I'm the first official vibe coding engineer at Lovable. >> You're at the top.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 by coder first. You've never coded. You don't want to look at the code. >> Coding is going to be like calligraphy. People 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 ven 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. >> 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. Genie makes me 13 ft tall because I was not specific. Hey, I just don't understand. What do 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 Yavanovich. 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 I've 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 replet, bold, gamma, nadan, linear, devon, post talk, superhuman, descript, whisper flow, perplexity, warp, granola, magic patterns, ray, catch, chap, your de mo, and stripe atlas. Head on over to lennisnewsletter.com and click product pass. With that, I bring you Lazaranovich after a short word from our sponsors. This episode is brought to you by Stella, the customer research platform built for the AI era. 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Run your next study at strea.io/lenny. That's s str.io/lenny. Today's episode is brought to you by some sorrow. 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. Some SAR 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 the intent and answers questions like, did that truck driver break 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 constraints at a planetary scale, some Sara 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/lenny to learn more. That's samsar.com/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 of lovable. She mentioned that she works with a professional vibe coder. You I had so many questions. I almost wanted to like go on a tangent with her to try to understand this role. Instead, I asked you to come on the podcast. Uh there's so much I want to talk about. I want to talk about just this career path and just how you got into it, how other people might get into it, where you think this is all going, this whole vibe coding thing. Also, I want to get into what you've learned about it being successful using all these AI tools because this is your job. First, I want to just start with understanding this actual job. Just like what is it that you do day-to-day? You're basically being paid a full-time job to VIP code. Incredible. What are you responsible for? What are you doing dayto-day? >> Well, as you said it, like it's the it's a dream job, right? I get paid to do what I would have done anyways, right? It's the best job in the world. I get to use tools like globable every day to push projects to production whether for internal or external use. Those could be ranging anything from like different templates on marketing side, sales side or whatever or they can be as deep as like 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 it's an ideas role. A lot of people have a lot of great ideas but they don't know how to build them and 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 quality and security that they should have in order to be available for users in production. >> And 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 and it's like sort of a product level products. >> Yeah, definitely. Like some of the stuff that I've shipped that are public are like when we launched our Shopify integration, most of the if not all the templates that users were remixing were built by me, right? So stuff like that or like the merch store cuz 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 uh they would have bought it from a store that was built by me. But then on again on the internal side we we want to track a lot of things like one of the the cool things that we want to uh build now for example is like feature adoption matrix like if if we build a feature how many people are actually using it adopting it like 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 build or adopt faster than I would have built it myself like at this point I'm at a stage where like if it takes me an hour or two hours to set up like a big enterprise account somewhere. I'm just going to build it myself faster. So, you know, I'm I'm in that position of like build versus buy. I I'm I'm in the build boat uh so to speak. Yeah. >> And then who do you report to? Are you kind of this rover that helps wherever or you would with a specific team? >> I I'd say probably closer to the former, right? Uh I started in growth, right? Elena brought me on uh early on and you know to to cuz she has so many great ideas and like she just needed somebody with the right type of mind mindset and velocity and ownership to just take them away, build them up, get them into production whether they're like based on education or or anything go to market or whatever. But then obviously the when you're able to ship fast, everybody needs that in an environment that we as a company are now living in which is where the fastest growing startup in history. So every department needs a Lazar now or yesterday. So now I'm like 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'll I'll 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 kind of thrive in that environment where like 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 VIP coder. But I want to start with because you do this full-time. You're you're at the top.1% elite level of vibe coding. You're doing this full-time. They hired you to do this as a job. 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 that help you be really good at at this job? >> The first understanding that I had very early on, even though I just in full transparency before we begin, I don't I don't have a technical background. I never wrote a single line of code in my life almost like I've I've, you know, written a couple console logs manually and that's about it, right? So like I I'm very much lean on to AI assistance. >> Let me actually follow that thread because that's such a good point and 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 this space. >> Yeah. Yes. I 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. Um let me give you an example like six seven months ago somebody in our community was like oh I wish lovable can build Chrome extensions right and then folks that are not technical were like well why why is that not possible right and then people that are technical start explaining you oh well you know it's a react it's different stack it's this you and people like me including myself would just go in to lovable and like 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. Right. Our community manager with me at one point she was like building this presentation deck for for something. She's like, would it be cool if this was a video, right? And then she just prompted her way into building a 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 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 like that's just the the pursuit that I have in my mind uh that has helped me among other things that we will chat today I think to excel in this role that that I have at Labo. Two of the I think concerns maybe traps people that don't have a technical background fall to in theory is one is if you get blocked it's not obvious how to solve a problem and two is just are you building like uh this like teetering slop that will collapse someday because you don't know you know system architecture you don't know if this is going to scale all those sorts of things. So coming back to what you've learned about how to be successful and build successful products, talk us through just things you've done and things you've learned for how to avoid those sort of things and what you do when you get stuck is is one example. I I'm happy that you mentioned like those 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, right? I I didn't come into this. Yes, I am delusional as I mentioned in the sense that I 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 for my own point of view in my own sake. 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, right? Like the output that AI can do is much faster than human output anyways. So like very early on I started leveraging chat mode and to this day I can say I spent 80% of my time in planning and chatting and only 20% in executing the plan actually right 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 just I came into lovable that was my first exposure to to to this I've tested and and played around with all the tools obviously but like whether somebody's doing it in curs or cloud code 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 cuz these are still just tools yes AGI is coming but it's not there yet so like until it's here you're still steering the ship in order for you to steer the ship you kind of have to know the instructions right 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. Like 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 in in LLM and AI these days. And I I understand that there may be some people that 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 and because of the two limitations that I want to tackle tackle on next, right? The first one being that the the there's a limitation when you work with LLMs. So that's there's a machine level limitation and there's a human level limitation, right? The first one is there's something that that is known as the context memory window, right? And for nontechnical people, I like to use the Aladdin and the genie analogy when I explain, right? It's very simple. Everybody knows the story line. You you rub the the lamp, a genie comes out and tells you, "Okay, I'll grant you three wishes. Not 3,000 wishes, not three million, just three at a time." Right? To me, when I translate that data 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, like 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 is AI spends to read stuff, another to browse the web, another to uh 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 uh analogy of the genie and the Aladdin. I asked the genie for the first wish and the first wish is I want to be taller. And guess what happens? 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 I'm a dysfunctional human being. Right. Right. Because I was not specific. Right. Like 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 a I just don't understand. What do you mean when you say you know what I mean? like you do when I tell you that we as humans we have I'm 36 so I have 36 years of experience of human uh 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 you know because I can't control the first part which is the token memory window the quality of the LLM models you are 100% control of the ladder. And that's what I I want to dive into today as well and just try to teach people, okay, if I'm the malible part, how do I how do I fix that part, right? I think that's the the key lesson here. This is so helpful and I love this metaphor of the genie. The 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. 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 um 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 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. Like I knew when even before I joined Lovable, I was like, okay, even before I started using Lovable or any AI tools, first thing that I knew was like I don't know how to code, right? So my first thing was like, "Oh, I can build. Wow, amazing." But a week later, it was like, "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, I my development cycle that I'm in began and it's still ongoing, which is wait a minute, should I have I even built this in the first place? Because like at once you figure out that we solved for the how which is you AI assistant or rapid engineering. Call it whatever you want. You can call it vibe coding if you want to. But like we solved for that. Now we got to solve for everything else. And everything else is what matters. Good design, good taste, good user experience. Like 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 I think that the the core skill there to work on and develop today isn't again coding. Although I have nothing against traditional engineering and and I'll and I'll say later why I'm actually a big fan of it of elite engineering. But like people like me, people watching that are like, should I start learning how to code? If you haven't done it yet, I' i'd honestly say no. Like you're you're optimizing for the wrong skill set. We won't be rewarded in the word 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 uh useful. >> So, okay. So, what I'm hearing here is uh 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 you've never coded. You don't want to look at the code. you don't care about what's happening there that 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 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 This is something GM Marous uh shared on 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 uh I don't know website. So, I I really like that advice because those act it's so actionable. Okay. I just going to I'm going to spend more time with stuff that's great to inform my my taste and judgment. And then on the clarity piece, uh I want 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 mind 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, you just dictate the hell of it, and just press send. Right? Don't even wait for it to finish. Open a new window again. Lable.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 deliverability. like I know which features I want, which pages I want. Maybe I can even find a good reference. Maybe I can go on Maven, maybe I can go on dribble, 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 started. Now things are even more clear. Now you you 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 I'll go to and find a library 21st dev or uh um or a 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 it will interpret it better than your English or Spanish or whatever language that you use uh 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 uh uh 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 a an empty piece of paper or maybe a maybe chatting just with Chad 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 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 uh 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 the uh 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 this hack they're like oh but that doesn't that cost more. I'm like, yes, upfront 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 this the the the second layer. But I assume you may have some questions on this one. >> Questions and also just wow this is so such a great uh 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 whisper flow or use the built-in uh 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 Mobin and Dribble. Those are the two that you go to. >> Yeah, most times. Yeah. >> Okay. And then the fourth, and these are all in parallel. It's great. Uh 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 you got? 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 uh this 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 I 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% that I'm actually saving people. Like I'm I'm actually going against what I should be saying. If I was thinking about lovables, I would 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 they want. And then you you know it's my personal mission that resonates with me cuz if if there wasn't lovable um I would have never built anything potentially in my life. And I don't I don't think that that would have been a fun life to live. So um you know I I guarantee people like 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 kind of I don't know as you said I attribute it to nontechnical 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. It 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 when 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 the question in return is the obvious one, which is how do you context switching? Like you talk about context so much yet you're keep switching between apps. How do you manage to do it and do it in a way that's productive? You're 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 you've built hundreds of projects like I did, like you you 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 Chad GPT 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're are project requirements documents or for me I call them like sources of truth right what 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 loable 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 build it in 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 a 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 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 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 um uh you know uh markdown. Basically, I'm just using markdown format because I've learned that AI likes to read markdown. And what that serves is it serves as 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 or 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 there's a separate menu for that in your project settings where you can define project uh 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.mmd to see which task is next then execute on that next 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 put comes into play. I've told the a 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 to do with them. And at that point, I'm just sitting and reading. All I don't prompt anymore. At from that point on, I can switch as many windows as I like. My prompts have become proceed with the ne next task. I don't need the context. I outsourced that and delegated that to to the agent. The agent need 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 3 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 that work with AI. They're like 60% in my mind, maybe even more in how your output's 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 is the the one that that like requires better decision- making rather than better output or better uh alignment. >> Oh my god, there's so much here. This is so awesome. Okay. So, essentially what 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 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 in sific sophisticated tools that can build incredibly powerful things like you can do all 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 that this three wishes rule is really important the reason you're doing this in in large part beyond just being really clear about the plan is this idea of one task at a time keeps the agents context window uh 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? It is. Yes. Because again, if let's say you didn't do this, let's let's hypo 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 you're not referencing files or architecture at all. You're just describing the issue. Here's what's going to happen. Any tool loadable or cursor or clo whatever tool you 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 when you first start, you have like 20 files. It's it can read 20 files. But what what happens when you have I'm just building a project right now that has like 60 70 edge function functions, right? What happens then when I say this broke and there's no reference 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, an LLM expert in the comments may say that I'm wrong, but this is my best guess as a non-educated person. The f 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." When a lot of times when they don't, people blame the machine. And to to an extent, I 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 into the mud, right? And 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, didn't 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 it does it 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 that's if you ever read like a stream of Chad GPT'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 I just fell for the the the worst drink at the book. I 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 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 uh uh use all the agent files that you can because the that 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 uh 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 uh 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 real uh right after this, but uh the idea here is just like laser show point the laser at 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 in the context window. It start 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 it's 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 handholding 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 over and over and over." And I've seen that because again all I do is read the 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 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 file 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 uh agents MD rules MD. Say you wanted to just like move one step forward and be better at this stuff. What are the 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 it's a 10,000 foot overview, right? It it really high level explains the intent that I have with this app. >> This is master plan MD. Is that what you call it or? >> Yes. Yeah. Master plan.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 master plan as like this highlevel overview right to to get the agent into oh okay yeah we are building XYZ right um 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, um you're never going to get to the finish line. >> And this is tasks.md. Is that what you call this? >> No, that that's the implementation plan. I call it implementation plan. Yeah. >> Okay. >> And implementation plan is kind of uh in service of the future tasks.md if that all of these files are in service of building tasks. Mmd. 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 back end 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 like again just think of it as saving a I'm an ideas guy. I'm sitting with a technical guy. It's me and you. We're building our startup. You I know you're a software engineer by background and I'm telling you my idea, right? I'm giving you the master plan. And you come to me back and you're like, "Okay, if you want to do this, it's doable. Here's how I would order it." Like you have a road map. You you're not you didn't open your linear and started writing features and and uh RFC's and whatever. You're just high level talking about the 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 I like to see um lovable or any other tool chat GPT is good at it I even have my I built like custom GPT so if people want to start somewhere before they even get into any tool uh they can go to Chad GPT store and for GPTs and just type lovable bass prom generator or lovable prd generator and find those that I built and just like brain dump in them and then get these files as output. Right? So I I I like to see some elements of CSS in in design guidelines because you know you with design is a little bit it's a little bit tricky. AI is sometimes over creative. So I that's where I'm doing a little bit more technical steering right. Um and then finally it's just the user journeys just like if we know how things look like if we know how uh they feel if we know what we're building high level like high level just very high level again how do people navigate what what are some of the the features in there you know and stuff like that and then tasks.mmd gets into the nitty-gritty of 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 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 the writing you as a as somebody who directs and builds this product product, you need to know again what what 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, you 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 of these tools cuz that's how you get to clarity not just by reading but also by by doing as well. Here's a puzzle for you. What do OpenAI, Cursor, Perplexity, Verscell, Platt, and hundreds of other winning companies have in common? The answer is they're all powered by today's sponsor, Work OS. If you're building software for enterprises, you've probably felt the pain of integrating single signon, skim, arbback, audit logs, and other features required by big customers. Work OS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SAS. 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It also this shows you just what professional vibe coding looks like. You know, everyone imagines VIP code. I'm just sitting here typing stuff going do this. 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 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 the 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 it 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 we're at our stage where 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 uh I'm not going to name names but like uh leading right share companies of the world leading 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 our uh you know people in go in go to market or or sales or uh HR or whatever roles are now just confidently building internal stuff for us to manage our expenses or manage employee on boarding or like there's so many use cases like that where like you're seeing lovable uh and and other tools for that matter being used to to push things into production. >> Yeah. >> To help people do this workflow that you're describing with all these MD files. Do you think you could share uh 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 uh lovable 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 Levelable or other tools. >> Yeah, it's trained on 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 unblocked yourself because there's a whole other series of tips you have there. But I just want to reflect um it's so interesting how one you're kind of from first principles un 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 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 it feels 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 you built a SAS, who cares how it looks like? It works. It it does stuff like oh my god, I'm so much more productive today. Like if if good enough was here, let's say 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 worldclass and magic is the 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 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 be make better emotional decisions. And I think design is all about emotion. And that's where like the level up, 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 upskill?" Let's like 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 I you always use the analogy of like I 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 uh uh, 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 a an amazing newsletter like oh and to teach you how to and and learn how to prompt for good design, learn about design styles. I didn't know what Bow House meant or glass morphism. Had no idea. So like 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.loable.app. I don't know what it is. Like that 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 stilting? Do you feel like that goes away based on your experience? >> It it never goes away. We will need elite engineering more than ever. Like cuz 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 going to be a thing uh uh 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. And not to mention that in a 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 going to 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 going to 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 whatnotss, like, so I think there's going to be room for it. But I 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, not don't don't go and get a CS degree, you learn uh learn a good trade, you know, cuz uh the new generation of millionaires in the US are actually electricians and plumbers and whatnotss, 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. Will we need engineers to build actual products the the application layer? That's that's the question. Like is everyone going to be like you? is ever going to be or designers just going to be all we need. Everybody's gonna become an engineer and let's let's let's speak to that end like I'm an I feel like I'm a I'm a rapid engineer like I I I'll refer to myself as 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 you want to call 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 a on a canvas and people 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 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 a a an a forward 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. >> Oh man. Essentially these ven 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 going to like they can all do the same thing. Essentially all the roles 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 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? You have kind of a workflow you go through of unblocking yourself. >> Yes, great question and absolutely true. Like no matter how good of a plan you have in place, you're going to run into problems eventually. And I have like a small little framework that I call 4x4. Just again analogies, right? 4x4. If you if you have it on your car, you're going to get yourself out of the mud much easier than than uh the other way around. So um 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 the problem persists and sometimes even the problem persists but Lovable's agent's unaware that it persisted so there's no more try to fix buttonable thinks everything's is 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 the function that's broken rightclick 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 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 AI is like okay got it found it fixed it right but then there are situations when even that's not sufficient so you're like okay I need to go even deeper and that's where like code reviews and evaluations come into play my go-to tool today for that is codeex openai right what I do is like any any build that I do I will export it to GitHub like loable allows you to own your code cursor as well all of these tools allow you to have 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 codeex since beta like I import it in there and then I'm using an external tool. So I'm I'm like in the first try as if you remember like I used the tool and I was like total vibes I'm I'm relying on the tool right in the second try I use myself as the awareness uh uh a facilitator in the third one I'm using an external tool as a facilitator which is like I'll either connect to codeex and chat with codeex to then fix the problem in lovable right I don't allow codeex 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 uh workflow that I had before codeex and before cloud code which is there's a tool called repo mix which allows you to like compress every your entire codebase into a single file you download it and then I upload it claude just 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 cuz your team just can't handle it >> right >> and then the fourth one is is usually the best one because of the time when there are problems it's my fault like no matter how your ego is big guys that you're watching this it's your fault trust me 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, illovable and 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 got 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 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 Lola. 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 at doing things the right way if they are used the right way. It's always our fault. It's aund 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 it the right way. For me, as a non-designer, I don't know any of the terminology. Like none of the headings and whatnotss, and I still don't know it to this day. So, when I'm 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 gonna happen to everyone. One is just ask the tool to try to fix it. And often times 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 codeex 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 and 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 power 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 Codeex 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. And then this final step is so great and this is where I want to go. They what 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 cuz I won't remember to prompt you better two days from now." Put this into rules. put this what we just learned into rules.mmd 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 run 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. This something I've seen other people Ben Tossel who I think is a factory now shared this recently. He's also basically vip coding all the time. He was brilliant into no code tools before and now he's all about 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 pseudo code. It's interesting that that's where feel 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 comes from again learning how these tools work. Uh you need to know what's possible. We talked about it and I I know I may s 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 OpenAI um uh started uh uh or better say released image generation natively in the app, right? So you could go to Chad GPT and be like gen 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 Lullabable without thinking that OpenAI did not release an API for that just yet. So I spent at least a week trying to brute 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 layer 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 uh uh 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, VIP coding is just they had all these workflows, all these files and then cursor adds them. Loveable adds them and >> 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 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 or like I have a YouTube channel a little bit appreciate but like there's a there's a like a 7day learn how to vibe 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 just works right So um that's why as I say like um it's the it's the horses 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 clogged 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 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 cuz 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 may 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 going to suck as well. That's why like I never become good enough at cloud code, I feel cuz 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 I still see myself being good at like using tools that are uh 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 the 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 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 just craving humans naturally are going to want to do live stuff more. So anything human to human is going to be a big thing to scale up on understand the dynamics. anything regarding math. If it's a math problem, I think Peter Steel 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 AI is got got 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 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 3 seconds. And we're only a couple years in. So like 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 I don't like to seeing it. I want raw that raw human experience. So, I think human skills I 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 Karpathy 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 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'm I don't know whatever. So yeah, I think we're going to 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 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 going to die. People writing jokes, comedians are not. AI is never going to be able to write a good joke. Never, never, never. 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 going to be awful. But if you use AI to translate things from one language to another, it's very good at it. Like 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 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 like somebody who's a amazing writer is going to 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 going to write good comedy. It's impossible. And like so try to find your analogy in your industry. Like I just gave you one for uh writing skills, so to speak. So writing jokes, super good skill to have. Um translating, I'm sorry to say, but like you're not going to have a job for much longer. Like uh you better find something else to do. Um but yeah, that's that's how I look at it. >> The the comedy piece is interesting. I had one of the founders of the data labeling company, I don't know if it was Merkore or maybe Serge, and he said that um I think it was Anthropic hired a bunch of national lampoon comedy writers uh to help them train models. And so they're working on it. I'm so I I love this strong prediction you 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, 3 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. Like I I've done so many things in 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 uh I waited tables a long time so you develop some human skills you understand what people like what they don't like right 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 slum dog millionaire the movie storyline which is like everything that happens to the character brings them into a position to be able to answer the qu 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 the eyeballs, right? and get attention which is going to it is this most scarce resource and it will be even more scarce but like going back to the vibe coder role if somebody's 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 I did chat with Elena once only once and like why me there are so many good vorters I how did you pick me out of the crowd And I think you know obviously you know she gave me a couple of reasons but like to translate it into like 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 used social media a lot like LinkedIn was my go-to because 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 to on point to be successful at X. So, I'm not. So, I guess, you know, it's just like build in 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 and hiring in 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.appd 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 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 um I I mean skill-wise, again, we're just recating ourselves here, but I think it's important to repeat it as many times as possible. Really develop good judgment, right? Really uh understand in a deeper sense how uh how things translate when wife Cody comes into play. Right? There's a company out there, I'm not going to name them, but like um 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 free vibe coders full-time all they do is like translating the old uh codebase into uh onto lovable is bringing everything their CRM CMS everything that 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 in job descriptions too like saying hey lovable skills skills are you know um recommended in the recommended tab, right? So, yeah, to to to 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 vibeer first. I think the reason why it I clicked with Anton and with Elena and with everyone else because I was already doing it like all all I did I just changed the the the the vehicle but I was already doing it professionally before I got hired. So that's kind of the the the key like uh do the job you you you would have done anyways. >> What a mindexpanding 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 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 it's it's because like look, um, 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 it's it's something more powerful than the internet in my mind because like uh internet allows us to consume. Lovable allows us to build and and in our nature and human nature is to build to create, right? And and the fact that there's a tool today that you can go into and dump an idea in and 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 uh you it was very very uh complicated and honestly the last 5 to 10 years I gave up on that dream almost I I thought I'm never going to build anything like I've tried I I 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 again like that that kid like I dream every day like I it's it's amazing what this enable us to do and I anybody that's scared like just try it. It switches from fear to excitement immediately because then you see what's possible firsthand just go in build something build anything and the 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 we we shall wrap it up. Um, I'm gonna 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. Uh, text tag 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 matter, 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 you better start learning how to produce magic cuz 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 uh publishes videos almost daily 40 50 minutes long of him designing. I want to see how a worldass 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 backend are you using, which front end are you using. That doesn't matter. Quality, taste, design. That's all you need to optimize for in the future that's ahead of us. Lazar this uh I think we're going to leave a lot of line 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 6 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. Uh so um I mentioned it already. LinkedIn is probably the best place uh to find me on. You know, I'm very responsive there. Um if you want to follow me, I hope to re-engage my YouTube channel a little bit more. I think I have a lot of cool tips and tricks that I that I want to share and teach people how to u use lovable and and just vibe code in general and level up and on how people can uh 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 cuz 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 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? Um and uh you know and I need to say this too because a lot of people may be inspired uh 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. We're 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 a about a problem today and you you're like, man, I I think I can solve it, come join us. Help us build and shape the future of software development. >> Incredible. And uh what's the site? I imagine it's just a link on Lovable's website to find the the open rolls. 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'spodcast.com. See you in the next episode.