Lenny's Podcast: Product | Career | Growth
00:06:55 · Lazar Jovanovic, Lenny Rachitsky
[00:06:55] S0: Yeah, definitely. Some of the stuff that I've shipped that are public are like when we launched our Shopify integration, most of, if not all, the templates that users were remixing were built by me. So stuff like that. Or the merch store, because we wanted to obviously prove the concept that, "Hey, Lovable and Shopify just works. It's so simple, anybody can do it." I vibe coded our merch store. So all the merch, including this shirt that people were buying online, they would've bought it from a store that was built by me. But then again, on the internal side, we want to track a lot of things. One of the cool things that we want to build now, for example, is feature adoption metrics. If we build a feature, how many people are actually using it and adopting it? And that's a pretty custom build. 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've built it myself. At this point, I'm at a stage where if it takes me an hour or two hours to set up a big enterprise account somewhere, I'm just going to build it myself faster. So I'm in that position of build versus buy. I'm in the build boat, so to speak. Yeah. [00:08:10] S1: And then who do you report to? Are you this rover that helps wherever, or are you with a specific team?
00:08:10 · Lenny Rachitsky, Lazar Jovanovic
[00:08:10] S1: And then who do you report to? Are you this rover that helps wherever, or are you with a specific team? [00:08:15] S0: I'd say probably closer to the former. I started in growth. Elena brought me on early on because she has so many great ideas and she just needed somebody with the right type of mindset and velocity and ownership to just take them away, build them up, get them into production, whether they're based on education or anything, go-to-market or whatever. But then obviously when you're able to ship fast, everybody needs that in an environment that we as a company are now living in, which is we're the fastest growing startup in history. So every department needs a Lazar now or yesterday. So now I'm shifting a little bit into some of the go-to-market roles and even building some, again, internal tools for enterprise team. I'm working on some community tools as well right now as we speak. So I'm a little bit all over the place, but I thrive in that environment where I'm given a rough concept, a rough idea, and I'm just tasked to bring it to life as soon as possible.
00:09:18 · Lenny Rachitsky, Lazar Jovanovic
[00:09:18] S1: Okay. I'm hoping with this chat, we create a lot more Lazars and I want to get to the career path, how you got to this and what it takes to actually become a full-time vibe coder. But I want to start with... because you do this full-time, you're at the top 0.1% elite level of vibe coding. You're doing this full-time. They hired you to do this as a job. I'm so curious what you've learned. What are some pro-tips that you've developed for being successful with AI tools, Lovable, and also just more broadly? What are maybe two or three things you've learned that help you be really good at this job? [00:09:51] S0: The first understanding that I had very early on, even though just in full transparency before we begin, I don't have a technical background. I never wrote a single line of code in my life almost. I've written a couple of console logs manually and that's about it. So I very much lean onto AI assistance. [00:10:10] S1: Let me actually follow that thread because that's such a good point. And 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.
00:10:20 · Lazar Jovanovic
[00:10:20] S0: Yeah. Yes. I honestly feel that it is because people like me don't know that they are not supposed to be building X, Y, Z. And that's how we actually are able to build it. Let me give you an example. Six, seven months ago, somebody in our community was like, "Oh, I wish Lovable can build Chrome extensions." And then folks that are not technical were like, "Well, why is that not possible?" And then people that are technical start explaining to you, "Oh, well, it's React, it's different stack, it's this." And people like me, including myself, would just go into 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. Our community manager with me, at one point, she was building this presentation deck for something. She's like, "Wouldn't it be cool if this was a video?" And then she just prompted her way into generating an actual video inside Lovable before that was available. Now that's a feature. Now you can prompt Lovable to do it. But back in the day when she did it, even I thought it was impossible. I never tried it. So I think that's the advantage that we have over people that are technical. We're just coming to this completely unbiased and very positively delusional, which I think you have to have when working with AI tools. You have to come with this delusion that absolutely everything is possible until proven wrong. And that's just the pursuit that I have in my mind that has helped me, among other things that we'll chat today, I think to excel in this role that I have at Lovable.
00:12:02 · Lenny Rachitsky
[00:12:02] S1: Two of the, I think, concerns maybe traps people that don't have a technical background fall into in theory, one is if you get blocked, it's not obvious how to solve a problem. And two is just, are you building this teetering slop that will collapse someday because you don't know system architecture, you don't know if this is going to scale, 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 as one example.
00:12:36 · Lazar Jovanovic
[00:12:36] S0: I'm happy that you mentioned those limitations. I have some other ones that I want to bring in, but let's address this one first, which is the most important one. And that is you have to be self-aware. I didn't come into this... Yes, I am delusional, as I mentioned, in the sense that I just don't want to accept something's not possible, but I'm also well aware that I need to be better in order for it to become a reality from my own point of view and 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. The output that AI can do is much faster than human output anyways. So 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. I'm optimizing for the right kind of speed. Most people optimize for the wrong one. That's the first lesson that I learned literally on day two, because I came into Lovable, that was my first exposure to this. I've tested and played around with all the tools, obviously, but whether somebody's doing it in Cursor or Claude 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 because these are still just tools. Yes, AGI is coming, but it's not there yet. So until it's here, you're still steering the ship. In order for you to steer the ship, you have to know the instructions, 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. The syntax is none of my interest. It's what the agent tells me that matters to me. I put a lot of trust in LLMs and AI these days, and I understand that there may be some people that are not as confident as I am. I just feel that the models today are good enough for me to trust in their syntax output. However, I'm concerned about the agent output because of the two limitations that I want to tackle on next. The first one being that there's a limitation when you work with LLMs. So there's a machine level limitation and there's a human level limitation.