How Coinbase scaled AI to 1,000+ engineers | Chintan Turakhia

1. 0:00 – 2:38

   Introduction to Chintan

   1. CVClaire Vo0:00

      People are skeptical that large, established, highly technical, highly capable engineering organizations can deploy AI at scale and get any effect. But I think you've proven it's possible.

   2. CTChintan Turakhia0:11

      It's not only possible, it's adapt or die. It's just been such a huge superpower for the team.

   3. CVClaire Vo0:17

      How many engineers are we talking about here?

   4. CTChintan Turakhia0:19

      A thousand plus.

   5. CVClaire Vo0:20

      So we're not messing around here.

   6. CTChintan Turakhia0:21

      The company tried to adopt other AI tools, and we saw this uptick in adoption. People opened it up, checked the box, did kind of like a hello world thing, but it didn't stick. My biggest thing is how do I make this damn thing stick? Because there's something here.

   7. CVClaire Vo0:34

      I do think that it's really important when you're doing this organizational transformation that you have a single person with incredible conviction at the leadership level who is also hands-on the metal.

   8. CTChintan Turakhia0:45

      Show the engineers, not just tell. And the worst thing any eng leader could do is just be like, "I decree you must use AI." Come on, no one's gonna listen to you.

   9. CVClaire Vo0:57

      [upbeat music] Welcome back to How I AI. I'm Claire Vo, product leader and AI obsessive here on a mission to help you build better with these new tools. Today we have Chintan Turakhia, senior director of engineering at Coinbase, and he's gonna show us, yes, it is possible to drive AI adoption and higher velocity in an engineering organization of thousands of engineers. He's also gonna show us the new expectations for engineering managers and engineering leaders, which is less meetings and more code. Let's get to it. This episode is brought to you by WorkOS. AI has already changed how we work. Tools are helping teams write better code, analyze customer data, and even handle support tickets automatically. But there's a catch. These tools only work well when they have deep access to company systems. Your copilot needs to see your entire code base. Your chatbot needs to search across internal docs. And for enterprise buyers, that raises serious security concerns. That's why these apps face intense IT scrutiny from day one. To pass, they need secure authentication, access controls, audit logs, the whole suite of enterprise features. Building all that from scratch, it's a massive lift. That's where WorkOS comes in. WorkOS gives you drop-in APIs for enterprise features so your app can become enterprise-ready and scale upmarket faster. Think of it like Stripe for enterprise features. OpenAI, Perplexity, and Cursor are already using WorkOS to move faster and meet enterprise demands. Join them and hundreds of other industry leaders at WorkOS.com. Start building today.

2. 2:38 – 8:00

   How Coinbase approached rewriting their app with AI assistance

   1. CVClaire Vo2:38

      Chintan, thank you so much for joining. What I love about what we're gonna talk about today is we spend so much time talking about the individual vibe coder or the non-technical person becoming a software engineer, and still people are skeptical that large, established, highly technical, highly capable engineering organizations can deploy AI at scale and get any effect. There's still so much skepticism, but I think you've proven it's possible, and you're hopefully gonna show us the way.

   2. CTChintan Turakhia3:10

      I, I think it's, uh, not only possible, it's, you know, adapt or die. Um, y- d- like it, i-it's, uh, it's just been such a huge superpower for the team, and we've gotten so much efficiency out it, and, and there's just, like, ways to approach it. Um, I was... I think I was reading a tweet yesterday, uh, just about a very, very long story at Microsoft and... or someone, like, pulling Copilot into their organization, and it was just, like, just a fun tweet of just like, "Yep, we're gonna make graph go up and to the right," but, like, the actual adoption wasn't good. And, and so, like, I've been spending the last year just absolutely obsessing about it, and you can do it. People can do it.

   3. CVClaire Vo3:50

      So how, how can you do it? Because, you know, how many engineers are we, we talking about here?

   4. CTChintan Turakhia3:56

      A, a thousand plus.

   5. CVClaire Vo3:57

      Yeah.

   6. CTChintan Turakhia3:57

      Yeah.

   7. CVClaire Vo3:57

      So we're not, we're not messing around here.

   8. CTChintan Turakhia3:59

      Yeah, we're not messing around.

   9. CVClaire Vo4:00

      This is a, this is a real, this is a real team working on real products who know what they're doing, who have built great software. And so where did you start, either culturally, from a product perspective, from a tools perspective?

   10. CTChintan Turakhia4:12

       So I think a lot of it actually just started around this time last year. Um, we had some changes, uh, to align, like, the product I'm responsible for, and a big part of that was effectively, like, rewriting the entire product from scratch, uh, from, uh, turning it from a self-custody wallet to actually a social consumer app that just happens to use crypto. And, you know, we're using React Native, um, but we made a lot of decisions for a self-custody wallet. But to become a consumer app, you gotta, like, rethink everything. That was one. Two, we needed to do it in, like, six to nine months, so we were going head-to-head with, like, the big social players out there that have multi-thousand person teams, uh, that have a 10-year head start, and we were really trying to just do something big and new and crazy. Like, absolutely just crazy. And, and, uh, a big part of this is, like, how do we rewrite the app so that it is the best possible app out there, like consumer grade, and do it in this insane timeline? And the team is cracked. They're amazing. But, like, you know, we, we be... we became a smaller team as a result of, of some of these changes. And so I started just looking at, like, ways to accelerate. And, and, you know, like, I don't know, my, my team knows me well, and if you, if you know me, like, I obsess about efficiency. Uh, and I think that's, like, so critical to, like, make teams accelerate their v-velocity. Um, but in, in, in ways that make sense, uh, for tool... in using the tool. So at around this time, I think Cursor had come out with their sort of initial release. It was around, like, November of last year. We, we all tried it, right? 2024. And it kinda sucked. And it, it's not like... I love Cursor. I love Cursor. Uh, the models weren't there. The, just the models weren't there. Like, the, the models couldn't even, you know, really write a unit test right well. And, you know-You're an engineer, um, and you understand, like, once, once an engineer tries a tool and, and they're like, "Ugh, this is not so good," like, it's very quickly and very easy to write it off, right? It happens. And so we, we kind of went through this, like, trough of sorrow of just like, "Okay, goddamn it, AI tools are not here. The models aren't ready. What are we going to do?" And, you know, for even a year prior to this event, like, the company tried to ado-adopt other AI tools like GitHub Copilot, and we saw this, like, uptick in adoption. Like, people opened it up, checked the box, did kind of like a hello world thing, but it didn't stick, right? And, and, like, my, my biggest thing is how do I make this damn thing stick, right? Because there's something here, right? And my mental model was just always the models will-- the, the foundational LLMs will always get better. And it's like going to the gym. You need to go and build your reps and try, and that's okay, and the cost of doing it is, like, nothing. It's just a little bit of wasted time. We're not worried about compute right now because it's so early. And so, like, from basically January all the way to, like, March or April of 2025, I just changed the, the mindset and the mentality. I, I was, like, in Cursor every single day, every single hour of the day, and I was like, "How do I make this work?" Right? Like, you know, it was great because I was writing code again. It was great because, you know, it was unlocking all these, like, use cases. Like, we were doing interviews, like, interviewing candidates and, and just, like, I don't wanna necessarily write up all the notes, right? That takes a long time. But I intuitively, I like, I know. I've assessed, right? So I would use it for, like, tactical day-to-day paperwork kind of things

3. 8:00 – 10:30

   The importance of leadership conviction and hands-on demonstration

   1. CTChintan Turakhia8:00

      to accelerate me. Uh, but also from, like, a coding perspective, would just pick up bugs and be like, "Hey, let's try this," right? What's gonna happen? What can I learn? What are the tips and tricks to, like, show the engineers, not just tell? And the worst thing any eng leader could do is just be like, "I decree you must use AI." Like, come on. No one's gonna listen to you.

   2. CVClaire Vo8:24

      I have to empathize with this because I also, running a large, like, multi-hundred person engineering organization, you know, was experiencing even early versions of these tools and had such innate conviction-

   3. CTChintan Turakhia8:37

      Yeah

   4. CVClaire Vo8:37

      ... that it would, of course, transform how we did work. Like, that was very obvious to me. I don't know it's obvious because of experience or obvious because it was just obvious, but-

   5. CTChintan Turakhia8:47

      Yeah

   6. CVClaire Vo8:48

      ... but then, you know, you just had these experiences as leaders, especially in the, you know, maybe 12 months ago. [tsking] One engineer tries it, doesn't work. It's not just that engineer throws it away. It's everybody else says, "Well, I think... You know, I trust their opinion."

   7. CTChintan Turakhia9:04

      Right.

   8. CVClaire Vo9:04

      "And if they say it's not gonna work-

   9. CTChintan Turakhia9:06

      Yeah

   10. CVClaire Vo9:06

       ... it's not gonna work for me." And I do think that it's really important when you're doing this organizational transformation that you have a single person with incredible conviction at the lo-leadership level who is also hands on the metal. Because until you can say, "Well, I understand it didn't work for that, but it worked for these three things," or, "I actually figured out how to make it work for that because we tried A, B, and C," I think it's just the only way. You cannot be in philosophy. You cannot be in, you know, someday in the future you figure it out. You have to actually get back to it. And then I think, like, bonus points, so many of us in engineering leadership have, like, been pushed away from c- from coding-

   11. CTChintan Turakhia9:48

       I know. I was happy to get back in it

   12. CVClaire Vo9:49

       ... into meetings. And I'm like, "I just wanna code again." Like, give me some joy.

   13. CTChintan Turakhia9:53

       Yeah.

   14. CVClaire Vo9:53

       Give me some time. Um-

   15. CTChintan Turakhia9:54

       Yeah. And, and so-

   16. CVClaire Vo9:55

       And so I think that's a benefit as well.

   17. CTChintan Turakhia9:57

       And you, you have to show, not tell. And, and so I did, and, like, I think what I learned very quickly is, like, okay, there's something here. There's, there's a there, right? And then we just started picking off, like, one or two use cases, and, and the best way to get to an engineer is just give them the tools so they stop doing the shit work and so that they can build the stuff they love, right? Right? And so, like, we would just, like, pick off unit tests. We'd pick off, like, linting, all these, like, little things that just, like, paper cut and suck the soul out of you as a builder. But the engineers

4. 10:30 – 17:57

   The “PR speed run” technique that transformed team adoption

   1. CTChintan Turakhia10:30

      and, you know, like, the team just wants to move faster. The team wants to build better things. And so we started leaning into, like, Cursor rules for some of these things. Even the simplest thing. I re- I remember, like, I think I remember my aha moment, which was, like, popping in some bug report, working through it, and then I didn't think about it. I just did it. I was like, "Just create a draft PR. Here's the ticket. Here's kind of the PR de- like, and, you know, here's the PR description I want." And it just did it. And I was like, "I never need to remember git status, git rebase." Not-- Like, why is anyone doing this anymore? Like, like, what are we doing? And it took-- A funny thing is it took some convincing of me to the, the team. Like, "Guys, just type create draft to PR. Like, create a draft PR, and it'll be done for you." And like, like, "Well, you know, I kinda have my workflow." I was like, "Cool, cool, cool, cool. I get your workflow. You can modify it. You can use Cursor rules. It's okay."

   2. CVClaire Vo11:30

      Like, no one's getting bonus points for memorizing Git commands. [chuckles]

   3. CTChintan Turakhia11:33

      Exactly. Exactly. And, and so, like, we chipped away, and we put in a bunch of rules, like Cursor rules, and that, that helped so much. And then, like, we ha-- I, I was, like, sensing. I was like, "Okay, I have, I have enough, like, folks on the team that are like, 'Yep, this is unlocking stuff.'" And they would post in the team channel, like, "Look what..." We had a, literally a channel called cursor-wins, and, like, everyone was just posting in the channel, like, "I just did, like, you know, 20 unit tests and then went and had a coffee. This was great. Like, I love it." And so people started seeing it in action. And then we hit this, like, point, and I was like, "Okay, how do I speed run now the whole team?" There's a, there's a little bit of conviction here.So we just-- And I remember this. Like, I think I had landed. I was going to the East Coast. I landed, um, from my flight, got into an Uber, hopped on, like, an entire team all hands, like, speed run. We called it-- It was, like, basically Cursor speed run. And I was in the Uber using Cursor, putting up a PR. And the goal of the speed run was every single person would just pick up the most trivial thing. It could be, like, copy change, a bug, whatever, and just put up the PR. And we ended up, I think in 15 minutes, I think 100 people had joined. In 15 minutes, we ended up putting up, like, 70 PRs. And we broke GitHub too, which was cool because we learned, like, our infrastructure needed improvement.

   4. CVClaire Vo13:00

      So I wanna, I wanna pause real quick because, again, How I AI, a little bit about tactical techniques, and you've used a couple that I have used, which is, like, one, high conviction leader with hands on the metal that just says, like, "We just gotta do this." Access to tools, focus on toil I think is very important. You called out linting. You called out tests. Another one I would call out is, like, design debt, where, you know, front-end engineers or designers have just lived with parts of the app they hate.

   5. CTChintan Turakhia13:28

      Yes.

   6. CVClaire Vo13:28

      Um, that is another really great one. But, and then, and then a, a shared Slack channel. And one, like, you know, riff I would make on your Cursor Wins channel is we made ours Wins and Losses. And so we were very clear, like, just post what you did-

   7. CTChintan Turakhia13:43

      Oh, yeah. Sure

   8. CVClaire Vo13:43

      ... and when it worked and when it doesn't.

   9. CTChintan Turakhia13:45

      Yeah.

   10. CVClaire Vo13:45

       Because when it didn't, people would be like, "Oh, yeah, but you could try XYZ," or, "I have a Cursor rule for you," or whatever. But what I haven't heard that I want people to just, like, perk their ears on and pay attention to is this, like, idea of a PR speed run, which is, like, do a time down time. Everybody boot up whatever tool and just speed run some fixes. Because how much conviction does an org have to get going from, look, I've been there, like, the, the doldrums of, like, quarterly planning, and this will be in four months and blah, blah, blah, blah, blah, to just, like, we just got 70 PRs that we've been sitting on out, out the door in, in 30 minutes. I just... That has to be such a transformational moment for an eng team.

   11. CTChintan Turakhia14:27

       You know, there was a success rate on those, on, uh, merging those PRs, and, like, it was just like, shit, this is possible. They're, they-

   12. CVClaire Vo14:34

       Yeah

   13. CTChintan Turakhia14:34

       ... like, everyone's eyes lit up. And it was really sort of a death to status updates, long live building moment.

   14. CVClaire Vo14:41

       Yeah.

   15. CTChintan Turakhia14:41

       Right?

   16. CVClaire Vo14:41

       Yeah, and, and this is the other thing I wanna call out because I think you all have a really special culture there. But so often, we in product engineering design orgs get, like, really wrapped around the axle on, like, the rules of engagement. Like-

   17. CTChintan Turakhia14:55

       Yep

   18. CVClaire Vo14:55

       ... well, I'm not allowed to build it unless the product manager says it's important. Or, like, I can't really make that decision about what color that button is because design hasn't weighed in. And, like, I do think these moments where you just break all the rules, and you're like, "Guess what? Remember, you can just ship code."

   19. CTChintan Turakhia15:10

       Use these. You can just-

   20. CVClaire Vo15:11

       You can just ship code. Like, put AI aside. AI maybe enables it and makes it, like, a much less costly ex- you know, um, expense. But, like, just doing that is so powerful for velocity and for, I also think for quality. Like, people just take more radical ownership of things. Um, so I'm gonna 100% steal this.

   21. CTChintan Turakhia15:33

       You sh- I mean, I want everyone to steal it. Like, you know, I, I really like the way you just put it, right? This is a moment where we should be breaking the rules.

   22. CVClaire Vo15:41

       Yeah.

   23. CTChintan Turakhia15:41

       Because AI is breaking the rules for us, and if we don't adapt to how, like, we can use it, we're toast, right? And, and we as, like, a very collective, like, whoever's not adapting is gonna fall behind kind of thing, right? And what all of this, like, ends up unlocking is, is, like, the reduction in coordination overhead. So, like, one thing I've been obsessing about a lot is like, okay, cool. Great. Good job on the speed run. Yes, we got a lot of stuff done. We started then seeing those wins. More and more people adopted. Brian then, you know, we, we were sharing some information with Brian, like, how adoption's going, and then we just did a company-wide speed run. And at that moment, like, there was, like, 800 engineers on the call, and we ended up pushing up forward, like, three, 400 PRs in 30 minutes. And yes, again, we broke GitHub. And that's fine. That's good. Like, this is pressure testing. We should be designing ourselves to break the rules, right? But the thing I've been obsessing about is, like, how do you, how do you measure any of this, like, in terms of output, right? There's, there's this, like, tension where, okay, the more AI we use, well, does that count as a replacement for people? And, like, I'm in the camp of absolutely not. AI is an accelerant, right? AI is an accelerant because there will always be more work to, like, to do, right? And so the way I think about it, at least for, for my team and, and what I'm pushing across the board, is really, like, time from ticket to when the change lands to the user. Like, that actually encompasses every single piece you need, right? And today, like, even if you go from, like, ticket backlogs and stuff like that, like, there's, "Oh, do I... Should I..." Like, like you said, "Should I prioritize this? Is this important? Let me ask my PM," or, "Let me ask the pro- the pro- program product manager, project manager," whatever. And now the whole team, like, fast-forward from back then to now, we just see someone give us feedback, and literally within, like, seconds, we're like clo- like, we, we built this internal bot I'm excited to show you. And within seconds, like, the PR's

5. 17:57 – 19:20

   Measuring success

   1. CTChintan Turakhia17:57

      being authored, right? An agent picks it up, and within seconds, that feedback is, like, acted on. And so we crunch the time to action, the time then from ticket to the, the PR being ready for review, then the review time. Like, all my devs complain review times take too long. We found some solutions, actually. I think we are doing average of, like-150 hours like was the cycle time for a PR review because there was so much. We reduced it by 10X down to like 15 hours or so roughly. And then the last piece is like from that merge, how do you do like that OTA update? And you squeeze that whole cycle again, and then the team is like just literally unlocked with sheer velocity.

   2. CVClaire Vo18:44

      Yeah.

   3. CTChintan Turakhia18:45

      That's it.

   4. CVClaire Vo18:45

      And then you get stuff in front of customers.

   5. CTChintan Turakhia18:47

      Yes.

   6. CVClaire Vo18:47

      And then you have the velocity of like actual market ideas.

   7. CTChintan Turakhia18:50

      Yes. And you get that feedback-

   8. CVClaire Vo18:51

      Yeah

   9. CTChintan Turakhia18:51

      ... and like the, the-- we're obsessing also about how fast can we take like in real life feedback-

   10. CVClaire Vo18:56

       Yeah

   11. CTChintan Turakhia18:56

       ... and then actually just fix it right then and there.

   12. CVClaire Vo18:59

       Yep.

   13. CTChintan Turakhia19:00

       I think, I think there was another aha moment. I was on a call with, with like a, a user of our product, right? And they're like, "Hey, it'd be cool if you like changed X, Y, and Z." And like literally while I was on the call, I just put up a PR and pushed it. And they're like, before the call ended, it was 30 minutes, so I was like, "Just, you know, reload the app. It's fixed."

   14. CVClaire Vo19:19

       Okay. Before we

6. 19:20 – 23:14

   Demo: Real-time feedback-to-feature implementation

   1. CVClaire Vo19:20

      turn this into an hour of, you know, like two, two eng product leaders being like, "Just ship really fast. We'll go into the, uh, merits of reducing PR cycle time," all that fun stuff, let's actually show a couple of things you built because I think the kind of meta commentary on like you can do this in engineering organizations, there are steps to it, there are measures you can take, I think are things that everyone can learn from. But you also have been building. So let's talk about how you used actually Cursor to drive how you drove this into the organization and understand adoption of AI.

   2. CTChintan Turakhia19:54

      Yeah, for sure. Um, I think a lot of it just comes like from honest curiosity and figuring out, um, where the bottlenecks are.

   3. CVClaire Vo20:03

      Yep.

   4. CTChintan Turakhia20:03

      Like, why aren't folks adopting? How are people using it? Et cetera, et cetera. I wanna show you like I think the be-- the, the kind of crazy thing I'm about to walk you through is like I just got this harebrained idea. Cursor has like great analytics, right? And so you go to the admin panel, you look at the analytics, and aw- you know, awesomely, they let you download it into a CSV. I was like, what if I just use Cursor to figure out what my team is doing in terms of using Cursor, but not in just like from a vanity metric point of view of like lines of code committed by AI. I think that's like kinda misleading, actually digging more into, um, how they're using Cursor and how do we sort of like replicate power users. So let's see. Uh, we have some, some data. It's in this file here. Uh, a- and it's just a like a standard CSV from Cursor that you can like download from their, their site, um, like your admin panel. And then there's also here a bunch of different sort of fields. Um, so like accepted lines, chat lines, chat lines deleted, um, various like data elements. But, you know, one thing like I just sort of started with, I wanna understand the usage of Cursor, right? And I already know, um, we have like light users all the way to power users. And one of the things I really wanted to figure out was like what are the natural clusters of usage? Can you find them across the team? What is the best way to cohort them? Right. And I'm just gonna pick up the standard analytics file here, maybe pop in another one here. And then I love Opus High. I also love Plan Mode because it gives you a chance to like see what it's thinking through. So we can let this cook and see what it comes back with.

   5. CVClaire Vo22:00

      And what I wanna call out here for engineering managers or engineering leaders is this is the kind of quantitative analysis that we would all have loved to be able to do across a bunch of engineering metrics-

   6. CTChintan Turakhia22:13

      Yes

   7. CVClaire Vo22:13

      ... at, at some point, right? Like, how often do we get asked by the board or our boss like, "What's velocity? What's cycle time? Which of our engineers are super, you know, like are, are really at the far edge of the curve in terms of effi- efficiency? How are our junior engineers ramping into the repo?" All that kind of stuff, and that kind of analysis is actually really onerous and hard to get at because of the structure of the data and the nature of the analysis. And so what I love about just LLMs in general, and in particular using something like Cursor, is you can get to really nuanced cohorting analysis on human behavior and human analytics as a manager in a way that I think has been really challenging to do before.

   8. CTChintan Turakhia22:53

      Yeah. I totally agree. And like the beautiful thing is now with MCPs, with data accessibility, like I think of tools like Cursor as just my daily operating system.

   9. CVClaire Vo23:06

      Yeah.

   10. CTChintan Turakhia23:06

       If I have a question, it doesn't matter if it's technical or not, I just go into Cursor and ask it. Um, and so it's like super, super powerful

7. 23:14 – 33:15

   Using Cursor to analyze AI adoption patterns

   1. CTChintan Turakhia23:14

      that way. Okay, so it's asking me a little bit about like what outputs do I want. I do wanna enrich CSV, um, just it makes it easier. I do want a static dashboard just for fun. Like, I'm not really trying to create a brand new dashboard right now. But my main goal here is just honestly, honestly, like find natural cohorts, right?

   2. CVClaire Vo23:35

      Yeah.

   3. CTChintan Turakhia23:35

      Um, and so it's gonna kinda try to do a light, moderate, active, power user. It's gonna look at lines suggested, so volume, sophistication, agent mode, model preference, acceptance rate, and breadth. What features are they using? It'll spit out, you know, a CSV dashboard. It'll likely generate a Python script too that I can reuse. So I'm just gonna kick off build mode. While that's cooking, I do wanna just maybe bop over to like... It's gonna create all this stuff in Python, create the scripts for me. Awesome. But we can look here at some of the, the information, right? So like this is all sort of random made-up data. It's like sample data. But what it did was in a previous run, it, it looked at all the data, generated the Python script, which is great, super simple, and it sort of just did some like-High-level status metrics like AI code percentage, again, on all this made-up data, AI lines per week, composer lines. This is when you're using the agent mode in Cursor or tab lines, right, when you're hitting Tab. Uh, one, one of my team members actually got the cool Cursor Tab award, which is, which is very great. And so it sort of breaks all this down, and then what it really segmented around was, like, agent-heavy users, which is folks who really lean into agent usage. There's also tab-heavy users. This is, like, a different cohort. They just lean into tab usage, and they, they maybe want really just a bit more control and maybe haven't gotten yet used to, like, how to let go with an agent. Uh, you have balance users that try both, and then you have sort of, like, maybe Cursor curious or maybe not Cursor pilled or, you know, LLM pilled right now. And so I generated this whole script. It's great, and now let me show you sort of a bit more analysis I wanna do here. So let's do this. Run the analysis on... I have, um, a sample user set, and generate the HTML as well, and let's... We're actually... Like, this is sort of the output of the analysis script that was generated in Python, which is already cooking in parallel.

   4. CVClaire Vo25:44

      Got it. So what you've done here is you've taken some raw data from Cursor. You've asked one kind of agent to do a cohort-based analysis and generate a enriched CSV essentially-

   5. CTChintan Turakhia25:57

      Yeah

   6. CVClaire Vo25:57

      ... with some data, and then you're cr- kicking off another agent to actually do the analysis on that and generate sort of an HTML view of it so you can visualize the data.

   7. CTChintan Turakhia26:08

      That's right. That's right. What it did was the Python script that was generated, right, it found these natural cohorts, these natural cohorts of super user, regular user, power user, light, inactive. Again, this is just honestly sample data, um, but based on, like, real information, real schema, real Cursor, um, data fields. And it came up with, like, 70% are in agent heavy in the sample data, 20% are minimal, 4% are balanced. We have some room to improve here on the sample, right?

   8. CVClaire Vo26:41

      Yeah.

   9. CTChintan Turakhia26:41

      Like, not enough people are using it. Um, and so it does a bit of a breakdown, which I kinda like, you know. Kind of a recap of metrics. Yeah, we have a lot of lines of code in this data. We have 520 power users. Again, made-up names, but, like, this person is crushing it. I wanna know what this made-up [chuckles] person, Gabriel Diaz-

   10. CVClaire Vo27:04

       Yeah

   11. CTChintan Turakhia27:04

       ... is doing, right? Awesome thing here, it generated a little, a visual dashboard. Nothing fancy, something just really simple to look at, right? Total lines-

   12. CVClaire Vo27:15

       Mm

   13. CTChintan Turakhia27:15

       ... composer lines, tab completion, a little bit of breakdown, some structuring on the tiers and usage, right? But what I really kinda wanna understand is, like, what is Gabriel Diaz doing, right? This made-up user-

   14. CVClaire Vo27:30

       Yeah

   15. CTChintan Turakhia27:30

       ... who's just, like, crushing it.

   16. CVClaire Vo27:32

       Yep.

   17. CTChintan Turakhia27:33

       How about based on the data, generate guidance for each user cohort, what, you know, they should do to advance and graduate to super user. I'm looking for explicit guidance. Effectively, like, I wanna turn this into some type of playbook, right? So let's let this cook, and then in parallel, what I also wanna do is I like visuals. And there's something intuitive here where, like, as we look at the data itself, right, we, we know that the, like, the path to this super user over here, it's, it's not like you go inactive to, to light to regular to power to super. We know it's not linear like that, right? Right. There may be, like, forks from light to straight to power user. Regular user seems to be, like, balanced on the tiering. But what I wanna know is, like, what are the special things these folks are doing, and how do I sort of shift the curve, right? And so I'm also gonna throw another question in parallel, like, create a mermaid diagram, uh, for all the different sort of paths a user can take from light to power, and it's... I'm assuming it's not linear. And let's just see what this cooks up, too. Okay. This is really working hard.

   18. CVClaire Vo28:47

       [laughs]

   19. CTChintan Turakhia28:47

       Really working hard.

   20. CVClaire Vo28:48

       Oh, Opus 4.5.

   21. CTChintan Turakhia28:50

       Yeah. Oh, Opus is, Opus is-

   22. CVClaire Vo28:52

       Thank you

   23. CTChintan Turakhia28:52

       ... really working hard on this. But, um, yeah, let's, let's see where it goes. [chuckles]

   24. CVClaire Vo28:57

       Well, you know what's really interesting. I'll give you a, a, a, a, a shorter hack on this one. So I think what this is generating is, like, an HTML playbook that you could-

   25. CTChintan Turakhia29:05

       Exactly

   26. CVClaire Vo29:05

       ... share out that has, has things. I will tell you what I would do in this use case, and I've done this a couple times, um, with cust- like customer QBRs, is I say write a Slack post that I can put in my engineering channel on a couple of these stats and, you know, how we can get people to move from A to B, and it'll write me like a, a short little Slack post. So I've-

   27. CTChintan Turakhia29:28

       Ah, yes

   28. CVClaire Vo29:28

       ... I love this idea of going from something like a CSV to a really deep analysis to an HTML, like, visualization to, like, three bullet points I can send in Slack.

   29. CTChintan Turakhia29:38

       Yeah.

   30. CVClaire Vo29:38

       And as a manager, each one of those steps would've taken just forever to do, and now you can get them all done in Cursor.
8. 33:15 – 36:00

   Quick recap and appreciation

   1. CVClaire Vo33:16

      Okay. So just to, just to recap again. [laughs] We're doing, we're doing, uh, free, free product work for Cursor here. We, we took... You know, your ultimate problem was, like, how do I drive up adoption of these tools? And you're like, "Of course, I'm gonna use the tool to understand adoption and then figure out ways to try adopt- drive adoption." We did analysis. We created a visualization of the, um, the data itself. You identified cohorts and power users, which would've been very tedious to do if you were gonna do manually.

   2. CTChintan Turakhia33:45

      Yeah.

   3. CVClaire Vo33:45

      And then you created a hosted playbook-

   4. CTChintan Turakhia33:51

      Hehe

   5. CVClaire Vo33:51

      ... um, as well as a series of motivational statements, which we can either give to our friends at Cursor for free or trademark right now and ma- make a little- [laughs]

   6. CTChintan Turakhia34:04

      Royalties

   7. CVClaire Vo34:04

      ... a little money. Um, agent everything. Tab without thinking. BugBot always on. Iterate prompts. Love it.

   8. CTChintan Turakhia34:11

      It's kind of fun.

   9. CVClaire Vo34:11

      And this, you know, again, what I think is fun... Well, let me talk about what I think is fun about this. One, everybody who has been in engineering leadership knows this is the kind of stuff you get asked to put in a board meeting. You get asked by your boss, like, "What percentage of our engineers are using Cursor? Do we have power users? Are we actually getting value?" And we're talking about an AI use case right now, but again, across management, there are actually measurable things you can do about the performance and efficiency of your team.

   10. CTChintan Turakhia34:43

       Yes.

   11. CVClaire Vo34:43

       And I think it's been so impossible to get before. Two, it would be no fun if you didn't get to do it with code, which you get to do [laughs] now. You get to do it with code.

   12. CTChintan Turakhia34:54

       Yeah, like actually that is the thing.

   13. CVClaire Vo34:55

       It's not tedious.

   14. CTChintan Turakhia34:56

       You can solve problems with just code now, right? You can just do things. I, I... Y- you know, you're so right. Like, I, I think this... I underappreciate what, exactly what you're saying right now, and, and I just wanna r- repeat it, because normally you would be asked this, and then you would have to go pull an IC to do that. And like, what? What?

   15. CVClaire Vo35:16

       Yeah.

   16. CTChintan Turakhia35:16

       Like, come on. Like, no, you can just do things right now.

   17. CVClaire Vo35:20

       Well, and, and again, it's, like, not f- the... I, I, I think people underappreciate the velocity creation of a fun task.

   18. CTChintan Turakhia35:28

       Yeah.

   19. CVClaire Vo35:28

       Which is, like, at the end of the day, like, this is silly, but also-

   20. CTChintan Turakhia35:32

       True

   21. CVClaire Vo35:32

       ... the, like, little fun bits of it, you're like, "Great, I wanna go to the next level 'cause I got, like, a little dopamine hit from this dark mode playbook that's kinda funny." And I, I think people underappreciate, like, that iteration speed that can just come with, like, a fast feedback loop-

   22. CTChintan Turakhia35:46

       Yeah

   23. CVClaire Vo35:47

       ... when you're building something, and the fast feedback loop when you're building something that has high quality against it, which, like, something designed like this does. It's so much more fun to look at than a Google Doc-

   24. CTChintan Turakhia35:57

       Yeah, I totally agree

   25. CVClaire Vo35:58

       ... or a spreadsheet or a dashboard.

9. 36:00 – 40:50

   Demo: Building a live feedback capture system using AI transcription

   1. CVClaire Vo36:00

      So we, we did it. We did it. We... Again, you and I are twin stars, I think, here, and so we could probably go all day on the things that we find fun. But let's go to a second use case that I think people are gonna wanna see, and let's see how fast we can do this use case, which is you were talking about the speed of feedback to feature, and you, you said some fighting words out there. You were like, "We're really compressing the time from feedback to feature." So how does that actually work?

   2. CTChintan Turakhia36:28

      Those were, those were some fighting words. Um, and, you know, I think-You know this, right? You want, you wanna build this for your users, right? And you want to create the best damn product out there as fast as possible. And the way to, like, make that cycle work really well is genuinely how fast you can move on feedback.

   3. CVClaire Vo36:51

      Okay.

   4. CTChintan Turakhia36:52

      Okay. But I wanna start from how does, like, feedback even normally come in, right? So you, you know, normal, like, teams and, and culturally, like, you'll have dogfooding or bug bash sessions, right? You'll get on a meet or get in a room, keep using the product, blah, blah, blah, all that jazz, and then someone has to, like, collect the bugs in a Google Doc, and then take those bugs in a Google Doc and put them into a ticket system.

   5. CVClaire Vo37:17

      Yep.

   6. CTChintan Turakhia37:18

      Right? Uh, okay, and then there's a whole discussion around is this important? Is this not important? Okay, should we pick it up in this sprint? Should we wait for another sprint? And by that time, your user has churned out. They're like, "You guys didn't fix this. I kinda hate it. Moving on," right? Everyone's attention is, like, so, so, so short. And right now, like, the whole team, uh, we're all preparing for a big launch. Um, and we wanted to get together and do this thing called a surge, and this is where we, like, just bring the team together, and we do very, very long days, um, using all this AI and just shipping, like, massive amounts of code. Uh, and fun fact, like, during these surges, we end up shipping, like, more than three to four X more PR volume in the same time. But the other thing we wanted to do was bring people into the office, and we set up this thing called, like, a feedback cafe. And so we'd invite externals, internals, et cetera, and we'd dogfood with them, and we'd show them the app, and, like, here's just, like, a couple seconds of, you know, what it looks like. We're just standing there collecting information, doing all this, like, live dogfooding. And the hard part, though, is especially in real life, how do you actually capture that information? Because it's voice, it's video. How do you translate it into a system? Okay, so I just spent, like, half a weekend and built a tool to capture feedback live. Let's just pick something. I'm gonna pick... I'm gonna, I'm gonna pick a new thing. How I AI. Testing with Claire. Awesome. So let's do that. It's gonna create a little session. Perfect. Very simple. And we have two modes. You can, like, you can use this on your mobile phone. That's what the team did, uh, when they were in real life. But for this, I'm just gonna, like, capture some audio, and let's see. What's, what's-- Actually, I wa- maybe I can just hear from you, like, a fun little bug or something, uh, of a product that you, you think you wanna fix. So we're gonna start capturing audio.

   7. CVClaire Vo39:36

      There is a AI chatbot that I use where my account, when switched to business account, forces me to clear all my chats, and I think we should fix that bug so that I can access my existing chats.

   8. CTChintan Turakhia39:51

      We're gonna start capturing audio. We're gonna... Okay, cool. We captured it. It's basically taking the audio. I did a system prompt, sends it to an LLM.

   9. CVClaire Vo40:02

      Mm-hmm.

   10. CTChintan Turakhia40:02

       Um, and then what we do is it-- the prompt is basically saying, "Go and identify the bugs."

   11. CVClaire Vo40:07

       Yep.

   12. CTChintan Turakhia40:08

       Right? And then it'll create it. I'm gonna do one while it's processing. Right now I'm using the app. I'm on the Trade tab, and I'm clicking the From field, and I'm typing in numbers, but the numbers are not showing up, so that's not letting me make a trade. So I think in our first example, the audio is a little hard to capture-

   13. CVClaire Vo40:27

       Yeah. It was for me

   14. CTChintan Turakhia40:27

       ... just 'cause it's going through the system.

   15. CVClaire Vo40:28

       Yep.

   16. CTChintan Turakhia40:28

       But let's look at the second example. It calls it out really clearly. "On Trade tab, typing into From field does not display entered numbers. User cannot initiate a trade." Cool. Really, really clean.

   17. CVClaire Vo40:39

       Yep.

   18. CTChintan Turakhia40:40

       I hit Create Linear Ticket. It even gives, like, a suggested title. The user journey I care about for this is trade. Boom. I create the ticket itself.

10. 40:50 – 47:10

    Using custom Slack bots to automate engineering workflows

    1. CTChintan Turakhia40:51

       Awesome. I pop over. The ticket is all here. The file is there. Linear is a incredible tool. It's doing some triaging. But the thing I wanna now hop over to is we're gonna just create the PR. So we have this tool we built in-house. We call it Claude Bot. It's actually, like, using all sorts of underlying models.

    2. CVClaire Vo41:13

       [laughs]

    3. CTChintan Turakhia41:13

       Uh, it's not something that is specific to, to, to Claude. Um, so Claude Bot, create PR. I know the repo for this is Wallet Mobile. And here's the ticket. Oh, that's not the ticket.

    4. CVClaire Vo41:27

       That's not the ticket.

    5. CTChintan Turakhia41:27

       The ticket is, boom, here. Great. Cool. So I just went from a bug report to a ticket.

    6. CVClaire Vo41:39

       To a PR.

    7. CTChintan Turakhia41:39

       To the PR is cooking.

    8. CVClaire Vo41:42

       Okay, so I have to pause because if you, um, are new to How I AI, you have not seen my signature move when I really love something, which is this.

    9. CTChintan Turakhia41:50

       [laughs]

    10. CVClaire Vo41:51

        And I was doing this because I was just thinking about this, this little micro app that you have on the left side, which is, you know, live user feedback, totally unstructured, right? Video or audio. Run a little baby LLM on it. Get not only a summary of the issue, but a good recommendation on how you might fix it. Very quick beep boop to Linear. We love our friends at Linear. I think it's a great platform for agents. And then a little custom agent in your Slack that can read those Linear tickets and just execute on them. And again, so traumatized by the past, maybe, [laughs] which is like this process would have been, you know, somebody-S- manually summarizing-

    11. CTChintan Turakhia42:40

        Yep

    12. CVClaire Vo42:40

        ... what came out of a research session.

    13. CTChintan Turakhia42:43

        Yep.

    14. CVClaire Vo42:43

        Some document being written. Somebody actually making explicit decisions about what to include and not include.

    15. CTChintan Turakhia42:48

        Yeah.

    16. CVClaire Vo42:48

        I think that's something-

    17. CTChintan Turakhia42:48

        The decision-making is gone

    18. CVClaire Vo42:50

        ... people don't appreciate. Yeah. Like, no filter anymore. You don't get that like, "Well, you know, if I make this five pages long, no one's gonna read it, so I'm really gonna focus on the top 10 things." It's like, let's capture everything and then just burn through it. And then I have to ask you, why did you all build your own little bot to do this? What was the advantage of, of building the bot?

    19. CTChintan Turakhia43:12

        So this, this is, like, in-house, um, and we built it. You know, it all started, um, around, like, middle of this year. I created this, like... I, I was just obsessing so much about AI. And I was like, how do I, how do I create better tooling for the team, for the company, so everyone can be accelerated? So I invented, actually, like, I put a call out on Twitter. I invented this role called super builder. And the single job, single most important job of a super builder is to create more super builders. So we, we hired our first super builder, and they, um, we, we talked about some ideas, and one of the biggest things, because most of our company uses Slack, we're all in Slack. And Slack, you know, I'm a strong, like, strong believer it's just a bunch of humans pretending to be systems, right? And the cost of writing th- something in Slack is zero, but the cost of answering something in Slack is enormous, and most of it is noise, right? And so one of the things was just like, how do we bring the workflows that we are all so used to, um, and how do we, like, sort of capture that and then add AI on top of it? So we had, like, various reason. We know, like, lots of companies have, uh, background agents, Cursor, et cetera, et cetera. Uh, w- we just have, like, different sort of security requirements right now that we just couldn't launch with, and that's fine. So we, we built this in-house, and we have these, like, feedback channels, right? "Hey, there's a bug here. There's a bug here." And so now all we just do is like, "Claude Bot, go and do something with that." Or if someone is, um, like, "Hey, we just got out of this meeting. Here's a summarized transcript." We're like, "Awesome. @Linear agent, go break this down into tickets." And then just, like, you know, you know the look you, you showed, like, right? Like, everyone is just doing that emoji of, like, the head exploding, right? Because then now we have, like, 20 tickets, and then we do fun things like this, which is just go, like, bonkers, where we just fire off tons and tons of calls, right? To just... And so we, we built this plan mode. So cl- this bot has a create PR, which, um, it's cooking. Um, it has a... And also the cool thing about create PR is when it's done, it will respond back. It will show you a link to, like, the Cursor branch using Cursor's deep link. And when then the one-off build is ready, it will show the QR code so you can just scan and start playing with the fix, right? There's a plan mode, which is very much like Cursor's plan mode. It just comes up with, like, a plan. And then we also have, um, explain as well, where it's like, oh, I wanna debug something, so, like, um, why is Chintan's app not working right now? Um, Chintan dot base dot east as an example, right? And it has, like, all the skills, all the MCPs. And so the thing, the thing I realized is context is the most important thing.

    20. CVClaire Vo46:33

        Mm.

    21. CTChintan Turakhia46:33

        So the place where we capture all of our context is Linear. And then y- this agent that we built, um, we added skills and MCPs. So if we can capture context through Linear, then we can trigger the agent, uh, using all the context from Linear, and then it goes off into all the MCPs, like Datadog, Sentry, Amplitude, um, our internal Snowflake databases, et cetera. And it has the ability to pull context from the rest of the company, and it can work across multiple code bases, and then boom, like, it's, it's like a s- it's a, it's, it's a super builder.

    22. CVClaire Vo47:09

        This

11. 47:10 – 50:00

    Advice for driving AI adoption within your organization

    1. CVClaire Vo47:10

       is, this is awesome. And so before we move, move on, I think what I wanna call out here are a couple things that I hope people didn't miss. One is right now, if I can give people career advice, you wanna be, like, the, the top three most AI-pilled people [laughs] in your engineering organization. I'm sorry. I just have to say it.

    2. CTChintan Turakhia47:29

       Agree.

    3. CVClaire Vo47:29

       Like, I, you know, I, whenever I to- you know, pulled an engineering leader aside or someone aside who's, like, maybe a little AI skeptical, and I said, like, "I want you to lead this," I wasn't doing it... Yes, of course, I wanna do it because I think it has high impact on the company, but I felt like I was doing people a career favor by giving them this role. And so if you can find companies that are hiring super builders that will put you in the role of driving AI across an organization where you can learn these skills, I tell you, it is a incredible benefit to your overall career, and I don't think people appreciate how much that is pretty still rare right now. So if you can find it, I would just beeline directly, directly to it. I think the other thing, and we've seen this a couple times, we saw this, Amplitude actually did it, building your own agents is not impossible for organizations. And so if you do have security compliance, data access restrictions, you can't use cloud agents, you can't use these things, it is not impossible to build these things yourself. And there are lots of, like, really great SDKs out there too that you can use, um, to do so. And then, you know, three, like, I do think some of these platforms, Linear and Slack, are just friction reducers to access to AI. And so-If you are thinking about driving AI adoption in your organization, like figure out how you can get the right platforms in place that can unlock access to agents. Because if you ask somebody to open or learn a new tool, it's just gonna create too much friction, um, to, to move forward.

    4. CTChintan Turakhia49:01

       I think there's like one super important thing. Like this, this is a channel where we call CloudBot Playground, and I'm scrolling through fast just to show you like how much people are using... This was one night.

    5. CVClaire Vo49:14

       [laughs]

    6. CTChintan Turakhia49:14

       I was up at like 1:00 AM just pushing... This w- we got like 200 bugs, right?

    7. CVClaire Vo49:19

       Yeah.

    8. CTChintan Turakhia49:19

       From this tool I showed you, and I just kicked them all off in like one solid go just to get things cooking, and like it was great. Uh, let's see if a plan came out here. Yeah, so like there's a, there's a plan that comes. It actually creates the plan in the Linear ticket.

    9. CVClaire Vo49:37

       Yep.

    10. CTChintan Turakhia49:38

        This, the trick here, why Slack, is because Slack is how things go viral within your company.

    11. CVClaire Vo49:45

        Yep. Totally.

    12. CTChintan Turakhia49:46

        If you have pulled out the magic into some separate tool that others can't see-

    13. CVClaire Vo49:52

        Yep

    14. CTChintan Turakhia49:52

        ... it doesn't happen. And so by getting things into Slack-

    15. CVClaire Vo49:56

        Do it in public

    16. CTChintan Turakhia49:56

        ... people just like, "Holy shit. This is possible?"

    17. CVClaire Vo49:59

        Yeah.

    18. CTChintan Turakhia49:59

        "Let's go."

12. 50:00 – 55:23

    Personal use case: AI for wine selection based on taste preferences

    1. CTChintan Turakhia50:00

       And it's like, it's really cool.

    2. CVClaire Vo50:01

       I, I completely agree. Okay, so we have just seen about everything I wanted to see from the engineering side. But before we get out of here, I want you to spend just a couple minutes on a personal use case.

    3. CTChintan Turakhia50:14

       Okay.

    4. CVClaire Vo50:15

       [laughs]

    5. CTChintan Turakhia50:15

       Let's go. I, I think the one that resonates probably for everyone is getting... I- if you have kids, getting the school emails, that it's like, oh, here are 50 events that are about to land. Uh, here are the dates. I've just started taking a picture of it and then throw it into ChatGPT and say, "Create the calendar invites."

    6. CVClaire Vo50:34

       100%.

    7. CTChintan Turakhia50:35

       Right?

    8. CVClaire Vo50:35

       [laughs]

    9. CTChintan Turakhia50:35

       It's like, it's the dumbest thing, but oh my God. And then the shared calendar dance happens, and it's like, it's so great. Another thing, though, like I love food and wine. I really do. And like, um, I've done like sommelier training, et cetera, et cetera. And I, and I realized like, you know, I went to New York recently with one, one of my buddies. He, he's, uh, he's learning about AI, but he's like, "What are, what are some of the real use cases that would resonate with me?" And I was like, "Well, like one of the biggest sort of anxieties people have is when they go to a restaurant, they're handed the wine menu, right?" And they're like, "What do I pick? What if I pick the wrong thing?" So, uh, uh, with my friend in New York, we went to some, uh, like champagne sta- tasting. And so, like I just took notes. There's like this whole notebook, right? I just did this like an hour ago. And I was like, "Oh, here's a great producer. Single star means like, yeah, it's good. And then here's another one. Oh, see, I wrote amazing buy. Like, this is someone I've actually never tried before, but I loved, loved their champagne. Let's see. It was just super yummy. Here's another one," right? Effectively, then I just like pop this right in, and I said, "Here are a bunch of champagnes that I tasted. Figure out from my notes, like what are my taste preferences?"

    10. CVClaire Vo51:48

        Wow.

    11. CTChintan Turakhia51:48

        Really simple. Because, you know, like when I, when I did like sommelier classes, the biggest thing that it teaches you is the vocabulary to describe the stuff you like, right? And then, so it just took the images. It figured out the producers, and this is actually like spot on. The fun thing I did with my friend while I was in New York was like we were just... He was, he's, he actually is the real life version of ChatGPT, and it's, it's what inspired me to do this, which is he's always trying to figure out my taste preferences. And so, you know, this is like my strongest signal. I, I love like these wines that have very little sugar, that are like really ripering acidic. I love some aging. I love growers, right? Grower champagne, not like the big houses that are like very sweet. It even went into like a certain subcategory of like, you know, the chalky style. This specific producer that I wrote amazing buy for. And it also called out something I learned in real life, which is like I do like Pinot Meunier but only like with this sort of characteristic, right? Kind of crazy. All right, fine. And so then it came up with like a little bit of like a champagne profile. Cool. And if I'm buying stuff, you know, here's, here's what I would buy. All of that's fine, okay? Like, why on earth would anyone do this, right? Like, like people must be listening and be like, "Okay, maybe just drink a little less champagne, dude." But like the fun thing is, let's say you took, you went to a restaurant, right? And I just did this for this like example here, and you just like dropped in, took a picture of, uh, the wine menu, right? And it's like a big old menu. Some of them are like size of a dictionary. Some of them are simple. But like you don't wanna make a choice, especially you just wanna be with like talking to the company that's in front of you, not like staring at the wine Bible. You drop it in, and boom, what it actually comes out with, and, and I think the prompt I asked is, "What would I like from this list? What are good values?" And it kind of just went through this really fast based on my preferences. Like, and it's right. Like, I would love this. I have, I have had it, and it's great, and it's fun. It shares the price. Absolute no-brainer. Another example. Another example. And then it kind of gets into a bit more detail, like categorically. Like look, if you want a value one and just like want a bunch of bottles, go for this. Like, everyone's gonna love it. If you want something a bit like more splurgy, try these, right? Um, and very much like it kind of talks about what, why you'll like it. What I love the most, though, it says, "This is the stuff just to stay away from," right? And, you know, if it's a big night, then just go get these six bottles and, and call it a day. And so like that's the fun thing here for me.

    12. CVClaire Vo54:37

        So what I have to call out for folks is we've actually seen not this particular use case, but this flow before, which is like how you reverse engineer your own taste.

    13. CTChintan Turakhia54:46

        Yes.

    14. CVClaire Vo54:46

        So we saw Hilary at Whoop-Show how to reverse engineer her own taste on slides. Um, we saw, I forget, somebody else reverse engineered photographic styles. Um, Ravi, uh, reverse engineered photographic styles and said, like, "Here's a photo."

    15. CTChintan Turakhia55:02

        Mm-hmm.

    16. CVClaire Vo55:02

        Like, "Tell me, explain to me how to, how to describe this." But you are the first person that has reverse engineered their own taste in wines, and I love this. And now you can pick yummy stuff to get for-

    17. CTChintan Turakhia55:15

        Yes

    18. CVClaire Vo55:15

        ... and you know what? Six bottle cart. I'm going out with you next time. [laughs]

    19. CTChintan Turakhia55:19

        Let's do it. I know. We'll, we'll, we'll celebrate AI adoption or something

13. 55:23 – 58:57

    Lightning round and final thoughts

    1. CTChintan Turakhia55:23

       like that. [laughs]

    2. CVClaire Vo55:24

       This has been so great. I have one, uh, two lightning round questions for you. We'll keep them very short, and then we'll get you out of here. My first one is, if you look back two years ago to now at work, how are you, how are you spending your time differently? Like, how has all this changed how you personally spend your time?

    3. CTChintan Turakhia55:43

       My calendar's empty.

    4. CVClaire Vo55:44

       Oh.

    5. CTChintan Turakhia55:44

       Like, almost empty.

    6. CVClaire Vo55:45

       We love it.

    7. CTChintan Turakhia55:45

       And the reason why is 'cause the coordination overhead of like, "Hey, let's prioritize this. Let's change this. Let's change the roadmap." No, you just do things. That's one. Two, I'm writing way more code. The team knows, like, uh, if their [laughs] contributions fall below mine, like, that's, that's-

    8. CVClaire Vo56:01

       [laughs]

    9. CTChintan Turakhia56:01

       We, we gotta, like, help on the AI. But, like, look, like, I'm also jumping in. The team is doing incredibly hard work. I am spending way more time in the code base fixing bugs, trying things, coming up with, like, technical approaches. I am not a replacement for, like, the insane amount of talented, cracked engineers on my team, but I'm able to move things forward much faster and cut through the bullshit.

    10. CVClaire Vo56:27

        If, if AI has done anything for us, canceling meetings would be-

    11. CTChintan Turakhia56:31

        Yeah

    12. CVClaire Vo56:31

        ... would be the gift that, that I want. Okay, my last question is, when AI is not listening to you, when it gives you a really dumb playbook for your engineers, what is your prompting technique?

    13. CTChintan Turakhia56:43

        It, it depends on, like, how many tries, times I've tried to convince it. But generally, it's like, okay, one, you're clearly not listening to me. This is what I said. Two, yeah, I know I'm absolutely right, but, like, stop being stupid. I need your help. And three, I, I... Like, the nuclear option is I threaten it, and I say-

    14. CVClaire Vo57:03

        [laughs]

    15. CTChintan Turakhia57:03

        ... um, "Claude," if I'm using, like, Claude Opus 4- 4.5 High. Like, "Okay, I'm gonna stop using you, Claude. I'm gonna switch to Gemini." And then it gets its shit together. [laughs]

    16. CVClaire Vo57:15

        I love it. I don't know what that says about either parenting or management style, but I think that it's-

    17. CTChintan Turakhia57:20

        Thank you

    18. CVClaire Vo57:20

        ... I think it is effective. Well, this has been great. Where can we find you, your team, and how can we be helpful?

    19. CTChintan Turakhia57:27

        Yes. Um, so I'm on Twitter, uh, @ChintanTurakhia. We are building the Base App, uh, used to be known as, uh, Coinbase Wallet.

    20. CVClaire Vo57:37

        Okay.

    21. CTChintan Turakhia57:37

        And I think by the time that when this, uh, episode airs, it will be live to the general public. Use it. It is a consumer social app that happens to use crypto, and it's enabling creators to earn and be valued. Um, and we're excited to launch it, and we think it's, like, a real big paradigm shift in crypto consumer apps. So give us the feedback, give it a shot, post, uh, see the magic happen. And we are hiring too, uh, cracked front-end, back-end design engineers, ML engineers.

    22. CVClaire Vo58:09

        Super builders.

    23. CTChintan Turakhia58:09

        Super builders. I have two super builders. Happy to bring in a third one. But, like, it is, it is really, really fun, uh, to work here on this team, and, and it, it... It'll be, it'll be awesome. So come join us.

    24. CVClaire Vo58:22

        Amazing. Well, thanks for joining us.

    25. CTChintan Turakhia58:24

        Thank you. This, um, this was such a, a great way to cap off the week.

    26. CVClaire Vo58:28

        [upbeat music] Thanks so much for watching. If you enjoyed the show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiai pod dot com. See you next time.

Episode duration: 58:57