over time these tools will do more and more of what you do manually. I've heard this from other people that are doing this full-time. Basically, vibe coding is just they had all these workflows, all these files, and then Cursor adds them, Lovable adds them, and yeah, it's sad. Oh shoot, I had this cool workflow, but on the other hand, it's like, okay, now it's just doing all these things. A year ago, if we had an interview, your mind would be blown. Stuff that I had to do as workarounds to address shortcomings, I built a very successful course on that with Starter Story. For a year, people were like, oh my god, you're the only guy in the world that knows this secret. Now Lovable natively addresses 99 of it. I can almost say most of the stuff that I was teaching people was like—I have a YouTube channel, a little bit appreciated—but there's a seven-day learn-how-to-vibe-code-with-Lovable series that I did in March, completely obsolete. None of it is true. None of it is a problem anymore. All the things that I was like, oh, well, this is missing and that is missing, it's not missing anymore. It's natively in the product. You don't have to work your way around it. It just works, right? So that's why, as I say, it's the horse analogy. I don't know if you've heard of it. A lot of people are tweeting about it, which is like, we started building the steam machine in the 1700s, right? It took us about 200 years to build it. When engines got built and cars were put on the roads, I think that 90 of the horse population got eradicated in the US within 20 years. The person that tweeted this works at Claude Code, right? So he was like, now when I translate it into AI, I was hired to do a job, technical job, technical writer, whatever. I became obsolete six months later. Humans did not get the 20 years that horses did. The guy that was hired to do a thing is like, six months later, I need to reinvent my role. I need to evolve it into something else, right? So I think there's just an evolution that's coming really, really fast. A lot of people are scared, and I'm just super excited because don't you see our roles are finally going in a direction where we're outsourcing what we hated doing anyway, right? Sitting in meetings, taking notes, doing spreadsheets. Maybe there are people that like that, but most people don't. We're just getting into a place where we're rewarded for what really matters: clarity, judgment, thinking. We're actually going to be paid to think longer and ponder longer because the longer an idea simmers and gets broken down, the better, because building it is going to be instant, right? It's going to be like this. It's just a matter of you having so much clarity around it because, guess what, if a tool is super powerful and you give it a wrong input, the output's going to suck as well. That's why I've never become good enough at Claude Code, I feel, because I don't start my projects with enough clarity, and the tool is so powerful that I just get misdirected completely from the get-go, and I was like, oh shoot, this is not what I wanted to do. So that's why I still see myself being good at using tools that are a little bit on the exploratory prototyping path more than on the path that elite engineers will use, for example. I love your optimism and excitement about this stuff. I think for a lot of people, say current software engineers, PMs, designers, there's a lot of fear about the future of their careers. Are they going to be relevant? Will my software engineering skills disappear? So to follow this thread a little bit, if you were to give someone advice on which skills you think will be most valuable, or where AI will take on more and more this kind of momentum you're seeing of where AI is filling in more and more gaps, what would your advice be of what you think people should focus on? What will continue to be valuable in the future? Yeah, emotional intelligence for sure. Just understanding human nature, real-life stuff. I think we're all going to get so tired of everything fake: fake images, fake posts, fake profiles, fake this, fake that, fake videos. Everything is becoming fake and AI-generated. I think humans, naturally, are going to want to do live stuff more. So anything human-to-human is going to be a big thing to skill up on. Understand the dynamics. Anything regarding math, if it's a math problem, I think Peter Thiel said it recently, people that just do math stuff, AI is going to come for you. Anything that's very deterministic, meaning X input equals Y output, and the line is pretty clear, AI has got you eaten for lunch, right? But if you understand how X to Y goes in the human dynamic, human relationship layer, I think that's where things are going to become good. So if we translate it again to a specific skill, I'll say it again: good design, really good design, great design. When I say design, that's images, fonts, as well. Copy. Copy is a big one. We all now, we're like two years into AI, I'll bet you me and you, if people put 10 pieces of copy in front of us, we could tell what's AI and what isn't in like three seconds, and we're only a couple years in. So really good copywriting is going to be a very good skill to have because people are just going to know after three words or three sentences that it's AI-written, and even I don't read AI output anymore. I... anything that's very deterministic, meaning x input equals y output, and it's pretty clear AI has got you eaten for lunch, right? But if you understand how x to y goes in the dynamic human relationship layer, I think that's where things are going to become good. So if we translate it again to a specific skill, I'll say it again: good design, really good design, great design. When I say design, that's images, fonts, as well as copy. Copy is a big one. We're all now two years into AI. I'll bet you, me and you, if people put 10 pieces of copy in front of us, we could tell what's AI and what isn't in three seconds, and we're only a couple years in. So really good copywriting is going to be a very good skill to have because people are just going to know after three words or three sentences that it's AI-written. Even I don't read AI output anymore. I don't like just seeing it. I want that raw human experience. So I think human skills—I don't even know how to describe it because I don't think we're doing an awesome job putting labels onto what humans are good at natively, but I think we will. I think we will describe job descriptions better. We will have human-first engineers, or human designers, or I don't know how to describe those roles. Same way how Carpathy coined vibe coding: I was vibe coding before he did. I didn't know how to call it. I started vibe coding in July of 2024, and I think he coined it sometime in early 2025. So I was doing it for seven months, and I was teaching people how to do it for about three or four with courses, and I didn't even know how to call it because there was no name. It was like, oh, I'm just using AI to do this for me. I don't know, whatever. So yeah, I think we're going to reinvent some of the terms, roles, and whatnot. But stuff that's human to human is here to stay. Stuff that's like, oh, you're a middle manager, you're a middleware person that's just translating stuff—I can use that analogy again. Translators are going to die. People writing jokes, comedians, are not. AI is never going to be able to write a good joke. Never, never, never. It just doesn't have that layer. It just doesn't understand what's funny. If you ever try to use AI to write jokes, they're awful. They're always going to be awful. But if you use AI to translate things from one language to another, it's very good at it. AI is going to replace translators. It's going to replace most journalists because it does good research, it can write good copy, whatever. Not elite journalism. It's not going to be able to replace all the writers. It's going to amplify great writers that can train AI on how to write books. So somebody who's an amazing writer is going to all of a sudden write seven books a year instead of one, right? So that's dangerous if you're an average writer. Be careful. There's zero comedians being replaced, zero. That's just my personal belief. AI is never going to write good comedy. It's impossible. So try to find your analogy in your— I think it was Anthropic hired a bunch of National Lampoon comedy writers to help them train models. And so they're working on it. And I love this strong prediction he made. I'm so curious in a year to look back and be like, he was completely right, or no, they got that one too. I'll be wrong on 95% of the things I said today three months from now. That is the only thing I can say very, very confidently. Yeah. That seems right. Okay, so speaking of career, one interesting career option is to do what you're doing. As you said, this is a dream job for you. It's a dream job for so many people. What is your path to this job, and what do you think it takes for someone to actually do this as a profession? Well, my personal path and personal journey was anything but linear, right? I've done so many things in life—blue-collar jobs, worked even at Subway while I was studying, and stuff like that. I'm an engineer by trade, but not a software engineer. I'm a forestry engineer. So no coding, but still, engineering is engineering. I feel you still develop a certain set of skills doing that. I waited tables a long time, so you develop some human skills. You understand what people like, what they don't like. Again, blue-collar jobs teach you hard work. As I said, the path was not linear, but I feel almost like a Slumdog Millionaire movie storyline, which is like everything that happens to the character brings them into a position to be able to answer the questions in the quiz better. I feel the same way. I've done a lot of stuff. The last seven to eight years were obviously spent in startups, but doing everything but code writing. I started in community management, social media. Again, distribution matters a lot. That's something we haven't touched upon at all. In a world when everybody's building and there's roughly the same amount of consumers in the world, how do you get in front of the eyeballs, right, and get attention, which is the most scarce resource and will be even more scarce? But going back to the vibe coder role, if somebody is saying, okay, well, I have a pretty diverse background too, and I'm vibe coding, how does this become a job? Well, for me, I feel like it became a job by building in public. I did Chat with Elena once, only once. So why me? There are so many good vibe coders. How did you pick me out of the crowd? I think, obviously, she gave me a couple of reasons, but to translate it into one concept, it was like I was building in public and sharing. As I said, I made a YouTube channel, and I shared all the failures and all the knowledge, all the projects that I was building. I use social media a lot. LinkedIn was my go-to because I just have that type of cadence. As you can see, all my answers are very long, and X doesn't cut it for that. You need to be very on point to be successful at X, so I'm not. So I guess it's just building in public, sharing your knowledge, giving away all the secrets. There are no secrets whatsoever. If you're sitting on a good concept, you're missing out. Just share it immediately. If you figure something out, I recognized that very early on. I think a lot of people participate in hackathons these days. I want to encourage people to do them. Find those opportunities locally to connect with other builders. Lovable is hiring across the board. Check out our open positions. It's as easy as that, right? Just apply, really. Find companies that are hiring and hiring in different roles. I've seen people do something—I'm going to give people a secret away. A couple of hires stood out by not sending resumes, but sending Lovable apps. They built Lovable apps to show why they're a good fit for a role. And we as Lovable employees will always open an app that uses lovable.app domain. Always. If you send me a DM, send me a Lovable app. Don't send me anything long. Send me an app that tells me what you want from me or how you see us collaborating and working together, right? So there are people finding creative ways to get in front of the eyeballs of decision-makers like Elena, right? Skill-wise, again, we're just repeating ourselves here, but I think it's important to repeat it as many times as possible. Really develop good judgment, right? I think it's important to really understand, in a deeper sense, how things translate when vibe coding comes into play, right? There's a company out there—I'm not going to name them—but that uses Lovable religiously. It's going to be one of our main case studies, actually, where they actually hired vibe coders before Lovable did. I'm the first official vibe coding engineer at Lovable with that title, but I've met people in companies where they hired them before us. People that are just vibe coders, people that just understand that speed matters, right? It still matters a lot to be fast. There's a company out there with three vibe coders full time. All they do is translate the old code base onto Lovable. Really develop good judgment. Right? And I think it's important to really understand, in a deeper sense, how things translate when vibe coding comes into play. Right? There's a company out there. I'm not going to name them, but that uses Lovable religiously. It's going to be one of our main case studies, actually, where they actually hired vibe coders before Lovable did. I'm the first official vibe coding engineer at Lovable, with that title. But I've met people in companies where they hired them before us. People that are just vibe coders, people that just understand that speed matters. Right? It still matters a lot to be fast. And there's a company out there with three vibe coders full time. All they do is translating the old code base onto Lovable. This is bringing everything. There's CRM, CMS, everything. All the tool sets that they have, and they need it. There are people now actively just migrating everything. Everything over. There's S&P 500 companies that are putting Lovable in job descriptions too, saying, hey, Lovable skills are recommended in the recommended tab. Right? So yeah, to go back to how to become a vibe coder professionally. Well, you don't need a company to hire you. You can hire yourself as a professional vibe coder first. I think the reason why I clicked with Anton and with Elaine and everyone else is because I was already doing it. All I did, I just changed the vehicle, but I was already doing it professionally before I got hired. So that's the key. Do the job you would have done anyway. What a mind-expanding conversation. I love just how passionate and excited and motivated you are about all this. It feels like there are so many people out there right now that are so burnt out, disillusioned, scared. And you're the opposite of that. You're just leaning into this, taking advantage. You're not sure where it's going to go, but following the path. Yeah. And I don't want to interrupt you, but it's because, look, Lovable specifically isn't a company. You can talk about it as a company. I don't see it as a company. It's an idea. It's a mission. It's something more powerful than the internet in my mind, because the internet allows us to consume. Lovable allows us to build. And in our nature, in human nature, is to build, to create. Right. And the fact that there's a tool today that you can go into and dump an idea in, and something comes out of it, and somebody uses it and finds it useful, to me, it's the craziest concept ever. It's my only life's dream. I had my first computer when I was six, and I was convinced my whole life that I'm going to be a software engineer or that I'm going to be building. But life wasn't as simple as that for me. It was very, very complicated. And honestly, the last five to 10 years, I gave up on that dream. Almost. I thought I'm never going to build anything. I've tried. I've tried to build with technical co-founders. I just couldn't find alignment. I just gave up on it. And now, at 36, 30 years later, I feel like I can. That kid, I dream every day. It's amazing what this enables us to do. And anybody that's scared, just try it. I think it switches from fear to excitement immediately, because then you see what's possible firsthand. Just go in, build something, build anything, and the fear goes away. You should only be afraid if you're doing nothing. If you're doing absolutely nothing, yes, be terrified by all means. Be terrified, and then take a step toward doing something about it. And trust me, the leap is no longer as big as it used to be. It's as big as you come in and you just say what's on your mind and just ship. I think a big part of this is just stop listening to this podcast. Go do stuff. Would you actually try to, right? Ideally, people stop right now. They've heard enough. I gave them the best that I could. Just stop listening and just go. All right. Bye, everyone. Okay, I'm just joking, but let's wrap it up. I'm going to skip the lightning round just to keep this episode shorter. Before we wrap up, is there anything else other than just go build some stuff? Anything else you want to say? Anything else you want to leave listeners with? Otherwise, we'll let you go. Yeah. Tech stack doesn't matter anymore. Right. It doesn't matter. People obsess over, oh, is this written in HTML? Is this written in React? It doesn't matter. It never mattered, but now it matters even less. The end user just wants a stellar experience. We live in a world where anybody can produce good enough. So you better start learning how to produce magic, because otherwise you're just going to end up in a crowd with millions and millions of others. But at the same time, if you don't know what magical looks like, don't be discouraged to start building anything, and start from good enough and level up. The best way to level up: exposure time. Set aside more time on learning than building. Read the agent output, learn how it's thinking so that you know what's possible, but then also go and get inspired. Follow good designers on X. Find tools where great designs are produced and follow their creators. There's a tool where I'm following the actual person that built it because he publishes videos almost daily, 40, 50 minutes long, of him designing. I want to see how a world-class designer does it. I want to see him talk to the tool. I want to see him prompt. And that's how I learn to become better at it. So again, exposure time. Just deliberately set more time aside to learning than coding, because you can code fast, but you can code garbage fast as well as magic fast. It's the same amount of time. It's you and your input that matters. Forget about decisions on tech stack. Forget about which back end you're using, which front end you're using. That doesn't matter. Quality, taste, design. That's all you need to optimize for in the future that's ahead of us. Well, Zara, I think we're going to leave a lot of minds buzzing after this conversation. You blow my mind in so many ways. What a fascinating topic, conversation. What a glimpse into the future. What an interesting point in time. I'm so curious, in six months, where things are and revisiting this conversation. I really appreciate you coming on, sharing all of this. You're awesome. Where can folks find you if they want to reach out, maybe ask some follow-up questions, and how can listeners be useful to you? Awesome. Yeah. So I mentioned it already. LinkedIn is probably the best place to find me on. I'm very responsive there. If you want to follow me, I hope to reengage my YouTube channel a little bit more. I think I have a lot of cool tips and tricks that I want to share and teach people how to use Lovable and just vibe code in general and level up. And on how people can be useful to me, well, I'm very passionate about making sure that everybody experiences what I've experienced that day when I got my first prompt in. I envy the person that is going to try Lovable for the first time after watching this episode, because the feeling is just unmatched, of you going from a consumer to a builder. But in that process, there's going to be some battles to fight. I want to reduce the amount of those battles and hurdles. So if you can help me in any way, message me what could have been better in that experience, especially if you just watched this and you're like, I'm going to do it. I was on the fence, and I'm going to do it. If something breaks, if something doesn't connect and relate, I need to know what that is. My job is 100% to empower you to build the best work of your life. Right. And I need to say this too, because a lot of people may be inspired not by building or using Lovable, but rather building Lovable. Come join our team. Again, we're hiring across so many things. I think a lot of people should feel inspired, because I hope that the energy that I bring to the table will resonate. This is how it feels working at Lovable. This is how it feels working with the best minds, the brightest minds of the world. We're not number one by accident. It's not a coincidence. The best people are gathering, and we want you to be a part of it too. So if the energy and the conversation resonates with you, or if you heard about a problem today and you're like, man, I think I can solve it, come join us. Help us build and shape the future of software development. Incredible. Right. I need to say this, too, because a lot of people may be inspired not by building or using lovable, but rather by building lovable. Come join our team again. We're hiring across so many things. I think a lot of people should feel inspired because I hope the energy I bring to the table will resonate. This is how it feels working at lovable. This is how it feels working with the best minds. The brightest minds in the world are not here by accident. It's not a coincidence. The best people are gathering, and we want you to be a part of it, too. So if the energy and the conversation resonate with you, or if you heard about a problem today and you're thinking, “Man, I think I can solve it,” come join us. Help us build and shape the future of software development. Incredible. And the site is just a link on lovable's website to find the open roles. Yeah, we'll link folks there. Yeah. Incredible. Lazar, thank you so much for being here. I appreciate the opportunity. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast dot com. See you in the next episode. Bye. Bye. I'd honestly say no. Like, you're, you're optimizing for the wrong skill set. We won't be rewarded in the world of AI for faster raw output. We will be rewarded for better judgment. So I think that better judgment comes with, again, to go back to your question, like, how are you solving for that? How are you solving for this? Well, it starts with exposure. So I'm deliberately exposing myself to people and resources that I need, know I need to consume to level up. And then a lot of it just comes from building as well. You know, if we're honest, like, it's a muscle. Everything is a muscle. You need to practice. You need to see what's possible. And, you know, though, that's where some of the techniques and mindset shifts that I want to also use an opportunity today to ingrain into people's minds later down the call may be useful. So, okay, so what I'm hearing here is because coding is now essentially a solved problem. I love that you don't look at the code. You don't even, like, you've never coded. You don't want to look at the code. You don't care about what's happening there. That instead of you're watching this agent output, I want to actually ask about that. But what I'm hearing here is the areas you are investing in, building in yourself is at the front end, clarity around what it is. And I want to hear how you actually do that. What you do there, you have a really cool system there. And then there's like the taste and judgment of knowing, is this the thing I want? It feels like those are the two sides now that are more and more important. And on the taste judgment side, you share this concept to something Guillermo Rauch shared in our conversation, this idea of exposure time, exposure hours, being exposed to great stuff. Here's a great user experience. Here's a great onboarding club. Here's a great, I don't know, website. So I really like that advice. It's so actionable. Okay, I'm going to spend more time with stuff that's great to inform my taste and judgment. And then on the clarity piece, let's actually talk about that. Just what do you, what do you do there to be clearer with Lovable and other AI tools to help it build the right thing? This is the first mindset shift that I want to put into people's minds, right? If you just have a vague idea, let that be your first version of the project. Open, cursor, Lovable, whatever it is that you're using and just input a brain dump prompt, right? Just talk into it. Lovable specifically, I don't know about the other tools, has a really cool voice function. You click it and just dictate the hell of it and just press send, right? Don't even wait for it to finish. Open a new window. Again, Lovable.dev in here, you're like, okay, as I was brain dumping, I think I found a good thread, right? I think things are getting clearer. So let me start another project now with more clarity, more deliberability. Like I know which features I want, which pages I want, and maybe I can even find a good reference. Maybe I can go on Moven, maybe I can go on Dribbble, maybe I can go wherever, get a good screenshot, get a good animation and attach it because most of these tools accept files as a part of the input. So like you have the second project starting. Now things are even more clear. Now you expose yourself to quality and now you're like, well, what if I, what if I found a template that actually is already out there? Why reinventing the wheel? I'm building a platform that somebody else built. Why not expose AI to what quality looks like, right? So what I'll do is I'll go to and find a library, 21st dem or, or a dot build or like whatever places which allow me not to export screenshots, but export code snippets. Because guess what? Even though English is the number one programming language, Lovable and all other tools still communicate in code the best. If you want to get pixel perfect results, just give them code. It will interpret it better than your English or Spanish or whatever language that you use in these tools. So that's the third way. You're like, okay, now I'm even more deliberate. I'm not, I'm not even going as, as wide as like giving it vague concepts. I'm giving it code snippets. Like I want this exact design. I want this exact type of functionality. So that's your third project. And then by the time you do all of these three, you're already at a level of clarity that you wouldn't have if you just sat with an empty piece of paper or maybe, maybe chatting just with chat GPT, but not taking action. I think taking action is so, so cheap these days and free, by the way. Like all the tools I mentioned have free plans. Like most times you would be able to do this without spending any money at all just by starting multiple projects because guess what? That doesn't also cost anything either or doesn't incur additional cost except for builder credits. You're going to get three, four, five, six different concepts that you can compare as you're comparing them. Clarity just keeps coming like, and things get better and better to understand. And you're also solving for one big problem that you mentioned. You used the term AI slop, and I like it because a lot of people when they say AI slop, they don't refer beautifying the code, but beautifying the design. Right? This process that I just mentioned actually gives you four or five different design options and in the long run save you massive amounts of credits because a lot of people obsess over the concept of, oh, when I give them this hack, they're like, oh, but doesn't that cost more? I'm like, yes, up front, it may cost a little bit more. In the long run, if you really want to finish this project, you're actually saving hundreds of credits and maybe even hundreds of dollars, not to mention the amount of days simply because you started from a point of better clarity and better refinement process. Right? So like, that's the first step of solving for clarity. There are more. right? Which is the second layer, but I assume you may have some questions on this one. Questions and also just, wow, this is such a great, it shows you the power of having someone come into this world without an engineering background. This advice of just build it five times in parallel. You ask AI to try all kinds of stuff. Like, this is not how someone that has been a software engineer or a PM or designer would approach stuff. So your advice here, which is so fun, is as you're getting started with a project, just run five different approaches at it to start. One is just brain dump. Here's what I'm thinking. Here's a general idea. Like, use Whisperflow or use the built-in mic. And then two is, okay, now I have a general idea. Let me try to type it out. Like, actually thinking through the prompt. Three is, let me find a mock design somewhere online. And the sites you suggested were Mobbin and Dribble. Those are the two that you go to? Yeah, most times. Okay. And then the fourth, and these are all in parallel. It's great. Is find like actual code template that looks similar to the thing you want to build, download like the zip file basically and put, attach it. Or is it just HTML and CSS? Is that kind of what? Anything. Anything you got. Cool. Here you go. Okay. And then cool. Here's the prompt here. Make me what I want. And what I love is there's two wins here. One is just, it helps you clarify the idea as you see the tool build it. Like, oh no, that's not what I mean. Let me try it again. And then two is you pointed out you can pick the right direction so that you're not locked into your first design and first architecture. To your point, if you then spend all this time trying to fine tune design and direction, it's like all these tokens are being lost. You could have just started over. This is so great. Someone may think, okay, of course, you're just getting us to spend all these lovable tokens. This is what a lovable person would tell me. But what I'm feeling is this is where you could save the most money because if you get a correct in the beginning, you save so much work trying to get it back to where you want it to go. A million percent that I'm actually saving people. Like I'm actually going against what I should be saying. If I was thinking about lovable, I'll be like, no, no, just try to fix it in perpetuity. But that's not, we're not in business of doing that. We're in business of empowering anybody to build anything that they want. And then, you know, it's my personal mission that resonates with me because if there wasn't lovable, I would have never built anything potentially in my life. And I don't think that that would have been a fun life to live. So, you know, I guarantee people, like I've tested this framework with many people and everybody telling me the same thing. Eye opener. So simple, yet unintuitive, as you said, even though for me, it's kind of, I don't know, as you said, I attribute it to non-technical background. To me, that was the first thing that I would do. Like, I just did it. I never thought about it. Like, oh, I'm developing this amazing hack. I was just like, I'm waiting all this time for these agents to finish. I might as well start another project and another one and another one. And it's also a productivity hack. Like, that's what people ask me, like, wow, how do you ship so many things? I'm like, I never built just one project at a time. I built five or six. I have six lovable tabs and I just switch between them. And that's the next hack that I want to talk about if you allow me, which is, the question in return is the obvious one, which is, how do you context switching? Like, you talk about context so much, yet you keep switching between apps. How do you manage to do it and do it in a way that's productive and not produce bad code or bad product? And that's how I solve for that LLM problem. Again, the Aladdin and the magic lamp and all that, which is, if there's a limited token window, how do I make it dynamic? And what do I mean by that is this. If you just go and you prompt and you prompt and you prompt and you prompt, you realize that no matter what tool you use, the memory just isn't infinite, right? By the time you reach message number 10, 15, 20, 30, 40, snippets of early messages sort of get lost in the translation because Agent is optimizing for speed, right? If it had to read the entire conversation and the entire stream of requests that you made, developing anything viable or large would be impossible because it's just like consuming a lot of time and a lot of memory and a lot of tokens. So, again, something that I just figured out very early on as I was building was like, okay, if it can't remember things, my job is to provide it with reference. So, let me treat Lovable or any other tool as an engineer that I'm supposed to be providing perpetual context as the project goes and you can do that in many ways but the most efficient way that I found was like, I would do the four parallel builds. Like, let's continue off of that example. Very quickly after you've built hundreds of projects like I did, like, you see the winner. Like, the winner is so obvious. It's not even a competition. You maybe do one or more two prompts to calibrate it and when you're like, okay, the winner is here at that point, I either ask the tool that I'm using or I'll maybe, let's say, go to ChatGPT or whatever and ask the LLM to produce a series of PRDs. What PRDs are for, again, people that are not familiar with the terms, there are project requirements documents or for me, I call them like sources of truth, right? What needs to be true for this project to be successful from a couple of perspectives? I usually build something that I call a master plan. It's basically a compass saying, here's what we're building, right? It's like talking to a human. I really treat Lovable like a human being. So it's like, this is what we're building. Then I build an implementation plan, which is, this is how we are going to build it and this is the sequence, right? It's very important to me, again, going back to quality, taste, human, nature. I need to define because I'm still working with a system that is not emotionally intelligent yet. I need to define how I want the app to look and feel. So another PRD that I build is design guidelines. And then finally, something that just circles it all around, which is like, okay, when we know how things look and when we know how we're building it, how does the user journey look like, right? I use the registers and then what? And then when they register and do that first step, what's the second step and what's the third step and whatnot. So I built at least four PRDs, right? And then when these are built, I read them. That's the planning, chatting part. Like, that's where I'll spend a lot of time now on. When I nail down that first design, I'll spend an entire day if I need to, just planning this part out, like documentation and breaking things down because that's how I'm setting the course. like everything's going to be dependent on this particular part of the process. When I'm done doing that, I build one final document, which I call either plan.md or tasks.md and .md part is, you know, markdown. Basically, I'm just using markdown format because I've learned that AI likes to read markdown and what that serves is a source of truth on like actual tasks and subtasks that it will need to execute to get to the finish line, right? And then there's the final, final layer, which is depending on what tool you use, Cloud Code or Cursor have what's known as rules.md or agent.md. What you're basically doing with rules or agent files is you're letting the agent know how you want it to behave and what it should focus on in the long run so that you don't have to repeat yourself with every prompt, right? So in Lovable, there's a separate menu for that in your project settings where you can define project knowledge and usually what I'll say, hey, read all the files before you do anything. Like don't do anything before you read all the PRDs, read tasks.md to see which task is next, then execute on that next set of tasks and when you're done, tell me what you did and how I should test it. And that's where that conversation about I religiously read the agent output comes into play. I've told the, I gave the agent everything, all the tools and resources that it needs to succeed. I gave it the rules, I gave it the docs, I told it what to do with them and at that point I'm just sitting and reading. I don't prompt anymore and from that point on I can switch as many windows as I like. My prompts have become proceed with the next task. I don't need the context. I outsource that and delegate that back to the agent. The agent needs context and I need to make sure that it's dynamic. I need to make sure that I'm regularly updating the documents from time to time so that we shift that token window it uses and how it uses it over time but I'm not prompting, I'm not interrupting the flow. Yes, I'll go in, test, maybe put a prompt in here or there but that's how I can build five projects simultaneously and never lose the productivity part which is again as I said I do this today manually. Call me to talk three months from now an agent will do this for me. I'll be out of job pretty much. That's why I don't optimize for this skill at all. Like I'm using it today to bypass the shortcomings of human nature and LLMs but I'm optimizing 100% of my time today on good judgment, clarity, quality, taste, good copy, good fonts. Like people don't talk about fonts at all that work with AI. They're like 60% in my mind maybe even more in how your output is going to look like. That's my obsession. Like I don't obsess over these things that I'm talking today because I know what's coming. Like the agents are going to get better. The models are going to get better. They're not going to need me to extend the context. They're going to do it themselves. So for me, the skill that I optimize for is the one that requires better decision making rather than better output or better alignment. Oh my God. There's so much here. This is so awesome. Okay. So essentially what's happening here is you start a project, try a bunch of stuff, pick a direction that feels most correct. And once you have a set direction, you spend essentially a day not building but working with this AI agent to plan. And then, and well, I want to talk about that. And once you have the plan, then it's, and it's amazing that you could do stuff like this with what people may, some people may feel are not sophisticated tools that can build incredibly powerful things. Like you can do a lot of this with tools like Lovable, like have plans and rules and MD files. Like, you know, a lot of people may not think, may not know that. And so the idea is, okay, spend all this time planning because again, that'll save you a lot of time down the road. And then only once you have a plan, you have, you get it going. And a key part of this, this three wishes rule is really important. The reason you're doing this in large part beyond just being really clear about the plan is this idea of one task at a time keeps the agent's context window small so that it doesn't lose track of where it's at. That part seems important, right? It's like, do this thing and then, okay, cool. Now do the next thing. Right? Yes. Yes. Because again, if, let's say you didn't do this, let's, let's hyper, let's talk about you ignoring this. You're like, I just want to vibe my way. Okay, great. No problem. You work, you work, you work. At one point, something breaks, right? You haven't documented anything. There's not, there's no reference points. You report a problem. You're not referencing files or architecture at all. You're just describing the issue. Here's what's going to happen. Any tool, Loddable or Cursor or whatever tool you talk about is going to do this. It's going to be like, okay, let me start investigating. And then your code base gets bigger and bigger and bigger and bigger and bigger. Like when you first start, you have like 20 files. It can read 20 files. But what happens when you have, I'm just building a project right now that has like 60, 70 edge functions, right? What happens then when I say this broke and there's no reference which edge function does what? Guess what? Loddable is going to read all of those and it's going to consume 80% of the token allocation on reading to get clarity, leaving only the final 20% for thinking and executing. What I'm guessing and I can't prove this and all I'm expert in the comments may say that I'm wrong, but this is my best guess as a non-educated person. These tools are very obedient and very agreeable. They're going to lie to you. They're going to tell you that they fixed the problem even though they didn't. They're just going to try to make you feel happy and say, yes, I found what the problem is and I fixed it. A lot of times when they don't, people blame the machine and to an extent, I will say that's true. It's your fault, my friend. You did not provide any clarity or context to this tool. You just used its raw power and dug a deeper hole with your spinning your wheels into the mud, right? And, you know, obviously, I think we're heading into a world where AI is more honest than obedient and saying, hey, I only partially fixed this. you know, you did not give me enough of a context, right? The bigger mistake that people make then is like, they trust the tool fixed it. They test, they see it, then they get mad at it, start cursing and yelling, as we say, and then it gets even worse because guess what? Another bad trait of AI is. It's best not to hurt your feelings and never say, you're the dumb one. It says, no, I'm the dumb one. So it focuses in the next request. Instead of focusing on reading, it spends another 30% of tokens trying to come up with an apology, right? Again, I'm not educated, but if you ever read like a stream of ChatGPT's thinking in thinking models, you see exactly what I mean. Like when I insult it, I see that the first message is, okay, the user is mad, so I need to think of ways how to reduce their anxiety or whatever. I'm like, oh man, I just fell for the worst drink of the book. I made it spend the most scarce resource, which is those tokens, on thinking how it should address my anxiety versus focusing on the actual problem. So my advice for people is like, yes, vibe your way for fun and vibe your way while you're prototyping because that's the exploration part. I love that part. But when exploration is done, please, please, please use referencing, documentation, use all the agent files that you can because that token allocation is so scarce. Like, it's going to get expanded over time. Things are going to get cheaper, faster, but right now it's still so valuable and precious. You really need to make sure that they are allocated in the right direction. This is hilarious. I think the the genie metaphor is so good here. Just thinking about this genie is you're trying to be clear about what it is you want. And if you're just like vibe, you know, vibe wishing, it'll do the wrong thing. So the advice here is be as give it as much context about what you want it to do as possible. And these files we'll talk about right after this. But the idea here is just like laser show the point the laser where you want it to fix the problem. Don't just assume it'll go figure out because it will. and it'll try really hard to and it'll waste all your tokens. It'll fill the context window. And I remember at one point you mentioned before this recording that because it starts to run out of space in the context window it just like the solution ends up it doesn't actually work that hard on figuring it out in the end because it spent all this energy on reading and thinking and then it's like okay here at the last second here's a solution. I think it just picks the first thing it thinks is broken that just again this is me completely uneducated coming in into the conversation and just thinking out loud that's just my gut feeling and the way I think logically about it which is hey if it consumes most of its window and knows that it's running out of it maybe it's aware that it's running out maybe it isn't but either way I've had the experience anecdotally to where like my request is unclear I feel it takes the easiest fix in the book just the easiest versus the other way around where I'm like spending so much time finding the right file referencing that file like really putting in the effort of hand holding it in dark maybe giving it a flashlight and then saying here's the problem I think that this is the problematic file and then it's like oh yeah you're right and now I'm gonna actually fix over and over and over and I've seen that because again all I do is read the output agent makes me learn how to use it by so people read I don't know what people read but all I read is the output like I don't read the code and it's later down the road because like I know that it can do that much better than I can again I feel if there's a good quote I've read I can't I apologize to the author because I can't attribute it off the top of my head but it's like the ceiling on the AI isn't the model intelligence it's what the model sees before it acts right so that's the ceiling right now like what do what are you exposing we talk about exposure time for humans what are you exposing your agent to as well is as important if not even more important before it makes code edits yeah coming back to these files I think this is really important so let's think about just like what's like the MVP for someone that wants to do this better you listed all these kind of files these MD files essentially that you're building over the course of a day before you start actually building the thing you had design guidelines the user journey tasks agents MD rules MD say you wanted to just like move one step forward and be better at this stuff what are the files you'd create and then what do they roughly look like what's inside these files yeah so the master plan is the first one which is like it's a 10,000 foot overview right it really high level explains the intent that I have with this app and this is masterplan.md is that what you call it yes yeah masterplan.md and it's like it's really just like hey this is why I'm doing this this is who I'm doing it for this is how I want them to to you feel and a lot of times in the master plan I will reference the other PRDs I'll be like the design needs to feel modern and slick but for exact you know parameters consult and read design guidelines .md right so I'm using just the masterplan as like this high level overview right to get the agent into oh okay yeah we are building xyz right then there's the implementation plan because you know there needs to be some order if you just like dump stuff on top of each other without any order you're never gonna get to the finish line and this is tasks.md is that what you call this no that's the implementation plan implementation plan yeah okay and implementation plan is kind of in service of the future tasks.md if that all of these files are in service of building tasks.md when you build tasks.md then the rest is almost irrelevant it's just a basis for you to build tasks to execute right the implementation plan is kind of the first layer which is again higher level overview it doesn't go into the depth of like how to get there it just goes into the explaining of like oh well if we're building this I think we should start with the backend and we should start with tables and then later authentication and then after that we're gonna bring in the API and then after that we're gonna do this it's like again just think of it as having I'm an ideas guy I'm sitting with a technical guy it's me and you we're building our startup I know you're a software engineer by background and I'm telling you my idea I'm giving you the master plan and you come to me back and you're like okay if you wanna do this it's doable here's how I would order it like you don't have a roadmap you're not you didn't open your linear and started writing features and RFCs and whatever you're just high level talking about the order of things and then me and you again as two co-founders we talk and say okay well if we agree on this like how how should this look like how should this feel right let's describe it high level but now because I use AI I can go a little bit deeper and that's where like I like to see Lovable or any other tool ChatGPT is good at it I even have my I built like custom GPT so if people wanna start somewhere before they even get into any tool they can go to ChatGPT store and for GPTs and just type Lovable Base Prom generator or Lovable PRD generator and find those that I built and just like brain dumping them and then get these files as output right so I like to see some elements of CSS in design guidelines because you know with design it's a little bit tricky AI is sometimes over creative so that's where I'm doing a little bit more technical steering right and then finally it's just the user juries just like if we know how things look like if we know how they feel if we know what we're building high level like high level just very high level again how do people navigate what are some of the features in there you know and stuff like that and then tasks that MD gets into the nitty gritty like oh if you want these user journeys and you want the backend built first here's a set of tasks that I need to do like it just takes that as an input I'm just making the tool do the do the you know that gritty work that humans used to spend so much time on like I feel like with these tools we're all becoming product managers on steroids you know like we're just leveraging AI but like good product managers I think are not compensated for writing good PRDs they're compensated again for good judgment right somebody else can do the writing you as somebody who directs and builds this product product you need to know again what's going to be useful what's going to be tasteful what's going to be something that actually moves the needle I will say one thing though just because I put so much emphasis on like oh you need to acquire taste oh that doesn't mean you shouldn't build you get better at this by building actually so everybody listening to this should like literally go and build something today one two three four five projects test all these tools because that's how you get to clarity not just by reading but also by doing as well here's a puzzle for you what do OpenAI Cursor Perplexity Vercel Plat and hundreds of other winning companies have in common the answer is they're all powered by today's sponsor WorkOS if you're building software for enterprises you've probably felt the pain of integrating single sign-on SKIM RBAC audit logs and other features required by big customers workOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS whether you're a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally workOS is the fastest path to becoming enterprise ready and unlocking growth they're essentially striped for enterprise features visit workOS.com to get started or just hit up their slack support where they have real engineers in there who answer your questions super fast workOS allows you to build like the best with delightful APIs comprehensive docs and a smooth developer experience go to workOS.com to make your app enterprise ready today I'm imagining people hearing this may start to feel like this is so much work I just have to sit here and create all these rules and figure out all these little details like in one sense it is in another sense this is like you spend a few hours maybe a day planning and then you have AI build this thing that would have taken somebody weeks months right like the amount of investment to achieve this thing is absurd ROI it also this shows you just what professional vibe coding looks like you know everyone imagines vibe I'm just sitting here type of stuff go do this good if you want to actually build something really great that moves the needle as you said that solves people's rules problems that lasts that you know scales this is this is how you do it if you really want to do this as a as a real as a job and also if you want to build things that are really great yeah and don't get me wrong like there's there's obviously a ton of value in prototyping like there are a lot of people maybe watching this that are like okay I want to use lovable at work but I I can't or whatever you know there's there's different reasons there's maybe you're in healthcare or finance or there's something regulatory that just prevents you from pushing to production like building for the sake of prototyping is one of the best use cases we our motto for 2025 was demo don't memo which is like instead of writing all these documents and talking and sitting on meetings with your engineers trying to get your vision as a marketer or a sales guy in the office across go into lovable and build the prototype in 30 minutes and just hand it over like and I have a real like job that I held before lovable that's exactly what happened like this time last year I needed something built enterprise grade really and lovable and myself were not there yet to build it at that point but I have I had a team of engineers that I worked with I built the prototype in four hours and they actually were able to replicate it six to seven months later into production with with connecting all the pipes and everything but like if I had to describe it I would say took me it would take me at least a week or two just to get the words out there I just sat and built it in four hours and that's like lovable January last year this lovable today January 2026 is like ages ages ahead with functionalities like it's so much better it's not even a contest right so I think now what our stage were like for instance there's I'd say at least to best of my knowledge at least half of S&P 500 companies have people working in them that are using lovable to some extent right and we have a lot of enterprise companies that are actually on enterprise plans with lovable that are creating super meaningful projects like I'm not going to name names but like leading ride share companies of the world reading telecommunications companies of the world leading leading companies of the world in many many aspects healthcare finance like are actively with their teams using lovable and it's always the same feedback which is yes we may not be able to push to prod but like our marketers are no longer waiting for engineers are you know people in go to market or sales or HR or whatever roles are now just confidently building internal stuff for us to manage our expenses or manage employee onboarding or like there's so many use cases like that where like you're seeing lovable and other tools for that matter being used to to push things into production you know to help people do this workflow that you're describing with all these MD files do you think you could share after we record this just templates like simple templates of what these files look like for people just to look at and copy I would literally go to chat GPT as I said and brain dump into it in my just type lovable GPT lovable a PRD generator you'll see my name there right and and and that I'm the author go in brain dump it will ask you a couple of questions to get clarity and just produce four files for you and you can just go ahead and and upload those amazing cool we'll link to that so so it's not it's not just here's a bunch of files let's go talk to this thing it'll generate the right files for you and then you plug that into lovable or other tools yeah it's trained it's trained to think like I do so yeah oh amazing okay that is perfect by the way I want to talk about like how you unblock yourself because there's a whole other series of tips you have there but I just want to reflect on it's so interesting how one you're kind of from first principles under learning how to build product as a PM as an engineer as a designer and you're kind of figuring out a workflow where AI is helping fill in all the gaps that you don't have for as an engineer as a PM helping you craft PRDs and design so I think that's so interesting just this it's interesting that these functions still work and are necessary now it's you and AI help create all this basically this triad that's always existed product manager engineering and design and something I've always thought is that there's this question of which background will be most valuable in this future is it a PM is it an engineer is it a designer my mind has always been the PM function is like their job is clarify figure out what to build clarify what to build be really clear about the requirements figure out what success looks like it feels like that's where the skill is most needed there's also a design component of like make this look awesome and I feel like that's going to be an emerging uh that the value of that being really good at design and taste and judgment is only going to go up uh before we get to things you've learned about unblock yourself because a lot of times you know things don't go in the right direction there's a bug without being an engineer what do you do before we get there is there anything else you wanted to share around just like tips for being successful if we measure success in the right terms again um AI as you pointed out regardless of your background is an amplifier so you know if if you don't know what you're doing you're just going to produce garbage faster one thing again I just want to double down on is in the old world um good enough was good enough right because even producing good enough was not easy right 10 years 15 years ago just producing was more than plenty more than good enough you built a sass who cares how it looks like it works it does stuff like oh my god I'm so much more productive today like if good enough was here let's say like let's let's visualize it for people like if this was like pretty pretty bad could be better mediocre good enough world class if this was the gap between good enough and world class well guess what the gap is now this because everybody produces good enough with AI absolutely everyone does it so now learning and optimizing for how do I produce world class and magic is that the key lesson to take away today as you pointed out I think PMs are the winners of AI today because they bring clarity if I was a betting man as they say I'd bet that the next class that wins our designers because we're training these tools to be more clear to be better to make better technical decisions I don't think we will train them just yet to be make better emotional decisions and I think design is all about emotion and that's where like the level of the skill up needs to come that's the biggest level up if you asked me like oh what is the main thing you figured out when you joined lovable like what's the biggest personal up skill let's say it's like working with Felix nad abby all of the people that are designers just really what moved and shifted the needle for me I'm like oh so this is how world class looks like and this is what it takes right I you always use the analogy of like I wanted to steal one of their designs and bring it into my lovable project so I went into figma and I was like let me just take this background like and just put it in there I went in and realized that what could be you know interpreted as a pretty simple or rather simple gradient took 50 different layers to produce so I clicked on that component I was like oh my god this is not three colors this is 50 colors and not just 50 colors 50 colors with different gradients of levels of opacity so I was like oh okay well that and that's the big disconnect that I've had all along so like um again if you if I'm answering your question directly of like okay what are some of the other tricks what are some of the other things design guys just expose yourself to exquisite designs follow Felix from from lovable he has an amazing newsletter like oh and and to teach you how to and learn how to prompt for good design learn about design styles I didn't know what baha baha bahaus meant or glass morphism had no idea so I built an app as well for that in lovable I was like I needed to build an app to learn these styles so now it's public anybody can see it it's like some ui style dot lovable dot app I don't know what it is like it has like 18 different styles and prompts to replicate them so like learn what good design means learn all the design styles learn how to prompt to get them is probably uh what I would what I would optimize for um at this stage yeah while we're on this topic what's your sense of just engineering as a function do you feel like there will be a future where software engineers are still thing do you feel like that goes away based on your experience it never goes away we will need elite engineering more than ever like because let me tell you this in a world where everybody builds and everybody's building everything who's doing the maintenance right taining code bases uh scaling code bases maintaining projects um you know they're still gonna be a thing uh uh definitely and obviously ai is gonna be good at this but again that requires a different level of skills right it's one skill to build something it's a completely different set of skills to expand it extend it and maintain and not to mention that you know world where everybody's building infrastructure suffers right like we all know and experience like cloudflare went down two or three times in the last two or three months the whole internet goes down elite engineers are the ones fixing this lovable experiences massive amounts of influx of new users infrastructure there suffers elite engineers are the ones building the infrastructure to hold the fort right so like i think we're gonna need a lot of people with really good skills of like hey who actually builds the world that needs to support billions of builders now because everybody's gonna want to learn how to build stuff like how do we teach them how do we maintain everything that they need the hostings the the security the email the connectors the apis the whatnot it's like so i think there's gonna be room for it but i'm also on the boat of people like if i had an 18 year old brother and he asked me what should i do i would tell him hey go become a plumber you know don't don't go and get a cs degree you learn uh learn a good trade you know because uh the new generation of millionaires in the us are actually electricians and plumbers and whatnot right so it's like uh you know it's a balancing act i'd say i don't know like i i do still think that good engineers with with good sense of like um understanding where the future is going are always going to be needed and and scarce such an interesting question i think to your point there's definitely going to be people need to keep building the machines that power all this stuff well we need engineers to build the actual products the the application layer that's that's the question like is everyone going to be like you is everyone going to be our designer is just going to be all we need everybody's going to become an engineer and let's let's let's stand them let's speak to that end like i'm an i feel like i'm a i'm a rapid engineer like i i'll refer to myself as a rapid engineer in in a year from now because vibe coding is just coding in 12 months from now and even today we spoke about this before like how many elite elite engineers are publicly admitting they're no longer hand coding or manually coding whatever we're going to call it they ai writes all the code i i use the analogy here of like coding is going to be like calligraphy you writing code is going to be the equivalent of like you write you fine printing like on on a canvas and people are like oh my god you wrote that code that's so amazing it's going to be so rare that it's going to become an art right it's not going to be it's going to be commoditized completely like it already is in a sense most elite vibe coders rely on ai again it's an amplifier right so i think um everybody becomes an engineer in in the in the world of the future a designer a pm uh everybody is uh uh uh an afford deployed engineer or an ai assistant engineer or an llm engineer or a vibe coder the term is irrelevant we're all using llms for raw output based on good judgment or bad judgment no man essentially these venn diagrams of engineer designer pm they're used to be very separate now they're converging and people with a specific with deeper pm engineering design background are gonna like they can all do the same thing essentially all the rules are converging what a what a time to be alive and it's so hard to predict exactly how this all goes but but it's fun to pontificate i want to get back to when you get blocked speaking of elite engineers uh things that there's like in the in reality you're still writing code using these tools sometimes code goes things go wrong bugs are introduced there's a weird database thing there's like some network issue what do you do when you get stuck do you kind of a workflow you go through unblocking yourself yes great question and absolutely true like no matter how good of a plan you have in place you're gonna run into problems eventually and i have like a small little framework that i call four by four just again analogies right four by four if you if you have it on your car you're gonna get yourself out of the mud much easier than than uh the other way around so in that sense four different ways to debug attempt one of each only once and i'll explain why in the end um first one is again every tool is different i'll i'll reference lovable's workflow um which is when something breaks lovable's agent is smart enough to say hey i made a mistake it will label that message in orange and uh have this little button usually which is uh called try to fix so you agent basically admits it made a mistake you click on a button and most times when it's a smaller issue it corrects the course fixes it no problem right now there are situations obviously when the problem is a little bit deeper than that right you click to try to fix but the problem persists and sometimes even the problem persists but lovable's agents unaware that it persisted so there's no more try to fix button lovable thinks everything's working but in reality it isn't and the the culprit there is usually you're using a third-party integration you did not give enough context to lovable where what to observe and what to see so it can't see that the problem exists because lovable cursor cloud code you name it all these tools are good enough today to fix any problem they're aware of again awareness is the key here right so when they're unaware of it there comes the second part which is okay i need to bring the awareness layer and what i do there is i go and very simply open the preview sandbox dev environment of my app whatever try to run the the function that's broken right click read the console log right every every browser allows you to just go and read the console log and a lot of times it will record stuff if it doesn't you can prompt any tool and say hey i don't think you're seeing the problem so instead of me yelling at you let's find it together right i think it's a problem with xyz i want you to write console logs in relevant files so that we can monitor every step along the way let's just bring awareness layer into the equation it writes the console logs you rerun it guess what now you have a full history of everything that was happening you copy that you paste it inside your chat 99 of the time that's enough that's that's already enough i was like okay got it found it fixed it right but then there are situations when even that's not sufficient so like okay i need to go even deeper and that's like code reviews and evaluations come into play my go-to tool today for that is codex open ai right what i do is like any any build that i do i will export it to github like lovable allows you to own your code cursor as well all of these tools allow you to have a copy of the code that you can export to github and then import it into wherever you want to so i i you know use codex since beta like import it in there and then i'm using an external tool so i'm like in the first try as if you remember like i use the tool and i was like total vibes i'm relying on the tool right in the second try i use myself as the awareness uh a facilitator in the third one i'm using an external tool as a facilitator which is like i'll either connect to codex and chat with codex to then fix the problem in lovable right i don't allow codex to make code changes for me a lot of people will say why don't you like it's a good good model i just don't know its agent well enough like i don't want to go and and and use a tool that i don't know how to steer so i use it only for diagnostic purposes and i'll also do it manually it's an old workflow that i had before codex and before cloud code which is there's a tool called repo mix which allows you to like compress every your entire code base into a single file you download it and then i upload it to claude just call regular claude or chat gpt and i'm like this is what i'm building read it and and this is the problem that i have these are the console logs again it's almost like having an external consultant at that point like you're hiring help elsewhere because your team just can't handle it right and then the fourth one is is usually the best one because one of the time when there are problems it's my fault like no matter how your ego has been guys that you're watching this it's your fault trust me you you had a bad prompt you you premised your request in the wrong way you just don't want to admit it or you can't remember that you did but it's your fault so again illoveable and in the all these other tools you can revert back there's version control built into lovable cursor cloud code you go and say okay i tried these three things i'm just going to take three steps back and i'm going to think about my prompt a little bit more take a couple breaths go for a walk have some coffee come back with a clear mind and try again because guess what ai is just writing code very fast and sometimes it stumbles on a very small rock and it only happens then and never again so you just gotta make the same request again and usually that just fixes the problem it's just a snag it's a syntax error it's it's something minute right and then i do the final thing which is this and this is the key one actually when the problem gets fixed i go into the chat mode and i and i ask lolo i say okay i needed to do four different things to fix this how can you help me learn how to prompt you better so that next time i have a problem we do it in one go 99 of the time i get such a great answer that i don't have the problem of not knowing what to do next time right like again you need we all need to be aware and realistic these tools are so good at doing things the right way if they are used the right way it's always our fault it's a hard i say 90 but honestly it's 100 our fault right because they're good enough it's just that i'm not dynamically shifting token allocation i didn't reference the right file i didn't say the right way for me as a non-designer i don't know any of the terminology like none of the headings and whatnot and i still don't know it to this day so when i'm i struggle with prompts a lot of times i use chat mode to help me craft a good prompt i mean anybody can do this too if you are just stuck it's 10 p.m and you don't know what to ask switch to chat mode brain dump and be like help me draft a better prompt help me prompt you better and let the tool effectively prompt itself a lot of times you're going to solve your problems by not introducing them at all with with bad inputs so oh my god everything you share so interesting i just want to i want to keep digging so just to reflect back the the sequence and then i want to follow up with another question the sequence you go through when you get stuck which is going to happen to everyone one is just ask the tool to try to fix it and oftentimes it's telling you something is wrong can i fix it for you and you're like please fix sometimes that'll work two is work on adding more debugging messages to the console log and this advice i love of just ask it to add more debugging lines to its own console log to help see what's going on and then you can ask it okay now that you're looking watch look at all the output of your console log see if you can help find the problem and then step three is go to codex which is which is so funny and and i hear this a lot that codex is like the the most elite engineer as an ai karpathy tweeted this once that we had the head of codex on the podcast too by the way uh that he's like anytime i have the most gnarly bug i just go to codex let it run for half an hour and it solves it unlike any other tool out there and so it makes sense that that's where you go so the idea here is you point codex to your code you showed all the console output logs tell it what the problem is and just have it go figure it out sweet and then this final step is so great and this is where i want to go to do that which you use this as a learning opportunity so that next time you solve the problem more quickly or avoid it completely so what you do there is you ask the agent okay here's what happened what can i do what could i have said how could i have prompted you better to have gotten this immediately solved yeah and then even more even deeper than that is like once you go through this conversation you're like okay let me eliminate myself again completely out of the equation because i won't remember to prompt you better two days from now put this into rules put this what we just learned into rules.md because i i'm making you read the rules every time anyways so you might as well just record it there so i'm not going to prompt you better you're just going to learn that i'm stupid and you're going to prompt yourself better right again just eliminate yourself and and move the context you solve 99 of the problems with ai today so idea here is uh help it build its own brain and and rules and way of thinking based on problems you're into so great okay so so i want to come back to this point you've made a couple times which is so interesting this idea that you watch the output of the agent to learn what is going on there's something i've seen other people ben tossle who i think is a factory now share this recently he's also basically vibe coding all the time he was brilliant to know code tools before and now he's all about by coding and he shared basically like he's learning how things how coding works and learning how systems work by watching the agent output and this connects to something michael terrell shared the ceo of cursor when he was on the podcast he had this vision of cursor becoming basically what comes after code what's the layer that we are adding on top of code where people don't need to worry about code anymore and at that point it was like a year ago that we chatted and it feels like this is the layer is the agent conversation of what it is what it's thinking and then what you tell it back so essentially it's english in a conversation which is like it's not even pseudocode it's interesting but that's where it feels like things are heading the layer over code is just it's thinking and your conversation with it yeah yeah exactly i mean um again in in a way i really optimize for for good judgment and part of good judgment is it comes from again learning how these tools work you need to know what's possible we talked about it and i know i may sound sound contradictory sometimes right but it's because as you said it's so interesting the world we live in that things contradict to each other it's an advantage not to know what's possible but then at the same time you cannot be completely uh oblivious to uh something that's like a a factual thing so let me talk about a failure of mine that came from being delusional um back in the day when open ai um uh started uh or better said released image generation natively in the app right so you could go to chat gpt and be like generate an image of xyz the whole world exploded like that was like the biggest thing ever obviously first thing that comes to my mind is like i want to build a lovable app i just want to build a wrapper and i want to build an image gen with lovable without thinking that open ai did not release an api for that just yet so i spent at least a week trying to brute force my force brute force my way into making this work instead of just waiting for another week because a week later they had an api and i built this app in 30 seconds the problem was that like i tried to do it when it was impossible impossible like so um i think again you know uh uh it's just a matter of really learning what's possible through communicating with the agent player and lovable and all the other tools are agentic now which means like they don't just write code they can browse the web they can read files they they have reasoning and thinking capabilities so that's why i'm so invested into that conversation because a lot of times it will tell me hey what you're trying to do is just undoable at the moment because of xyz so like i always use those as a learning opportunity and i just level up most by being in chat mode for planning and learning purposes and and because it just again develops your clarity your judgment capabilities uh rather than coding capabilities yeah the other point you made here uh that i think is really important is that over time these tools will do more and more of what you do manually i've heard this from other people that are doing this full-time basically vibe coding is just they had all these workflows all these files and then cursor adds them level adds them and yeah it's like sad oh shoot i had this cool workflow now but on the other hand it's like okay now it's just doing all these second a year ago a year ago if we had an interview your mind would be blown stuff that i had to do as workarounds to for to to address shortcomings like i built a very successful course on that with starter story like for a year people were like just oh my god you're the only guy in the world that knows this secret now lovable natively addresses 99 of it i can almost say most of the stuff that i i was teaching people were like i have a youtube channel a little bit appreciated but like there's a there's a like a seven day learn how to buy code with lovable series that i did in march completely obsolete like it none of it is true none of it is a problem anymore all the things that i was like oh well this is missing and that is missing it's not missing anymore it's natively in the product like you don't have to work your way around it it it just works right so um that's why as i say like um it's the it's the horse's analogy i don't know if you heard of it like a lot of people are tweeting about it which is like we started building the steam machine in 1700s right took us about 200 years to build it when it got to when when engines got built and put cars were put on the roads i i think that 90 of horse population was got eradicated in the us within 20 years the person that tweeted this works at cloud code right so he was like now when i translated into ai i was hired to do a job technical job technical writer whatever i became obsolete six months later like humans did not get the 20 years that horses did the guy that that is was hired to do a thing is like six months later i need to reinvent my my role i need to evolve it into into into something else right so you know i i think there's just an evolution that's coming really really fast but like a lot of people are scared when i'm just super excited because don't you see our roles are finally going in a direction where we're outsourcing what we hated doing anyways right sitting in meetings taking notes doing spreadsheets like nobody maybe there are people that like that but like most people don't we're just getting into a place where we're rewarded for what really matters like clarity judgment thinking we're actually going to be paid to think longer and ponder longer because the longer idea simmers and gets broken down the better because building it is going to be an instant right it's going to be like this it's just a matter of you having so much clarity around it because guess what if a tool is super powerful and you give it a wrong input the output's gonna suck as well that's why like i've never become good enough at cloud code i feel because i don't start my projects with enough clarity and the tool is so powerful that like i just misdirected completely from the get-go and i was like oh shoot this is not what i wanted to do so that's why i still see myself being good at like using tools that are uh a little bit on the exploratory prototyping path um uh more than like on the path that you know elite engineers will use for example i love your optimism and excitement about this stuff i think for a lot of people say their current software engineers pms designers there's a lot of fear about the future of their careers are they going to be relevant will i will my software engineering skills disappear so to follow this thread a little bit if you were to give someone advice on which skills you think will be most valuable slash where ai will take on more and more this kind of momentum you're seeing of where ai is filling in more and more gaps what would your advice be of what you think people should focus on what will continue to be valuable in the future yeah emotional intelligence for sure just understanding human nature real life stuff i think we're all gonna get so tired of everything fake fake images fake posts fake profiles fake this fake that fake videos everything is becoming fake and ai generated i think humans just craving humans naturally are gonna want to do live stuff more so anything human to human is gonna be a big thing to skill up on understand the dynamics anything regarding math if it's a math problem i think peter still said it recently like people that just do math stuff you know ai is gonna come for you like anything that's very deterministic meaning x input equals y output and it's pretty clear the the line is pretty clear ai has got you eaten eaten for lunch right but if you understand how x to y goes in human dynamic human relationship layer i think that's where things are going to become good so if we translate it again to a specific skill i'll say it again good design really good design great design like how how and when i say design that's images fonts as well copy copy is a big one like we all now we're like two years into ai i'll bet you me and you if people put 10 pieces of copy in front of us we could tell what's ai and what isn't in like three seconds and we're only a couple years in so like really good copy writing is going to be a very good skill to have because people are just going to know after three words or three sentences that it's ai written and even i don't read ai output anymore i i don't like just seeing it i want raw that raw human experience so i think human skills i don't even know how to describe it because i don't i don't think we're doing an awesome job uh putting labels onto what humans are good at natively but i think we will i think we will describe job descriptions better um we we will have like uh uh human first engineers i don't know or human designers or i don't know how to describe those roles same way how uh carpathy uh coined vibe coding i was vibe coded before he didn't i didn't know how to call it like i started vibe coding in july of 2024 and i think he uh he coined it sometime in early 2025 so i was like doing it for seven months and i was teaching people how to do it for about three or four with courses and i didn't even know how to call it like because there was no name it was like oh i'm just using ai to to to do this for me i don't know whatever so yeah i think we're gonna reinvent some of the the terms rules and whatnot but uh stuff that's like human to human is here to stay stuff that's like i think like oh you're you're just doing you're you're a middle manager you're a middleware person that's just translating stuff um and i can use that analogy again translators are gonna die people writing jokes comedians are not ai is never gonna be able to write a good joke never never never it just doesn't have that layer that just doesn't understand what's funny like if you ever try to use ai to write jokes like they're awful they're always gonna be awful but if you use ai to translate things from one language to another it's very good at it like ai is gonna replace translators it's gonna replace most journalists because it does good research it can write good copy whatever not not elite journalism it's not gonna be able to replace all the writers it's gonna amplify great writers that can train ai on how to write books so like somebody who's an amazing writer is gonna all of a sudden write seven books a year instead of one right so that's that's dangerous if you're an average writer be careful there's zero comedians being placed zero and that's just my personal belief like ai is never gonna write good comedy it's impossible and like so try to find your analogy in your that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that that Um, I think it was Anthropik hired a bunch of National Lampoon comedy writers, uh, to help them train models. And so they're working on it. And I'm so, I, I love this strong prediction he made. I'm so curious in a year to look back and be like, he was completely right or no, they got that one too. I'll be wrong on 95% of the things I said today, three months from now. That is the only thing I can say very, very confidently. Yeah. That seems right. Okay. So speaking of career, so one interesting career option is to do what you're doing. As you said, this is a dream job for you. It's a dream job for so many people. What, what is the kind of your path to this job and what do you think it takes for someone to actually do this as a profession? Well, my personal path and personal journey was anything but linear, right? I've done so many things in life, like, um, blue collar jobs, looked at even at subway while I was studying and stuff like that. Like, um, I'm, I'm an engineer by trade, but not a software engineer. I'm a forestry engineer. So no coding, but still engineering is engineering. I feel you still develop certain set of skills doing that. Um, I waited tables a long time. So you develop some human skills. You understand what people like, what they don't like. Like I've again, blue collar jobs, like teach you hard work. And like, it's, as I said, the path was not linear, but I feel almost like a slumdog millionaire, the movie storyline, which is like everything that happens to the character brings them into a position to be able to answer the quite the questions in the, in the quiz better. I have, I feel the same way of like, I've done a lot of stuff last seven to eight years, obviously spent in startups, but doing everything but code writing, like started in like community management, social media. Again, distribution matters a lot. That's something we haven't touched upon at all. Like in a world when everybody's building and there's roughly the same amount of consumers in the world. How do you get in front of the eyeballs? Right. Like, and get attention, which is gonna, is this most scarce resource and it will be even more scarce, but like going back to the vibe quarter role. If somebody is like saying, okay, well, I have, I have a pretty diverse background too. And I'm vibe coding and like, how do I, how does this become a job? Well, for me, I feel like it became a job by building in public. Like I did chat with the lane of once only once. So like, why me? There are so many good vibe quarters. I did. How did you pick me out of the crowd? And I think, you know, obviously you're, she gave me a couple of reasons, but like to translate it into like a, a, a, a, one concept. It was like, I was building in public and sharing. I, as I said, I made a YouTube channel and I, I shared all the failures and all the knowledge, all the projects that I was building. I use social media a lot. Like LinkedIn was my go-to cause I, I just have that type of cadence. Uh, as you can see, I, all my answers are very long and X doesn't, doesn't cut it for that. Like you need, you know, you need to be very, uh, too on point to be successful at X. So I'm not, so I guess, you know, it's just like building public, share your knowledge, give away all the secrets. Like there are no secrets whatsoever. Uh, if you're sitting on a good concept, you're missing out. Let's just share it immediately. If you figure something out that I recognize that, um, very early on. And, you know, just like, I think a lot of people participate in hackathons these days. I want to encourage people to do them. Like find those, those opportunities locally to connect with other builders. Lovable is hiring across the board. Check out our open positions. It's as easy as that, right? Like just apply really find companies that, that are hiring and hiring in different roles. And I've seen people do something. I'm going to give people a secret away. A couple of hires stood up by not sending resumes, but sending lovable apps. They built lovable apps to show what they're, why they're good fit for a role. And we as lovable employees will always open an app that uses lovable.app domain. Always. If you send me a DM, send me a lovable app. Don't send me anything long. Send me an app that tells me what you want from me or how do you see us collaborating and working together. Right? So there's people finding creative ways to get in front of eyeballs of decision makers like Elena. Right? And I mean, skill wise, again, we're just repeating ourselves here, but I think it's important to repeat it as many times as possible. Really develop good judgment. Right? And I think it's important to really understand in a deeper sense how things translate when vibe coding comes into play. Right? There's a company out there. I'm not going to name them, but like that uses lovable religiously. It's going to be one of our main case studies actually where like they actually hired vibe coders before lovable did. Like I'm the first official vibe coding engineer at lovable, like with that title. But I've met people in companies where they hired them before us. People that are just vibe coders, people that just understand that speed matters. Right? It still matters a lot to be fast. And like there's a company out there with three vibe coders full time. All they do is like translating the old code base onto lovable. This is bringing everything. There's CRM, CMS, everything. All the tool sets that they have and they need it. There are people now actively just migrating everything. Everything over there's S&P 500 companies that are like putting lovable in job descriptions too. Like saying, hey, lovable skills are, you know, recommended in the recommended tab. Right? So yeah, to go back to the how to become vibe coder professionally. Well, you don't need a company to hire you. You can hire yourself as a professional vibe coder first. I think the reason why I clicked with Anton and with Elaine and everyone else, because I was already doing it. Like all I did, I just changed the vehicle, but I was already doing it professionally before I got hired. So that's kind of the key. Like do the job you would have done anyways. What a mind expanding conversation. I love just how passionate and excited and motivated you are about all this. It feels like there's so many people out there right now that are so burnt out. I don't know, disillusioned, scared. And you're the opposite of that. You're just leaning into this, just taking advantage, taking, you're not sure where it's going to go, but following the path. Yeah. And I don't want to interrupt you, but like it's because like, look, Lovable specifically isn't a company. You can talk about it as a company. I don't see it as a company. It's a it's an idea. It's a mission. It's something more powerful than the Internet in my mind, because like Internet allows us to consume. Lovable allows us to build. And in our nature and human nature is to build, to create. Right. And the fact that there's a tool today that you can go into and dump an idea in and something comes out of it and somebody uses it and finds it useful to me. It's just it's the craziest concept ever. It's my my only life's dream. I had my first computer when I was six and I was convinced my whole life that I'm going to be a software engineer or that I'm going to be building. But like life wasn't as simple as that for me. Like you. It was very, very complicated. And honestly, the last five to 10 years, I gave up on that dream. Almost. I thought I'm never going to build anything like I've tried. I've tried to build with technical co-founders. Like I just couldn't find alignment. I was I just gave up on it. And I now like at 36, like 30 years later, I feel like I can like that that kid like I dream every day. Like I it's it's amazing what this enables us to do. And I anybody that's scared, like just try it. I think it switches from fear to excitement immediately because then you see what's possible firsthand. Just go in, build something, build anything. And the fear goes away. You should only be afraid if you're doing nothing. If you're doing absolutely nothing. Yes. Be terrified by all means. Be terrified and then take a step towards doing something about it. And trust me, the leap is no longer as big as it used to be. It's as big as you come in and you just say what's on your mind and and just ship. I think a big part of this is just stop listening to this podcast. Go just do stuff. Would you actually try to, right? Ideally, people stop right now. They've heard enough. I gave them what I I gave them the best that I could just stop listening and just go. All right. Bye, everyone. Okay, I'm just joking, but let's but let's we shall wrap it up. I'm going to skip the lightning round just to keep this episode shorter before we wrap up. Is there anything else other than just go build some stuff? Anything else you want to say? Anything else you want to leave listeners with? Otherwise, we'll let we'll let you go. Yeah. Tech stack doesn't matter anymore. Right. It doesn't matter. Like people obsess over, oh, is this written in HTML? Is this written in React? It doesn't matter. Like it never mattered, but now matters even less. The end user just wants a stellar experience. We live in a world where anybody can produce good enough. So you better start learning how to produce magic because otherwise you're just going to end up in a crowd with millions and millions of others. But at the same time, if you don't know what magical looks like, don't be discouraged to start building anything and start start from good enough and level up. The best way to level up exposure time. Set aside more time on learning than building. Read the agent output, learn how it's thinking so that you know what's possible, but then also go and get inspired. Follow good designers on X. Find tools where great designs are produced and follow their creators. There's a tool where where I'm following just the the actual person that built it because he he publishes videos almost daily 40, 50 minutes long of him designing. I want to see how a world class designer does it. I want to see him talk to the tool. I want to see him prompt. And that's how I learn to become better at it. So again, exposure time just deliberately set more time aside to learning than coding because you can code fast, but you can code garbage fast as well as magic fast. It's the same amount of time. It's you and your input that matters. Forget about decisions on tech stack. Forget about which back end are using, which front end are using. That doesn't matter. Quality, taste, design. That's all you need to optimize for in the future. That's ahead of us. Well, Zara, this I think we're going to leave a lot of lines, a lot of minds buzzing after this conversation. You blow my mind in so many ways. What a fascinating topic conversation. What a glimpse into the future. What an interesting point in time. I'm so curious just, you know, in six months where things are and revisiting this conversation. I really appreciate you coming on, sharing all of this. You're awesome. Where can folks find you if they want to reach out, maybe ask some follow up questions and how can listeners be useful to you? Awesome. Yeah. So I mentioned it already. LinkedIn is probably the best place to find me on. You know, I'm very responsive there. If you want to follow me, I hope to reengage my YouTube channel a little bit more. I think I have a lot of cool tips and tricks that I want to share and teach people how to use lovable and just vibe code in general and level up and on how people can be useful to me. Well, you know, I'm very passionate about making sure that everybody experiences what I've experienced that day when I got my first prompt in. I envy the person that is going to try lovable for the first time after watching this episode because the feeling is just unmatched of you going from a consumer to a builder. But in that process, there's going to be some battles to fight. I want to reduce the amount of those battles and hurdles. So if you can help me in any way, message me what could have been better in that experience, especially if this is your you just watch this and you're like, I'm going to do it. I was on the fence and I'm going to do it. If something breaks, if something doesn't connect and relate, I need to know what that is. My job is 100% to empower you to build the best work of your life. Right. And, you know, and I need to say this, too, because a lot of people may be inspired not by building or using lovable, but but but rather building lovable. Come join our team again. We're hiring across so many things. I think a lot of people should feel inspired because I I hope that the energy that I bring to the table will resonate. This is how it feels working at lovable. This is how it feels working with the best minds. The brightest minds of the world were not number one by accident. It's it's not a coincidence. The best people are gathering and we we want you to be a part of it, too. So if if the energy and the conversation resonates with you or if you heard about a problem today and you you're like, man, I think I can solve it. Come join us. Help us build and shape the future of software development. Incredible. And what's the site that I imagine is just a link on lovable's website to find the open roles. Yeah, we'll link folks there. Yeah. Incredible. Lazar, thank you so much for being here. I appreciate the opportunity. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast dot com. See you in the next episode. Bye. Bye.