Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO)

0:00 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. You guys hit four million ARR in the first four weeks, you hit ten million ARR in the first two months, with just fifteen people. You're the fastest growing startup in all of Europe. How did you decide on lovable is the name? It's so sweet. 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 in People wonder just what jobs will be more important, what skills will be less important. Doing a bit of everything being in generalist is I think much more important than it used to be. If I'm putting together a product team today, I I would r really obsess about getting as many skill sets as possible for each person I hire. With so few people. People love the product. That's the driver of of the growth.

1:12 Today my guest is Anton O C. 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.

1:30 It's one of the fastest growing product in history. The fastest growing startup in Йorop 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 four million ARR. In the first two months, crossed 10 million ARR.

1:55 All with just fifteen 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 does most enable their team to scale this quickly with so few people? Pro tips for using Lovable?

2:09 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.

2:38 Of perplexity. And notion and superhuman and linear. And granola. Check it out at Lenny's Newsletter.com. With that, I bring you

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5:10 dot com slash Lenny. Anton, thank you so much for being here and welcome to the podcast. It it's a pleasure to talk to you, Lenny. Great to be here. I don't know how you have time to do this podcast. Your life must be insane these days with the uh

5:27 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 ten thirty your time is when we're doing this. Mostly from the

5:42 The crazy head pace of everything, but yes. We're gonna this is gonna be a invigorating conversation. Yeah, not gonna be able to sleep. I'm sure I'm sure. 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. I I'd say lovable is your personal AI software engineer, you describe

6:05 an idea and then you get a fully working product. That from the yes. And What this means is that Entrepreneurs actually

6:13 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 show to their teams. And and some of them become f founders because of like their the empowerment from this. Um but also developers themselves.

6:33 They actually writing call or creating products much faster. And um I mean the the reason It's pretty obvious.

6:42 For me, so I'll spell it but I'll spell it out. Th the reason why we're doing lovable Is that I don't know about your mom, but like my mom doesn't write

6:54 m 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 ninety nine percent of the population. who don't write write code.

7:09 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.

7:19 That's how we see it. I love the way that you guys describe it and uh you didn't mention it, but it I think it's like building the last piece of software ever. How do you how do you how do you phrase that? Yeah, w we say we say we're building the last piece of software. The last piece of software. Okay.

7:33 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. Yeah, so we launched Lovable Three. Less than three months ago.

7:46 And now we have three hundred thousand monthly active users and thirty of those thirty thousand of those are actually Uh paying. Uh and the in it's growing on the s at the same rate like you just for uh almost only through uh organic word of mouth.

8:02 Okay. And uh I'll share a couple of stats in terms of revenue. Just so folks know this, and we'll have this in the intro too. I think you guys hit Four million air in the first four weeks. You had ten million ARR in the first two months. With just fifteen people.

8:17 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? That that's right. Yeah. People were saying like Oh you're shipping so fast and we were all quite frustrated. Because

8:32 We wrote our service in kind of scripting language and then As we started scaling we we just now we have to throw everything away and rewrite it in in a more performant way. Okay.

8:44 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 slash businesses That have launched off of Lovable now are actually companies. I I mentioned designers using Lava Blend and and one of our early users Harry he he started shipping

9:04 real web apps to his clients, instead of just shipping the signs. And Then he went on to say, Okay, wait, I'm going to start an AI startup. And and his he his company he like launched on Product Hunt and everything and making Money is

9:18 Just like lets anyone upload their photo library. And then it's cat like the AI is parsers and cat categorizes it. And if you go to launch dot lovable app. Like this is an app built by Lovable, which is a is again a product product hand version where you can see a lot of uh businesses uh small size uh featured there.

9:38 Okay, cool. So we're gonna come back to some of the stuff. But let's get into uh 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.

9:54 And a lot of people hear about Oh yeah, AI's 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. Uh so Slenny, I was thinking Um did you ever consider making a

10:09 Copy. And Build your own R B D. I haven't. But go on. How how about you do that?

10:18 Let's do it. Let's do it. Okay, so we're gonna make our own Airbnb. Okay. So I I just put in The first prompt. For an R B D clone.

10:27 Okay. And what and what is the prompt? Just to for folks that aren't watching. Two words, Airbnb clowns. That's the problem. I like She starts simple.

10:35 the is that the AI says, Okay, I'm going to I can go through What what does a beautiful IB and B 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

10:52 Just uh UI that is I mean it has all the n the the nice things you would expect from a uh R B and B clone where Um You see

11:01 different categories and you can see two listings from I will be with the login buttons and everything. So far it doesn't have the functionality of R B and B, it just has the UI. I would now Ask for An improvement on

11:16 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. Okay, and so you had this pre loaded, so you didn't see 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?

11:31 The f the first prompt takes thirty seconds. Thirty seconds. Okay. And it's like a very good copy of Airbnb. Yeah. I love that you didn't have to show it a design, you just tell it Airbnb and those. Okay, so your question is what would I want to add to my own version of Airbnb?

11:48 What 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 list of So Let's let's

12:02 We we add I mean prompting is important here, so let's be s specific. But we would ask Um Creating a add a button on the listing which has purchas this this uh Rb home. Is that it?

12:16 Perfect. Is it add I've got a list. I mean Oh. Be more even more specific. It will pop up

12:26 A model. Um To purchase The listing. Perfect.

12:33 And I love so I think some things as you're typing, I'm just gonna share thoughts as you're doing this, so The site that you Ask this AI. engineered to build. Like it's actually a functioning website. You can browse around. It's not just a design.

12:46 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.

12:54 And you want it to start adding Like actual homes that plug into this. How does that sort of step work? Mm. So y as you say, this is just Yeah, kinda the mock up UI.

13:07 But it's also interactive. If I want to And Login. And uh listing management.

13:15 Then We will connect uh something called the backend. So where data is stored, where users log information is stored. And I I can show you how to how to do that. Um first let's just try out where we got with this short prompt of adding the adding the purchase uh listing.

13:34 And it It didn't do exactly what I wanted. I said Uh and um a button Or I didn't say what a button should say. Here, but it says book now. And if I click book now.

13:44 I get uh The booking confirmation. So the the AI was like okay, uh it didn't really it was probably surprised by you wanting to buy the listing since it's R B B, right? So it still says book the listing, but I it's shows a pr a pretty model where I can click confirm and pay.

14:03 And then it says booking confirmed. I'll just say real quick, I love that this is actually a really good example of why Being a good product manager is important. Uh, a lot of wasted time happens when you're not clear about the problem you're trying to solve and why you're trying to solve it and all that kind of stuff. So It's really cool that this is a use case where you have to be

14:22 Really good at Explaining what it is you want. And it's interesting you don't have to tell uh this A this A AI why. You know, humans want to understand why is this important. Uh. Mostly you need to be very clear about what it is you're doing.

14:34 Uh and I love that's a really strong PM skill. Yeah. PM's really good at that. So we have to explain exactly what you Expect and what you're not getting is even more important with AI than with the humans. But um So the l uh are going to Cooking up More of the

14:52 actual functionality. But first I'll actually show you something Um that w like how what's the fastest way to change what went wrong. It's it's it's created uh buttons that say book now. And I want them to say

15:05 Um buy now. And Uh what I could do is select this item and say change it to buy now. But what we just released is that you can actually edit these Like this is a fully functioning product.

15:20 But you can edit it visually. Like you're gonna like you do in Squarespace and Wix and so on. So I'll just change the text to buy now. And then it instantly s changes. Uh it actually changes the Okay.

15:32 deep down in the code base, but it it's very fast to to do that. So I think people listening to this and seeing this. If you're not aware, like this is the cutting edge of tools like this. No other tool. out there let you

15:45 generate code from an AI engineer and then actually just like change a small Element of it. of every other tool that I'm aware of, you have to like ask the agent Do this for me and then you hope that it does the right thing. So this is a huge deal which you just showed. Right. Yeah.

16:00 No says by now. Oh, okay, and that's something you just launched. Yeah. Correct. We just launched this a few days ago. But uh I won't go into full building the full functionality, but what it looks like is that you connect Um

16:14 An open source backend as a service. And uh that's called Superbass. Uh and I have this Instance. To connect to that completely empty, just like one click to set that up.

16:25 And now it's connected to Uh the back end it's Just like it. automatically generating and explaining Um ex generating some code and explaining

16:34 What I can do next. And what I would do now it Say Let's let's add login, that's it. That's our login. And where is it actually hosted?

16:43 On the back end. Yeah. In general. Yeah, so yeah Everything Can be one click. deployed and then it's running it's hosted by I think

16:53 A cloud vendor, uh which is hosting I think a huge chunk of the internet. It's called Cloudflare. Um and the back end is hosted by The Yeah. A good cloud provider which is called Superbase.

17:07 Amazing. Okay. Uh Let's wrap up the demo. That was unless there's anything else. Was there anything else really important that you wanted to show? No, I mean I I'll just explain what you what I would do next. I would say, Okay, let's add login. Um let's make the listings

17:21 editable by the users, so users can upload listings and Um then this is going to take a bit more time, but it be with patience and Um good prompting skills you're going to get to a full working Airbnb. That was a really good uh uh piece to add. So basically like this is getting to a place where it actually is

17:40 Not so different from actual Airbnb. Uh people can log in, they can add their home. You can add internal tools to add listings for your say sales team, ops team. Basically it just will allow you to build a marketplace. Uh great. That looks a lot like Airbnb. Um

17:56 Amazing. Okay. Thank you for the demo. I think for a lot of people they're like Yeah, I've seen this kind of stuff. For most people Like holy shit. It's unreal what Like it's almost like we're taking for granted now.

18:08 You can ask uh An app. To build you a whole website. And that costs probably like a few pennies. It took like five minutes.

18:17 Versus like It would have been tens of thousands and like weeks and weeks and months even build just a prototype. I mean these tools as we see here, they're already very good. Like it it looks really good as well. Um but mainly I s I would say the getting

18:32 Yeah. Better very, very fast. And I I'd say like one of the bigger bottlenecks is now They're not integrated into

18:40 the current way that you have your existing products and so on. But sin getting better so fast so so fast. I think the best thing For people who are interested in this or like interested in just being a part of the future economies.

18:55 get your hands very dirty with these tools because being in the top ten percent in using them is going to be to absolutely set you apart in the coming uh months and and years. So let's let me follow that thread. You are magically able to sit next to Everybody that is

19:12 uh using lovable for the first time and you could just whisper a tip in their ear. To be successful. Lovable. What would that tip be? It it takes a lot to master using tools like Lovable and being very curious.

19:25 Impatient 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'm w what I want here. Um am I missing something? What should I do? Yeah is

19:39 Is is the best way to be productive is also a pr one of the best ways to just learn about how software engineering works which is And you don't have to write the code anymore, but it's it is useful to understand how software and or how building products works. So that so I think that's The patients and

19:56 Curiosity is is uh Uh super useful. The sec the second part that that we spoke about is I would if I would sit sit next to you, I would probably say like hey

20:08 You you're not being super clear here. Like for example, don't say It doesn't work. Explain exactly what you're expecting and which parts are working and which parts are not working. Uh and that's a lot of th that's something that

20:21 A lot of people Don't do naturally. I love that like when you have an engineer you're working with that does a very expensive uh mistake to miscommunicate something, to just forget about a feature, to forget about a requirement, and here it's

20:35 You do that and then like I was thirty seconds later you're like oh okay, sorry, that was wrong and then you could just try again. Yeah, that's true. It might it might be more costly with humans. Mm. Uh okay. And the first step so the first step is chat mode. So you could just

20:48 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? Uh yeah, lovable Just Lovable. Yeah. Okay. So you're talking about Lovable. By the way, uh w where did you how did you decide on Lovable is the name? It's so sweet. I think

21:07 It's all about building I mean great product. Um that's what I w I want more people to be able to do. And the best word for a great product is that it's lovable.

21:20 Yeah, the a lot of jargon that I like to use to like emphasize what we should be striving for is building a l 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 in uh the company name. That is great. Absolute level product. A L P

21:39 Then he is the new M MVP. Okay. So we talked about this the scale you guys have hit at this point. I imagine it's far beyond ten million AR. Do you share that at this point or are you keeping that private? We we we don't anchor on the numbers, but I I mean I could probably do a Twicks tweet about this quite soon, yes.

21:57 Okay, so it's far beyond ten million error at this point. Uh It's uh one of the fastest growing startups in history, the fastest growing startup in Europe. I wanna zoom us back to the beginning. What is the origin story of all? How did it all begin? What was the journey to today?

22:14 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 uh when ChatGPT came out They were starting to get really good at taking a human instruction and spitting out code.

22:30 And then And people in my team, I was the CTO at a Y C startup. They felt like oh Nanton, y you're exaggerating, this is not going to change anything in the coming years. So I wanted to prove a point. And I created the

22:46 uh open source tool called GPT Engineer. Where You you could write something like Create a Snake game. And then it spits out a lot of code, a lot of different files, and then Opens the snake game.

22:59 And then I g tweeted a video about that. And Um Just but engineer. is to date the m most popular

23:07 uh open source tool to uh should showcase the ability for large language models to create applications. Is that like fifty to something fifty something thousand GitHub stores. And like dozen of academic references. And I know that I'll just add that it like GitHub shut you down because it thought it was some kind of attack. the the like how many stars you're getting, how many people are using it. Right. Yeah, so that was that that came later. That that's lovable. So this was plurable. Lovable.

23:35 Um earlier was always creating new projects on GitHub when someone they used Lovable and it was the we ask them Is it fine? Like how what's the limits here? They said are there no limits? But once we started creating fifteen thousand

23:50 Project per Day. Yeah. So they were Yeah, a lot of usage then Some engineer.

23:57 When it was on on call, maybe they woke up in the night. And they saw their servers are We're taking too much load. Because of us. So then would they sh they shut off down completely and

24:08 We got this email that said, Oh, you broke some kind of rules and we didn't know what was going on. That's similar to a story I heard when uh ChatGPT was originally being trained. Microsoft servers or uh Sh blocked it because they thought it was some crawler. And it was just actually like the very first version chat GPT being trained on.

24:26 On data. Anyway, keep going. And so I I built this tool called GPT Engineer. And Um

24:35 I was thinking about mean we're go we're s seeing the biggest change humanity will ever see, I think. Where um like before you had manual labour being uh uh taken over by s by machines, but now it's actually cognitive labour being taken being done better than humans. Five machines.

24:52 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. Microsoft to build co pilot and so on. But it is to Enable those who have had like such a hard time finding

25:12 people who are good at creating software, that's been their absolute bottleneck. And Letem. Mm, take their ideas and their dreams into reality. So

25:22 enabling more entrepreneurship and the innovation by f building the A AI software engineer for for any anyone. And then I I put I grabbed Uh previous colleague of mine Who has also been a founder, uh Fabian And I said we should we should build Something like GPT Engineer, but it's it has to be for

25:38 The people who don't write code. That's the story. Okay. And then that became lovable. There's like the shift from open source into a product. That anyone can use but also pay for.

25:50 Make sense. Okay, so from that point Uh I saw stat that you started making a a million dollars in AR per week. And once you launch lovable, is that true? Yeah, so we launched

26:02 Um We so we actually call the first version of the product like GPT Engineer app. Uh and that was that's that's was it was very different in some ways. Um and we'll we'll launch that under a wait list and so like oh yeah we have this wait list. And we got a lot of feedback and iterated.

26:18 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 twenty first of November, so that's almost three months ago.

26:33 We uh just hit like one million million error in a week and then it kept go grow going at that that pace. It's k still going at Even faster than that pace. Faster than one million ARR per week.

26:46 Holy shit. Okay. That sounds like product market fit to me. You said that you did a lot of work on the back end. 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 the successful.

27:08 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

27:22 Um Generally. when you have AI building something is that it can get stuck in s in some place. It starts i is super good in the beginning and then it gets back.

27:33 Pain thinkingly. Identify places where it goes stuck. And Um there there's a different approaches but address like different ways how we do it, but address

27:44 the places where it gets like tuned entire system Quantitatively. And having a very fast feedback loop to improve it in the areas where it got stuck, the most important areas.

27:55 It still does get stuck sometimes, but that's The scaling though. And Um We're still early in that scaling law, I would say.

28:04 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? It introduces some kind of bug. And then Um It's not smart enough to figure out how to get out of that bug. I see. And this is a common

28:23 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

28:38 Make the A I smarter to get To avoid more and more of these places they get stuck. Another is people just learning how to Get AI.

28:48 Unstuck. Uh, there's 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.

29:03 I mean this is something that uh is a problem today. And uh the frontier of Where this is the problem. So

29:16 Uh what we did was we identify the most important areas. Like oh so specifically Adding login. Quite data persistence. Adding

29:27 payment with strike. Like those those are the things that we uh make sure it doesn't get stuck on, for example. Um and the places Where it gets stuck today. Um is currently the something that we're You

29:39 Can use being very good at understanding and getting unstuck. But in the future it'll it won't be so important. The the system is just going to not get stuck. And I know you're you're not talking in 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.

29:58 So I wanna zoom back to The pace of growth that you guys have seen. One of the big stories, everyone's always looking at you guys have like fifteen people. Ten million ARR in two months.

30:10 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 of 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. I'd like to take credit of I having done everything end to end in the product.

30:30 Um but What Mm but we're building on top of uh taking on the oil here, which is we have discovered oil, which is are the foundation models. Right. Um and then what we'd what we've done is that we're obsessed about what's the right

30:44 way to present this to a user was 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 to be done.

31:01 And then People love the product. That's what that's the driver of of the growth. Uh the for Getting awareness. Well.

31:11 We've mainly been posting what we've shipped on social media. That's that's how people know about us. So building in public is is 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

31:29 innately interesting and shareable. Mm. 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. The I mean the the team is

31:45 Everything in building a a great product. So I I just give uh The k give a big shout out to to teen that has written the code. I I had written the code, recent much of the code recently, I would say. Um and the I mean you you won't

32:01 People who And I have I have good taste for like what this Simple, what's the right abstractions. And I think that's what we've done uh differently. And I have have this obsession for make us making it better and better and better. This episode is brought to you by the Fundrise Flagship Fund.

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33:22 Okay, I'm gonna come back to the team'cause I know you have a lot of thoughts there. In terms of writing code, how much do you guys actually use AI to write the code that is building lovable? Like how does that work on your team? We have set up lovable so that we can change lovable with itself. Yeah, we have done that. Um

33:38 Then There is a lot of I Hyper specific things. um in terms of running a separate

33:47 Basic we spin up this dedicated computer for each user. Uh it's doesn't do everything. Novel doesn't do everything. So we use like m co the tools that are for developers, not for the ninety nine percent. Most of the time most of the time. And Uh everyone uses AI all the time in in writing code.

34:07 It's also a great course for experimentation. And are there tools like Cursor and stuff like that? Like any tools you can change. I think Cursor is The um the one that almost everyone everyone uses in the in the team. Yeah. Okay, cool. We I did a survey recently and on tools that my listeners and readers use in cursor like

34:25 Seventeen percent of all people that read my newsletter use Cursor already, which is absurd. And you guys were in there too. Okay, so kinda along these lines, there's obviously other competitors and companies in the space, so everyone's always wondering. Uh U, Bolt, Replit, cursor is a different kind of thing. What's the simplest way to understand maybe how lovable might be different from, say, Balden

34:47 Ruplet, which I think are probably the closest. The Packaging for non technical people is what we what we aim for. And And I showed you in the demo that

34:58 you can edit the text, like you can s change the colours and so on. Yeah instantly. With without having to go into like a code editor Um without having to wait this. About thirty seconds for the AI to do the full change.

35:11 So Uh that's the The big way that we think about background. And then For

35:18 Mm. 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 the uh GitHub and that means that you can use cursor if you're or the people in your team that are yeah, that want to

35:37 be more low level, they can use cursor. And while the people who Don't wanna mess and set up their local file system and commit to GitHub and så you can use Slab of Boss. not getting stuck is I I I think the most important thing for people and that's why

35:52 We came we can enter the the space late. We haven't done the same type of marketing th as many others and we still Um Talk to you.

36:02 R ranked as uh the one that works most reliably. 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.

36:15 Is is a really big point. And you're saying other comp other companies aren't as good at that. I don't know if any other does that. Oh yeah, let you do that. Amazing. Okay. And then I had what's kinda like the vision for a 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, five ten years? I mean, I have to say so we're building the last piece of software and it is inherently very hard to predict the how the world looks like in five years these days. It's very hard.

36:47 And 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 the product or what to 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.

37:07 Already today you can just ask Ad chat with open AI and then you get the chat with open AI uh in your in your product. But um That's like just work working perfectly is the

37:21 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 the engineering side, right? And

37:34 I think Um And AI can be very useful in Aggreg aggregating and understanding your users. So

37:43 Like i if you uh if you use the analytics tools, you know that there's something quite common which is to Yeah, see how users have interacted with the product. AIs can do that on absolutely massive scale. And propose changes to human to to say like oh yeah, that sounds like a good change to make it a bit more Intuitive.

38:01 And it can also automatically run spin out. A B tests. So that you can see with the with data. Or these improvements to the product. So tha that's I think that's on the horizon as well quite soon.

38:13 Mm-hmm. Like what's interesting about this in in one way is People will wonder just what jobs will be more important, what skills will be less important. Let me share a thought I have and then I'm I want to get your take and see where you go with this. It feels like What is getting more valuable is

38:29 Being good at figuring out what to build. And then knowing if the thing you have built is correct and good and ready. So it's like discovery, ideation uh idea

38:42 Launching a product. And then it's like haste 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

38:58 figure out what to build. You could sit there and you you could just tell it what to build. And it 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. Yeah, it's like that's the thing now. So I just reactions to that and thoughts on what skills will matter more. Unless you're not.

39:13 I mean if you're if you want to if you're a founder or you want to build something, yeah, I I I totally agree with that. figuring out what else what our pain Yeah. Pain points and Seeing

39:23 Big. there are pro often currently solutions to every some kind of solution to everything. What is the And how can you make this 10X better somehow? Like figuring that out is super important. When you have um an existing product Then I think taste and like refining the tasting what is what is good is even um more of the Important part.

39:45 The Tech like the engineer skill set is still going to be important. Because that that helps you understand what are the constraints so what you can build And I just think

39:57 A lot of software engineers are probably a bit scared now, like okay, I am I out of a job and what's going to happen. But They should see themselves as the people who translate the the problems that are stated by a by human probably Um

40:11 to a technical solution. And N but they do have to s 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 p technically people who are very good at understanding what are

40:26 the constraints technically, and they should see themselves as that. Translators. Is there like a like is it almost like you wanna be Learn the end manager skill. of overseeing engineers versus like the actual engineering skill or is

40:40 You think it's still gonna be really important to learn how to code and be really good at that? I mean Doing a bit of everything, being in 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 will r we obsess about getting as much of as many skill sets as possible.

41:01 For each person I hi I hire, right? They should know How architecting a system works preferably. They should know the sign. They should know they should have product taste, they should know how to talk to users.

41:13 I think everyone should be able to n should know a bit about all of that preferably. Easier said than done. It's hard to find people that know all these things. So it's segue to hiring and and how you hire. How many people do you have at this point? Is that Some sure?

41:27 Yeah, n now we're at eighteen. Eighteen okay, wow. So I love that you It sounded like you're about to say, Oh, we have a hundred people now. No eighteen. Okay. So you went from fifteen to eighteen. Uh okay, great. So

41:41 What do you look for when you're hiring people? The way I saw you describe it on Twitter is you look for cracked engineers the best. Crack team in Europe, things like that. I guess just specifically what are you looking for when you're hiring? I think the most important thing is That's People care a lot and

41:57 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.

42:10 And that's You're always contributing to making the team work more productively together. And the that's Like care or preferably obsession.

42:21 Yeah. Gets you a very long way. Mm. And You do

42:27 often want to have like absolute absolute um superpower in of some dimension. To be able to understand and do as many possi things as possible. Like have this generalist uh brain that that quickly any skills but be super, super good in in one dimension. And that's for us that's of m that's mostly cramming as much out of AI out of the large language models. I'm just standing there.

42:51 Um the entire parameter space of what you can change to make the s the our product perform better. So how do you actually test for these things? I know you know, like some of these things described I think everyone's looking for, like they care about the user, they want to collaborate well. Just like when you're'cause But like you have eighteen people building in a company that's growing more than a millionaire every week, like that's an absurd

43:13 Uh Uh scale. And The people you've found are clearly. World class.

43:20 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? I

43:33 Ask people what they've done before and they these people that I'm ex describing, they have often done something where they care a lot. Yeah, about what they've done before. Uh and dig into details about But they

43:47 The technical things that they did. 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

44:02 something that I I think is more uh uncommon is that we do I Pretty much always I have people join the work simulation for at least a day. of an awful week.

44:13 Awesome. Okay. So work trial. That's awesome. So basically they work with the team for at least a day. You said pretend uh like uh sometimes a week. Yeah. And uh I love this point you made about they show

44:25 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. Mm. Like what percentage are engineers at of these eighteen? Uh

44:39 Twelve. at least write code in uh at least part time. Twelve out eighteen. Okay, cool. Uh you're when we were setting up you're like, Oh, our engineers creating content now.

44:51 Yeah. I think that's a cool example of of how people do a lot of different things. Yeah. Uh Also, okay, so I have your job posting that you shared once of like The actual job description. I'm gonna read a few lines from it. It's Uh very inspired by Shackleton, right?

45:07 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 an engineering job description fact. Let me read it to you. I don't even know you may not know what I'm referring to. Uh I'll read a few lines here.

45:20 Long hours, high pace. Candidates must thrive. Under a 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. Collaboration with other exceptional minds purpose larger than any normal engineering role.

45:39 Generous share in the venture success. Amazing. Thank you. Thoughts. Yeah, so I d I did the I did get some help with the the formatting of this, but then I Uh it was mostly m me doing the the exact tracing of the different sentences.

45:55 So good. And uh I love that, you know, to some people it's gonna be like holy shit, I'm not gonna sign 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. Correct. Amazing. Yeah. Okay, cool. So so it feels like one of the elements of hiring here is Uh create a really good filter.

46:15 To be clear about just how intense This is. So that the people that want that are the ones drawn to you. Okay. And then you're also you're in Sweden. Uh Fastest growing startup in Europe ever.

46:29 Thoughts on building in Europe. Slash Sweden versus the US slash San Francisco. Yeah, so this this ambition level that you you're talking about in the job ad i is more uncommon in Sweden. I and I think That is the Like the biggest unlock.

46:45 Um Like The time. in human history when you have the most impact for at work.

46:56 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

47:08 Right. Inspiring People to be this ambitious. Um in a place where the the average ambition is lower, but the talent the the raw talent is

47:18 um much more available. Yeah. Is is a great recipe. I think that's a great recipe. So Then and that's what's Mm.

47:27 I think it's some kind of advantage there. But it's some kind of advantage. Like there's there's incredible people in Europe. They're just not uh The they're harder to find, and what I'm hearing is like The key is how do you suss them out.

47:44 And get them. To to want to talk to you. Yeah, the the most people in Europe they haven't thought that oh do going on an extremely ambitious mission is what I wanna do. So that's uh Figuring out who those are uh is is a big part of it.

48:01 Awesome. Okay. 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.

48:12 You must have a bazillion things that People ask you to build it. You want to build What's your approach to deciding what to purchase and actually build? I j just top line. I think identifying what is the

48:25 biggest bottleneck was the biggest product problem and iterating or fast on saying, Okay, this is the biggest problem, let's really really solve that that problem and then pick picking the next one. Um Uh and not overthinking, not like dreaming out a long road map. That's my my default. There's a very, very simple algorithm. Um

48:44 understanding what is the most big the biggest problem is not the s always a simple simple problem. I think Yeah, so we spend time uh one shed on uh talking to users, the list reading up on what people are writing. Uh we have we have the uh feature board for where people do a lot of requests, as you say. And then

49:05 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

49:15 Uh product managers that are not engineered say, Oh, this is what we should do now because The right solution. to the problem. mm might be entangled in things that uh

49:29 All right. Um Technical details, or if they might be entangled in technical details of like okay, yes, this is 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 a it's quite engineering led um compared to many other product companies. As it should. I'd be sh I'd be uh worried if you guys had a product manager at this point. That wouldn't make no sense right now.

49:55 I imagine the answer is it's chaos and there's no actual Uh 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 sh build it, spec it, launch it? Just like what does that look like?

50:09 If you have something. If if you look back uh like three months we mainly said, Okay, let's do this weekly planning. Uh we have we do have like a

50:22 uh main problems and then we have kind of ranked them which are which ones do we focus the one we're focused on next or this week. Um and then we have a Uh demo where we say okay this or are this the things we ship this week so they get everyone on the same page. And we do have

50:39 A bit more of a roll up now. And where we say Like here are we going to make so sure you can support custom domains next, they're going to add collaboration Uh After that.

50:51 And um the b like the biggest problem now or the b the biggest initiative now that's all The biggest problem is Making the system more identific. Um

51:01 And that has a a bit of a longer roadmap, but we still do the cadence of weekly planning. Yeah, 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.

51:16 We're fixing the bags and and polish this week. And that was the planning on Monday. That was actually this week was uh Polish. Polish week. I love that. Uh, how far is this roadmap that you're now having? I mean, it's uh clear over the coming months.

51:33 But it stretches out three months and then but within it with in in one month it's probably going to look a bit different. Okay. And then what are the tools you use just for folks that want to understand like the latest? Tools so you said thick jam, what else is in that stack of tools? I mean w we do so many things in our company in linear. Because it's just an amazing product. So

51:52 We we do talent application. Cation tracking in linear. Yeah well. And after going through and and this single of the other two. Yeah.

52:01 Custom made tools for that. Uh linear and then Uh fake jumps. So simple. Uh how soon until one of your engineers is an a agent.

52:11 Engineer, an AI engineer, do you think? Do you have a sense? I love to dig into what what does that question actually mean. Um I think y y we've been talking about like oh AI That would require

52:24 Um or P something playing chess, that's a that's AI. Like if you if an A if a computer can play chess, that's AI. And now that's like oh no, that's a the chess uh program. And we always s shifting this forward and forward. Um I think

52:40 Anything. That a human doesn't do It's just A smart computer system, right? So I what isn't

52:51 Wha wh wh when is some when is an a software engineer and and Agent. I think it's always going to be just We're building in lovable is just an interface. That humans interact with.

53:04 to create the software that they want. And then how we solve that, is that going to be an agent under some definition? Yeah, sure. I think so. But uh That's less important to me. Okay. I I like that.

53:18 Let me ask this. You guys are moving super fast scaling like crazy. You described a little bit about your process, weekly planning. uh big jamboard of ideas and now there's a roadmap that you're kinda 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.

53:35 over the small team you have to ship quickly and move fast uh that you haven't already mentioned. We we work from the office most of the time. I think it's it's pretty nice. Then you can 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.

53:54 Is a pretty productive uh Yeah. You're cross pollinating. I mean, people are constantly thinking, uh, subconsciously as well about the How to solve these different problems and which the most important ones are and then being in office.

54:08 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. I love that. Uh The answer to uh the CO of a company that's the m one of the most advanced AI tools in the world is

54:25 One of your answers to how to move fast is Like lunch together. I love that. It's so human and so uh s it makes all the sense in the world, but I love that that's still a part of this. Yeah.

54:35 Yeah. Okay. You talked about this kind of on the same thread. You talked about If you were to start in

54:42 a team, like a new product team today. Say you were head of product somewhere. We're ahead of RPM uh VP of product somewhere. Building a new product team, scaling a product team. What would you do?

54:55 going forward that's different from what people have done in the past in terms of who you're hiring. How you're 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

55:10 All the other stuff that's going on. I mean, everyone should be excited about using AI. Think that's a pretty big one. Um Mm-hmm.

55:18 And then And the team working really well together is is uh the what the lunch you have to uh like to sit down and solve problems together. Um You should Yeah.

55:31 The bottleneck For most products these days it's not going to be as much on engineering, but having Good taste, good intuition about your users. And

55:43 Um That's I mean engineers and everyone preferably in the team should have that. willingness at least to w to want to go through that motion and listen to the users. Um and truly understand what what the they care about.

56:00 What's kinda 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 contest, stuff like that. I don't know. Like what are some attributes of the folks you've hired so far? I think law kognitivity is the strongest. The strongest.

56:26 correlate of being at loveable lovable Uh but There there is this start up mindset that I I think is also very strong. Being a bit more being being m much more interested in moving very fast and uh iterating fast than having like s a lot of structure, a lot of process.

56:46 And thinking about the business as a whole more than thinking about my specific profession, my specific craft that I'm and see myself like wanting to dig in Into only. Amazing. Okay. So smart, like very smart.

57:01 Entrepreneurial Acts like an owner. Yeah. Isn't just like this isn't just a job, but they feel like they actually Have agency.

57:09 Okay. This is great. 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.

57:22 Basically ninety nine percent of people. Is there anything along those lines that you think is important to share? It's very clear to most people who have been engineers or been founders that they Um there's s so many that have failed in their endeavors because they didn't have um someone that know how to solve the technical parts. And

57:45 People know that it's those like know that it still exists and they work they solve everything. And it's going to be an Cambrian explosion of uh like in entrepreneurship and better s software product. Uh we're not going to settle for all the

58:01 Annoying. bad technology that we that we use today. And Um Everyone.

58:10 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 This is the best version of the product or what our company should be doing. Instead of having long meetings or like s writing up documents. So it it's um going to be empowering uh across a lot of different co professions and and uh places in the world.

58:32 What's what's next for Lovable? What's kinda like the next few things they might launch? As this episode comes out. I I mentioned this agentic behavior. And that when I say agentic what it means is that the you give more freedom to the system to decide what what happens next.

58:48 Yeah, it m it might want to run write a test, run those tests and see like oh the tests fail, l let's fix those. So that so that's Um one of the big unlocks for Getting further faster.

58:59 And On Then there's some More like obvious things that you want to do. Yeah.

59:06 to go all the way to That easily go all the way to making money with lovable. And That's like that's like how do you set up so that it's hosted on your specific uh domain?

59:18 How do you collaborate the seamlessly with your team? Um making that that easier so that the the are just Obvious things. Um and Something.

59:29 We're thinking about is to help the founders succeed after they built their first version. And like how do they get more users, how do they get to get feedback? Uh, how do they get the word out if they build something useful? I was just gonna say that. That's exactly where my mind went, is like everyone's gonna be building all these things, no one's ever gonna

59:48 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, reality referrals. That is very cool. Okay.

1:00:07 Yeah, we already have. On playbooks that we that we help the people building with how how do you do those things that you can find up on a rug? Oh, interestingly, this makes me want to buy m some Meta stock because you're 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 got. Uh I wanna come back to you said that you can work on your existing code base. That's actually a big question for a lot of people.

1:00:32 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. Let me correct you there. You use it on any connected existing code base.

1:00:46 Um we have kinda have a research preview of of importing your code base. But what you can do Is if you start in Lovable Then you can have engineers editing it how in whatever tool they want to use for editing it.

1:00:59 Okay, cool. That's great clarification. So I guess just four people,'cause a lot of like most listeners here are not building something. Brand new, they're working within an existing product. So You're saying that that is coming. You can use Lovable in the future.

1:01:12 in some form with your existing app and product. Correct. Wow. That's huge. Okay.'Cause that's basically the most most people.

1:01:21 So that's gonna be a big deal. Okay. Uh Final question. We have the segment on this podcast called Failure Corner.

1:01:29 Okay. 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 uh this is a good example just Up and to the right, the the fastest growing product ever. Uh

1:01:42 What's an example when something Totally failed. in the course of your career and and what did you learn from that? I ha I'm a bit hard pressed to find something that Totally failed. But I I think there's a bit of a product lesson.

1:01:54 Um we're I was the first employee at an AI startup here in Stockholm called Fana Labs. And

1:02:02 The premise. Okay, so humans learn in different ways. You we if you personalize then you get two standard deviations. More uh effec effective learning.

1:02:14 So they're Yeah, there's a lot of Products like Um that is not personalized.

1:02:23 And we could build we were building an API to personalize learning. Uh and The I mean The AI and so on because

1:02:32 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 built a product or some some way to learn where be it like English

1:02:46 And think doing it. And then The people that have the product have to Use this advanced AI APIs. To start p making it personalized.

1:02:59 And It was it's a very hard like retrofitting, like oh you have to switch out the engine and put in this AI and Yeah. It's m well the big learning here is in that it didn't work v very well.

1:03:11 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 th that was a big learning for me that Um

1:03:28 You y you really wanna see They're W how the w what is the big picture of the user, what's the big picture of how this should sh how do you think the user experience should be? And then add something with AI. uh to solve specific problems.

1:03:44 And now Sun Labs is doing great, but it's it's the not on top of that product specifically. It'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 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.

1:04:02 If it's not solving a problem for them. Yeah, there's like a lot of novelty products that like everyone 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

1:04:17 is likely to help your product be successful. And an app like Lov 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?

1:04:32 How many people have this problem? How Much does this matter to them. Maybe we should add like the Lenny mode. It activates in Lovable, it activates like this. Product ma product coach.

1:04:45 And then questions you're like No wait hold on why are you Why let's take a step back. Yeah. Get out of my way. Yeah, exactly.

1:04:58 Mm-hmm. What's your ex yeah, what's your experiment by? Uh that's actually I think there's actually a big opportunity there to say people'cause Yeah, there's like a play around with this thing and then there's like okay, but really is this anything people actually want?

1:05:09 Can we can we call it Lenny mode? Is that a fine with you? One hundred percent. Let's do it. I'll license you no cost. Sure. Okay. Okay. We made a deal here. Let's do it.

1:05:19 Okay. Uh Anton, is there anything else that you wanted to share? Anything you want to leave listeners with? Uh before I let you go and go to sleep. I think again

1:05:30 The the world is changing quickly and it's very fun. You should see that like have fun in all all of this change. Um and The best thing you can do for the your current profession or if you want to have a new job is to be in the top one percent in knowing how to use the AI tools.

1:05:48 So go out there, use uh use lovable, use the other A tools and become um make sure to understand or try to understand as much of as possible in how to use them productively. Um That that's that's something I I tell all my friends in it generally and I I like love the audience to know as well. Okay, well I gotta make try to make this even more specific for people.

1:06:09 Uh, how do you know if you're in the top one percent? Like what's like a heuristic almost of like Slash how do you Get there. Is it just use it a a hundred times a day? What else? What can you recommend? Yeah, I I think if you spend a full week on trying to reach an outcome. I the best way to learn is like I wanna do this thing.

1:06:28 And then I'm gonna use AI to do that thing. Uh And then you've spent a full week, you're you're in the top one percent in the gl global population. If you have friends that uh you surround yourself with friends who ha who have this obsession or they also care a lot about this.

1:06:43 uh then you'd be quickly in the top uh point one percent. So what I'm hearing is like find a problem that it that need that can be solved. Like find a problem, a pain point for yourself or someone. Yeah. 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.

1:07:01 Yeah. And you're in the top one percent. Yeah, I I think at the top yeah the top one percent was just spending it. Uh a full week and Making like asking AI if you don't understand. So makes su making sure that you understand.

1:07:15 Yeah. Like that's the thing people forget you just ask. Like Like you c would would you ask the chat feature of lovable in this case, or would you go to Cloud or Chat GPT to ask for advice? I mean my recommendation here, if you're in product is too Yeah. lovable to build software and l and learn that AI tool. If you're

1:07:33 And then you should use ch chat mode and and chat mode, I have to add, is something you activate in your user profile. It's not launched like In the f in the main problem product. So it's in in the in lab. But if you ex add the uh that flag, then you can use chat mode. If you're if you want to learn some other AI tool.

1:07:53 Then You should I mean y ask that tool or ask uh Claude B about how how how that topic, that domain works. Okay, amazing. Uh, where can people find you? Where they can where can they find lovable and how can listeners be useful to you? L lovable posts, updates and memes on lovable underscore dev on Twitter.

1:08:15 Uh we post things on LinkedIn as well and they're a lot of a lot of things coming out uh and changing in how we build software. So you can follow Lovable underscore dev and you can follow me at Anton. Oh seek. Right. At Twitter.

1:08:29 Um I'd love more feedback. Um what people like where people Sí. This is huge change for them. And we there are lot of a lot of people posting about that on Twitter, but the there's that we have a Discord where you can share like all th this is how I use Lovable and will super useful to me. Um and

1:08:48 Yeah. Feedback. Dot lovable dot dev. You can give You can ask for

1:08:54 uh new features. You there's a lot of people asking in off 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. 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 more chapters of this journey. I have a lot more to that. As do we all. That's why people listen to this podcast. Uh Anton, thank you so much for being here.

1:09:17 Thank you so much, Lenny. 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.

1:09:37 You can find all past episodes or learn more about the show at Lenny's Podcast.com. See you in the next episode.

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