Head of Claude Code: What happens after coding is solved | Boris Cherny

22:01And so nowadays seeing these gains of just hundreds of percentage points is just absolutely insane. What's also insane is just how normalized this has all been. And like we hear these numbers, like, of course, AI is doing this to us. It's just it's so unprecedented. The amount of change that is happening to software development, to building products, to just the world of tech. It's just like so easy to get used to it. But it's important to recognize this is crazy. This is something like I have to remind myself once in a while. There's sort of like a downside of this because the model changes. So there's actually like there's many kind of downsides that we could talk about.

22:34but I think one of them on a personal level is the model changes so often that I sometimes get stuck in this like old way of thinking about it and I even find that like new people on the team or even new grads that join do stuff in a more kind of like AGI forward way than I do so like sometimes for example I had this case like a couple months ago where there was a memory leak and so like what this is is you know like quad code the memory usage is going up and at some point it crashes. This is like a very common kind of engineering problem that, you know, every engineer has debugged a thousand times.

23:07And traditionally, the way that you do it is you take a heap snapshot, you put it into a special debugger, you kind of figure out what's going on, you know, use these special tools to see what's happening. And I was doing this, and I was kind of like looking through these traces and trying to figure out what was going on. And the engineer that was newer on the team just had Quadco do it. And it was like, hey, Quad, it seems like there's a week and you figure it out. And so like QuadCode did exactly the same thing that I was doing. It took the heap snapshot. It wrote a little tool for itself so it can kind of like analyze it itself.

23:37It was sort of like a just-in-time program. And it found the issue and put up a pull request faster than I could. So it's something where like for those of us that have been using the model for a long time, you still have to kind of transport yourself to the current moment and not get stuck back in an old model because it's not Sonnet 3.5 anymore. The new models are just completely completely different. And just this mindset shift is very different. I hear you have these very specific principles that you've codified for your team that when people join you, you kind of walk them through them. I believe one of them is what's better than doing something, having Claude do it.

24:14And it feels like that's exactly what you described with this memory leak is just like, you almost forgot that principle of like, okay, let me see if Claude can solve this for me. There's this interesting thing that happens also when you underfund everything a little bit. because then people are kind of forced to clodify. And this is something that we see. So, you know, for work where sometimes we just put like one engineer on a project and the way that they're able to ship really quickly, because they want to ship quickly, this is like an intrinsic motivation that comes from within. It's just wanting to do a good job.

24:44If you have a good idea, you just really want to get it out there. No one has to force you to do that. That comes from you. And so if you have clod, you can just use that to automate a lot of work. And that's kind of what we see over and over. So I think that's kind of like one principle is underfunding things a little bit. I think another principle is just encouraging people to go faster. So if you can do something today, you should just do it today. And this is something we really, really encourage on the team. Early on, it was really important because it was just me. And so our only advantage was speed.

25:16That's the only way that we could ship a product that would compete in this very crowded coding market. But nowadays, it's still very much a principle we have on the team. And if you want to go faster, a really good way to do that is to just have Claude do more stuff. So it just very much encourages that. This idea of underfunding, it's so interesting because in general, there's this feeling like AI is going to allow you to not have as many employees, not have as many engineers. And so it's not only you can be more productive, what you're saying is that you will actually do better if you underfund.

25:47It's not just that AI can make you faster, it's you will get more out of the AI tooling if you have fewer people working on something. Yeah, if you hire great engineers, they'll figure out how to do it. And especially if you empower them to do it. This is something I actually talk a lot about with, you know, with like CTOs and kind of all sorts of companies. My advice generally is don't try to optimize. Don't try to cost cut at the beginning. Start by just giving engineers as many tokens as possible. And now you're starting to see companies like, you know, at Anthropic, we have, you know, everyone can use a lot of tokens.

26:19We're starting to see this come up as like a perk at some companies. Like if you join, you get unlimited tokens. This is a thing I very much encourage because it makes people free to try these ideas that would have been too crazy. And then if there's an idea that works, then you can figure out how to scale it. And that's the point to kind of optimize and to cost cut, figure out like, you know, maybe you can do it with Haiku or with Sonnet instead of Opus or whatever. But at the beginning, you just want to throw a lot of tokens at it and see if the idea works and give engineers the freedom to do that.

26:49So the advice here is just be loose with your tokens with the cost on using these models. People hearing this may be like, of course, he works at Anthropic. You want us to use as many tokens as possible. But what you're saying here is the most interesting innovative ideas will come out of someone just kind of taking it to the max and seeing what's possible. Yeah. And I think the reality is at small scale, you're not going to get like a giant bill or anything like this. Like if it's an individual engineer experimenting, the token cost is still probably relatively low relative to their salary or, you know, other costs of running the business.

27:24So it's actually like not a huge cost. As the thing scales up, so like let's say, you know, they build something awesome and then it takes a huge amount of tokens and then the cost becomes pretty big. That's the point at which you want to optimize it. But don't do that too early. Have you seen companies where their token cost is higher than their salary? Is that a trend you think we're going to find and see? You know, at Anthropik, we're starting to see some engineers that are spending, you know, like hundreds of thousands a month in tokens. So we're starting to see this a little bit. There's some companies that we're starting to see similar things.

27:56Yeah. Going back to coding, do you miss writing code? Is this something you're kind of sad about that this is no longer a thing you will do as a software engineer? It's funny. For me, when I learned engineering, for me, it was very practical. I learned engineering so I could build stuff. And for me, I was self-taught. I studied economics in school, but I didn't study CS. But I taught myself engineering early on. I was programming in middle school. And from the very beginning, it was very practical. So I actually learned to code so that I can cheat on a math test. That was the first thing. We had these graphing calculators, and I just programmed the answer into...

28:37TI-83? TI-83 plus. Yeah, yeah, exactly. Plus, yeah. So I programmed the answers in, and then the next math test, whatever, the next year, it was just too hard. I couldn't program all the answers in because I didn't know what the questions were. And so I had to write a little solver so that it was a program that would just solve these algebra questions or whatever. And then I figured out you can get a little cable, you can give the program to the rest of the class, and then the whole class gets As. But then we all got caught and the teacher told us to knock it off. But from the very beginning, it's always just been very practical for me where programming is a way to build a thing.

29:13It's not the end in itself. At some point, I personally fell into the rabbit hole of the beauty of programming. So I wrote a book about TypeScript. I started the... Actually, at the time, it was the world's biggest TypeScript meetup just because I fell in love with the language itself. and I kind of got deep into functional programming and all this stuff. I think a lot of coders, they get distracted by this. For me, it was always sort of... There is a beauty to programming and especially to functional programming. There's a beauty to type systems. There's a certain kind of like this buzz that you get when you solve a really complicated math problem.

29:55It's kind of similar when you kind of balance the types or the program is just like really beautiful, but it's really not the end of it. I think for me, coding is very much a tool and it's a way to do things. That said, not everyone feels this way. So for example, you know, like there's one engineer on the team, Lena, who, you know, was still writing C++ on the weekends by hand because, you know, for her, she just really enjoys writing C++ by hand. And so everyone is different. And I think even as this field changes, even as everything changes, there's always space to do this. There's always space to enjoy the art and to kind of do things by hand if you want.

30:34Do you worry about your skills atrophying as an engineer? Is that something you worry about? Or is it just like, you know, this is just the way it's going to go? I think it's just the way that it happens. I don't worry about it too much personally. I think for me, like programming is on a continuum. And, you know, like way back in the day, you know, like software actually is like relatively new, right? Like if you look at the way programs are written today, like using software that's running on a virtual machine or something. This has been the way that we've been writing programs since probably the 1960s.

31:02So, you know, it's been, you know, like 60 years or something like that. Before that, it was punch cards. Before that, it was switches. Before that, it was hardware. And before that, it was just, you know, like literally pen and paper. It was like a room full of people that were doing math on paper. And so, you know, programming has always changed in this way. In some ways, you still want to understand the layer under the layer because it helps to be a better engineer. And I think this will be the case maybe for the next year or so. But I think pretty soon it just won't really matter. It's just going to be kind of like the assembly code running under the programmer or something like this.

31:36At an emotional level, you know, I feel like I've always had to learn new things. And as a programmer, it's actually not, it doesn't feel that new because there's always new frameworks. There's always new languages. It's just something that we're quite comfortable within the field. But at the same time, this isn't true for everyone. And I think for some people, they're going to feel a greater sense of, I don't know, maybe like loss or nostalgia or atrophy or something like this. I don't know if you saw this, but Elon was saying that why isn't the AI just writing binary straight to binary? Because what's the point of all this programming abstraction in the end?

32:12Yeah, it's a good question. I mean, it totally can do that if you wanted to. Oh, man. So what I'm hearing here is in terms of there's always this question, should I learn to code? Should people in school learn to code? What I heard from you is your take is in a year or two, you don't really need to. My take is I think for people that are using that are using quad code, that are using agents to code today, you still have to understand the layer under. But yeah, in a year or two, it's not going to matter. I was thinking about what is the right historical analog for this? Because somehow we have to situate this thing in history and kind of figure out when have we gone through similar transitions?

32:52What's the right kind of mental model for this? I think the thing that's come closest for me is the printing press. And so if you look at Europe in the mid-1400s, literacy was actually very low. There was sub 1 % of the population, it was scribes, that they were the ones that did all the writing. They were the ones that did all the reading. They were employed by lords and kings that often were not literate themselves. And so it was their job of this very tiny percent of the population to do this. And at some point, Gutenberg and the printing press came along. And there was this crazy stat that in the 50 years after the printing press was built, there was more printed material created than in the thousand years before.

33:38And so the volume of printed material just went way up. The cost went way down. It went down something like 100x over the next 50 years. And if you look at literacy, it actually took a while because learning to read and write is quite hard. It takes an education system. It takes free time. It takes not having to work on a farm all day so that you actually have time for education and things like this. But over the next 200 years, it went up to like 70 % globally. So I think this is the kind of thing that we might see is a similar kind of transition. And there was actually this interesting historical document where there was an interview with some scribe in the 1400s about how do you feel about the printing press.

34:22And they were actually very excited because they were like, actually, the thing that I don't like doing is copying between books. The thing that I do like doing is drawing the art in books and then doing the book binding. And I'm really glad that now my time is freed up. And it's interesting, like as an engineer, I sort of felt like a peril with this. Like this is sort of how I feel where I don't have to do the tedious work anymore of coding because this has always been sort of the detail of it. It's always been the tedious part of it and kind of like messing with a git and kind of using all these different tools.

34:52That was not the fun part. The fun part is figuring out what to build and coming up with this. It's talking to users. It's thinking about these big systems. It's thinking about the future. is cooperating with other people on the team. And that's what I get to do more of now. And what's amazing is that the tool you're building allows anybody to do this. People that have no technical experience can do exactly what you're describing. Like I've been doing a bunch of random little projects and it's just like anytime you get stuck, just like help me figure this out. And you get unblocked. Like I used to, I was an engineer for an earlier in my career for 10 years.

35:27And I just remember spending so much time on like libraries and dependencies and things and just like, oh my God, what do I do? And then looking on Stack Overflow. And now it's just like, help me figure this out. And here's a step by step one, two, three, four. Okay, we got this. Yeah, exactly. I was talking to an engineer earlier today. They're like, they're writing some service in Go and it's been like a month already and they built up the service. Like it's working quite well. And then I was like, okay, so like, how do you feel writing it? And he was like, you know, like I still don't really know Go, but.

35:55And I think we're going to start to see more and more of this. It's like, if you know that it works correctly and efficiently, then you don't actually have to know all the details. Clearly, the life of a software engineer has changed dramatically. It's like a whole new job now as of the past year or two. What do you think is the next role that will be most impacted by AI within either within tech, like, you know, product managers, designers, or even outside tech? Just like, what do you think? Where do you think AI is going next? I think it's going to be a lot of the roles that are adjacent to engineering.

36:26So yeah, it could be like product managers, it could be design, could be data science. It is going to expand to pretty much any kind of work that you can do on a computer because the model is just going to get better and better at this. And, you know, like this is the co-work product is kind of the first way to get at this, but it's just the first one. And it's the thing that I think brings AI, agentic AI, to people that haven't really used it before. And people we're starting to just to get a sense of it for the first time. When I think back to engineering a year ago, no one really knew what an agent was.

36:59No one really used it. But nowadays, it's just the way that we do our work. And then when I look at non-technical work today, or maybe semi-technical like product work and data science and things like this. When you look at the kinds of AI that people are using, it's always these conversational AI. It's like a chatbot or whatever. But no one really has used an agent before. And this word agent just gets thrown around all the time. And it's just like so misused. It's like lost all meaning. But agent actually has like a very specific technical meaning, which is it's a it's an AI, it's an LLM that's able to use tools.

37:34So it doesn't just talk, it can actually act and it can interact with your system. And you know, this means like it can use your Google Docs, and it can it can send email, it can run commands on your computer and do all this kind of stuff. So I think like any kind of job where you do, you use computer tools in this way, I think this is going to be next. This is something we have to kind of figure out as a society. This is something we have to figure out as an industry. And I think for me also, this is one of the reasons it feels very important and urgent to do this work at Anthropic, because I think we take this very, very seriously.

38:08And so now, you know, we have economists, we have policy folks, we have social impact folks. This is something we just want to talk about a lot. So as a society, we can kind of figure out what to do because it shouldn't be up to us. So the big question, which you're kind of alluding to is jobs and job loss and things like that. There's this concept of Jevons paradox of just as we can do more, we hire more. And it's not actually as scary as it looks. What have you experienced so far, I guess, with AI becoming a big part of the engineering job? Just are you hiring more than if you didn't have AI?

38:39And just thoughts on jobs? Yeah. I mean, for our team, we're hiring. So QuadCode team is hiring. If you're interested, just check out the jobs page on Anthropic. Personally, all this stuff has just made me enjoy my work more. I have never enjoyed coding as much as I do today because I don't have to deal with all the minutiae. So for me personally, it's been quite exciting. This is something that hear from a lot of customers where they love the tool they love quad code because it just makes coding delightful again uh and that's just that's just so fun for them but it's hard to know where this thing is going to go and again i just like i have to reach for these historical analogs uh and i i think the printing press is just such a good one because what happened is this technology that was locked away to a small set of people like knowing how to read and write became accessible to everyone.

39:32It was just inherently democratizing. Everyone started to be able to do this. And if that wasn't the case, then something like the Renaissance just could never have happened. Because a lot of the Renaissance, it was about knowledge spreading. It was about written records that people used to communicate. Because there were no phones or anything like this. There's no internet at the time. So it's about what does this enable next? And I think that's the very optimistic version of it for me. And that's the part that I'm really excited about. It's just unimaginable. You know, like we couldn't be talking today if the printing press hadn't been invented, like our microphones wouldn't exist.

40:09None of the things around us would exist. It just wouldn't be possible to coordinate such a large group of people if that wasn't the case. And so I imagine a world, you know, a few years in the future where everyone is able to program. And what does that unlock? Anyone can just build software anytime. And I have no idea. It's just the same way that, you know, in the 1400s, no one could have protected this. I think it's the same way. But I do think in the meantime, it's going to be very disruptive and it's going to be painful for a lot of people. And again, as a society, this is a conversation that we have to have.

40:40And this is a thing that we have to figure out together. So for folks hearing this that want to succeed and, you know, make it in this crazy turmoil, we're entering any advice. Is it, you know, play with AI tools, get really proficient at the latest stuff. Is there anything else that you recommend to help people stay ahead. Yeah, I think that's pretty much it. Experiment with the tools, get to know them, don't be scared of them. Just, you know, dive in, try them beyond the bleeding edge, beyond the frontier. Maybe the second piece of advice is try to be a generalist more than you have in the past.

41:13For example, in school, a lot of people that study CS, they learn to code and they don't really learn much else. Maybe they learn a little bit of systems architecture or something like this. But some of the most effective engineers that I work with every day and some of the most effective, you know, like product managers and so on, they cross over disciplines. So on the code team, everyone codes, you know, our product manager codes, our engineering manager codes, our designer codes, our finance guy codes, our data scientist codes, like everyone on the team codes. And then if I look at particular engineers, people often cross different disciplines.

41:47So some of the strongest engineers are hybrid product and infrastructure engineers, or product engineers with really great design sense and they're able to do design also. Or an engineer that has a really good sense of the business and can use that to figure out what to do next. Or an engineer that also loves talking to users and can just really channel what users want to figure out what's next. So I think a lot of the people that will be rewarded the most over the next few years, they won't just be AI native and they don't just know how to use these tools really well, but also they're curious and they're generalists and they cross over multiple disciplines and can think about the broader problem they're solving rather than just the engineering part of it.

42:28Do you find these three separate disciplines still useful as a way to think about the team? They're, you know, engineering design, product management. Do you find like those, even though they are now coding and contributing to thinking about what to build, do you feel like those are three roles that will persist long term, at least at this point? I think in the short time it'll persist. But one thing that we're starting to see is there's maybe a 50 % overlap in these roles where a lot of people are actually just doing the same thing. And some people have specialties. For example, I code a little bit more versus CAD RPM does a little bit more coordination or planning or forecasting or things like this.

43:04Stakeholder alignment. Stakeholder alignment. Exactly. I do think that there is a future where I think by the end of the year, what we're going to start to see is these start to get even murkier, where I think in some places the title software engineer is going to start to go away and it's just going to be replaced by builder or maybe it's just everyone's going to be a product manager and everyone codes or something like this. Who says hiring has to be fair? Every founder and hiring manager I've been speaking with these days is feeling the same pressure. Hire the best people as fast as possible.

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44:15That means hours saved for hire and a team focused on what matters most, winning the right candidates. Don't let your competitors out hire you. MetaView customers close roles 30 % faster. Try MetaView today for free and get an extra month of sourcing at metaview.ai. That's M-E-T-A-V-E-W dot A-I slash Lenny. You talked about how you're enjoying coding more. I actually did this little informal survey on Twitter. I don't know if you saw this, where I just asked, I did three different polls. I asked engineers, are you enjoying your job more or less since adopting AI tools? And then I did a separate one for PMs and one for designers.

44:53And both engineers and PMs, 70 % of people said they're enjoying their job more. And about 10 % said they're enjoying their job less. Designers, interestingly, only 55 % said they're enjoying their job more. And 20 % said they're enjoying their job less. I thought that was really interesting. That's super interesting. I'd love to talk to these people, you know, both in the more bucket and the less bucket, just to understand. Did you get to follow up with any of them? A few people replied and we're actually doing a follow-up poll that we'll link to in the show notes of going deeper into some of the stuff.

45:26But a lot of, there's like, you know, the factors that make it more fun and less fun. The designers, they didn't share a lot actually of just like the people that are actually asked just like, why are you enjoying your job less? I didn't hear a lot. So I'm curious what's going on there. Yeah, I'm seeing this a little bit with, at Anthropik, I think everyone is fairly technical. This is something that we screen for, you know, when people join. There's a lot of technical interviews that people go through, even for non-technical functions. And, you know, our designers largely code. So I think for them, this is something that they have enjoyed from what I've seen, because now instead of bugging engineers, they can just like go in and code.

46:06And even some designers that didn't code before have just started to do it. And for them, it's great because they can unblock themselves. But I'd be really interested just to hear more people's experiences because I bet it's not uniform like that. Yeah. So maybe if you're listening to this, leave a comment if you're finding your jobs less fun and you're enjoying your job less. Because what you're saying and what I'm hearing from most people, 70 % of PMs and engineers are loving their job more. That's like if you're not in that bucket, you could something's going on. Yeah, yeah. We do see that people use also different tools.

46:35So, for example, our designers, they use the quad desktop app a lot more to do their coding. So you just download the desktop app. There's a code tab. It's right next to CoWork. And it's actually the same exact quad code. So it's like the same agent and everything. We've had this for many, many months. and so you can use this to code in a way that you don't have to open a bunch of terminals but you still get the power of quad code and the biggest thing is you can just run as many you know quad sessions in parallel as you want we can you know we call this multi-quadding so this is a it's a little more native i think for folks that are not engineers and really this is back to bringing the product to where the people are you don't want to make people use a different workflow you don't want to make them go out of their way to run a new thing it's whatever people are doing if you can make that a little bit easier then that's just going to be a much better product that people enjoy more and this is just this principle of latent demand which i think is just the single most important principle and product can you talk about that actually because i was going to go there explain what this principle is and and and just what happens when you unlock this latent demand latent demand is this idea that if you build a product in a way that can be hacked or can be kind of misused by people in a way it wasn't really designed for to do kind of something that they want to do, then this helps you as the product builder learn where to take the product next.

47:54So an example of this is Facebook Marketplace. So the manager for the team, Fiona, she was actually the founding manager for the Marketplace team, and she talks about this a lot. Facebook Marketplace, it started based on the observation back in, this must have been like 2016 or something like this, that 40 % of posts in Facebook groups are buying and selling stuff. So this is crazy. It's like people are abusing the Facebook group's product to buy and sell. And it's not abuse in kind of like a security sense, it's abuse in that no one designed the product for this, but they're kind of figuring it out because it's just so useful for this.

48:29And so it was pretty obvious if you build a better product to let people buy and sell, they're going to like it. And it was just very obvious that Marketplace would be a hit from this. And so the first thing was buy and sell groups, so kind of special purpose groups to let people do that. And the second product was marketplace. Facebook dating, I think, started in a pretty similar place. And I think that the observation was if you look at people looking at, if you look at profile views, so people looking at each other's profiles on Facebook, 60 % of profile views were people that are not friends with each other that are opposite gender.

49:00And so this is this kind of like, you know, like traditional kind of dating setup, but, you know, people are just like creeping on each other. So maybe if you can build a product for this, it might work. And so this idea of latent demand, I think it's just so powerful. And for example, this is also where coworker came from. We saw that for the last six months or so, a lot of people using quad code were not using it to code. There was someone on Twitter that was using it to grow tomato plants. There was someone else using it to analyze their genome. Someone was using it to recover photos from a corrupted hard drive.

49:33It was like wedding photos. There was someone that was using it for, I think, like, they were using it to analyze an MRI. So there's just all these different use cases that are not technical at all. And it was just really obvious, like people are jumping through hoops to use a terminal to do this thing. Maybe we should just build a product for them. And we saw this actually pretty early. Back in maybe May of last year, I remember walking into the office and our data scientist, Brendan, had a quad code on his computer. He just had a terminal up. And I was shocked. I was like, Brendan, what are you doing?

50:10You figured out how to open the terminal, which is, you know, it's a very engineering product. Even a lot of engineers don't want to use a terminal. It's just like the lowest level way to do your work. Just really, really kind of in the weeds of the computer. And so he figured out how to use the terminal. He downloaded Node.js. He downloaded quad code. and he was doing SQL analysis in the terminal. It was crazy. And then the next week, all of the data scientists were doing the same thing. So when you see people abusing the product in this way, using it in a way that it wasn't designed in order to do something that is useful for them, it's just such a strong indicator that you should just build a product and people are gonna like that.

50:48It's something that's special purpose for that. I think now there's also this kind of interesting second dimension to latent demand. This is sort of the traditional framing is look at what people are doing, make that a little bit easier, empower them. The modern framing that I've been seeing in the last six months is a little bit different. And it's look at what the model is trying to do and make that a little bit easier. And so when we first started building quad code, I think a lot of the way that people approached designing things with LLMs is they kind of put the model in a box. And they were like, here's this application that I want to build.

51:21Here's the thing that I wanted to do a model. You're going to do this one component of it. Here's the way that you're going to interact with these tools and APIs and whatever. And for quad code, we inverted that. we said the product is the model. We want to expose it. We want to put the minimal scaffolding around it, give it the minimal set of tools so it can do the things. It can decide which tools to run. It can decide in what order to run them and so on. And I think a lot of this was just based on kind of latent demand of what the model wanted to do. And so in research, we call this being on distribution.

51:48You want to see like what the model is trying to do in product terms. Latent demand is just the same exact concept, but applied to the model. You talked about co-work, something that I saw you talk about when you launched that initially is your team built that in 10 days. That's insane. It came out, I think it was like, you know, used by millions of people pretty quickly, something like that being built in 10 days. Anything there, any stories there other than just, it was just, you know, we used Cloud Code to build it and that's it. Yeah, it's funny. Cloud Code, like I said, when we released it, it was not immediately a hit.

52:19It became a hit over time and there was a few inflection points. So one was, you know, like and it just keeps inflecting. The growth just keeps getting steeper and steeper and steeper every day. But for the first few months, it wasn't a hit. People used it, but a lot of people couldn't figure out how to use it. They didn't know what it was for. The model still wasn't very good. Cowork, when we released it, it was just immediately a hit, much more so than Cloud Code was early on. I think a lot of the credit honestly just goes to Felix and Sam and Jenny and the team that built this. It's just an incredibly strong team.

52:54And again, the place Co-Work came from is just this latent demand. Like we saw people using quad code for these non-technical things. And we're trying to figure out what do we do. And so for a few months, the team was exploring. They were trying all sorts of different options. And in the end, someone was just like, okay, what if we just take quad code and put it in the desktop app? And that's essentially the thing that worked. And so over 10 days, they just completely used quad code to build it. and you know co-workers actually there's this very sophisticated security system that's that's built in and essentially these guardrails to make sure that the model kind of does the right thing it doesn't go off the rails so for example we ship an entire virtual machine with it and quad code just wrote all of this code so we just had to think about all right how do we make this a little bit safer a little more self-guided for people that are not engineers it was fully implemented with quad code took about 10 days we launched it early you know it was still pretty rough and it's still pretty rough around the edges.

53:49But this is kind of the way that we learn, both on the product side and on the safety side, is we have to release things a little bit earlier than we think so that we can get the feedback, so that we can talk to users. We can understand what people want and that'll shape where the product goes in the future. Yeah, I think that point is so interesting and it's so unique. There's always been this idea, release early, learn from users, get feedback, iterate. The fact that it's hard to even know what the AI is capable of and how people will try to use it is a unique reason to start releasing things early.

54:23That'll help you as you exactly described this idea of what is the latent demand in this thing that we didn't really know. Let's put it out there and see what people do with it. Yeah. And for anthropic as a safety lab, the other dimension of that is safety. Because when you think about model safety, there's a bunch of different ways to study it. Sort of the lowest level is alignment and mechanistic interpretability. So this is when we train the model, we want to make sure that it's safe. we at this point have like pretty sophisticated technology to understand what's happening in the neurons to trace it.

54:52And so for example, like if there's a neuron related to deception, we're starting to get to the point where we can monitor it and understand that it's activating. And so this is just this alignment. This is mechanistic interpretability. It's like the lowest layer. The second layer is evals. And this is essentially a laboratory setting. The model is in a Petri dish and you study it and you put in a synthetic situation and just say, okay, like model, what do you do? And are you doing the right thing? Is it aligned? Is it safe? And then the third layer is seeing how the model behaves in the wild.

55:21And as the model gets more sophisticated, this becomes so important because it might look very good on these first two layers, but not great on the third one. We released Claude Code really early because we wanted to study safety. And we actually used it within Anthropic for I think four or five months or something before we released it because we weren't really sure. Like this is the first agent that, But the first big agent that I think folks had released at that point, it was definitely the first coding agent that became broadly used. And so we weren't sure if it was safe. And so we actually had to study it internally for a long time before we felt good about that.

55:56And even since, there's a lot that we've learned about alignment. There's a lot that we've learned about safety that we've been able to put back into the model, back into the product. And for co-work, it's pretty similar. The model's in this new setting. It's doing these tasks that are not engineering tasks. It's an agent that's acting on your behalf. It looks good on alignment. It looks good on evals. We tried it internally. It looks good. We tried it with a few customers. It looks good. Now we have to make sure it's safe in the real world. And so that's why we release a little early. That's why we call it a research preview.

56:24But yeah, it's just, it's constantly improving. And this is really the only way to make sure that over the long term, the model is aligned and it's doing the right things. it's such a wild space that you work in where there's this insane competition and pace at the same time there's this fear that if you get if the you know the god can escape and cause damage and just finding that balance must be so challenging what i'm hearing is there's kind of these three layers and i know there's like this could be a whole podcast conversation is how you all think about the safety piece but just what i'm hearing is there's these three layers you work with uh there's kind of like observing the model thinking and operating there's tests evals that tell you is doing bad things and then releasing it early.

57:04I haven't actually heard a ton about that first piece. That is so cool. So you guys can, there's an observability tool that can let you peek inside the model's brain and see how it's thinking and where it's heading. Yeah. You should, you should at some point have Chris Ola on the podcast because he's just the industry expert on this. He invented this field of, we call it mechanistic interpretability. And the idea is, you know, like at its core, like what is your brain? Like what are, what is it? It's like it's a bunch of neurons that are connected. And so what you can do is like in a human brain or an animal brain, you can study it at this kind of mechanistic level to understand what the neurons are doing.

57:40It turns out, surprisingly, a lot of this does translate to models also. So model neurons are not the same as animal neurons, but they behave similarly in a lot of ways. And so we've been able to learn just a ton about the way these neurons work, about, you know, this layer or this neuron maps to this concept, how particular concepts are encoded, how the model does planning, how it thinks ahead. You know, like a long time ago, we weren't sure if the model is just predicting the next token or is doing something a little bit deeper. Now, I think there's actually quite strong evidence that it is doing something a little bit deeper.

58:13And then the structures that way to do this are pretty sophisticated now, where as the models get bigger, it's not just like a single neuron that corresponds to a concept. A single neuron might correspond to a dozen concepts. And if it's activated together with other neurons, This is called superposition. And together, it represents this more sophisticated concept. And it's just something we're learning about all the time. And for Anthropik, as we think about the way this space evolves, doing this in a way that is safe and good for the world is just, this is the reason that we exist. And this is the reason that everyone is at Anthropik.

58:47Everyone that is here, this is the reason why they're here. So a lot of this work, we actually open source. We publish it a lot. And we publish very freely to talk about this. just so we can inspire other labs that are working on similar things to do it in a way that's safe. And this is something that we've been doing for Cloud Code also. We call this the race to the top internally. And so for Cloud Code, for example, we released an open source sandbox. And this is a sandbox they can run the agent in. And it just makes sure that there are certain boundaries and it can't access everything on your system.

59:20And we made that open source. And it actually works with any agent, not just Cloud Code, because we wanted to make it really easy for others to do the same thing. So this is just the same principle, race to the top. We want to make sure this thing goes well. And this is just the lever that we have. Incredible. Okay. I definitely want to spend more time on that. I will follow up with this suggestion. Something else that I've been noticing in the field across engineers, product managers, others that work with agents, is there's this kind of anxiety people feel when their agents aren't working. There's a sense that like, oh man, Niza has a question and answer or it's like blocked on something or it's or I just like I'm like, there's all this productivity I'm losing.

1:00:03I can't like I need to wake up and get it going again. Is that something you feel? Is that something your team feels? Do you feel like this is a problem we need to track and think about? I always have a bunch of agents running. So I got the moment I have like five agents running and at any moment, like, you know, like I wake up and I start a bunch of agents. Like the first thing I did when I woke up is like, oh man, I really want to check this thing. So like I opened up my phone, quad iOS app, code tab, you know, like agent do blah, blah, blah. Because I wrote some code yesterday and I was like, wait, did I do this right?

1:00:32I was like kind of double guessing something and it was correct. But now it's just like so easy to do this. So I don't know, there is this little bit of anxiety maybe. I personally haven't really felt it just because I have agents running all the time. And I'm also just like not locked into a terminal anymore. Maybe a third of my code now is in the terminal, but also a third is using the desktop app. And then a third is the iOS app, which is just so surprising because I did not think that this would be the way that I code even in 2026. I love that you describe it as coding still, which is just talking to Cloud Code to code for you, essentially.

1:01:08And it's interesting that this is now coding. Coding now is describing what you want, not writing actual code. I kind of wonder if the people that used to code using punch cards or whatever, if you show them software, what they would have said. I remember reading something. This was maybe like very early versions of like ACM, like magazine or something where people were saying, no, it's not the same thing. Like this isn't really coding. And, you know, they call it a programming. I think coding is kind of a new word. But I kind of think about this. Like back in the, you know, my family is from the Soviet Union.

1:01:43I was born in Ukraine. And my grandpa was actually one of the first programmers in the Soviet Union. And he programmed using punch cards. My mom, growing up, told these stories of like... She told these stories of when she was growing up, he would bring these punch cards home. And there was these big stacks of punch cards. And for her, she would draw all over them with crayons. And that was her childhood memory. But for him, that was his experience of programming. And he actually never saw the software transition. But at some point, it did transition to software. And I think there's probably this older generation of programmers that just didn't take software very seriously.

1:02:19And they would have been like, well, you know, it's not really coding. But I think this is a field that just has always been changing in this way. I don't think you know this, but I was born in Ukraine also. Oh, I don't know. Yeah. Which town? I'm from Odessa. Oh, me too. What? Yeah, that's crazy. Wow. Incredible. What a moment. maybe related in some small way. What year did you leave and your family leave? We came in 95. Okay. We left in 88 a little earlier. What a different life that would have been to not leave. I feel so lucky every day that I get to grow up here. Yeah. My family, anytime there's a toaster or a meal, they're just like, to America.

1:03:07It's like, okay, enough about that. Yeah, but you get it, you know, once you start really thinking about what life could have been. Yeah, yeah, exactly. Yeah, we do that. We do the same to us, but it's still vodka. It's still vodka. Absolutely. Oh, man. Okay. Let me ask you a couple more things here. You shared some really cool tips for how to get the most out of AI, how to build on AI, how to build great products on AI. One tip you shared is give your team as many tokens as they want. Just like let them experiment. You also share just advice generally of just build towards where the model is going, not to where it is today.

1:03:39What other advice do you have for folks that are trying to build AI products? I'd probably share a few more things. So one is don't try to box the model in. I think a lot of people's instinct when they build on the model is they try to make it behave a very particular way. They're like, this is a component of a bigger system. I think some examples of this are people wearing very strict workflows on the model, for example, you know to say like you must do step one then step two then step three and you have this like very fancy orchestrator doing this but actually almost always you get better results if you just give the model tools you give it a goal and you let it figure it out i think a year ago you actually needed a lot of the scaffolding but nowadays you don't really need it so you know i don't know what to call this principle but it's like you know like ask not what the model can do for you maybe maybe it's something like this just think about how do you give the model the tools to do things don't try to over-curate it.

1:04:27Don't try to put it into a box. Don't try to give it a bunch of context up front. Give it a tool so that it can get the context it needs. You're just going to get better results. I think a second one is maybe actually like a more, even more general version of this principle is just the bitter lesson. And actually for the Quadco team, we have a, you know, hopefully listeners have read this, but Rich Sutton had this blog post maybe 10 years ago called the bitter lesson. And it's actually a really simple idea. His idea was that the more general model will always outperform the more specific model.

1:05:01And I think for him, he was talking about like self-driving cars and other domains like this. But actually, there's just so many corollaries to the better lesson. And for me, the biggest one is just always bet on the more general model. And, you know, over the long term, like don't don't try to use tiny models for stuff. Don't try to like fine tune. Don't try to do any of this stuff. There's like some applications, you know, there's some reasons to do this, but almost always try to bet on the more general model if you can, if you have that flexibility. And so these workflows are essentially a way that, you know, it's not a general model.

1:05:34It's putting the scaffolding around it. And in general, what we see is maybe scaffolding can improve performance maybe 10, 20%, something like this. But often these gains just get wiped out with the next model. So it's almost better to just wait for the next one. And I think maybe this is a final principle and something that cloud code, I think, got right in hindsight. From the very beginning, we bet on building for the model six months from now, not for the model of today. And for the very early versions of the product, I just wrote so little of my code because I didn't trust it. Because, you know, it was like Sonnet 3.5, then it was like 3.6 or forget 3.5 new, whatever name we gave it.

1:06:17These models just weren't very good at coding yet. They were getting there, but it was still pretty early. So back then, the model used Git for me. It automated some things, but it really wasn't doing a huge amount of my coding. And so the bet with quad code was at some point, the model gets good enough that it can just write a lot of the code. And this is the thing that we first started seeing with Opus 4 and Sonnet 4. And Opus 4 was our first kind of ASL 3 class model that we released back in May. And we just saw this inflection because everyone started to use quad code for the first time. And that was kind of when our growth really went exponential.

1:06:53And like I said, it stayed there. So I think this is advice that I actually give to a lot of folks, especially people building startups. It's going to be uncomfortable because your product market won't be very good for the first six months. But if you build for the model six months out, when that model comes out, you're just going to hit the ground running and the product is going to click and start to work. And when you say build for the model six months out, what is it that you think people can assume will happen? Is it just generally it will get better at things? Is it just like, okay, it's like almost good enough and that's a sign that it'll probably get better at that thing?

1:07:29Is there any advice there? I think that's a good way to do it. Like, you know, obviously within an AI lab, we get to see the specific ways that it gets better. So it's a little unfair. But we also, we try to talk about this. So, you know, like one of the ways that it's going to get better is it's going to get better and better using tools. and using computers. This is a bet that I would make. Another one is it's going to get better and better for long periods of time. And this is a place, you know, like there's all sorts of studies about this, but if you just trace the trajectory or, you know, maybe even like for my own experience, when I used Sonnet 3.5 back, you know, a year ago, it could run for maybe 15 or 30 seconds before it started going off the rails.

1:08:09And you just really had to hold its hand through any kind of complicated task. But nowadays with Opus 4.6, on average, it'll run maybe 10, 30, 20, 30 minutes unattended. And I'll just start another quad and have it do something else. And like I said, I always have a bunch of quads running. And they can also run for hours or even days at a time. I think there are some examples where they ran for many weeks. And so I think over time, this is going to become more and more normal, where the models are running for a very, very long period of time. And you don't have to sit there and babysit them anymore.

1:08:38So we just talked about tips for building AI products. Any tips for someone just using Cloud Code, say, for the first time, or just someone already using Cloud Code that wants to get better? What are a couple of pro tips that you could share? I will give a caveat, which is there's no one right way to use Cloud Code. So I can share some tips, but honestly, this is a dev tool. Developers are all different. Developers have different preferences. They have different environments. So there's just so many ways to use these tools. There's no one right way. You sort of have to find your own path. Luckily, you can ask Cloud Code.

1:09:10It's able to make recommendations. It can edit your settings. It kind of knows about itself. So it can help with that. A few tips that generally I find pretty useful. So number one is just use the most capable model. Currently, that's Opus 4.6. I have maximum effort enabled always. The thing that happens is sometimes people try to use a less expensive model like Sonnet or something like this. But because it's less intelligent, it actually takes more tokens in the end to do the same task. And so it's actually not obvious that it's cheaper if you use a less expensive model. Often it's actually cheaper and less token intensive if you use the most capable model because it can just do the same thing much faster with less correction, less handholding and so on.

1:09:48So that's the first step is just use the best model. The second one is use plan mode. I start almost all of my tasks in plan mode, maybe like 80%. And plan mode is actually really simple. all it is is we inject one sentence into the model's prompt to say, please don't write any code yet. That's it. Like there's actually like nothing fancy going on. It's just the simplest thing. And so for people that are in the terminal, it's just shift tab twice and that gets you into plan mode. For people in the desktop app, there's a little button on web. There's a little button. It's coming pretty soon to mobile also.

1:10:21And we just launched it for the Slack integration too. So plan mode is the second one. And essentially the model would just go back and forth with you. Once the plan looks good, then you let the model execute. I auto accept edits after that, because if the plan looks good, it's just going to one shot it. It'll get it right the first time almost every time with the Opus 4.6. And then maybe the third tip is just play around with different interfaces. I think a lot of people, when they think about Cloud Code, they think about a terminal. And, you know, of course, we support every terminal. We support like Mac, Windows, you know, like whatever terminal you might use, it works perfectly.

1:10:54But we actually support a lot of other form factors too. Like, you know, we have like iOS and Android apps. We have a desktop app. There's, you know, the Slack integration. There's all sorts of things that we support. So I would just like play around with these. And again, it's like every engineer is different. Everyone that's building is different. Just find the thing that feels right to you and use that. You don't have to use a terminal. It's the same quad agent running everywhere. Amazing. Okay. Just a couple more questions to round things out. What's your take on Codex? How do you feel about that product?

1:11:23How do you feel about where they're going, just kind of competing in this very competitive space in coding agents? Yeah, I actually haven't really used it, but I think I did use it maybe when it came out. It looked a lot like quad code to me, so that was kind of flattering. I think it's actually good to have more competition because people should get to choose and hopefully it forces all of us to do an even better job. honestly for our team though we're just focused on solving the problems that users have so for us you know we don't spend a lot of time looking at competing products we don't really try the other products i you know you kind of you want to be aware of them you want to know they exist but for me i just i love talking to users i love making the product better i i love just acting on on feedback so it's really just about building a building a good product Maybe a last question.

1:12:14So I talked to Ben Mann, co-founder of Anthropic. What to talk to you about. He had a bunch of suggestions, which I've integrated throughout our chat. One question he had for you is, what's your plan post AGI? What do you think you're going to be doing? What's your life like once we hit AGI, whatever that means? So before I joined Anthropic, I was actually living in rural Japan. And it was like a totally different lifestyle. I was like the only engineer in the town. I was the only English speaker in the town. It was just like a totally different vibe. Like a couple of times a week, I would like bike to the farmer's market.

1:12:47And, you know, you like bike by like rice paddies and stuff. It was just like a totally different speed than just complete opposite of San Francisco. One of the things that I really liked is a way that we got to know our neighbors and we kind of built friendships is by trading like pickles. So in that in the town where we lived, it was actually like everyone made like miso, everyone made pickles. And so I actually got decently good at making miso. And I made a bunch of batches and this is something that I still make. Miso is this interesting thing where it teaches you to think on these long time skills that's just very different than engineering.

1:13:23Because a batch of white miso takes at least three months to make. And a red miso is like two, three, four years. You just have to be very patient. You kind of mix it up and then you just let it sit. You have to be very, very patient. So the thing that I love about it is just thinking in these long time skills. And yeah, I think post-AGI or if I wasn't adanthropic, I'd probably be making miso. I love this answer. Ben asked me to ask you about what's the deal with you and miso. So I love that you answered it. Okay, so the future might be just going deep into miso. Getting really good at making miso.

1:14:02Amazing. uh boris this was incredible i feel like we're we're brothers now from ukraine uh before we get to our very exciting lighting ground is there anything else that you wanted to share is there anything you want to leave listeners with anything you want uh you want to double down on yeah i i think i would just like underscore you know like for for anthropic since the beginning this idea of like starting at coding then getting to tool use then getting to computer use has just been the way that we think about things. And this is the way that we know the models are going to develop or the way that we want to build our models.

1:14:35And it's also the way that we get to learn about safety, study it and improve it the most. So everything that's happening right now around just like quad code becoming this huge multi-billion dollar business. And now all of my friends use quad code and they just text me about it all the time. So just like this thing getting kind of big. In some ways, it's a total surprise because this isn't kind of the, we didn't know that it would be this product. We didn't know that it would start in a terminal or anything like this. But in some ways, it's just totally unsurprising because this has been our belief as a company for a long time.

1:15:10At the same time, it just feels still very early. You know, like most of the world still does not use quad code. Most of the world still does not use AI. So it just feels like this is 1 % done and there's so much more to go. oh man that's insane to think seeing the numbers that are coming out you guys just raised a bazillion dollars uh i think cloud code alone is making two billion dollars in revenue you think anthropic i think the number you guys put out you're making 15 billion in revenue it's uh insane to just think this is how early it still is and just the numbers we're seeing yeah yeah yeah it's crazy and i mean like the the way that quad code has kept growing is honestly just the users.

1:15:48Like we, so many people use it. They're so passionate about it. They fall in love with the product. And then they tell us about stuff that doesn't work, stuff that they want. And so like the only reason that it keeps improving is because everyone is using it. Everyone is talking about it. Everyone keeps giving feedback. And this is just the single most important thing. And, you know, for me, this is the way that I love to spend my days, just talking to users and making it better for them. And making me so. And making me so, well, the, you know, the me so is like not super involved. It just, You just got to wait.

1:16:17Well, Boris, with that, we've reached our very exciting lightning round. I've got five questions for you. Are you ready? Let's do it. First question, what are two or three books that you find yourself recommending most to other people? I'm a big reader. I would start with a technical book. One is it is Functional Programming in Scala. This is the single best technical book I've ever read. It's very weird because you're probably not going to use Scala. And I don't know how much this matters in the future now. but there's this just elegance to functional programming and thinking in types and this is just the way that i code and the way that i can't stop thinking about coding so you know you could think of it as a historical artifact you could think of it as something that will level you up i love this uh never before mentioned book my favorite oh amazing amazing uh okay second one is uh accelerando by strass this is probably you know like my my big genre is uh is sci-fi uh like probably sci-fi and fiction.

1:17:11Accelerando is just this incredible book. And it's just so fast-paced. The pace gets faster and faster and faster. And I just feel like it captures the essence of this moment that we're in more than any other book that I've read, just the speed of it. And it starts as a liftoff is starting to happen and, you know, it's starting to approach the singularity. And it ends with like this like collective lobster consciousness orbiting Jupiter. And, you know, this happens over like the span of a few decades or something. So the pace is just incredible. I really love it. Maybe I'll do one more book. The Wandering Earth by Sixin Liu.

1:17:48So he's the guy that did Three Body Problem. I think a lot of people know him for that. I think Three Body Problem was awesome, but I actually liked his short stories even more. So Wandering Earth is one of the short story collections, and he just has some really, really amazing stories. stories. And it's also just quite interesting to see Chinese sci-fi because it has a very different perspective than Western sci-fi and kind of the way that at least he as a writer thinks about it. So it's just really, really interesting to read and just beautifully written. It's so interesting how sci-fi has prepared us to think about where things are going.

1:18:19Just like it creates these models of like, okay, I see. I've read about this sort of world. Yeah. I think for me, this is like the reason that I joined Anthropic actually, because uh you know like like i said i was living in this rural place i was thinking these long time skills because everything is just so slow out there at least compared to sf um and just like all the things that you do are based around the seasons and it's based around this food that takes many many months that's the way that kind of like social events are organized that's the way you kind of organize your time you like you go to the farmer's market and it's like it's persimmon season and you know that because there's like 20 persimmon vendors and then the next week the season is done and it's like grape season and you kind of see this so it's like these kind of long time scales and i was also reading a bunch of sci-fi at the time and just like being in this moment i was like you know just thinking about these long time scales i know how this thing can go and i just i felt like i had to contribute to it going a little bit better and that's actually why i ended up at ant and ben man was also a big part of that too i feel like i want to do a whole podcast just talking about your time in japan and the journey of boris through japan to anthropic but we'll keep it short.

1:19:26I'll quickly recommend a sci-fi book to you if you haven't read it. Have you read Fire Upon the Deep? This is Vinge, right? Yeah, it's great. Okay. That one's like, it's like so interesting from an AI, AGI perspective. So a few people have read that. So I love that. Yeah, it's like... I really like... Yeah, yeah, yeah. I like Deepness in the Sky also. I think those purchase the sequel, right? Yeah. Yeah, yeah, yeah. I think so. Yeah. It's very long and complex to get into it's so good. Okay, we'll keep going through our lightning round. Do you have a favorite recent movie or TV show you've really enjoyed?

1:19:59So I actually don't really watch TV or movies. I just don't really have time these days. I did watch, I'm going to bring up another Sishin Liu, but the Three Body Problem series on Netflix, I really loved. I thought that was like a great rendition of the book series. So the common pattern across AI leaders is no time to watch TV or movies, which I completely understand. Is there a favorite product you've recently discovered that you really love i'm gonna like show a little bit and just say co-work because this is this is legitimately the the one product that's been pretty life-changing for me uh just because i i have it running all the time and the chrome integration in particular is just really excellent so it's been like it paid a traffic fine for me it like canceled a couple subscriptions for me uh just like the amount of like tedious work it gets out of the way is awesome i also don't know if it's a product but maybe i'll also another podcast that I really love obviously besides Lenny obviously it's the acquired podcast by Ben and David it's just like super awesome I feel like the way that they get into business history and bring it alive is really really good and I would start with a Nintendo episode if you haven't listened to it great tip with co-work just so people understand if they haven't tried this like basically you type something you want to get done and it can launch Chrome and just do things for you.

1:21:19I saw one of the someone went on path leave from Anthropic and he had to fill out these like medical forms for him. These like really annoying PDFs where it just like loads up the browser, logs in, fills about some bits of. Yeah, exactly. Exactly. And it actually just kind of works. Like we tried this experiment like a year ago and it didn't really work because the model wasn't ready. But now now it actually just works. And it's amazing. I think a lot of people just don't really understand what this is because they haven't used Agent before. And it just feels very, very similar to me to Cloud Code a year ago.

1:21:48But like I said, it's just growing much faster than Cloud Code did in the early days. So I think it's starting to break through a bit. And there's also this Chrome extension that you mentioned that you could just use standalone that sits in Chrome and you could just talk to Claude, looking at your screen, at your browser and have it do stuff, have it tell you about what you're looking at, summarize what you're looking at, things like that. Exactly, exactly. For people that are just starting to use CoWork, the thing I recommend is, so you download the quad desktop app, you go to the cowork tab, it's right next to the code tab.

1:22:17The thing that I recommend doing is like start by having it use a tool. So like clean up your desktop or like summarize your email or something like this, or you know, like respond to the top three emails. Like it actually just responds to emails for me now too. The second thing is connect tools. So like if you connect, like if you say, look at my top emails and then send Slack messages or, you know, like put them in a spreadsheet or something. Or for example, like I use it for all my project management. So we have a single spreadsheet for the whole team. There's like a row per engineer. Every week, everyone fills out a status.

1:22:45And every Monday, cowork just goes through and it messages every engineer on Slack that hasn't filled out their status. And so I don't have to do this anymore. And this is just one prompt. It'll do everything. And then the third thing is just run a bunch of quads in parallel. So it can cowork, you can have as many tasks running as you want. So it's like start one task, you know, I have this project management thing running, then I'll have it do something else, then something else, and I'll kick these off. And then I just go get a coffee while it runs. There's a post I'll link to that shares a bunch of ways people use what was previously cloud code and now just you could do through co-work.

1:23:17Because a lot of this is just like, oh, wow, I hadn't thought I could use it for that. And once you see, like these examples I think are where people need to hear, I'm just like, oh, wow, I didn't know I could do that. Yeah, I think a lot of this was also, some of this was also inspired by you, Lenny. You had this post about, it was like 50 non-technical use cases for quad code or something like this. So we actually, one of our PMs used that as a way to evaluate Cowork before we released it. And I think at the point where we hit, where Cowork was able to do like 48 out of the 50, they were like, okay, it's pretty good.

1:23:46Wow. I did not know that. That is awesome. I've become an eval. Yeah. How does that feel? Amazing. I feel like I'm valuable to the future of AI. This is like reverse breaking through. Wow, that is so cool. Wow. Okay. I wonder what those last two are. Anyway, okay. Two more questions. Do you have a favorite life motto that you often come back to in work or in life? Use common sense. I think a lot of the failures that I see in, especially in a work environment, is people just failing to use common sense. Like they follow a process without thinking about it. They just do a thing without thinking about it, or they're working on a product that's like not a good product or not a good idea.

1:24:29And they're just following the momentum and not thinking about it. I think the best results that I see are people thinking from first principles and just developing their own common sense. Like if something smells weird, then, you know, it's probably not a good idea. So I think, I think just this, this is the single advice that I give, you know, to coworkers more, more than anything too. And I feel like that alone could be its own podcast conversation. What is common sense? How do you build it? But we'll keep this short. Final question. So you've been, got more active on Twitter slash X. I'm curious just why, and just what's your experience been with Twitter, the world of Twitter, because you get a lot of engagement on Twitter slash X.

1:25:06So for a long time, I used threads exclusively because I actually helped build threads a little bit back in the day. And I also just like the design. It's like a very clean product. I just really like that. I started using threads because actually I was bored. So in December, I was in Europe. You started using Twitter, you mean? Oh, yeah, yeah, yeah. I started using Twitter because I was bored. So my wife and I, we were traveling around in Europe for December. We're just kind of nomading around. We went to Copenhagen, went to a few different countries. And for me, it was just like a coding vacation.

1:25:36So every day I was coding and that's like my favorite kind of vacation is to just like code all day. It's the best. And at some point I just kind of got bored and I ran out of ideas for a few hours. I was like, okay, what do I want to do next? And so I opened Twitter. I saw some people tweeting about QuadCode and then I just started responding. And then I was like, okay, maybe actually a thing I should do is just like look for people, look for bugs that people have. Maybe people have like bugs or kind of feedback they have. And so kind of introduced myself, asked for if people had a bunch of bugs and feedback.

1:26:07And I think they were kind of surprised by like the pace at which we're able to address feedback nowadays. For me, it's just like so normal. Like if someone has a bug, like I can probably fix it within a few minutes because I just sort of quad. And as long as the description is good, it will just go and do it. And then I'll go do something else and answer the next thing. But I think for a lot of people, it was pretty surprising. So it was really cool. And yeah, the experience on Twitter has been pretty great. It's been awesome just engaging with people and seeing what people want, hearing about bugs, hearing about features.

1:26:38I saw a complaint to Nikita Beer the other day on Twitter of just, you're like posting many threads and it was breaking and just like, oh man, what's going on here? Yeah, there was a bug. I hope it's fixed now. Amazing. Oh man, Boris, I could chat with you for hours. I'll let you go. Thank you so much for doing this. you're wonderful where can folks find you online how can listeners be useful to you yeah find me on threads or on twitter that's the that's the easiest place and please just tag me on stuff send bugs send feature requests what's missing what can we do to make the products better what do you like what do you want I love love hearing it amazing Boris thank you so much for being here cool thanks funny 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.

1:27:29Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'sPodcast.com. See you in the next episode.