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Anton Osika: How Lovable scaled to 10M ARR with 15 people

Through clear, specific prompts and relentless reliability work; Lovable grew to 10M ARR mostly from people sharing demos, not from paid acquisition.

Anton OsikaguestLenny Rachitskyhost
Mar 9, 20251h 9mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 5:12

    Introduction to Anton and Lovable

    1. AO

      (laid-back music) Lovable is your personal AI software engineer. You describe an idea and then you get a fully working product. The reason is to enable those who have had, like, such a hard time finding people who are good at creating software, that's been their absolute bottleneck, and let them take their ideas and their dreams into reality.

    2. LR

      You guys hit 4 million ARR in the first four weeks. You hit 10 million ARR in the first two months with just 15 people. You're the fastest growing startup in all of Europe. How did you decide on Lovable as the name? It's so sweet.

    3. AO

      The best word for a great product is that it's lovable. A lot of jargon that I like to use to, like, emphasize what we should be striving for is building a minimum lovable product, and then building a lovable product, and then building an absolutely lovable product. So I, I took that jargon with me in the company name.

    4. LR

      People wonder just what jobs will be more important, what skills will be less important?

    5. AO

      Doing a bit of everything, being a generalist is, I think, much more important than it used to be. If I'm putting together a product team today, I, I would really obsess about getting as many skill sets as possible for each person I hire.

    6. LR

      What have you done that has allowed you to grow this fast with so few people?

    7. AO

      People love the product. (laughs) That's the driver of, of the growth.

    8. LR

      (instrumental music) Today my guest is Anton Ock. Anton is co-founder and CEO of Lovable, which is essentially an AI engineer that takes an English prompt and codes a product for you in minutes. You can then talk to it, iterate on the product, and then launch it to the world. It's one of the fastest growing products in history, the fastest growing startup in Europe ever. And as Anton describes, their goal for Lovable is for it to be the last piece of software that anybody has to write because it'll be able to create all future products for us. They launched just a few months ago. In the first four weeks, hit 4 million ARR. In the first two months, crossed 10 million ARR, all with just 15 people. Absurd. In our conversation, we covered a lot of ground, including a live demo of Lovable, how their team operates, how they hire, what has most enabled their team to scale this quickly with so few people, pro tips for using Lovable, how it all started, how he recommends you build product teams going forward with tools like this existing, what skills will matter more and less going forward, plus how to think about Lovable versus competitors, and so much more. If you're trying to wrap your head around how product building will change with the rise of AI tools, this episode is a must watch. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become a yearly subscriber of my newsletter, you now get a year free of Perplexity and Notion and Superhuman and Linear and Granola. Check it out at lennysnewsletter.com. With that, I bring you Anton Ock. This episode is brought to you by Sinch, the customer communications cloud. Here's the thing about digital customer communications. Whether you're sending marketing campaigns, verification codes, or account alerts, you need them to reach users reliably. That's where Sinch comes in. Over 150,000 businesses, including eight of the top 10 largest tech companies globally, use Sinch's API to build messaging, email, and calling into their products. And there's something big happening in messaging that product teams need to know about, rich communication services, or RCS. Think of RCS as SMS 2.0. Instead of getting texts from a random number, your users will see your verified company name and logo without needing to download anything new. It's a more secure and branded experience. Plus you get features like interactive carousels and suggestive replies. And here's why this matters. US carriers are starting to adopt RCS. Sinch is already helping major brands send RCS messages around the world, and they're helping Lenny's podcast listeners get registered first before the rush hits the US market. Learn more and get started at sinch.com/lenny. That's S-I-N-C-H.com/lenny. This episode is brought to you by Persona, the adaptable identity platform that helps businesses fight fraud, meet compliance requirements, and build trust. While you're listening to this right now, how do you know that you're really listening to me, Lenny? These days, it's easier than ever for fraudsters to steal PII, faces, and identities. That's where Persona comes in. Persona helps leading companies like LinkedIn, Etsy, and Twilio securely verify individuals and businesses across the world. What sets Persona apart is its configurability. Every company has different needs depending on its industry, use cases, risk tolerance, and user demographics. That's why Persona offers flexible building blocks that allow you to build tailored collection and verification flows that maximize conversion while minimizing risk. Plus, Persona's orchestration tools automate your identity process so that you can fight rapidly shifting fraud and meet new waves of regulation. Whether you're a startup or an enterprise business, Persona has a plan for you. Learn more at withpersona.com/lenny. Again, that's with P-E-R-S-O-N-a.com/lenny.

  2. 5:12 – 9:39

    Lovable’s rapid growth

    1. LR

      Anton, thank you so much for being here and welcome to the podcast.

    2. AO

      It, it's a pleasure to talk to you, Lenny. Great to be here.

    3. LR

      I don't know how you have time to do this podcast. Your life must be insane these days with the, uh, the pace at which you guys are scaling, just how much is changing in AI every day, uh, so I just extra appreciate you making time for this. I think you said it's, uh, 10:30 your time is when we're doing this.

    4. AO

      I'm a bit tired, yes.

    5. LR

      Oh, man. (laughs)

    6. AO

      Mostly from the, the crazy (laughs) pace of everything, but yes.

    7. LR

      We're gonna... This is gonna be a invigorating conversation-

    8. AO

      Yes.

    9. LR

      ... and you're not gonna be able to sleep. (laughs)

    10. AO

      I'm... (laughs) I'm sure. I s- I'm sure.

    11. LR

      So, okay, so for folks that are maybe a little bit familiar with Lovable or not at all familiar, what's just... What is Lovable? What's the simplest way to understand it?

    12. AO

      I... I'd say Lovable is your personal AI software engineer. You describe-... an idea and then you get a fully working product, th- that from the AI. And what this means is that entrepreneurs actually today, they turn their ideas into real businesses. Um, we have a lot of designers and product managers that, uh, create the first version of, of their product ideas to sh- show to their teams. And, and some of them become founders because of, like, their, the empowerment from this. Um, but also developers themselves, they're actually writing code or creating products much faster. And, um, I mean, the, the reason it's pretty obvious for me, so I'll spell it ou- but I'll spell it out, the, the reason why we're doing Lovable is that, I, I don't know about your mom, but, like, my mom doesn't write code and-

    13. LR

      Same. (laughs)

    14. AO

      ... you know, my friends, (laughs) almost all my friends from, throughout my life reached out for help, like, "Anton, I want, I do- I need to build something. How do I find a great software engineer?" And we're building for this 99% of the population who don't write, write code. Um, currently, if you're technically inclined, you get m- much further but over time naturally the way to build software is by just talking to an AI. Th- that's how is it.

    15. LR

      I love the way that you guys describe it and, uh, you didn't mention it, but I, I think it's like building the last piece of software ever. How do you, how do you, how do you phrase that?

    16. AO

      Yeah. W- we, we say, we say we're building the last piece of software.

    17. LR

      The last piece of software? Okay. We're gonna do a live demo, but first of all, can you just share some stats on the scale of this business at this point? Because it's quite absurd.

    18. AO

      Uh, yeah. So we launched Lovable thr- less than three months ago, and now we have 300,000 monthly active users and 30 of those, 30,000 of those are actually, uh, paying. Uh, and the, and it's growing on the s- at the same rates, like y- you adjust f- uh, almost only through, uh, organic word of mouth.

    19. LR

      Okay. And, uh, I'll share a couple stats in terms of revenue just so folks know this, and we'll have this in the intro too. I think you guys hit 4 million ARR in the first four weeks, you hit 10 million ARR in the first two months with just 15 people. You're the fastest-growing startup in all of Europe, and you guys had to rewrite your entire code base recently, and you couldn't ship any new features for a while. Is that right?

    20. AO

      Th- that's right. Yeah. Peo- people were saying like, "Oh, you're shipping so fast." And (laughs) we were all quite frustrated because we wrote our service in this, uh, you know, s- kind of scripting language and then as we started scaling, we, we were just, now we have to throw everything away and rewrite it in a, in a more performant way.

    21. LR

      Okay. Uh, before we get to the demo, last question. You shared there's some companies that have started based on Lovable. I didn't even know that. So what are some examples of companies/businesses that have launched off of Lovable and now are actually companies?

    22. AO

      I, I mentioned designers using Lovable and, and, and one of our early users, uh, Ha- Harry, he, he started shipping real web apps to his clients instead of just shipping designs. And then he went on to say, "Okay, wait, I'm going to start an AI startup." And, and his, hi- his company, he, like, launched on Product Hunt and everything and making, uh, money is just, like, lets anyone upload their f- photo library and then it's cat- like, the AI just i- parses and cate- categorizes it. And if you go to lauched.lovable.app, like, this is an app built with Lovable which have

    23. NA

      (instrumental music)

    24. AO

      ... is again a product, Product Hunt version where you can see a lot of, uh, businesses, uh, as, or small SaaS s- thing, um, featured there.

    25. LR

      Okay, cool.

  3. 9:39 – 18:34

    Live demo: Building an Airbnb clone

    1. LR

      So we're gonna come back to some of this stuff, but let's get into a, let's get into demo. I, I rarely do demos on this podcast, but I'm finding that, uh, I think it's really important for people to see these products in action because in a large part, this is the future of product building and a lot of people hear about, oh yeah, AI is coming, and I don't think a lot of people actually see what the latest tools are capable of. And so, uh, I love showing these sorts of things on this podcast.

    2. AO

      Uh, so, so Lenny, I was thinking, um, did you ever consider making a copy and build your own Airbnb?

    3. LR

      (laughs) I, I haven't, uh, but go on.

    4. AO

      H- how about you do that?

    5. LR

      Let's do it. Let's do it. Okay, so we're gonna make our own Airbnb.

    6. AO

      Okay.

    7. LR

      Okay, cool.

    8. AO

      So I, I just put in the first prompt for an Airbnb clone.

    9. LR

      Okay.

    10. AO

      And, and, yeah.

    11. LR

      Let's look. And what, and what is the prompt just to, for folks that aren't watching?

    12. AO

      Two words, "Airbnb clone." That's the prompt.

    13. LR

      Okay. (laughs)

    14. AO

      I, I like to (laughs) start simple. A- a- and then what you get there is that the AI says, "Okay, I'm going to, (laughs) I'm going to go through wh- what does an beautiful Airbnb clone look like?" And it, it goes through a bit of, like, decision, design decisions and then I'll, I'll zoom out to see more of it. Uh, we, we have this just, uh, UI that is... I mean, it has all the ni- the, the nice things you would expect from ar- uh, Airbnb clone where, um, you see different categories and you can see two listings from Airbnb with login buttons and everything. So far, it doesn't have the functionality of Airbnb, it just has the UI. I would now ask for an improvement on some of the functionality, like if I'm switching category, I want to see different listings, let's say. But if you, if you have any thoughts on what we should build next, let me know.

    15. LR

      Okay. And so you had this pre-loaded so you didn't see, uh, how long it would take, but how long would this normally take for it to just write all this code and have it for you?

    16. AO

      The s- the first prompt takes 30 seconds.

    17. LR

      30 seconds? Okay.

    18. AO

      Yeah.

    19. LR

      And it's like a very good copy (laughs) of Airbnb. Yeah. I love that you didn't have to show it a design, you just tell it Airbnb and it knows. Okay, so your question is, what would I want to add to my own version of Airbnb? Wo- I've always wanted to explore buying the place that I look at, just like, "Is this for sale?" So what if we see what that would feel like if you're just like, uh, a way to buy, buy a listing?

    20. AO

      Okay. Okay. Okay. S- so let's, let's, uh, how about w- we add... I mean, prompting is important here so l- let's be s- specific.

    21. LR

      Yeah.

    22. AO

      But we would ask, um-... creating a add a button on the listing which says, "Purchase this, this, uh, Airbnb home." Is that it?

    23. LR

      Mm-hmm. Perfect.

    24. AO

      Okay. So add a button. And I'll be more, even more specific. It will pop up a modal, um, to purchase the listing.

    25. LR

      Perfect. And I love ... So I think some things, as you're typing I'm just gonna share thoughts as you're doing this.

    26. AO

      Yeah.

    27. LR

      So the site that you asked this AI engineer to build, like it's actually a functioning website, that you can browse around. It's not just a design. The, say ... Obviously there's no, like, actual listings here. Like, there's no actual houses here. Say you were trying to, like, actually build Airbnb and you wanted to start adding, like, actual homes that plug into this. How does that sort of step work?

    28. AO

      Hmm. So as you say, this is just, kind of the mock-up UI, but it's also, also interactive. If I want to add login and add listing management then w- we would connect something called the backend. So, so where data is stored, where users' log- information is stored. And I, I can show you how, how to do that. Um, first let's just try out where we got with this short prompt of-

    29. LR

      Let's do it.

    30. AO

      ... adding the, adding the purchase, uh, listing. And it, it didn't do exactly what I wanted. I said, uh, add, um, a button. Or I didn't say what a button should say, but it says book now. And if I click book now, I get, uh, the, a booking confirmation. So the, the AI was like, okay ... It didn't really, it was probably surprised by you wanting to buy the listing since it's Airbnb, right? So it still says book the listing, but it's ... shows a, a pretty modal where I can click confirm and pay. And then it says, yeah, booking confirmed.

  4. 18:34 – 21:42

    Tips for mastering Lovable

    1. AO

      And I'd, I'd say one of the bigger bottlenecks is now they're not integrated into the current way that you have your existing products and so on. But since they're getting better so fa- so, so fast, I think the best thing for people who are interested in this or interested in just being a part of the future economies, get your hands very dirty with these tools, because being in the top 10% in using them is going to be... to absolutely set you apart in the coming y- uh, months and, and years.

    2. LR

      So let's... Let me follow that thread. So say you are magically able to sit next to s- everybody that is, uh, using Lovable for the first time and you could just whisper a tip in their ear to be successful with Lovable, what would that tip be?

    3. AO

      It, it takes a lot to master using tools like Lovable, and being very curious and patient. And I... We, we have something called chat mode where you can just ask and, like, to understand, like, how does this work? Like, is... I'm not getting what I want here. Um, am I missing something? What should I do? Eh, is, is, is the best way to be productive, is also fr- one of the best ways to just learn about how software engineering works, which is, I mean, you don't have to write the code anymore, but it's, it is useful to understand how software engine- or how building products works. So tha- so I think that's... The patience and curiosity is, is uh, uh, super useful. The se- second part that, that we spoke about is that being... I would, if I would sit, sit next to you, I would probably say like, "Hey, you, you're not being super clear here." Like, for example, don't say, "It doesn't work." Just explain exactly what you're expecting and which parts are working and which parts are not working. A- and that's a lot of... That's something that a lot of people don't do naturally.

    4. LR

      I love that, like, when you have an engineer you're working with, that is a very expensive, uh, mistake to miscommunicate something, to just forget about a feature, to forget about a requirement, and here it's you do that and then, like, 30 seconds later you're like, "Oh, okay, sorry, that was wrong," and then you could just try again.

    5. AO

      Uh, that's true. It, it might, it might be more costly with humans.

    6. LR

      (laughs) Uh, okay. And the first step... So the first step is chat mode, so you could just... So your advice is chat with the... What do you call it? Do you call it an agent? Do you call... What's, like, the, the term for the thing that you were talking with?

    7. AO

      Uh, yeah, Lovable is a name.

    8. LR

      Just Lovable?

    9. AO

      Yeah.

    10. LR

      Okay. So you're talking about Lovable. By the way, uh, where did you... How did you decide on Lovable as the name? It's so sweet.

    11. AO

      I think it's all about building, I mean, a great product. Um, that's what I wa- I want more people to be able to do, and the best word for a great product is that it's lovable. Yeah, the... A lot of jargon that I like to use to, like, emphasize what we should be striving for is building a minimum lovable product, and then building a lovable product, and then building an absolutely lovable product. So I, I took that jargon with me in, uh, the company name.

    12. LR

      That is great. Absolute Lovable Product, ALP. The new... It's the new MVP. Okay.

  5. 21:42 – 26:50

    The origin story

    1. LR

      So we talked about this, the scale you guys have hit at this point. I imagine it's far beyond 10 million ARR. Do you share that at this point or are you keeping that private?

    2. AO

      We, we, we don't ****** on the, the numbers-

    3. LR

      Okay.

    4. AO

      ... but I, I mean, I could probably do a 2X tweet about this (laughs) quite soon, yes.

    5. LR

      Okay. So it's far beyond 10 million ARR at this point. Uh, it's, uh, one of the fastest growing startups in history, the fastest growing startup in Europe. I want to zoom us back to the beginning. What is the origin story of Lovable? How did it all begin? What was the journey to today?

    6. AO

      I, I think I was not impressed by, like, what people were doing with the large language models when after... especially after... I, I was using them way back, but when ChatGPT came out, they were starting to get really good at taking a human instruction and spitting out code-

    7. LR

      Mm-hmm.

    8. AO

      ... and then, um, people in my team, I was the CTO at a YC startup, they felt like, "Oh, Anton, you're exaggerating. This is not going to change anything in the coming years." So I wanted to prove a point, and I created a open source tool called Ch- GPT Engineer, where you, you could write something like, "Create a snake game," and then it spits out a little code, a little different files, and then opens the snake game. And then I tweeted a video about that, and, um, GPT Engineer is to date the most popular, uh, open source tool to, uh, sh- showcase the ability for large language models to create applications. And it's at, like, 50 something th- 50 something thousand GitHub stars and, like, dozen of academic references.

    9. LR

      And I know that... I'll just add that it... Like, GitHub shut you down because it thought it was some kind of attack that, like, how many stars you were getting, how many people are using it.

    10. AO

      Right. Yeah, so that was... That, that came later. That, that's with Lovable.

    11. LR

      Oh, okay, okay.

    12. AO

      Th- so this was with Lovable.

    13. LR

      Okay. Okay.

    14. AO

      Lovable, um, oh, earlier was always creating new projects on GitHub when someone-

    15. LR

      Mm-hmm.

    16. AO

      ... uh, used Lovable, and it was the... We asked them, "Is it fine? Like, how... What's the limits here?" They said, "Oh, there are no limits." But once we started creating 15,000 g- projects per day, uh, so there were, uh, a lot of usage, then some engineer when he was on, on call, maybe they woke up in the night and they saw their servers are... were taking too much load because of us. So then when they, they shut us down completely and we got this email that said, "Oh, you broke some kind of rules." And we didn't (laughs) know what was going on.

    17. LR

      That's similar to a story I heard when, uh, ChatGPT was originally being trained. Microsoft servers were, uh, sh- blocked it, because they thought it was some crawler, and it was just actually, like, the very first version ChatGPT being trained on, on data. Anyway, keep going.

    18. AO

      Uh, so I- I built this tool called GPT Engineer and, um, I was thinking about... I mean, we're go- we're seeing the biggest change humanity will ever see, I think, where, um, like before you had manual labor being, uh, taken over by, by machines but now it's actually cognitive labor being take- being done better than humans by machines. And what's the best way to have some kind of positive impact here? It's not to make engineers more productive, which is... There's a lot of companies using AI to make engineers more productive in Microsoft, you build Copilot and so on. But it is to enable those who have, like, such a hard time finding people who are good at creating software that's been their absolute bottleneck and let them take their ideas and their dreams into reality, so enabling more entrepreneurship and i- innovation by building the A- AI software engineer for an- anyone. And then I, I put... I (laughs) grabbed a previous colleague of mine who has also been a founder, uh, Fabian, and I said, "We sh- we should build something like GPT Engineer but it sh- it has to be for the people who don't write code." That's the story.

    19. LR

      Okay. And then that became Lovable. It was like the shift from open source into a product that anyone can use but also pay for. Makes sense. Okay, so from that point, uh, I saw a stat that you started making a, a million dollars in ARR per week, uh, once you launched Lovable. Is that true?

    20. AO

      Yeah, so we launched... Um, we... So we actually called the first version of the product, like, GPT Engineer App, uh, and that was... that's, that's... it was very different in some ways. Um, and we li- we launched that under a waitlist and so like, "Oh, yeah, we have this waitlist," and we got a lot of feedback and iterated. Um, finally when we th- thought the product was really good, we said, "Okay, now we have a lovable product." And it was mainly on the AI that we did a lot of improvements. Uh, once we launched that, that was 21st of November, so that's almost three months ago, we, uh, just hit like one million, million ARR in a week and then it kept go- grow- growing at that, that pace. It ke- still growing at even faster than that pace.

    21. LR

      Faster than one million ARR per week?

    22. AO

      Yeah.

    23. LR

      Holy shit (laughs) . Okay. That sounds like product market fit to me.

  6. 26:50 – 33:20

    Scaling laws and getting AI unstuck

    1. LR

      You said that you did a lot of work on the backend. I saw you tweet about this, that you guys figured out some kind of unlock on scalability, like a new scaling law that allowed you to build something like this. What can you talk about there that kind of, on the technical element, allowed you to build something new and, and that's successful?

    2. AO

      There are many scaling laws, I would say, when you build AI systems and this one in particular is about when you put in more work, the pro- the product reliably gets better and better. And what you s- what you see in, um... Generally, when you have AI building something, is that it can get stuck in s- in some place. It starts, it's, it's super good in the beginning and then it gets stuck. What we did was to painstakingly identify places where it got stuck and, um, there, there's a different approaches but address, like, different ways how we do it, but address the places where it gets stuck. Tune the entire system quantitatively and having a very fast feedback loop to improve it in the areas where it got stuck, the most important areas. It still does get stuck sometimes but that's the scaling law and, um, we're still early in that scaling law, I would say.

    3. LR

      And so when you talk about things getting stuck, it's like the, the AI agent just saying, like, "I don't know what to do from this point" and... or like they introduce some kind of bug? Is that, is that an example of getting stuck?

    4. AO

      Yeah, i- introduces some kind of bug and then, um, it's not smart enough to figure out how to get out of that bug.

    5. LR

      I see. And this is a common, a common problem people have with tools like this, is they, like, get to a certain point and then it's like, "Well I don't know what to do. I'm not an engineer." Like, "Here's a bug it's running into," or the infrastructure's built the wrong way. And so it sounds like, uh, one of the paths to solving that is what you're describing, is you make the AI smarter to get... to avoid more and more of these places they get stuck. Another is people just learning how to get AI unstuck. Uh, this is something when we had Amjad on the podcast from Replit he said that this is, like, the main skill that he thinks people need to learn is how to unstuck AI when it runs into a problem. Uh, just thoughts there. I don't know. Anything along those lines come up as I say that?

    6. AO

      This is something that, uh, is a problem today, and i- the frontier of where this is a problem i- is very rapidly, like, receding back. So, uh, what we did was we identify the most important areas like, oh, so specifically adding login, creating data persistence, adding payment with Stripe, like, those, those are the things that we made sure it doesn't get stuck on, for example, um, and the places where it gets stuck today, um, is currently, it's something that we're... you can use being very good at understanding and getting unstuck but in the future it doesn't... it won't be so important. They... this is just going to not get stuck.

    7. LR

      And I know you're, you're not talking super in depth about this 'cause this is one of your unfair advantages, this kind of stuff you figured out, so I'm not gonna push too far. I don't know, I know you want not everyone to do exactly the same stuff. So I want to zoom back to the pace of growth that you guys have seen. One of the big stories, everyone's always looking you guys, of like 15 people, 10 million ARR in two months. That's absurd. It's something... I don't know if it's ever been done in history. If, if so, it's maybe a couple other AI startups recently.How have you been able to do this? What have you done that has allowed you to grow this fast with so few people?

    8. AO

      I'd like to take credit of, like, having done everything end-to-end in the product, um, but what's, uh, but we're building on top of, if they can, um, the oil here, which is, (laughs) yeah, we have discovered oil which is, are the foundation models. Right. Um, and then what you, what you've done is that we're obsessed about what's the right way to present this to a user who wants the interface for the human to get as much out of this as possible. Packaging together, I, I showed you in the demo that you, how you can add authentication and making this work seamlessly together as a whole. That, that's what we've done. And then people love the product. (laughs) That's what, that's the driver of, of the growth. Uh, the, um, for g- getting awareness, w- we've mainly been posting what we've shipped on social media. That's, that's how people know about us.

    9. LR

      So building in public is, is-

    10. AO

      Yep.

    11. LR

      ... is how people usually describe that. So it's like, uh, I think it's like you guys have the advantage of the demos are just like, "Holy shit, you can do that?" And then you guys share the numbers that you guys are growing at, so it's innately interesting and shareable.

    12. AO

      Mm-hmm.

    13. LR

      Uh, but I imagine most people have something interesting to share. I guess is there anything that you think you did that other companies maybe haven't done that make the product so, uh, lovable?

    14. AO

      The, I mean, the, the team is everything in building a, a great product. So I, I just give, uh, give a big shout-out to, to team that has written the code. I, I haven't written the code recent- much of the code recently, I would say. Um, and the, I mean, you, you want people who can ship really fast and have, uh, have good taste for, like, what is simple, what's the right abstractions, and I think that's what we've done, uh, differently, and have, have this obsession for ma- us making it better and better and better.

    15. LR

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  7. 33:20 – 36:25

    Reliability and unique features

    1. LR

      is a paid ad. Okay, I wanna come back to the team 'cause I know you have a lot of thoughts there. In terms of writing code, h- how much do you guys actually use AI to write the code that is building Lovable? Like, how does that work on your team?

    2. AO

      We have set up Lovable so that we can change Lovable with itself. Uh, we have done that. Um, since there is a lot of, like, hyper-specific things, um, in terms of running a separate ve- like, we spin up a dedicated computer for each user. Uh, it's, doesn't do everything. Lovable doesn't do ev- everything. So we use, like m- c- the tools that are for developers, not for the 99% most of the ti- most of the time. (laughs) And, uh, everyone uses AI all the time in, in writing code. It's also a-

    3. LR

      And ar-

    4. AO

      ... a great course for experimentations.

    5. LR

      And are there tools like Cursor and stuff like that, like any-

    6. AO

      Yeah.

    7. LR

      ... tools you can share right now?

    8. AO

      I think, I think Cursor is the, um, the one that almost everyo- everyone uses in, in the team.

    9. LR

      Yeah. Okay, cool. We, I did a survey recently and, on tools that my listeners and readers are using Cursor. Like, s- 17% of all people that read my newsletter use Cursor already, which is absurd. And you guys are in there too. Okay, so kind of along these lines, there's obviously other competitors and companies in this space, so everyone's always wondering, uh, you, Bolt, Replit, Cursor is a different kind of thing. What's the simplest way to understand maybe how Lovable might be different from, say, Bolt and Replit, which I think are probably the closest?

    10. AO

      The packaging for non-technical people is what we, what we aim for. And, um, I showed you in the demo that you can edit the text, like, ver- you can switch, change the colors and so on, uh, instantly with- without having to go into, like, the code editor, um, and without having to wait this, about 30 seconds for the AI to do the full change. So, uh, that's the, the big way that we think about packaging it. And then for, m- you know, making sure that this can be used as productively as possible in a larger team, uh, something that's different from, I think, the other, all the other tools is that it's, it is synchronized with, uh, GitHub. And that means that you can use Cursor if you're, or the people in your team that are, you know, that want to be more low level, they can use Cursor. And while the people who don't want to mess and set up their local file system and commit to GitHub and so, so on, they can use Lovable. Not getting stuck is, I, I, I think, the most important thing for people. And that's why we came, again, and we entered s- this space late. We haven't done the same type of marketing as, as many others and we're still, um, m- from the people that I talk to, r- ranked as, uh, the one that works most reliably.

    11. LR

      I l- I love it. Okay. So, uh, so this point about how you can just use Lovable to build a lot of it for you and then get into Cursor to edit and tweak is, is a really big point. And you're saying other com- other companies aren't as good at that?

    12. AO

      Yeah, I don't know if any other does that, uh-

    13. LR

      I didn't even let you do that, amazing. Okay.

  8. 36:25 – 38:14

    The vision and future of Lovable

    1. LR

      And then, how, what's kind of like the vision for Lovable? Like, what's the end state of this? Is this everybody can build anything they want sort of thing? What's the simplest way to understand where you're going in the next, I don't know, 5, 10 years?

    2. AO

      I mean, I have to say, so we're building the last piece of software, and it is inherently very hard to predict th- how the world looks like in five years these days. It's very hard. Um, but the last piece of software, how I see that is that, it, it's almost instant to go from what you want to change in a product or what you, what product you want to build, to having it fully working end-to-end, integrated with any of your existing systems or integrated with the, kind of the very powerful third-party providers. Already today, you can just ask, add in, uh, chats with OpenAI, and then you get to chat with OpenAI, uh, in your, in your product. But, um, that's like just work- working perfectly is the, something that's coming in the coming two years, I would say. Um, and then after that, there is a lot of things in building a product that is not just engineering side, right? And I think, um, an AI can be very useful in aggreg- aggregating and understanding your users. So (laughs) like if you, uh, if you use the analytics tools, you know that there is something quite common which is to, uh, see how users have interacted with the product. AIs can do that on, on absolutely massive scale and propose changes to humans to, to say like, "Oh, yeah. That sounds like a good change to make it a bit more intuitive." And it can also automatically run spin out A/B tests so that you can see th- the, the, uh, all these improvements of the product. So that, that, I think that's on the horizon as well quite soon.

    3. LR

      Mm-hmm.

  9. 38:14 – 40:30

    Skills and job market evolution in the age of AI

    1. LR

      Like, what's interesting about this in, in one way is, people wonder just what jobs will be more important, what skills will be less important. Let me share a thought I have, and then, um, I wanna get your take and see where you go with this. It feels like what is getting more valuable is being good at figuring out what to build and then knowing if the thing you had built is correct and good and ready. So it's like discovery, ideation, uh, idea, part of the step of launching a product, and then it's like taste and, and craft. Just like, is this the thing? Is this gonna solve people's problems? Because the, the building now is being done more and more. And it's interesting, it used to be the reverse engineering was the hardest, most valuable skill, and now it's like figuring out what to build. You could sit there and you, you could just tell it what to build. And then a lot of people get to your screen, I'm sure, and they're like, "I don't know what to build. I don't know what people want." And it's like, that's the thing now. So, uh, just reactions to that and thoughts on what skills will matter more and less.

    2. AO

      I mean, if you're, if you want to... If you're a founder or you want to build something, yeah, I, I totally agree that figuring out what are s- what are pain, uh, pain points and seeing like there are pr- often currently solutions to every, some kind of solution to everything. What is the, and how can you make this 10X better so- somehow? Like, figuring that out is, is super important. When you have, um, an existing product, then I think taste and like refina- ta- tasting what is, what is good is even, uh, more of the important part. The tech, like the engineer skillset is still going to be important because that, that helps you understand what are the constraints of what you can build. And I just think a lot of software engineers are probably a bit scared now, like, "Okay, am I, am I out of a job? (laughs) What's going to happen?" But they should see themselves as the people who translate the, the problems that are stated by a, a human probably, um, to technical solutions. And th- but they do have to abstract themselves up a few steps, not just like looking at the, in their tech stack, like, "Oh, I can just do the front-end changes." They, engineers or te- technical people are very good at understanding what are the constraints technically, and they should see themselves as that translators.

  10. 40:30 – 46:21

    Hiring philosophy and team dynamics

    1. AO

    2. LR

      Is there like a, like is it almost like you wanna be, learn the eng manager skill of overseeing engineers versus like the actual engineering skill? Or is, do you think it's still gonna be really important to learn how to code and be really good at that?

    3. AO

      I mean, doing a bit of everything, being a generalist is I think much more important than it used to be. And the, if, if I'm putting together a product team today, I, I would re-obsess about getting as much of, as, as many skillsets as possible for each person I h- I hire. Like, they should know how architecting a system works, preferably. They should know design. They should know, they should have product taste. They should know how to talk to users. I think everyone should be able to know, should know a bit of e- all of that, preferably.

    4. LR

      Easier said than done. (laughs) I mean, it's hard to find people that know all these things. So let's segue to hiring and, and how you hire. How many people do you have at this point? Is that something you share?

    5. AO

      Yeah. N- now, we're at 18.

    6. LR

      18. Okay. Wow. (laughs) So I love that you... It sounded like you're about to say, "Oh, we have 100 people now." No. 18, okay. So you went from 15 to 18. Uh, okay. Great. So what do you look for when you're hiring people? The way I saw you describe it on Twitter is you look for cracked (laughs) engineers, the best crack team in Europe, things like that. I guess, just specifically, what are you looking for when you're hiring?

    7. AO

      I think the most important thing is that people care a lot, and they're not just like, "Oh, I'm here for a job. I'm here for being as, as a passenger on this journey." But everyone should really care about the product, the users, and care a ton about the team, how the team works together, and that you're always contributing to making the team work more productively together. Uh, and th- then that, like, care or preferably obsession, eh, gets you a very long ways.And then, um, you do often want to have, like, absolute, absolute, um, superpower in, with some dimension. To be able to understand and do as many pos- things as possible, like have this generalist, uh, brain (laughs) that, uh, quickly learns any skill. But be s- super, super good in, in one dimension. And that's for us, that's of, that's mostly cramming as much out of AI or out of the large language models and understanding the, um, the entire parameter space of what you can change to make the sy- the, our product perform better.

    8. LR

      So how do you actually test for these things? And, uh, you know, like some of these, these things describe everything everyone's looking for. Like, they care about the user, they want to collaborate well. Just, like, when you're, 'cause th- like, you have 18 people building in a company that's growing more than a million ARR every week. Like, that's an absurd, uh, (laughs) uh, scale. And the people you found are clearly world class. And I think a lot of people are gonna, like, want to hire the type of people you're hiring. So when you're actually interviewing, how do you suss out some of these things like their AI cramming skills, their team building collaboration? What do you actually do?

    9. AO

      I ask people what they've done before and they, these people that I'm ex- des- describing, they have often done something where they care a lot about what they've done, uh, before. Uh, and then dig into details about the, the technical things that they did. And then, um, I mean, we do the normal thing of giving a, showing a very hard problem that is a bit, uh, unorthodox that someone hasn't seen before preferably, and see how they think through the tr- think and reason through that. I, then something that I think is more uncommon is that we do, I pretty much always have people join work simulation for at least a day, often a full week.

    10. LR

      Awesome. Okay. So work trial. That's awesome. So basically, they work with the team for at least a day. You said po- ten, uh, like sometimes a week.

    11. AO

      Yeah.

    12. LR

      And, uh, I love this point you made about they show they cared deeply about something they previously worked on and you can, you look for just like obsession with the thing that they built last or something they worked on.

    13. AO

      Hmm.

    14. LR

      Like what percentage are engineers at, of these 18?

    15. AO

      S- so 12 at least write code in, uh, at least part-time.

    16. LR

      12 out of 18. Okay, cool. Uh, you were, when we were setting up, you were like, "Oh, our engineer's creating content now." (laughs) I think that's a cool example of, of how people do a lot of different things.

    17. AO

      Yeah.

    18. LR

      Uh, also, okay, so I have your job posting that you shared once of, like, (laughs) their actual job description. I'm gonna read a few lines from it. It's, uh, very inspired by Shackleton, right?

    19. AO

      Mm-hmm.

    20. LR

      Would you agree? Cool. I love it. By the way, did you write this or did you have AI write this job description where you're like, "Create a engineering job description for..." In fact, let me read it to you. I don't even know if, you may not know what I'm even referring to. Uh, I'll read a few lines here. "Long hours, high pace, candidates must thrive under high urgency, under AGI timelines approaching. Uh, difficult mission ahead, honor and recognition in case of success. Those seeking comfortable work need not apply." And then there's a few other things.

    21. AO

      Yeah.

    22. LR

      "Collaboration with other exceptional minds, purpose larger than any normal engineering role. Generous share in the venture's success." Amazing.

    23. AO

      Thank you.

    24. LR

      Thoughts? (laughs)

    25. AO

      Yeah. So I, I did the, I did get some help with the, the formatting of this. But then I, uh, it was mostly me doing the s- the exact phrasing of the different sentences.

    26. LR

      So good. And I love that, you know, to some people it's gonna be like, "Holy shit, I'm not signing up for this." But to a lot of people, the people you want is like, "Yes, this is exactly what I want to be doing."

    27. AO

      Correct.

    28. LR

      Amazing.

    29. AO

      Yeah.

    30. LR

      Okay, cool. So, so it feels like one of the elements of hiring here is, uh, create a really good filter to be clear about just how intense this is so that-

  11. 46:21 – 48:02

    Building in Europe

    1. LR

      Okay. And then you're also, you're in Sweden. Uh, fastest growing startup in Europe ever. Thoughts on building in Europe/Sweden versus the US/San Francisco?

    2. AO

      Yeah, so this, this ambition level that you, you're talking about it in the job ad is more uncommon in Sweden. And I think that is the m- like, the biggest unlock that someone like me who (laughs) sees that this is the, um, like, the time in human history when you have the most impact per w- worked hour. And that's why we have to be super ambitious, like just up the ambition level. And then, then we can maybe retire and have AI take care of most, most things in society. Um, that and, and br- inspiring people to be this ambitious, um, in a place where the, the average ambition is lower, but the talent, the raw talent is, um, much more available, eh, is, is a great recipe. I think that's a great recipe. So the, and that's what's, I think it's some kind of advantage there. It, it's a bit of a double-edged sword, but, uh, but it's some kind of advantage.

    3. LR

      So what I'm hearing is, like, there's, there's incredible people in Europe, they're just not, uh, they're harder to find. And what I'm hearing is, like, the key is how do you suss them out and get them to, to want to talk to you?

    4. AO

      Yeah. The m- most people in Europe, they haven't thought that, "Oh, do- going on an extremely ambitious mission is what I want to do." So that's, uh, figuring out who those are, uh, is, is a big part of it.

    5. LR

      Awesome. Okay.

  12. 48:02 – 51:38

    Prioritization and product roadmap

    1. LR

      I want to talk about prioritization. I imagine all these things that I just shared about just like how, uh, ambitious this mission is, how much you're doing, the last piece of software. You must have a bazillion things that people ask you to build that you want to build. What's your approach to deciding what to prioritize and actually build?

    2. AO

      I ju- just top line, I think identifying what is the-... biggest bottleneck, what's the biggest product- problem, and i-iterating all fast on saying, "Okay, this is the biggest problem. Let's really, really solve that, that problem." And then pi- picking the next one. Um, uh, and not overthinking, not like dreaming up a long roadmap. That's my (laughs) my default. There's a very, very simple algorithm. Um, understanding what is the most big- the biggest problem is not just always a simple, simple problem. I think... Yeah, so we spend time as one should on, uh, talking to users, list- re- reading up on what people are writing. Uh, we ha- we have a, a feature board for where people do an auto request, as you say. And then, um, when we pick one of the problems, we're quite engineering led. Like, for a product like ours, it's hard to be- like have, uh, product managers that are not engineer- nears say, "Oh, this is what we should do now." Because like, the right solution to the problem m- might be entangled in things that, uh, are, um, like technical details (laughs) or they might be entangled in technical details of like, okay, yes, this is, uh, the biggest problem, but we should solve- we should have this larger technical initiative that's going to solve all of these problems. So it's, it's quite engineering led, um, compared to many other product companies.

    3. LR

      As it should. I'd be s- I'd be, uh, worried if you guys had a product manager at this point. Makes- that would not- Mm-hmm.

    4. AO

      (laughs)

    5. LR

      That would make no sense right now. I imagine the answer is it's chaos and there's no actual, uh, (laughs) defined process. But just like, what does it look like generally? Like, what's kind of the cadence you guys operate on? How do you take a idea to, like, build it, spec it, launch it? Just like, what does that look like if you have something?

    6. AO

      If, if you look back, uh, f- uh, like three months, we mainly said, "Okay, let's do this weekly planning." And we have, we do have like a (laughs) FigJam board where we have all the, uh, main problems and then we have kind of ranked them, which are, which ones do we focus, the one we focus on next or this week. Um, and then we have a, uh, demo of where we say like, "Okay, this- or are this- the things we ship this week." So to get every- everyone on the same page. Um, we do have a bit more of a roadmap now, and where we say like, "Here are- we're going to make so sure you can support custom domains next. Then we're going to add collaboration, uh, after that." And, um, the mi- like the biggest problem now or the bi- the biggest initiative now that solves the biggest problem is making the system more agentic. Um, and that has a, a bit of a longer roadmap, but we still do the cadence of weekly planning. Uh, these are the next- the things we're focusing on this week. It's mostly... There's a good word for this that you- w- I would want your help with, but polish, like ru- fixing the bugs and, and polish this week. And that was the planning on Mondays.

    7. LR

      That was actually this week was, uh-

    8. AO

      Yeah.

    9. LR

      ... polish, polish week. I love that. Uh, how far is this roadmap that you are now having?

    10. AO

      I mean, it's, uh, clear over the coming month, but it stretches out three months and then- but within, within (laughs) in one month it's probably going to look a

  13. 51:38 – 53:17

    Tools and work environment

    1. AO

      bit different.

    2. LR

      Okay. And then what are the tools you use just for folks that want to understand like the latest tools? So you said FigJam, what else is in that stack of tools?

    3. AO

      I mean, we do so many things in our company in Linear, because it's just a f- amazing product. So we e- we do talent application tracking in Linear.

    4. LR

      Oh, wow.

    5. AO

      (laughs) And after going through and d- and dissing a lot of the other t- uh, made- custom-made tools for that, uh, Linear and then, uh, FigJam.

    6. LR

      So simple. Uh, how soon until one of your engineers is an a- agent engineer, an AI engineer, do you think? Do you have a sense?

    7. AO

      I'd love to dig into what, what does that question actually mean?

    8. LR

      Mm-hmm.

    9. AO

      Um, I think y- y- we've been talking about like, oh, AI that would require, um... Or something playing chess, that's, that's AI. Like if you give an AI, if a computer can play chess, that's AI. And now that's like, oh no, that's a, uh, chess, uh, program and we're always shifting this forward and forward. Um, I think anything that a human doesn't do is just a smart computer system, right? So like what is an, uh, w- when is some- when is an a- a software engineer an agent? I think it's always going to be just, we are building... And Lovable is just an interface that humans interact with to create the software that they want. And then how we solve that is that go- going to be an agent under some definition? Yeah, sure. I think so. But, uh, that's less important to me.

    10. LR

      Okay. I re- I like that.

  14. 53:17 – 54:37

    Tactics for moving fast

    1. LR

      Let me ask this. You guys are moving super fast, scaling like crazy. You described a little bit about your process, weekly planning, uh, FigJam board of ideas, and now there's a roadmap that you're kind of thinking out in the future. Is there anything else that you found was- helps you move this fast that gives you a lot of leverage over the small team you have to ship quickly and move fast, uh, that you haven't already mentioned?

    2. AO

      We, we work from the office most of the time. I, I think it's, it's pretty nice. Then you can say like, "Hey, I think we're thinking wrong about this thing." Or like, "Shouldn't we actually do this other thing?" And especially I think lunch, uh, like eating lunch together is a pretty productive, uh, hour, uh, where you cross-pollinating. I mean, people are constantly thinking, uh, subconsciously as well about, uh, how to solve these different problems and which the most important ones are. And then being in office, um, has this like focus or m- most of the time you should be focused, but you also have this like high bandwidth where everyone has a bit unstructured communication.

    3. LR

      I love that, uh, the answer to, uh, the CEO of a company that's the m- one of the most advanced AI tools in the world is... One of your answers to how to move fast is like lunch together. I love that. That's so human and so es- it makes all the sense in the world, but I love that that's still a part of this.

    4. AO

      Yeah.

    5. LR

      Okay.

  15. 54:37 – 57:11

    Advice for building product teams

    1. LR

      Y- you talked about this, kind of on the same thread. You talked about if you were to start an, a team, like a new product team today. Say you were head of product somewhere or head of RPM, uh, VP of product somewhere. Building a new product team, scaling a product team. What would you do going forward that's different from what people have done in the past in terms of who you're hiring, how you're s- structuring them, that kind of thing? Just like, what do you think people should be thinking as they build product teams going forward, knowing tools like Lovable exist and all the other stuff that's going on?

    2. AO

      I mean, everyone should be excited about using AI. I think that's a pretty big one. Um, n- and then, and the team working really well together is, is, uh, the w- like the launch (laughs) . You have to, uh, like to sit down and solve problems together. Um, you should... At the bottleneck for most products these days is not going to be as much on engineering, but having good taste, good intuition about your users. And, um, that's when engineers and ev- everyone preferably in the team should have that, like willingness at least to w- to want to go through that motion and listen to the users, um, and truly understand, uh, wha- what, uh, they care about.

    3. LR

      What's kind of like the background of most of the engineers and people you hired? Are they like... Is there anything, like, in common? Are they just, like, super, uh, impressive humans generally, like, you know, champions of programming contests, stuff like that? I don't know. Like, what are some attributes of the folks you've hired so far?

    4. AO

      I think r- raw, like, cognitive, like, capability is the strongest, like, diamond and the, the strongest, uh, correlate of being at Luv- Lovable. Uh, but there, there is this startup mindset that I, I think is also, uh, very strong. Um, being a bit more... Being, being, um, much more interested in moving very fast and, uh, iterating fast than having, like, s- a lot of structure, a lot of process, um, and thinking about the business as a whole more than thinking about my specific profession, my specific craft that I'm, uh, see myself, like, wanting to dig in- into only.

    5. LR

      Amazing. Okay. So smart, like, very smart, entrepreneurial-

    6. AO

      Yeah.

    7. LR

      ... acts like an owner.

    8. AO

      Yeah.

    9. LR

      Doesn't just, uh, isn't just like this isn't just a job, but they feel like they actually have agency. Okay. This is great.

  16. 57:11 – 58:31

    Empowering non-technical founders

    1. LR

      There's something you said kind of along these lines that, uh, I think is important. That one of the things that gets you excited about what you're building is giving people superpowers and especially people that don't know how to code, basically 99% of people. Is there anything along those lines that you think is important to share?

    2. AO

      It's very clear to most people who have, uh, been engineers or been founders that they, um, there's s- so many that have failed in their endeavors because they didn't have an, someone that know how to solve the technical parts. And w- now that we're close to having people know that is those, like, know that is those exist and they work re- and they solve everything, uh, it's going to be an, can be an explosion of, uh, like, in- entrepreneurship and better software product. Uh, we're not going to settle for all the annoying bad technology that we, that we use today. And, um, everyone, uh, who has an idea is going to say like, "Okay, I'm gonna build this thing and show you that this is the best, uh, this is the best version of the product or what a company should be doing," instead of having long meetings or, like, s- writing up documents. So it's, um, going to be empowering, uh, like across a lot of different co- uh, professions and, and, uh, places

  17. 58:31 – 1:01:23

    Future developments and user support

    1. AO

      in the world.

    2. LR

      What's, what's next for Lovable? What's kind of like the next few things they might launch as this episode comes out?

    3. AO

      Uh, I mentioned this agentic behavior and, uh, when I say agentic what it means is that, uh, we give more freedom to the system to decide what, what happens next. And it, it might want to wr- write the test, run those tests and see like, "Oh, the tests failed. Let, let's fix those." So th- so that's, um, one of the big unlocks for getting further faster. And on... Then there's some more, like, obvious things that you want to do, uh, to go all the way to, l- easily go all the way to making money with Lovable. And that's li- that's like how do you set up so that it's hosted on your specific, uh, domain? How do you collaborate s- uh, seamlessly with your team, um, making that, that easier? So th- those are just obvious things. Um, and something we're thinking about is to help these founders succeed after they built their first version and, like, how do they get more users? How do they ge- get feedback? Uh, how do they get the word out if they build something useful?

    4. LR

      I was just gonna say that. That's, I think that's, that's exactly where my mind went is like everyone's gonna be building all these things. No one's ever gonna get any traction with these tools 'cause no one knows how to find users, get anyone to basically go to market and growth is, like, a whole different skill. So that is so cool that you're thinking about that. How do we run some paid ads for you? How do we think about SEO? How do we think about word of mouth, morality referrals? That is very cool. Okay. (laughs)

    5. AO

      And we already have some playbooks that we, that we help the people building with, like, how, how do you do those things, that you can find up on our blog.

    6. LR

      Oh, interestingly, this makes me want to buy m- some Meta stock because you're... (laughs) All these apps that everyone's building, they're gonna all be running paid ads on Facebook and Google. Oh my God. What a good business those other guys get. Uh, (laughs) I wanna come back to... You said that you can work on your existing code base. This actually a big question for a lot of people. They see all these tools. They're all, like, amazing for prototypes and concepting.You talked about how you can actually do this within your existing code base, use Lovable.

    7. AO

      Let me correct you there. You cannot u- use it on any kind of existing code base.

    8. LR

      Got it, got it.

    9. AO

      Um, we ha- uh, kind of have a research preview of, of importing your code base. But what you can do is if you start in Lovable-

    10. LR

      Yeah, yeah, yeah.

    11. AO

      ... then you can have engineers editing it in how, in whatever tool they want to use for editing it.

    12. LR

      Okay, cool. That's great clarification. So I guess just for people, 'cause a lot of, like most listeners here are not building something brand new. They're working within an existing product.

    13. AO

      Yeah.

    14. LR

      So, you're saying that that is coming. You can use Lovable in the future in some form with your existing app and product.

    15. AO

      Correct.

    16. LR

      Wow, that's huge. Okay, 'cause that's basically the most, most people, so that's gonna be a big deal. Okay,

  18. 1:01:23 – 1:05:20

    Failure corner

    1. LR

      uh, final question. We have this segment on this podcast called Failure Corner-

    2. AO

      Okay.

    3. LR

      ... where most people come on this podcast, they show all these stories of success and everything's going great, and here's all the things, always winning. You guys (laughs) this is a good example, just up and to the right, the fastest growing product ever. Uh, what's an example when something totally failed in the course of your career, and, and what did you learn from that?

    4. AO

      I, I'm a bit hard-pressed to find something that totally failed, but I think there's a bit of a product lesson, um, where I was the first employee at an AI startup here in Stockholm called Sana Labs.

    5. LR

      Hmm.

    6. AO

      And the premise was just, okay, so humans learn in different ways. You, we, if you personalize, then you get two standard deviations more, uh, effe- effective learning. So there, uh, there are a lot of products like s- education, software that helps you learn, um, that is not personalized. And we could buil- we were building an API to personalize learning. Uh, and the, I mean, the AI in Sana, I mean, it was, it was pretty good, but the thing that we were doing in the end was, uh, to say like, "Okay, here's this product. Here someone has to build a pros- product or some, some way to learn, or m- be it like English, uh, thing Duolingo." And then that, the people that have that product have to use this advanced AI API to start to making it personalized. And it was, it's very hard, like retrofitting, like, oh, we have to switch out the engine and put in this AI and, eh, it, eh, it's m- wha- well, the big learning here is, in that it didn't work v- very well f- or n- for the company. I mean, the company wasn't super successful in this. The big learning is that you have to start with like, how is this product working end to end? And then add AI, or think where should we add AI? So that, that was a big learning for me that, um, you, you really wanna see the, like fr- how the, w- what is the big picture of the user? What's the big picture of how this shou- sh- how do you think the user experience should be? And then add something with AI, uh, to solve specific problems. And thus, now Sana Labs is doing great, but it's n- it's, uh, not on top of that product specifically.

    7. LR

      That's, I, I think it's, a lot of people hear this and they're like, "Of course." But I think it's so hard to actually remember this point when you're, have some cool tech and you're like, "Holy shit, everyone needs to try this, they're gonna love it." And then you don't realize, like no one actually cares if it's not solving a problem for them. You know, there's like a lot of novelty products that like everyone will wanna use for a little bit and then like forget and it's not, I don't actually need this often. And so I, I, like what this makes me think about is there's all these product lessons for what is likely to help your product be successful. And an app like L- like a tool like Lovable can help you do this, because if someone is building something, you can guide them. Okay, what's the problem you're solving for somebody? How many people have this problem? How much does this matter to them?

    8. AO

      Maybe we should add like the Lenny Mode, so it activates in Lovable, it activates like this product ma- product coach.

    9. LR

      That would g-

    10. AO

      Like ask inf- infinite questions, and you're like, "No, no, wait."

    11. LR

      Yes, let's absolutely do this.

    12. AO

      "Hold on, wha- why are you doing this?"

    13. LR

      Why? So let's take a step back. (laughs)

    14. AO

      (laughs) Basically.

    15. LR

      Everyone's gonna be like, close...

    16. AO

      What's your hypothesis?

    17. LR

      Get out of my way.

    18. AO

      (laughs)

    19. LR

      (laughs) Yeah, exactly. (laughs) What's your ex- yeah, what's your experiment plan? Uh, that's actually, I think there's actually a big opportunity there to say people, 'cause-

    20. AO

      Yeah.

    21. LR

      ... you know, there's like a play around with this thing, and then there's like, okay, but really, is this anything people actually want? Uh, I love it.

    22. AO

      Can we ha- can we call it Lenny Mode? Is that a-fine with you?

    23. LR

      100%.

    24. AO

      Awesome.

    25. LR

      Let's do it. I'll license you, no cost.

    26. AO

      Sure. (laughs)

    27. LR

      Okay. Okay, we made a deal here. Let's do it.

  19. 1:05:20 – 1:09:47

    Final thoughts and advice

    1. LR

      Okay, uh, Anton, is there anything else that you wanted to share? Anything you wanna leave listeners with, uh, before I let you go and go to sleep?

    2. AO

      I think, again, the, the world is changing quickly and it's very fun. You should see this, like have fun in all, all of this change. Um, and the best thing you can do for your current profession or if you want to have a new job is to be in the top 1% in knowing how to use AI tools. So go out there, use, l- uh, use Lovable (laughs) use other AI tools and become, um, make sure to understand or try to understand as much of, as possible in how to use them productively. Um, tha- that's, that's something I, I tell all my friends in generally, and I (laughs) I'd like, love the audience to know as well.

    3. LR

      Okay. Well, I gotta make, try to make this even more specific for people. Uh, how do you know if you're in the top 1%? Like what's like a heuristic almost of like, slash how do you get there? Is it just use it 100 times a day? What else? What can you recommend?

    4. AO

      Yeah. I, I think if you spend a full week on w- trying to reach an outcome, I, the best way to learn is like, I wanna do this thing and then I'm gonna use AI to do that thing. Uh, and you've spent a full week, you're, you are in the top 1% in the gl- gl- global population. If you have friends tha- uh, you surround yourself with friends who ha- who have this obsession or they also care a lot about this, uh, then you'll be quickly in the top, uh, 0.1%.

    5. LR

      So what I'm hearing is, like, find a problem that, uh, that n- that can be solved. Like, find a problem, a pain point for yourself or someone.

    6. AO

      Yep.

    7. LR

      And then end-to-end, like, fully solve that problem. Spend a week getting from idea to, like, a thing that was actually, somebody's actually using.

    8. AO

      Yeah. Yeah, I-

    9. LR

      And you're in the top 1%. Okay.

    10. AO

      Yeah. I, I think I'm (laughs) at the top, yeah, the top 1% by just spending a, uh, a full week and making ch- like, asking AI if you don't understand, so mak- making sure that you understand.

    11. LR

      Yeah. And like, that's the thing people forget, you just ask. Like, like, you c- would y- would you ask the chat feature of Lovable in this case, or would you go to Claude or ChatGPT to ask for advice?

    12. AO

      I mean, my recommendation here if you're in product is to use Lovable to build software and, and learn that AI tool. If you're... And then you should use Ch- ChatMode. And, and ChatMode, I have to add, is something you activate in your user profile. It's not really laun- like, in the f- in the main Pro- product. So it's in, in, in labs. But if you ex- add (laughs) , uh, that flag, then you can use ChatMode. If you're, if you want to learn some other AI tool, then you should, I mean, you, you ask (laughs) that tool or ask, uh, Claude, uh, or ChatGPT ab- about how, how, how that t- topic, that domain works.

    13. LR

      Okay, amazing. Uh, where can people find you? Where they can, where can they find Lovable and how can listeners be useful to you?

    14. AO

      L- Lovable posts updates and memes on l- lovable_dev on, on Twitter. And we post things on LinkedIn as well, and there are a lot of, a lot of things coming out, uh, and changing (laughs) in how we build software. So you can follow lovable_dev, and you can follow me @antonosika, uh, at Twitter. Um, I'd love more feedback on what people, like, where people see this is huge change for them. Uh, we... There are a lot of (laughs) , a lot of people posting about that on Twitter, but the- there's a... We have a Discord where you can share like, "Oh, th- this is how I use Lovable and it was super useful to me." Um, and, uh, feedback.lovable.dev. You can give... Uh, you can ask for, uh, new features. You see, there's a lot of people asking about voting what features you want next. So... And that's super useful. That's the most important thing for us. We just want to solve people's problems.

    15. LR

      Amazing. Anton, you're doing incredible work. What a, what a journey. Uh, I'm excited to have you back some day when we, we see (laughs) more chapters of this journey.

    16. AO

      I have a lot more to learn.

    17. LR

      As do we all. That's why people listen to this podcast. Uh, Anton, thank you so much for being here.

    18. AO

      Thank you so much, Lenny.

    19. LR

      Bye, everyone. (instrumental music) 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 lennyspodcast.com. See you in the next episode.

Episode duration: 1:09:47

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