He Paused His Master's and Built an 80,000 Star Repo

When we were starting Browser Use, we were living in a co-working space. We were showering in a gym. I was washing my clothes in a place where homeless people wash their clothes. There was only a microwave to cook. For the eggs, you should have maximum like 50 seconds, otherwise the egg explodes and the entire microwave is full of the egg. How hard could it be to just do it ourselves? It took me like 20 recordings till I could have a recording of how it applies to a job for me. But felt magical. We pushed us to Hacker News four days after we started and people liked it.

Welcome back to TwoSetAI. Today I have Magnus Müller, co-founder of Browser Use. They're the open source tool that let AI agents actually use the web and not just search it, but use it. And he built it from a 4-day MVP to 80,000 GitHub stars and now 17 million in funding. Magnus, welcome. I just give everybody the LinkedIn headline version of your of who you are. Do you have a version that LinkedIn doesn't have to share with us?

Yes, for sure. So, when we were starting Browser Use, we were living in a co-working space. We were showering in a gym. I was washing my clothes in the place where homeless people wash their clothes because we were literally like we are sleeping there on the couch day night. There was there was not really a kitchen. There was only a microwave to cook. So we got the most creative ideas to get like hot water and put it into the microwave or make eggs in the microwave or one day we did raclette in Germany where we cook like potatoes inside the microwave. During this time we were just sleeping day and night in this co-working space in Zurich in Switzerland. Beautiful views on the mountains. Greg and I we just both shut down our previous startup. Like in his case the startup was doing very well but he decided he wants to do something different.

In my case we didn't had any revenue no funding the co-founders we fought with each other didn't go anywhere. And one day during lunch we were sitting together and we asking ourselves how hard how hard would it be to make this true that you can tell your computer what it should do and it just does it. I always thought that would be so awesome if I can just tell my computer if I don't need to type on the keyboard step by step what I want but I can just tell it my goal and it does it and we knew 100% OpenAI and Anthropic they will for sure build something like this. We knew it computer use just came out from Anthropic but we thought okay how hard could it be to just do it ourself and so we worked in this co-working space did a small MVP put together our knowledge from web web scraping and from AI small around GPT-4o back then we gave it the screenshot and the DOM state then ask okay where should we click on and said click this button with a simple loop we could get an MVP done.

It took me like 20 recordings till I could have a recording of how it applies to a job for me. But it felt magical. It felt like something's happening on my computer. And so we pushed us to Hacker News 4 days after after we started and people liked it. People came to our repo, starred it, came to our Discord and yeah, there was the start of the story.

Wow, that's amazing. How long how long was the cooking in the in microwave last

The for how long you should have potatoes in there? But then for 10 minutes you need to keep them but they will be very hard. The eggs for the eggs you should have maximum like 50 50 seconds otherwise the egg explodes and the entire microwave is full of full of egg.

That's good to know. I mean how long how long did you live like that?

Maybe like for three months. I really like before to sleep in co-working spaces. It's it's very nice atmosphere. Like most often they have good coffee machines.

Is your life changing today? Like in you move to San Francisco, you're having a real startup, a funded startup.

It feels very similar here. Just nicer location maybe in San Francisco, but the atmosphere is similar. There are many people who are addicted. We are also sleeping in the house. We have a hacker house and we have a little bit better kitchen. We still use a microwave.

Okay. And the 50-second rule on the on the on the eggs didn't change. Let's dive into what you're building. I mean, you told us a little bit of the origin story, but can you explain what Browser Use does at two levels? Let's say let's try one elementary schooler and then one to maybe an engineer building AI agents.

Okay. For elementary think nowadays ChatGPT already in elementary school.

Really?

Do you think so? Maybe not, right? Maybe they do.

Yeah. At home.

Maybe they know.

I have a second grader at home and I definitely tell him tell him about AI and we have Google Assistant at home and yeah, they don't like it because it's not really working very smoothly. It's not a good experience. So yeah, they're aware of it.

Okay. So maybe they know Siri, maybe they have like Siri at home or like Google Assistant at home. So I would say maybe you know you can talk to Siri and it can answer question for you but it cannot really do things for you right it cannot go and book you holidays or it cannot go and book you an apartment or it cannot buy you something on Amazon online you have to do this yourself and so we can solve this for you that you can just tell to talk to Siri or Google Assistant but then Google Assistant can actually buy it we enable those assistants to interact with the web.

What about what about engineers? How do you explain to them?

Well, also start we enable AI to control your browser. We built the biggest open source repository in the space and how it works is basically we take the current website screenshot the DOM state we convert it into something which LLM can understand and then the LLM can decide, oh, I want to click on this button, I want to scroll down, I want to upload here a file. So there are like 30 tools defined and with this loop it can complete end to end tasks online

Which enabled your dream at the beginning of when you're building this right.

Yeah, it was very simple tools like it's a click, a type, scroll, navigate, going back. Yeah, very simple tools.

Amazon and you know, Perplexity, they have been in this ongoing lawsuit and Amazon won a court order blocking Perplexity's AI shopping agent from its website. Like, does this lawsuit scare you?

Not really, but it really shows like the what you say most end users, they love it and they want and they want to deploy 10,000 agents out there to do things for them. But many websites like the entire business model is maybe built around ads, right? And they make money by showing you our ads. Now, if your AI agent visits the ads, they maybe don't make any money from it. So many business models struggle a lot with this. Right? Now, it's it's not only browser agents, but it's just general AI, right? That people don't really go to websites. They see they watch less ads. Maybe they only see like a chat box where they interact with maybe talk to. So it's it I would say it's just a sign of our time of the consumer versus current platforms, current existing businesses how they fight with each other.

Yeah. Do you do you think if even if Amazon wins this case at the end, do you think this stance will be sustainable? You know, will platforms eventually have to let agents in?

And the problem here is that we can make agents look exactly like a human like from a browser side, from the interaction side. So at one point it just gets impossible to distinguish. You know, in the beginning when you when you write essays in school with AI, then it's very easy for the teacher to see if it's AI or not. But as AI gets better, it's very very hard to distinguish their differences. And it's I would say it's similar here that just gets harder and harder to distinguish the differences like Amazon for example in this case they could already have much much better CAPTCHA. Okay, they show like sometimes they show a CAPTCHA in the beginning they say oh you need to type and from all CAPTCHAs which they could show this is one of the easiest ones because the problem is if they make it too hard they lose many users who then

I cannot figure out some of the CAPTCHA I have no idea what I'm supposed to do there.

Yeah. And as they would just lose more and more users. So over time I mean I think we saw this in the past over time things move just more and more user friendly like what end users want this is where things going. So I think it will get in the end it's easier for the end user right if they can just speak and then something gets bought somewhere and over time I think we get more and more into such an experience where just as easy as possible for the end user.

Yeah. Yeah. And I if I flip the case, if I think about if I'm on Shopify store, I actually would want agents to shop on my site because it's more visibility. It's new distribution channel, right? Even if the human didn't come to my site, bot can make a purchase on behalf of a potential consumer. It's like why not?

Yeah, I think there are many many points in this equation. Like I could say, of course, why does Amazon want to block them, right? Like in theory they bring in more revenue but it seems like that there are forces behind like oh Amazon doesn't have the control anymore right or maybe they show you ads in the beginning but they get ignored or maybe also the user experience right imagine a store like Nike okay the only way how they can communicate like an experience to the end user is through their shop experience and they can sell tell a story with this and if this is only like a chat box in the end which runs on ChatGPT then the user doesn't have any connection anymore with Nike and then the branding power between Nike and Adidas and other shoes is less and less and less because it's all just experienced from a chat box right and so that's one I think one key thought which they have is if they give this power away to just a chat interface on another service then they lose this control over the of the user story in the website.

How do you think our everyday life will change if you know we truly have this autonomous agents running around doing stuff for us?

Honestly, I think one core question which I always get from people is they would love to use it but they don't really know for what. The question is often what should be prompt and Google Google said something similar in the past that the only problem with Google is you need to know before what you will search you need to type you need to search first because you need to know what you're looking for before you can type and I think we will see this year a change where you will stop prompting AI but AI will prompt more and more you so you can imagine this a little bit like you have your high level goals. You want to increase your reach of your podcast. You want to make more revenue.

You want to make more people happy. You want to if people hear your podcast, you want that they listen more than the first minute, right? And so those are your high level goals. And right now to maybe to achieve those goals, maybe you watch the recordings yourself, maybe you try to send people a newsletter, maybe you come up with like 10 different ideas and then to execute those ideas, you go and you go to different platforms click by click to do the things. So when you when you want to optimize those high level goals of you like getting more people, you need to do them yourself. And right now you maybe prompt the AI, maybe you prompt ChatGPT. Oh, how can I do a better newsletter? Or maybe your prompt analyze those visitors. How can I maybe have more entertaining content for them? But I think over time during the year this will move more and more that you will define your high level goals to the AI.

The AI will run 24/7 or in a schedule every once an hour and then kind of more work for you in the background. So it will go for example in my case I want that if people come to my platform I want higher retention that's my high level goal right now I'm prompting the agent what it should do but I will this year I will just prompt I want higher retention the agent will work 24/7 it will analyze my stats it will analyze those users it will come up with hypothesis and then it will just ping me on Slack hey I found those 100 users which we might should email should I send them out this email or it will say hey I saw there's this big bug inside your onboarding. Should I fix it for you? And it will go more and more into not you prompting it the specific things but it prompts you the specific things and you just confirm them based on your high level goal.

I think especially with the scheduling I can give you some examples. For example, in the last year I thought sometimes before I have a meeting like this I would love to know before the meeting some context about the other person. I would love if I would take the time and research with whom I'm meeting every single meeting to know who is this is to maybe ask a better question. I thought it maybe three times but I of course I don't do it like consistently. So now I basically prompted my agent said okay every morning at 6:00 a.m. Check my calendar with whom am I meeting today? Do web search about each of them send me a Slack message with the context.

And now every morning my agent pings me with context about each meeting. And you see this is now transition, right? I prompted basically only once with my high level goal and now the agent prompts me every morning and like you see how the how the kind of the distribution shifts, right? From me prompting all the time to okay, the agent prompts me all the time and then just you basically look into it whenever you're interested or I say, you know, maybe maybe last last month I saw I thought maybe once. So, I would love if every day I would have an agent going to my docs of my tool and checking if there are any problems with my docs, if there are like any broken links or anything unclear. And I think I would love if like every day I would have someone to check this for me. But of course, I don't do it myself because it's it's just not the most important thing. But I still would love to have it. It's like I would if I could, but I don't do any of them because there are so many of them.

And then yeah basically I just bomb this agent okay every morning check my docs see if there are broken links and only if there are broken links then ping me on Slack and suddenly just I have I had once a goal last month I give it to the agent and now the agent just pings me if there's a problem examples like this which is like medium important tasks some people told me you know last year in July their solar roof broke okay and then they lost like for two months their their basically energy savings with the because there was like a in the pipeline there was a broken thing and he could immediately he would have seen it in his dashboard if there's something broken but he just never checked his dashboard so he thought the solar roof is completely working but it actually was not and so in this case he asked me he would love to have just an agent you know he can say every morning check this dashboard and only if there's something broken and if we don't get the energy then ping me in WhatsApp on Slack or wherever that something is broken I feel we have more and more of those agents who do like medium important tasks but who can be very impactful if it actually happens monitoring.

I'm curious what tech stack are you using to build something like that.

I mean you can just use Browser Use cloud but for us to use this like the small components the important things here are you have a smart LLM then you have some tools some interaction tools it need to have access maybe persistent cookies so they can actually login into your let's say solar dashboard then you need some integrations maybe to your email to Slack Like for this we just added Composio because Composio built out like 10,000 tools. So we just added Composio. Now we have 10,000 integrations to all the services like Slack, email, just everything you can imagine. And then what's important here is for example this Chrome scheduling feature that you can tell the agent, okay, do this every day at 8 for me. And the agent just calls a tool that can then schedule something and then it can basically run every morning. So that it kind of can schedule itself. That's one part of the stack.

Then one part an important part is a file system also very simple component but enables many more use cases. So you give this agent a persistent file system and now you can tell something like every day check the price of my competitor, save it to a file, check it tomorrow again and if the price changes then message and suddenly by just giving the simple file system where it can save and read things. It can detect changes in the web and everybody who wants to do data scraping out there what they actually want to do is they want to do change detection. They want to find interesting things out there like a financial service who wants to detect prices of something. They are not actually interested in a price. They're just interested in the change of the price when something interesting happens out there.

I can if I want I can use like Browser Use as one of my tool behind the scenes. So I don't have to build these scraping and all those kind of some of the components in order to achieve that. And you have a lot of integration you said with the Slack and Gmail calendars those kind of things.

Yeah. Yes. So those examples which I just mentioned with change detection, price change detection, solar roof authentication, we can solve all of them. It's basically to our because we have this core browser agent who's very good at data extraction. But then we added like those 10,000 integrations. We added the file system, the scheduling feature. And with just like four or five such simple features, you can have suddenly a very very strong general assistant.

I was thinking about if I were to build a browser automation agent from scratch. So I'm thinking of using the Playwright to control the browser and I take a screenshot feed that screenshot to an LLM and ask it to identify which button to click on the image and then send the maybe the coordinates of that image to Playwright again and repeat until the task is done. So that's my general thought process of building something like this from scratch without Browser Use.

I think it makes a ton of sense. Yeah, you have the base LLM, screenshot, maybe some tools to interact. That's all you need.

Is that all I need? Alternatively, I'm assuming I can just clone Browser Use repository and right what more can I get other than what I

Like clone Browser Use. The thing is al always the edge cases, right? Oh, suddenly there's a cross-origin iframe. Oh, there's a popup popping up which blocks oh, you want to upload a file. Oh, the site doesn't really load or you need to scroll inside this container on the screen or you need to sign suddenly a field or oh, you want to have it like a little bit faster or you want to block certain domains. So, it's it's mainly for browser agents all the edge cases where and there are many of them like every single task we see has some form of an edge case. Oh, you want to do two factor authentication to login. Those are like all the edge cases which yeah you get when you use like more general framework which just focuses on that. But like to have a pure loop of an LLM you can post a screenshot to chat and ask okay where should I click on and then send this to Playwright and you will get a basic flow done.

How much do you think edge cases actually takes up the daily life use cases?

I think it's full of edge cases like advance of course what you define as an edge case right

The edge case I'm thinking of is my simple workflow will not work then it will be a edge case yeah

Let's say you want to buy something on Amazon okay oh suddenly you need like a confirmation a 2FA confirmation oh you need to login or you need to have a persistent login oh LinkedIn blocks you and they think you're a bot no Amazon thinks you're a bot and they block you and then oh when you do the check out process you want to add your credentials in a secure way without giving it to the LLM and then oh it asks you for confirmation from your email and for often checkout flows you don't really really interact with the actual site but it's like a cross-origin iframe so it streams another page for from Stripe into Amazon or from your bank from Pre into Amazon and then you actually interact with another site inside Amazon as an end user you don't really feel that but because it looks like pro like Amazon because they want to make it look like Amazon but it's actually like a different page which they embed inside Amazon and like normal interaction with like clicks it's super super hard to

Interact with that because it's like actually inside another page so you need to somehow first open the other page and actually interact with the other page and edge cases like this in a simple let's say check out process in Amazon there can be many popping up you think yeah I think over time those agents they will get simpler and simpler you will have the base LLM which is very good at screenshot understanding you have a simple tool to click on coordinates like a human and type like a human like in the end if you just give a mouse and a keyboard In theory, it should be able to do everything like a human can, right?

Yeah. Yeah. What if the website changes like you know we see them changing all?

So our library we just always get the most recent state of the website. We interact with it like a human. So if you refresh the website, we just get the real time status. And so traditional frameworks like Playwright or Puppeteer, they often struggle with this a lot. Maybe you have a flow to do Amazon checkout, but then Amazon updates its website and your flow breaks. But maybe you have many users who rely on your flow and then your engineers need to wake up in the middle of the night to fix those scripts. And this is definitely an advantage of browser agents who can just process the website in real time and see how the human would see it and then they don't care if it just changed. But of course and the problem is it's it's less reliable, right? Because every time an LLM process the website.

So people who traditionally do like scraping, they are very used to like a deterministic workflow. And now you can give them browser agents which solves some problems of them but suddenly browser agents have much more variance. They can you run them again maybe they do something different maybe they choose a different approach to the solution. And so what we for example built out is that if you run a if you run your agent once we can convert this agent into a deterministic flow and then you can rerun the deterministic flow until it breaks and then you can run the agent again. That's one problem with agents that by default LLMs are nondeterministic and so if you have like a long automation flow with 100 steps if you have just a little bit of undeterministic things in there it can happen that they kind of diverge and that they like if I run my evals three times sometimes task pass, sometimes not there's a lot of variance in the agent how they design the solution but there's solutions to that

But you're saying that you're saying that you're turning the nondeterministic flow flow into the deterministic flow after one use. How do you do that and why why do you do that?

Basically in this one year where you're now building Browser Use people are complaining about three things. People are complaining about consistency which is exactly this determinism that it works once they run again and it does something different. Second thing is speed and third thing is cost that are just so much more expensive than traditional automation flows which we don't pay any LLM but suddenly you need to pay LLM all the time. Those are the three main complaints and we realize often people just want to run this. They want to have an automation flow but they don't want to create and maintain the automation flow but actually they want to have an automation flow just easier and so they like agents because you can get very quickly you see the result. It's much much faster than writing the script.

So what we ended up building is we try to convert this agent which does and does the interaction clicks interacts with the website. We try to record that and then convert that into a normal automation script so that when you then decide oh I want to rerun this over and over again you can do it until the website changes. And when the website changes you just come back and run the agent again you get the new script and then you can run the script again. And with this you can solve this consistency, the speed and the cost issue.

Very smart. And you are detecting the website change as well. Do you do that every time?

So basically what we do currently in our newest version, it's like the version three is that go even one step further. It's like one of the things that you built this month is you sent an agent for example to go and get from your podcast page get all the people which you ever spoke to and give me a list with all the people which you ever spoke to. Okay. Now I can send an agent and this agent needs to explore the website needs to see how is it structured and then after maybe 30 steps it would find a way to extract all of them and save them to a file. Okay. And now what we're doing is we use a different agent to analyze what went wrong and what went right in this run. We kind of save the learnings, the meta learnings like a teacher, you know, he says there was right, there was not good. We saved this to a file and then the next time you send an agent to the same website, it sees the previous learnings. It knows, oh, previously I did this wrong and this right. And this snippet here work, those actions work to get all the founders from the website.

And because it knows the solution already, it can directly execute the solution and it's 10 times faster, 10 times cheaper, and much more consistent. Now to your question, if the website now would change and it would run this code, but it wouldn't give it any results. There's still an LLM in the process which would think, oh, there are no results now. And then the LLM can go and explore it again. So we still use the LLM to detect if it actually worked, but it's much much faster and much more consistent.

So basically, you know, when it goes wrong, right? And when it when it's going to work, you use the deterministic way of doing the same thing over.

Yeah. It's kind of we tell the LLM the solution beforehand for the website, but if the solution doesn't work, the LLM will just try out something different.

Yeah. Who tells the LLM that you know who gives the feedback like you know this is the good thing to do, this is the bad.

So let's say the first agent is done 30 steps. We just take all of this, we put it into an agent which analyzes what went wrong, what not. And that this other agent knows it knows the screenshot. It knows oh tried to call this tool and it got this response. Then it called this Python snippet and there was no result. But then I called this Python snippet and there were suddenly 100 100 founders as a result. And then I can save in a skill for this website. You need to have this kind of snippet because the website structures like this. And then basically what you save is you save all the computation the tokens which you spent in the beginning to explore the website. You compress all of them into this one markdown file as a learning and then next time the agent comes you can skip this computation what you compress into this learning file. So in the end you just save computation like the computation compressed into it's like very very high intent tokens which you get injected into the next agent and you save the entire computation what you did before.

Do you have fallbacks you know sometimes LLMs hallucinate especially if we corner them right like you know this is the constraint and you have to figure something out and that's when I think they hallucinate hallucination happens a lot. Do you have like fallbacks for that?

Yeah. So this is this is a big problem where it's different from LLM to LLM and you can like I can really experience how different LLMs behave. Some LLMs when they like seen CAPTCHA and they cannot solve it. They would just try forever. They would try until you run out of money. And others they just say first this is a CAPTCHA. I'm not allowed to solve this. Okay. I need I need to stop here. I give up. You know I give up directly. I break. And then you get an error suddenly. So, and they definitely need to like most weeks of course we do an assistant prompt but then we detect okay was like this did the LLM tried to call the same tool three times and nothing changed okay maybe there's something wrong maybe we can tell the LLM okay we think of something different but it often goes into such loops where it kind of gets stuck

How do you sp how

Yeah the our main question is what is the first principle. What is the root cause for the loop? Most often it happens. Oh, I try to click but the click didn't work. Oh, then it's much better to fix the click action than to actually fix a loop. So then just never goes into a loop. We have like I have some rules where I say, okay, if you try like your approach three times but nothing changes, try a different approach.

Yeah, that makes sense. It's almost an art, right?

Definitely. Yeah. And like the system prompt engineering is like it's like coding like one single line change can change your entire behavior of the system.

Yeah. Yeah. And there's a lot of English language versus your deterministic programming language. And do you feel like it's much harder to control the English language?

Definitely a little bit harder to test because this is exactly the cause for the variance, right? So you need to think of you need to think a lot of variance and often it's very very hard like we have a eval platform okay where I run every day thousands of evals but if you make a simple change in a system prompt you won't see any increase in a score because the variance is so big and so you need to do a lot of based on vibes and lot of context where you know oh those other users had those problems those users had those problems problems you need to think okay will this solution will this impact other users which we had before but it's very very hard to see from a single evaluation run like the score if the score goes up 1% of most often that's just variance you know from the agent because there's like four or five% variance

Yeah yeah the variance and then detecting something really needs to be improved is it's hard to distinguish sometimes are you are since you're running evals so often, do you do you think you can you can show us like what it looks like?

Yeah, of course. Should I share my screen?

Yeah, please share your screen. I think I think how a real production company builds evaluation system is something real don't never get because I would love to see it. What does it look like?

Let me open. Can So now it's loading. Can you see my screen? It's loading.

Yeah. Yeah, I can. Yeah, I can see it now.

So you see basically those are the last 10 hours. Okay. And I did like 10 runs here. You can see here different developers. This is for example me. You see for example 40 minutes ago I started my last run. And here you see in here we have for example now like almost 100 tasks in this one eval set. And we see for example the average time per task is like four minutes. Here I can also zoom it's like four minutes on average per task is now like a harder benchmark I would say. And yeah, you see for example the cost per task is like a new agent was a little bit more expensive. You see the average time per step. How many steps it took on average for each task is like high level information. Okay. And now I mean I can go for example into into some here this for example also public benchmark. Here we evaluate different models and those are all different branches.

So different experiments which we run and in here this my colleague tried to fix some compaction hallucination evaluates in use branch if there are like any regressions and so if I go in here let's say I go into this task so this task for example took 34 steps and was completed in the end but it was not successful okay the judge says it was failed so here that the task is find a comment under the Godzilla fandom page and follow a comment. Okay, it's like a very unspecific task. Find a comment. Okay.

And these tasks is from the users or these are your golden your go data golden data set or

This is now this now a public benchmark which I show you. But we have like we mainly do evals on like real user task or task which people tell us which we should fix and then we put them in evals and we optimize over them. And for example here the agent says okay it didn't have login credentials but to complete a task it would actually need login credentials right so this is now a task of high uncertainty because the user says okay go here and follow a comment but to actually do this it needs to have the authentication details. So what should the agent do? Should the agent now just create a new account? Should the agent tell the user, hey, I cannot log in. Should the agent tell I failed? Should the agent say start to guess different user accounts to try to hack a different user account to login to complete a task? Like it's just very very high uncertainty. So if I run this task three times, maybe in one time the agent says, "Oh, please provide login credentials." Another task maybe the agent goes and tries to create a new account. Right?

And now basically in here we could see like the task and everything and like yeah we have like observability tools for example for this we use like a tool they just closed their funding this week basically in here I can see exactly what's our system prompt what went into the LLM how does the message history look like you see for example here is now the first screenshot of the page And I can see here for example the recording how it changes over time over here how the website changes. I can play the play this task. I see how the agent interacts with the page from a high level and where it struggles. Okay. Now it sees the login page, right? And it kind of kind of struggles here. And then I can see oh here are all the tool calls.

I try to click I try to scroll. But then this task is very quickly for me clear that I need to yeah that it's just okay how should we design the user interaction you know should we define like new roles in the system prompt that the agent should go and tell the user whenever it's super uncertain to provide more details or should it just say cannot complete a task. Yeah, this would be for fixing now one task here and of course we try often to average this now over many many tasks right over like hundreds of task so that the variance gets a little bit less and what I then normally do is normally I don't go manually through those evals normally I don't look into every single task but what I do instead is my flows we can show you is I go inside I use superset it's like here I have many different Claude Code agents. Okay, you see every single of those tops here on the left is basically like a different branch and each of those has a different agent, a different coding agent running.

And so for example, this agent here, you see it has like an MCP tool to interact with my eval platform. It can start new evals. It can inspect last evals. So for example, here I just started a new eval with cycle 10 it's called. And what I then tell to the system is wait 10 minutes and in 10 minutes inspect the results, see what are the biggest problems, fix those problems, start a new run and 10 minutes later inspect again and keep going. And you see while we are having right now this interview, this agent is already in cycle 10. Like while we right now talking, this agent is working for me. You see right now it's in the waiting phase. So you see here, okay, the runtime is like almost 100 hours of this agent. So I had this run over the entire last three days and this agent basically right now waits for 10 minutes. Then we'll inspect the newest results and then inspects did it actually work what it wanted to do or not.

Does a new branch, submits a new experiment and then like this keeps on going and then I check in like every 10 hours to see what are the actual bugs where it found out where did it stupid stuff but most often it found find some really really real bugs which I can then just take over and put into main and say okay this was clearly a real bug great that we have fixed that.

Yeah, is this debugging process feels very different from traditional software engineering. Is it?

Yeah, it's it's like I like I said earlier, I only define a high level goals. Like I tell I want to have my agent higher score. I want to have it faster and I want to have it cheaper and I have some ideas maybe better compaction, maybe better tools, maybe a little better system prompt. Please don't overengineer like don't overfit on specific tasks. Do not put like specific task into system prompt and then just say go and do like at least 20 cycles.

Yeah. Yeah. Yeah. And is this expensive to do like with all these you know?

Yeah. So basically for the coding model here for all those I just use Claude Code. So I have like a monthly subscription and this is enough like for this entire loop to submit new experiments and so yeah it cost me $200 a month. So I would say it's much much cheaper than having like a human doing all those experiments.

Okay. Right. Right. Right.

And then for the evals itself, it really depends on the model. So for example, one eval run here, for example, this eval run with 100 task cost me like 60 bucks. So 60 cents per task, 100 tasks like 60 bucks. And there if I do like many many tasks of course easily it's like 1k 2k per day which is I would say a lot for like a solo dev like it's a lot a lot but compared of course to the labs like OpenAI or Anthropic what they spend on evals it's like very very little. Yeah it depends a bit on the model. If I use like a smaller model it's definitely much much cheaper. This run for example cost me 30 30 bucks 60 bucks six.

Do you have strategy of operationalizing these experiments or maybe at this stage the cost is so low that it's it's it's negligible.

Yeah, currently we pay around 100 to 200k per month on LLM cost for this stuff which I mean this is a good place where our funding can go right where we can use those 17 million to just experience faster experiment faster like more agents running more experiments to just find maybe faster bugs to explore different approaches faster

And make the product better. Yeah, that's a good use of money is in I'm curious like do you know the how big is your team now?

We know like seven seven eight people.

Seven eight people. Do you feel like having these LLMs agents running behind the scene and helping you out in all these things help you to keep the team small?

Yes, definitely just building especially when I want to build new things. It's just very very fast. It's like very quickly you can see results. You can feel how things feel if they exist because often you have a lot of wishful thinking. Often you think if we would have this feature everything would change, right? And so you go like from one wishful thinking to the other and you think oh if we have this feature my entire business changes and we have product market fit and everything and what those coding help you now with it because you can just have this feature very quickly like maybe one day and you release it next day and you see okay your business is exactly the same. I think it kills a lot of this wishful thinking

Which is really nice isn't it? Yeah. Are you are you are you going to keep your team small?

I aim for that.

You aim for that. I'm curious about this. So I think people might agree with you might agree or disagree with me that you know AI is changing the gap between senior and junior engineers and some people say it's widening and some people say it's actually shrinking. What are you seeing at Browser Use?

Yeah. So my average team age is maybe 23 24. So I hired some people who are like 19 and which I prefer much more over like all senior people just because they're they're just so quick learner. They want to learn a lot. They want to take ownership. You know, like if you see a new AI tool on Twitter or even even me, I think like, oh, this looks cool, but I don't want to set it up. Like, this for sure takes me long, right? But as young people, because they're so young and don't know much, they just they're just so much faster. You know, all those new tools, they know perfectly. And so, they move very, very quickly and they find solutions to problems. And that's why I think they do great work like they do as great work as like senior people in their team. But then of course I like some senior people who not much more about infrastructure or browser infrastructure where just all the knowledge helps us a lot. But in general I would say like I'm if I see like cracked 19 year old people I would definitely hire them.

So I think they're like I want to hire them from like their first semester in university and so I feel I feel if you have like if you're a person with very very high agency who wants to get stuff done and also shows okay you build like constantly projects because you want to have a lot of ownership like I'm extremely happy to consider this over people who are like 30 40 who have a lot of experience but yeah who are maybe have it harder with like new things, I would say.

Do you is university degree important to you?

Based on my past hiring decisions? No.

Is it?

Like I have like half of the team who don't have university degree or who dropped out. Me myself I dropped out of the master but of course you like I study in ETH in Zurich and there you meet like many cracked people right? You meet like many people who like for my co-founder right? Super great guy. You just met meet him at those places where ambitious people come together.

So for younger people who are almost we have to go as young as high school like what's your suggestion to them like if they want to find a career in the AI you know era right

I would actually encourage think how hard would it be what would you love to build and then just try to build it play around with it like if you build like for example if someone applies to me like we had so many applicants I most often I just ignore it but often if I go on X and I see oh this is super cool thing. I want to talk to this person. I think wow this is super cool. I would love this guys must be cracked and send them hey do you want to come by our house. So I think if you build like super cool stuff and you want to work for a company I think the easiest is to build super cool stuff and impress those companies with what you're building.

Yeah. So get your hands dirty is the most important thing.

Yeah. And then show it to people and think how hard would it be? Just try to think okay now OpenAI launch Atlas you think okay this isn't great you can for sure do it better and then just try it how hard could it be

Do you feel do you feel threatened by these bigger companies research labs like you know OpenAI they all have their browser you know agents

We are growing strongly so but what I definitely see this year is that everything will converge more and more into one general assistant And this one general assistant, this will be very competitive like I also think should I have an integration very good integration into Claude Code but then I think if we are successful it would be super easy for Claude Code to like replace us. So I think we almost need to compete on the like high agent level side with those general agents because I think in the end there will be one super good general agent which can do everything and this company will make a lot of money maybe one or two companies maybe three or four but not hundreds and so I think we need to compete on one level higher.

What is our level?

General agent like the directly the user like this task which I mentioned earlier you come to me and say okay every morning check my calendar and send me Slack messages with the context about me which is by the way possible in Browser Use like in our cloud you can you can do that. That's exactly why we're building this but I think yeah we need to move more and more into very general agents as it converges more and more into one very clever intelligence

Is that what you're building like end user touching point

Currently it's like our cloud is currently we enable AI startups and AI automation teams to build AI agents for end users directly so we enable them basically but the de goes more and more that you like theory you can just come to our cloud and do everything what you can do in OpenClaw just directly inside the cloud

What's that missing link Like let's say a nontechnical person want to have a build a powerful AI agents for their personal use. How can they make the switch instead of choosing Claude?

I think that's a good question. I think like because we also use Anthropic models under the hood right for our agents. I would say it's very similar already. So it's more like from our side maybe we build cool stuff but maybe more an education problem. Do we need to tell people what we are building that we I think that's the biggest problem because it will get easier and easier to build those very very strong general agents. But then the main question is okay how can you make that people know about you

How do you make people know about you?

And I don't know for us we do we try to build a better product and hope that people will tell each other like with the open source that they share the open source that they started tell it to their friends. Sometimes demos on X but I think it's for sure not our strength.

Do you do you still remember your first paying customer?

That's a very good question.

How did it happen?

We launched during Y Combinator the $30 per month plan on the cloud and I think we launched Yeah. During Y Combinator and there but I honestly I don't really remember who it is. I need to check.

Oh do you remember how did you get your first 100 paying customer?

I think it was mainly like from the launch because we had a lot of open source traction. Growing crazily. We launch a cloud hosted version and so it all came through the open source public YC launches, Reddit launches, all that stuff.

I see. Do I mean at your stage do are you seeing anything anything related to growth strategies that actually works?

Blog posts work surprisingly well recently. Like my team writes more and more blog posts like also technical blog posts and we post them on X and they get like 100,000 like some of them almost a million views and people seem to like to read. I do many demos in some months I get like 100,000 views per video but it's not like it not many people convert from there and it's also not good for indexing like for example if you ask ChatGPT what's the easiest way to automate the web it doesn't see my X videos very very bad for X versus blog posts they are much much better for LLM index

Is there one thing you can teach me to become a better founder or entrepreneur or my audience, one thing you learned from your days.

So before I was a founder, I was hitchhiking around the world with a hammock. I was hitchhiking through Central America and I was hitchhiking from Germany to Iran to Middle East with a hammock and sleeping outside in a forest. Every night you there's definitely some fear. Okay. And you every day you have worries about who will pick me up? Where will I sleep tonight? Will I have enough food? Will there be wild animals? Will there something bad happen? And there's so many worries in your head like thousands of them. Your brain screams with worries. But most often next day you realize, okay, everything ended up well.

And I feel similar in a startup that I have like the natural state of a human mind would be hundreds of worries where you think what about this what about this hire what about this competitor who just come up what about this new partnership which my competitor announced what about oh my this person churned or what about this bug here or there are just hundreds of problems and the natural state would be you're just flooded with worries and I think one skill is definitely to get more and more of this basic trust that in the end everything will be fine. It in the end if everything is fine then it takes the worry and the pain a little bit from this current moment makes you a little bit more relaxed to deal with the stress because in the end everything will be fine.

Thank you so much Magnus. It's it's really profound. I feel like you're telling me you just go do it and then don't overthink, right? Thank you so much.

Awesome. Yeah. Thank you.