A (3:07)

Yeah, for sure. No, for us, it's definitely. I mean, as a bunch of programming nerds ourselves, you know, building an AI that can code is. Has got to be one of the coolest things that we could probably spend our time on. I wanted to show a couple of flows actually of how we use the Devin stack, because there are a few different pieces involved. There's flac and linear, there's the wiki, obviously, and then there's like, Ask Devin and then there's, you know, starting Devin sessions and getting pull requests out of it. I think there's some real. I think there's some real nuance. And like, what are the right flows of, like, how do you work with Devin as an employee? Because I think it really is quite different from a lot of the tools out there, which are much more kind of like an ide, for example, or like a terminal ui. Like, Devin is, I think, first and foremost almost like an engineer on your team.

B (3:51)

Yep, totally. So what are some of the things that you reach for with Devin and the capabilities that you think really make a difference for you as a software engineer?

A (4:04)

The way that we like to describe it is Devin is a junior engineer. And so Devin is not going to go. And, you know, we're working on getting Devin to senior engineer, obviously, you know, we'll get Devin the promotion and everything, but, but, but like, Devin is not going to go and solve some, you know, really hard architectural problem or make some big strategic decision that you, you know, you're going to make and then kind of like execute on for the next month. Like, you probably want to be involved in those as well. Devin can help you with the decision, obviously by, by kind of like referencing the right things or giving a few things as an input. But I Think where Devin really shines is one way that we say also is kind of like tasks, not problems. And so often when you have a very clear like here is exactly what we need to go do and here's the task and here's all the details of what we need. Devin is really great at going and executing that for you and makes that much faster. And so actually I think the next question that comes to mind then is like, how do you figure out the spec or the task exactly. That you want to do? And so a lot of the other tools like wiki and search really are here for you to be able to kind of like ask the right questions that you want about understanding the code base or what needs to be done and then putting a task together. I think in practice, like a lot of the use cases that we see all the time are, you know, probably number one is just crawling through your issue backlog. You know, whenever you have an issue that comes up or we have a lot of slack channels where we talk about issues and then on every single one of them we just tag Devin as the first pass. And so that's a big one. And so like, you know, someone says, oh, you know, we need to go fix this thing in the front end or you know, maybe we need to go support this other, you know, support this other MCP for example, which we'll show in a second, things like that. And then for a lot of the other kind of like I'll say like engineer and toil use cases, it also does really, really well. And so often that's like, you know, going and doing a version upgrade or added documentation throughout, you know, your, your, your repo or adding unit tests for a specific thing that you have up or responding to, you know, a crash report that just came up and trying to diagnose what went wrong.

B (6:09)

Yep. I, I, I love that what you said about, you know, Devin's a, a junior engineer. I say Devin's my favorite intern on my team and, and, and I have infinite of of them. And then I like this idea of scoping task not problem. And I do think it's something that people are working with AI and even you know, other, other AI tools not in the engineering space. Really thinking about task level orientation sets you up for success or at least a sequence of tasks can be very helpful. And so why don't you pick a task that you might bite off for, for your product and let's, you know, show us how you would work through that end to end.

A (6:48)

Yeah, yeah, let's do it. So, as you might know, I'm a huge fan actually of Chat prd. And the natural thing that came to mind for me was we need to integrate into Chat PRDS MVP server. And so I was looking into how to do that with Devin. And so the first thing that I always kind of go to as an initial thing is what we call the deep Wookie, which basically for any repo, this is true for public or private repository, you can come in and get a full AI generated documentation of the repo. And so in this case, this is the Devon web app repo. There's nothing too sensitive here, but it's basically, it explains Devin. It's pulling a lot of this information from the readme or understanding the system architecture. I can search this and pull up different things. And so if I want to understand how the MCP market purse is set up, it'll point out what particular components there are or what particular files are called here. And I can read up on this and kind of understand exactly how this is set up. And the natural question here that I might ask is, okay, cool, just show me where the MCP server list is implemented. And to this we'll look through our repo. And Devin at this point has done a lot of work in the dev web app, pretty standard. And so that helps a lot, which is Devin builds this representation of the code base over time and we can see what's going on here. It has all this.

B (8:15)

And so you're getting both like sort of a natural language explanation of how this server list is implemented. And then you also, on the right side of this, for folks that are watching, get the actual code snippets and reference files that you can view and really understand the deep layer of the code. So you have like sort of a combination of. Let me explain how it works. And then this is the nitty gritty.

A (8:40)

Yep. Combination of English and code. I think it's an interesting one where it's like, you know, I think someday it'll probably all be English, you know, but. But I think especially now, you know, in this current period, I think we're really in the era where obviously you have the. You, you as the engineer want to be looking at both English and code. And you can see here it's giving you kind of the answers of what's going on. And in particular, it'll point out, okay, here's, you know, our list of all the different Marketplace servers that we have. And we have an Atlassian MCP and there's a HubSpot MCP and so on, right? And from here, the natural thing that I'll want to do here, which is what we found to be a big flow for folks, is to use this to produce actually a prompt for deployment. And so the whole idea is now that we're in this context, we know what the questions were, we know what part of the code base that we're looking at. It gives a lot for Devin to be able to start from. And if we have an app for task in mind, then we can get that going. So I'll say please go research the chat PRD MCP server and add that into this. And so what this will do I used basically construct a devoprompt from this. And so this has my prompt here, which I just typed in, which is not super refined, but it also has all the detail about the part of the code that we're in and what components we're looking at and so on. And so then it will generate for me this prompt in Devin that I can just go ahead and use the Meteor and you can see here it'll tell you want to follow the pattern of existing servers like Atlassian and HubSpot. Here's the exact typedit structure that's being used here. Here are the functions that you should be working at and here's what you should check to make sure that it works.

B (10:27)

One of the things that I want to call out for folks in terms of a workflow that they should think about is a lot of people, myself included, sorry, Devin would have just sent that prompt, which is add, you know, add chat PRDS MCP server to the list. And I do think that one very short but important loop of take this prompt and turn it into an effective prompt given the context, you know, and then sending that into the task to do just saves you a lot of heartache. And it feels like extra friction at the time, but I think pretty soon is one going to be the job to be done of the the tool itself. So does that like loop become invisible either through these reasoning models or some application layer that manages it? And two, it's just worthwhile for people to do so. This, you know, when you're thinking about sending a five word prompt, think instead saying here's my five word prompt, build me a better prompt and sending them that into your system?

A (11:24)

Yeah, for sure. And I think it's, you know, it's a great call because you know, as we said, Devin is async. And so from this point onward, the nice thing about this is, you know, Once you kick off the Devin session, Devin's going to start working and looking through the code and reading online about chatbod, for example, right? And it's going to do all this, but you're not expected to be there with it, right? And so, you know, it's going to work on its own. It's just as if you gave the. Your intern a project and your intern is going and working on it, and so they can ping you on Slack and ask you if there's questions or something. Or you can go kind of like, you can go take a quick look and see how your intern is doing, but you don't have to be sitting there with Devin for every step of the way here. And so one way that we kind of describe it is for a lot of tasks, there's often this sync component, like the synchronic component, and then this asynchronous component. And a lot of what search and wiki is for is for doing the synchronous part of the task before you do the asynchronous. Right? And so, like, if you had an intern, for example, would you just send them 5 word slack message and just leave it at that? Maybe sometimes for something that is like, you know, super clear and then, you know. Exactly. Often what you actually would do is you would sit down with them, talk it through for two minutes and be like, okay, yeah, like, you know, you know how we have this MCP marketplace and then we go and look at it together, you know, we read the pushing error line of the code and then you say, okay, yeah, so let's add check PRD to this and you know, take a look at how that MCQ server is implemented and make sure we add it to the list. And then you kind of hand off there, right? So you kind of have the first two minutes of going back and forth with Devin, your intern, and then as soon as you hit go on the Devin prompt, you're kind of expecting it to be more of an asynchronous thing where you don't have to be in the loop.

B (13:01)

Well, and one of the things I want to call out for people that are building AI products out there, you know, like you, like me, is in these sync products, latency really matters. People get really frustrated with wait times. But if you set up your product to really be this asynchronous modality, you actually buy yourself a lot of user love on waiting time because there's not that expectation. Just like you would not say, hey, intern, okay, now go research this other MCP and do a PR for me and come back when it's ready. You know, just like that would not be something you expect an intern to come back to you immediately. You also, from a product perspective, don't expect Devin to come back immediately. Now one of the benefits of this from, from a how I AI use case is you can multi thread a lot with tools like this and set, you know, 2, 3, 4, 5, 10 of these going at once on different projects and not feel like you have to sit there and, and babysit things. And so I'm, I'm wondering, you know, while this is running, do you go pop off and go to a meeting or get a coffee? What has this sort of like asynchronous workflow enabled for you?

A (14:10)

For better or for worse, I'm in meetings for a lot of the day and it's great to be able to just kind of kick these off or you know, you had an issue backlog of, hey, there's these three or four things I was hoping to look at today. Right. And you kick off each one with Devin and then you know, these go and work asynchronously. Right. And it'll make the pull request for you in GitHub and it'll kind of show you the diff and what work it went through. If it's like a front end change or something like that, it'll send you the screenshots of what of the before and after. Right. You can see it's going and researching chat prd.

B (14:43)

Well, I will say just clearly my SEO on my MCP is not good, but Devin did make my MCP homepage, so it's in the top nav. Yeah, that's funny for me. So it should know.

A (14:56)

Yeah, cool.

B (14:59)

This is great.

A (15:00)

So I think for sure, often you're running like a few of these at once and like you said, it's just like a nice way to be able to kind of have multiple tasks going and then check in on each of them.

B (15:09)

Yep. And so what this would do and maybe we can come back to it later when it's. It's done thinking what this is going to go do is it's going to go do research, it's going to find my docs page on the MCP server that Devin did make for us and then it's going to pull that docs in and then you're going to get actual code out of this. Your goal for this is to get a pr, right?

A (15:31)

Yep. So this will produce a pull request for us and Then from there I'll be able to review the pull request and then if that looks good, then I'll merge it and then obviously we'll have this out in the next seven or eight weeks.

B (15:41)

Amazing. And then your prompts are going to be so much better. And I'm feeling guilty, so I am just going to slack you the the MCP homepage and you can give that to Devin to go.

A (15:54)

Yeah, sure, sure.

B (15:56)

You're getting a true live, true live demo here. Yes.

A (16:00)

This is when your intern comes back to you and said, hey, I was looking this up and like, I couldn't find it. Like, can you point me to where.

B (16:06)

Okay, you have it. Chatprd.AI/product/mcp.

A (16:11)

Okay.

B (16:13)

Has code snippets and everything.

A (16:15)

Okay. Okay, here we go.

B (16:16)

Great. So this, this is a good example of you didn't. You've done your research. You use that research to create a better prompt. You use that prompt to kick off a task. That task is worked asynchronously as a sort of like more junior engineer would work, including doing research in your code external to your, your business. And then it's going to go ahead with the context of your repo and do a PR and ship this feature. And otherwise you would have had to like ask somebody to do this. And I think about, for me, I think about the people that you'd have to involve in something like this. Like you'd have to go find the senior engineer that wrote the MCP server code.

A (16:57)

Yeah.

B (16:57)

And say like, please explain it to me. You'd have to, you know, get. You take the time to write out that nice spec of this is what you want to do. And then you'd have to task it to somebody to actually implement. And so I think you can press that workflow of a team of like, you know, three, even three people's time into, you know, about 10 minutes to get something done.

A (17:21)

Yeah, yeah, no. And I think often a lot of the folks who we see who really, really love Dedorant and use it this way especially are folks who are like tech leads or product managers or things like that. It's a great kind of intersection of one is, on the one hand, you're already used to the flow, kind of like figuring out an issue and getting into what is going on there and then handing off something of here's exactly what we need to build. And then I think too is naturally the async workflow for people who are in meetings or who have a lot of back to back going on is just a great way to kick off and check in on tasks quickly. And so from starting things from the web app or starting things from Slack, for example, is a nice lightweight thing. If you're not in your IDE all the time. You can start tasks from the IDE as well, obviously. But we see this kind of flow a lot with leads and PMs, basically, who are going back and forth with a lot of things. Yeah.

B (18:17)

And one of the things that I've been telling people more and more is as part of your PM onboarding, you should be now giving Everybody access to GitHub, which isn't something that typically happens in a lot of product organizations. Giving access to GitHub, giving access to tools like this, because I think it does enable product managers to do a lot more. So while this is running, what I wanted to talk about is before we got into the show, you and I are saying you're just a little bit busy, you know, over the last month, just doing a few, few interesting things with, with the business, in addition to, I'm sure, wanting to build and spending time with the team. And so, you know, this asynchronous nature and this junior engineer on demand. How do you actually use that day to day to just keep afloat on top of all the stuff that's coming in your team? You know, not. I have a feature I want to build. Let's go build it. We just saw that flow. But like the kind of reactive stuff in your company, how are you using AI to stay on top of that and keep the velocity high?

A (19:19)

For us, a lot of it is just setting the right workflows in our Slack and in our org and so on. And so Devin obviously has knowledge, which means it'll learn your code base over time as you keep working with it, or you can kind of give it more details about how certain things work. And a lot of things, it's almost just like institutionalizing Devin as first line of response is how I would describe it. And so I could show a few examples. The big thing is to really get to the point where for a lot of these different things that we file, you know, like Devin is first person that gets tagged on all of these. Right. Devin won't be able to do every single thing, you know, on one shot on the first try. But often you're working back and forth with Devin and Devin puts up a PR and if there's some slight touch up that you have to do at the end or that you have to build, then you're able to do that. And so we have a ton of channels where we go and talk about issues or various things that we need to build or things like that. We have one for all the crashes that come in. We have one for core infrastructure things that come up. This is the one for our web app, which is hopefully a little bit less sensitive. And you can see here basically every single thing that folks talk about. And remember, we do, you know, we start in Devin Session and so it's like, hey, you know, can you standardize the font size, spacing and style for these three levels? Right? And then, you know, we just go and start the Devin session and Devin will make the pr. It'll go through the pr. This one gets merged because. Because there's some back and forth feedback here. And so, so, so like Devin goes and edits. He imports up. And see, Devin made this br. There were a couple back and forth edits. And then Dave, our engineer went and merged this. This is often how we work. It tells. This is another good example. Hey Devin, can you make it so that when you come in, click on the notification, it takes you to that in a new tab natural feature. Probably one of our users requested it. And you just started Devin Session. And Devin will give you this progress update of. Here's what I'm doing so far. Here are the files that I'm looking at and here's what I see in this case, by the way, it's actually confidence medium. And then Walden says, oh no, no, no, you should take a look at this thing instead. One of the cool things I want to point out too is because of this, Devin is a naturally multiplayer experience. And so we will often have a few different folks going back and forth or if somebody else is looking at this issue, or if somebody else is the expert on this part of the code base, they'll go and give their own kind of input here and Devin will just go back and forth with them as well. And so really it is just a thread where a group of you are communicating and figuring out how to. How to work on this issue. And Devin is just one of the players in thread, right? And so, you know, Ethan comes into Walden's thread here and says, hey, make sure to use a link element from Tanstock Router and then gives that feedback, right? And then Devin goes and makes that change in the pull request. And so you can say, see, Devin had like an initial thing and then had some additional commits and it went and did this link from Tanstack Router instead.

B (22:30)

As an AI founder You're used to sprinting towards product market fit, your next round or that first enterprise contract. But speed isn't enough for AI startups. Buyers expect security, compliance and transparency from day one. That's why serious AI startups use Vanta. With deep integrations and automated workflows built for fast moving AI teams. Vanta gets you audit ready fast and keeps you secure with continuous monitoring as your models infra and customers evolve. AI innovators like LangChain, Rider and Cursor scaled faster and closed bigger deals by getting security right early. With Vanta, listeners can claim a small special offer of $1,000 off vanta@vanta.com howi AI. You know one of the things that I like about this and again, kind of a a shout out and our use case for folks that are trying to drive more AI adoption in their teams is doing this as much as possible in public is really helpful from a learning perspective. So one of the experiences I had running the engineering team at LaunchDarkly was when we started putting Devin and Devin like agents in public channels. We saw a lot more adoption and upskilling of our team on how to actually talk to these agents, how to get the right outcomes. And so, you know, I, we were talking earlier and I was saying I DM Devin all the time. It's because I have no employees, no one to talk to. He's my only buddy. Um, but I dm, I DM Devin all the time. And we have these sort of like side conversations. He's sort of my intern on the side, but in larger organizations I was very much a do it in public channels, do it where people can see it. Because not only does the work get done and it's nice kind of muscle memory to tag in these tools immediately, but also just learning how you use them, what is an effective prompt, what are the kind of things that it's good at and not good at is really useful for just overall engagement with these tools. And so I think hiding your AI use is kind of the worst thing you can do. You can do it in. Org. So I say do it all in public.

A (24:56)

Yeah, yeah, yeah. And I think there's two sides of it that I was going to say work. One is like the kind of like when we talk about these multiplayer experiences, right? I think there are two benefits, right? One is this kind of like the knowledge transfer for the agent itself, which I think more and more products are starting to have, which is, you know, one person uses Devin or uses this tool or that tool, right? And that adds to the knowledge of the tool itself so that, you know, a week later when somebody else does that session, Devin's like, hey, oh yeah, I just pressed this piece of the code last week. Like, I know exactly what you're talking about. Let me go and find that. And then the other side is kind of like educating the humans, right? Of like, you know, showing each other what your experiences are, you know, being able to work with one another in the same flows. And I totally agree. I think because of both of those, you know, I think we'll see a lot of experiences in AI productivity. Get more and more multiplayer.

B (25:49)

Yeah, yeah, that's my hope. Okay. Before we move on from, from Devin and your use of it for engineering, I want to get really specific. So you'll go. And then I'll go, what are your top five? Like, everybody can reach for them, tasks that Devin can do for you. And you pick kind of like five categories of tasks and I'll pick five.

A (26:09)

Okay, sounds good. Yeah. So top five, I think, miscellaneous front end fixes. It's amazing for. I mean, because often that full workflow is like, for various reasons, like you said, you have to get three different people involved and it's like, here's what we're going to do. And then you bring in somebody who looks at that code and there's somebody else who's reviewing or something. And now with this, you tag Devin, you explain. Here's a screenshot. I want to make this button a little bit more round or I want to touch up the design here. And I want to do X lines meet, right? And it'll go and do that. It'll find the right parts of the code, it'll do the implementation, but also it'll send you the before and after screenshots as well. Right. And so you can just kind of review it in line there. And that's just like a really, really great use case. Both, I think, because similarly, it's verifiable for the agent, but it's also verifiable for the human rights.

B (26:58)

And while, while you're. You're saying that, I will just pull up an example of this, which is let me share my screen, which I rarely get to do here. It's very exciting. Window. Let's do. Always thrilling to share your Slack. As you can see, my only friends are agents. But here's an example of it I just did very recently, which is I'm working on the chat parity homepage and Devin shoots back to me. Here's a new Hero image that I like. And I was able to give feedback on that so that this is kind of exactly what you're talking about, which is like, let's make changes and then get kind of that immediate feedback back right in your workflow.

A (27:41)

Yep, yep. Fixes new components, changes that you want to make in your front end. It's super, super nice because yeah, as we've seen, you can just kind of do this all inside, basically. And so that's probably number one for me. I think number two that comes to mind is version upgrades, migrations, things like that. And so, you know, like upgrading your Node version or getting onto the latest packages and so on, it's a big time saving. We all have to do it. And then somehow these new packages just come out so quickly. But obviously the devil is in the details of finding this new version. Will say, oh, every instance of this component is, we recommend that you use this structure instead or something. And Devin will be able to go through that and do the semantic search and find each of the components and make the right changes. Number three, I'd say is documentation. Big one as well. And so we have our Devin docs, for example, our own kind of docs page, like the external docs page. And I mean Devin has written the entire thing. DeepWiki itself obviously is kind of an extension of that. But even writing your own docs pages or putting materials together, a lot of what Devin does is go again, processing the code base and understanding this, references that, and here's what this does, and so on. And so it's a funny one in the sense that it's not strictly a writing code use case or it isn't always, but I think it's so closely related to it that a lot of the same capabilities are really valuable there. Number four, that I would say is incident response, actually. And so we have this set up so that whenever there's a crash, the first line defender, you know, on pagerduty basically is dev. And so Devin gets a page and Devin gets started, goes and you know, kind of runs a session. And obviously you probably want a human there too, you know, especially for these big incidents to make sure what's going on. But the nice thing is, you know, it's like 4:00am and you're kind of like half asleep and then you get to your computer and Devin has already written a report of like, hey, I looked at it. I think it was this change from like last week that happened or yesterday that happened. Here's exactly where the trace of the error Goes, we use that a lot. It's a huge lifesaver for us. Then number five, let's see. I would say adding testing is a big one for us. It's a very common thing where this is especially for individual engineers as they're going and working on things. You have your whole pr, you built things out, you built a new feature and always, you know, the last thing that you have to do before you ship it is you have to go and add your own unit tests and make sure your thing works right. And the nice thing again is like Devin will go and do that. It'll make the test and then it'll run the test locally itself and make sure those tests pass. And so we can iterate with you to make sure the LIN pass, make sure the CI passes and so on and just kind of like add those for you.

B (30:41)

All right, well, we're very close. My five are very close. So I love those. So to recap, and I'll augment yours with mine. So number one, front end fixes. My particular version of front end fixes is I think these AI tools can really help you do polish really nice interactive user experiences where you wouldn't normally be able to spend time on them. So any of those, like little magical moments that you don't want to like, toil in front end on, I think it's really good at docs, I think is underrated. I actually have a GitHub action that every PR gets opened, gets reviewed by Devin, gets the PR description rewritten by Devin, and then after the PR is closed, Devin goes and ships our documentation, internal documentation, into our repo so that Devin has access to the document. I think it's like an excellent technical writer. I too have Devin first line of defense for incidents. So Devin actually has a Sentry login and logs in to Sentry and goes through all of our open issues and starts to fix stuff for us. Um, definitely upgrades. And then the one that I didn't hear you say, but I just think is a. Is a more like operational and personal benefit is it's like 247 availability rubber ducking, which is like when you're working on something and you're just like, can you just look at this and see if I'm being crazy? Like, if this is crazy, you know, Sunday night, Monday night, Saturday morning, where you'd like really don't want to bother a colleague. I just think having something to like sort of rubber duck with is. Is really nice. And so those would be. Those would be my use cases. Very similar. Okay, Scott, we're going to close with just one, one really high level use case outside of the Devin ecosystem, which is voice. And you were telling me a really interesting ChatGPT voice use case that I hadn't heard before. So do you mind spending a few minutes just telling us about that?

A (32:36)

Yeah, for sure. So I'm a big fan of voice. I actually think there are a lot of interesting we've played around with. We have Voice and Windsurf now actually as of wave 11 too, and partially because of that. But in short, part of the way I'm describing is like I think Google itself 25 years ago was basically a better encyclopedia. We have all sorts of things that you want to look up and pull together and so on. And basically it got you a faster answer and it got it to you with more up to date information of what was going on. And I almost think of ChatGPT voice as a better Google. You can get an even faster answer. It's fully synchronous, you can do it in the conversation and then obviously you have all of the detail. It can go and research and do these other things too. What I'll often do is if I'm in a meeting and we'll be talking about things, there are always questions that come up. Yesterday I was in a meeting and we were talking about this, which is, you know, there's so many orgs out there with tons of software engineers. And so we were kind of thinking like, yeah, like what are all the companies that have, let's say 10,000 plus software engineers, you know, and how many are there in the world? Right. You know, obviously like, you know, the big banks out there, tens of thousands of software engineers, the big tech companies, you know, those are the first couple, maybe the Accenture Infosys, you know, that category, those are the first ones that come to mind. But like what are all these different companies that have it? And you know, naturally in a meeting it's kind of rude to just go on your phone and just kind of like, you know, be totally unresponsive for like two minutes as you're looking. So instead what, what I'll often do is I'll just pull out ChatGPT and go on voice and it's basically like adding ChatGPT every conversation, you know. And so when I say, hey, like, you know, can you please like tell us like how many companies out there have 10,000 plus software engineers? Right? And then, you know, whether it's voice to voice or whether it's, you know, Voice and then you kind of get the response in text. Like I use both of those modes a lot, but I find it to be like a very natural, a natural stepping stone where I just find that voice lowers the friction even further in a way that actually really matters. Like, like I was going to say it's like, you know, in the encyclopedia area, right? If you were going to look something up, it took like, I don't know, five minutes or something. So you have to go pull the right like letter of the Alphabet or something and find this. And then Google got it for like 10 seconds, you know, and like voice is kind of like getting it from like 10 seconds down to like one or two seconds where you can just get on instantly and just say what you want to say. And that actually matters, I think, for, for, for being able to go back and forth or, or just like having, you know, very off the cusp, like off the cuff questions that you, that you want to ask.

B (35:15)

Yeah, I was going to say I, you know, you've maybe changed my mind here because I used to think that voice mode was like super slow. Socially disruptive in that it feels so unnatural to like talk during a meeting. But if you flip it on its head and you're like, no, this is just another meeting participant that I'm putting into the room, it actually is. Is more socially inclusive. Everybody hears the result, right. You're not like slacking around links and then people are opening them up on their laptop and reading while somebody is talking. Like everybody's sort of like clued into the synchronous nature of this new, new information. So if I had people to be in meetings with and not to brag, but I have very few meetings, then maybe I will bring chat chatgpt into it. Okay, we will do.

A (35:55)

Must be nice. Must be nice, man.

B (35:57)

It's the dream, man. So, quick lightning round questions, we will get you back to your work. First one, it's like picking between your children. I know. Now, the ide, the terminal or the agent, what is going to be the form factor to rule? AI engineering.

A (36:14)

I really think of this in the future as we call it coding agent. And a lot of what this becomes is actually just the next generation of human computer interface. And the way that I like to say it is Tony Stark doesn't have a laptop. You don't need one. At some point, if you have your Jarvis plugged in and you're going back and forth with your agent and then go and do these things for you, and you can imagine that Builded software is just, you're not looking at your code, you're not looking, you know, you're just looking at your own product, right? And you're looking at your own product and you're saying, hey, let's make this button rounder. Look, let me add a new thing over here. Let's save this and you know, let's ask the user for this and that info, you know, and you're just making the changes in real time in your products and your agent obviously is going and implementing this for you. And so I think it's a, it's, it's certainly very agentic, but, but I think it's almost like we might, whether we call it an IDE or an agent or whatever, it really is basically just like a, a different human computer interface where you are just looking directly at the product rather than having to go through all your code or go through. And so I think that's the future version. Some years out, I think today, I would say, I think a lot of it depends on the cohort. And so I'm, for example, in meetings all the time. Unfortunately not that. But yeah, you know, and because of that, I actually think the slack agent workflow is a super supernatural one, you know, or, or like linear, for example, and tagging, you know, dev and from Linear, I think for an engineering ic, who's, who's, you know, gets to code for, for, you know, eight or ten hours a day, again, must be nice. But then the IDE is kind of the natural place where a lot of this starts, right? Which is, you know, you'll have these things that run in the background and you'll have these asynchronous processes that are going as you're doing your thing. But the natural place to get started for that is the IDE Today, I'd.

B (38:06)

Say I also just think what's nice about this era is like the form factor can come to you and you can decide what the interface is that works best for your workflow. Okay. As somebody, Devin is my buddy, I am sure you get lots of chats that would give us very good insight into my closing question, which is when you are frustrated with our sweet, sweet intern, Devin, what is, what is your prompting technique? And I know you all monitor this because when I get frustrated, sometimes I get little credits back. A little credits back. Like you did that wrong. I get credit back. So I know you see a lot of human language to agents, but what is your strategy? What do you find yourself doing in a moment of, you know, Frustration or being blocked.

A (38:51)

I can give some advice. I can't say that I've always followed my own advice, but a lot of what it looks like I'd say for an agent especially, is I think the agent is a little bit different from a chatbot in the sense that, like, a chatbot, there's less to go off of. Is kind of like how I want to say it, right? Where with a chatbot, it's like, you know, you ask a question, it gives you the wrong answer, and it's like, no, that was the wrong answer. And then that's all you can really say. With an agent, like, one of the nice things that you can do is you can go through and look through all the history of wanting to do it, right? And so, like, we had an example of that just now where, you know, Devin got stuck of, like, you know, I've seen a chat PRD page. It's hard to have an MCP server. I'm, like, trying to find the documentation on this. Right? And if we go and scroll through the logs and we'll see, like, what happened, that it Googled it and it found some other things. Right. And that was what the issue was. Right. And so from there, it's kind of like you take that information and then you understand, oh, Devin was missing the link to this page. And when you send that. And so I think a lot of it, actually, with agents is just. It's kind of like pair programming or pair debugging with an intern. Like, you want to, you know, first you get to go through and see, okay, here's all the steps that you took. Oh, by the way, it's like, you know, I think you missed this one file, which is you have the downstream reference of this, and that's why there was the bug or something like that. I think that's the biggest thing that will really move the needle.

B (40:12)

Okay, so review the history, figure out where it went wrong, and then. Then reinstruct. Okay. Scott, this has been so fun. Thank you for showing us where can we find you and how can we be helpful.

A (40:24)

Yeah, yeah. Prefer so. So we're Cognition and Devin on Twitter. We officially got the Twitter of slash Cognition, which is great. And then obviously it's. It's. It's Devin AI if you'd like to use the product.

B (40:37)

Great. Well, thank you so much and appreciate you spending the time with us. Cool.

A (40:41)

Thank you so much for having me.

B (40:43)

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