How Block’s custom AI agent supercharges every team, from sales to data to engineering | Jackie Brosamer & Brad Axen

Goose is our AI agent. We kind of designed it to be really agnostic. You tell it what you need it to do by connecting it to different capabilities, and it can just solve any problem. What I'm going to do is take that CSV that Jackie was working with. I'm just going to pop it over and I'm going to say, can you read through this data, use it to create items in my Square dashboard?

So we took a CSV that we didn't even have to look at that probably had data that was not in this exact format, and you created a product catalog.

What a lot of my work and what my team is trying to do is not just make it faster for someone like me or on my team who has data expertise to go through this, but to allow our finance team, our sales team, anyone in the company to be able to dig in and self-serve a lot of this data rather than having to ask an expert and wait for that to come back.

You have been the first person that's told me it was the salespeople that were begging for it. We hear so much, you know, engineers are leading in and they want to vibe code and they want to do all that. But it's nice to hear that the folks close to customers and close to revenue are also seeing the value. Welcome back to How I AI. I'm Claire Vaux, product leader and AI obsessive here on a mission to help you build better with these new tools. Today we're speaking with Jackie and Brad at Block, who are going to show us how their open source AI agent Goose can be used to do everything from vibe data analysis to vibe coding in MCP. Let's get to it. This episode is brought to you by CodeRabbit, the AI code review platform, transforming how engineering teams ship faster with AI without sacrificing code quality. Quality code reviews are critical, but time-consuming. CodeRabbit acts as your AI copilot, providing instant code review comments and potential impacts of every pull request beyond just flagging issues. CodeRabbit provides one-click fix suggestions and lets you define custom code quality rules using AST grep patterns. catching subtle issues that traditional static analysis tools might miss. CodeRabbit brings AI-powered code reviews directly into VS Code, Cursor, and WinSurf. CodeRabbit has so far reviewed more than 10 million PRs, been installed on 1 million repositories, and has been used by 70,000 open-source projects. Get CodeRabbit free for an entire year at coderabbit.ai and use the code HOWIAI. Thanks for being here.

Thanks for having us.

Yeah, excited to chat.

I am so excited to have you 2 on How I AI because I have been so impressed how Block has, as a full organization and a large organization at that, really embraced AI, it seems, everywhere and for everything. And, you know, everybody's worried about AI-native startups, but you're one of the companies that I really think is going there very fast as a much larger Organization. So how did you lean in as an org so fast and so broad?

I think it was a combination of both like bottoms up and tops down. We saw, you know, a lot of concentrated pockets once ChatGPT came out that were really finding a lot of value outta these tools. For example, salespeople, they're like, I, any tool, give it to me. Like I'll use it, help me get those leads. And we also saw a lot of engineers, you know, starting to lean in as these tools became really good at coding. And kind of as that started from the bottom, I think a lot of our leadership saw a lot of promise and they've been, you know, trying to make some of those practices a little bit more consistent among groups. I think that it's really a great time to focus on organizational transformation because to me, that's really what's going to define who ends up the winners in whatever this AI future is, is who not just leans into technology, but even more than that, leans into the organizational transformation, which can in a lot of ways be even harder. It's like really easy to get Technology go exponentially and humans don't go exponentially so well.

You have been the first person that's told me it was the salespeople, it was the salespeople that were begging for it. We hear so much, you know, engineers are leading in and they want to vibe code and they want to do all that. But it's nice to hear that the folks close to customers and close to revenue are also seeing the value.

Yeah, I think it's, you know, everyone finds like that different first use case that really gets them hooked. And one of the things I've been really impressed with, with some of our non-developers especially, like we all kind of know how people use it for vibe coding, and I think that's like well discussed, is non-developers are just so creative and so tolerant of like stringing together a bunch of different tools just in a way that you never would have imagined. And that's, I think, really going to be the power, is getting these tools closer to the people that actually understand the problems and see what wacky things they come up with with this new technology.

Not only are you all leading the way in terms of how organizations— and I totally agree with you, I think the biggest transformation is cultural and operational. The tech piece is like a lagging piece of it you can figure out. But what I also love about what you're doing is not just how you're embracing it internally, but how you're trying to drive and help others adopt it externally. And so I'm wondering if you could tell us a little bit about the adorably named Goose and what Goose is and why you all built it.

So Goose is our like AI agent. And there's a lot of people have a lot of different definitions for what an AI agent even is now. But I'll say specifically what we mean by that is it's powered by an LLM, but it has a whole collection of tools that it's gonna go use to solve problems for you. And, uh, this is like something we're really excited about, uh, in part just because it, it lets us like build for ourselves and like come up with workflows that work for our company. But like you said, we open sourced it because we think there's a lot of extensibility for everybody. And we kind of designed it to be really agnostic. Like you, you tell it what you need it to do by connecting it to different capabilities. And it can just kind of solve any problem. And I think we'll get to show some of that today. And, uh, that's all powered by MCP. So that's a big part of this like open source, uh, puzzle is how do we make this work no matter what you want it to do? And MCP really helped us get in like early into helping to establish the protocol and have a way for us to connect it to anything. And people can develop their own MCPs and just make things work.

Got it. So you, and I just want to show here, it's up on GitHub. It's open source. It's your agent, your agent framework powered a lot, it seems, by MCPs, which I think we're gonna talk about later, that anybody can, can use and extend, extend upon. And what, what made you all think about open sourcing this?

Yeah, open source is, I think, a really big emphasis just as a value for our company. And I think there's 2 ways we think of it. Number one, this is just like the right thing to do, you know, for the world in some way. But then there's also, we think that there's so much benefit in terms of having this open ecosystem. and really seeing these patterns emerge from not just, you know, the engineers that we have in our company, but from engineers all over the world. And I think we're really seeing that come to fruition with a lot of the growth that we've seen in MCP, you know, and all the MCP servers that are out there. And it's only been, you know, what, like 6, 8 months?

Yeah.

And then I think we also wanted to really lean into open source in order to take advantage of all these different models that are coming out. A lot of the tools that come bundled, you know, released by these different model providers can only use like that kind of models. And we know that every month a new model's coming out. One might be better at coding, one might be better at writing. And we think there's a huge amount of power in being able to bring together data across all these different MCPs and then also pair that with really any model that you want.

Okay. So speaking of data, I think you're gonna show us How to do vibe data analysis, which is a first for How I AI. So it's a flow that you use internally. So this is definitely something we're not making it up. You use this exact flow day to day, but we're gonna use some fake data so we can screen share and really get into how it all works. And so Jackie, what kind of business are we today that we're gonna do some data analysis with?

Yeah, so I tried to make this as realistic as possible. When I don't work in tech, I actually have a small farm. that is not very profitable. And so we're going to be doing some analysis that I've actually done with my own internal data that kind of shows us how we can look at some business data and make some recommendations. So what we're going to do is go ahead and look at this to try to find this local CSV of data that has July data for my farm stand, which does a bunch of vegetables. And what we've asked is we've asked Goose to use Pandas, which is a Python library, to tell me the items that have the best revenue and like what the busiest days of the weeks are. And then I left a general kind of question at the end, just what are some trends, because I think it's always interesting when you get a slightly different result as you run this. And so you can see the first thing that we're doing here is it's using ripgrep to find The CSV. We found that riprep is actually really effective, even though it's really simple. There's a lot you can get done with it versus something fancier like a semantic search. It identifies this July data at CSV, then it writes a Python command. It found some error where it didn't have this installed. Oh no, this is maybe going into cursed virtual environment territory, but it looks like it's going to solve itself. Sometimes I'll tell it like, check your Python environment so that it makes sure it sources it, but it decided to go ahead and create a new virtual environment here. One of the things I really like about Goose is I'm terrible at maintaining developer environments, and so it'll go through and try to just like debug that for me.

I mean, how many of us, maybe this is a very niche comment, have been victimized by trying to get the right version of Pandas installed? Because I definitely have.

Okay.

And just taking a step back, we didn't see the input data, but is this just like general day-by-day sales of different products?

Exactly. This was just a list of you know, item, quantity, date, price.

Got it.

And so from that, it's able to go through and say, okay, you know, your top generating, your top revenue generating items are berries. We love berries, very high margins. And it's got, the best days are like Thursdays. And so, and then I just had this, you know, bonus question of like, what are some key items and trends? It'll kind of lead into some stuff later. So cherries have the highest average value. It goes through, you know, what are your mid, high, low items, and then, you know, goes through some weekly patterns. And so without even asking, of course, it comes out with some recommendations, which I really love.

Let's just pause on these recommendations.

Yeah.

So what I love about this is you have this ugly file. You probably could have tossed it in a spreadsheet or done some of this analysis yourself, and you're doing sums and you're doing pivot tables and you're trying to explore this, and you probably have to do different tabs for all these different analyses. And this you can really drop in and it's maybe a, a relatively, if you scroll up, it's, you know, relatively simple analysis by day, by product. But under the hood you have this pretty powerful data analysis package. And so I'm presuming you can go from this level of analysis to something much, much deeper.

Yeah, that's right. And I, when I'm doing something like this live with something like Goose, what I'll usually do is kind of, you know, have it take a high-level first pass and then, you know, dive into specific sections and say like, hey, can we go deeper here? You know, use this particular type of method, you know, do a line regression that tries to figure out price elasticity or something like that.

Well, and I have to say, as a mom of young boys, I do feel like I spend, you know, $1,500 on strawberries every month. So this is really hitting home. Okay, so we can go down and then I'd love to see, I like the idea of when you're prompting a data analysis to say, give me the data analysis, but also give me some recommendations. And so it's really saying here, you know, high margin items, which is great. Uh, look at why Friday drops, cuz you know, maybe they would expect that to be a higher day, weekend sales, and then some, oh, price adjustments. That's really interesting. So what would you do next after this?

Yeah, so I don't think that this is like very good to share. And so a lot of times what we see is that people really like to share these kinds of analyses as local webpages, especially people who aren't technical or just a whole new world has opened up with them, to them, as they figure out how to use AI to turn some of the stuff into HTML and share it with each other. We see a lot, we have a way for people to use Goose to share these internal static websites, and we see that really popular, especially among, you know, some of our business users that want to kind of create their own dashboards for, you know, whatever.

While this is generating, I'm just reflecting on what I've seen so far. So it seems like Goose has sort of a virtual machine in which it can write code and wrestle with Python dependencies. Love that for Goose. It has the ability to spin up hosted HTML and websites. Is that right? Is that what it's kind of doing here?

So it's all running locally. I think that's one thing that's like really unique about Goose. And part of the reason, you know, we started that way is that we, a lot of our developer environment stuff happens locally. And so that was like a really natural fit. And then we also saw within the open source community that this has actually been really popular with kind of the crowd that really wants to maintain end-to-end control over their workflows. One of the things we've seen with Goose is that some of the most promising use cases are, you know, you tell Goose to do something, you kind of go away and, you know, wait for that task to complete as it does things like, you know, write code errors. Oh, nice.

And so it popped—

it's also cool because it can— it went ahead and popped this up for Yep. This is not the prettiest website it's ever made with this fake data, but you can see, you know, it's able to make these bar charts with Plotly. And I could go ahead and dig in and try to refine this if I wanted it to be, you know, a little bit more well-designed.

Got it. And, you know, if I was being cheap and easy about sharing it, I would just print as a PDF and shoot it over and it would look nice.

When we run this internally, we have an MCP that it connects to where you can just publish it to the company. Like to kind of keep it internally private, but share it with anybody.

And what are the MCPs sort of out of the box that if I cloned the Goose repo right away, would it, would it have ready to go? And then what are some kind of ones that you've either built or integrated specific to maybe what you do at Block?

Yeah, so we have like a collection of different MCPs. Some of them we've taken from the open source community. Some of them we've developed internally because, you know, for example, we have an internal data MCP that I use every day that helps, you know, sort through where's the revenue for X product. I mean, it knows all that context. And we're also working on trying to have external MCPs that, you know, connect to our business products. A lot of what people are using Use for and a lot where we saw the first value is just MCPs that connect to all of our key internal systems. So Google Drive, probably the one I use the most. Brad mentioned this one we have internally called BlockSell that's really popular because at the click of a button, you know, you can publish a website and send a URL to anyone in the company. We also have a developer extension that does a lot of this like command line and tool stuff. We also have a computer controller one that just did, and that's how it just opened this Chrome window.

Got it.

Cool. And that's, that's the stuff that bundles with Goose. So when you get it, it's like you're gonna have like 5 really simple, like memory developer.

Yep.

And, and then when we use it internally, we have like 40 or 50.

Yep. And, and Jackie, how often are you doing an analysis like this? In your week using Goose?

If I'm doing an analysis, I'm using Goose. The question is more like, how many analyses do I do per week? I would say that I probably actually use it more in my day-to-day life for writing, but there's people in my team that are professional analysts or professional data scientists that are doing this every day. And I think increasingly what a lot of my work and what my team is trying to do is not just make it faster for someone like me or on my team who has data expertise to go through this, but to allow our finance team, our sales team, you know, anyone in the company to be able to dig in and like self-serve a lot of this data rather than having to ask an expert and wait for that to come back. I think we've all been talking about like the age of self-serve data for a long time, and it's like kind of gotten better, but we all know that You usually still go ask a data scientist. And so I think it's really interesting to think about how can we, you know, kind of short circuit that and allow everybody to focus less on kind of the rote parts of data analysis and spend more time talking about, you know, the insights and the context and the creative parts that we all want to spend our days on.

Okay, so we've talked about the salespeople love their AI, the farmers love their AI, but can we please get back to what people can wrap their head around, which is the software engineers? So you So you have this, you have this great, great data, um, and you want to do something with it. And so you have this data, you know, maybe you want to do something with it to improve your business. Brad, what's the next step in your mind here?

Yeah, let me, let me share some, uh, on my screen this for this, and we can show a little bit about like how a developer can build MCPs and use that to actually like connect what someone is doing into like existing products or to like solve new problems. And this is really how I spend my day to day. Like, I feel like at this moment in time, I'm, I'm just developing MCPs, select and write.

And I'm going to just pause right there because no matter how many times people hear it, they still need an MCP explained to them. So we're going to, Brad, we're going to take your explanation of what is an MCP.

Let me do that by showing you the page for it in Goose here. So in, in this, we have a, just this huge collection of extensions in Goose, and these are all Implemented with Model Context Protocol. So each of these is an MCP server. And what that means is that it has a collection of tools that it exposes to the agent. And it can do a couple of other things like provide data resources and all that other stuff. And so it's, it's like a protocol that lets the agent talk to a third party and get things that it can do and, uh, data it can read. And, and most importantly, it's kind of the arms and legs for the model. So this is how the model goes and interacts with the real world and like makes changes in online systems.

Okay. So you're spending all your time building MCPs. It seems like everybody is spending all their time building MCPs. So why don't you walk us through how you're spending your, your days?

Yeah. And so let me start by showing you an existing one before we start building. And so I've turned on a real Square MCP server. So this is like from, this is available like as an online MCP server that people can use to like run their business. So this is my Square dashboard and it's totally empty right now. This is a kind of an account I set up for the sake of the demo. And we're going to connect that through our agent and actually have it fill things in. So what I'm going to do is take that CSV that Jackie was working with. I'm just going to pop it over and I'm going to say, can you read through this data and Use it to create items in my Square dashboard. And so this is something that you could do with all kinds of input formats. So like, for example, I could give it an image and like, uh, maybe a picture of a menu or something like that. Um, and so since it's multimodal, it's just gonna go figure out that source, translate it into the API calls that it needs.

And I'm thinking as a product builder who has built many Import your data products where you're always trying to say like, it has to be a CSV in this format and these columns with this column name. And what I've just realized this opens up for you is like, drop in your arbitrary, messy, whatever data, don't have to reformat it. Let the MCP figure out the translation.

Exactly.

And then get it into the system. Okay.

Yeah.

So it doesn't matter what you've got. It's going to figure out, it could be a PDF, whatever. It's going to do its best. to like figure out how to make that work. You know, the first thing it does is it just reads it.

Yep.

And, uh, it's then gonna go, this is actually a really interesting part. I think the Square team did a great job designing this MCP because it has so many different things that you can do on Square that it's like too much for the model to handle all at once. So what it actually does under the hood here is it, is it says like, oh, I, if you need to know about catalog, I give you a method to look up what you can do in catalog. And then under the hood, the LLM is seeing like all of these operations that it can now access. So all that stuff about like creating items, it figured out. Um, okay. And I actually see the, uh, it's like, this is a funny, I normally would just read the CSV. Um, so I'm like, I'm like laughing a little bit at like how much work it's going through for this. But, uh, it is what it is.

I know I have, I have that same experience using like a cursor max mode where I'm like, I just don't know if you need to work. Think to think that hard.

Exactly.

How to do this. But you know, it's not my brain, it's the AI's brain.

Yeah. Yeah. So we'll, we'll see. Let, I'll let it cook for a minute here. Okay. And see how it figures this out. But effectively what's happening is it's just deciding to use shell commands to grab the prices from that CSV. And then it's gonna turn that into an API request and actually send it to the Square dashboard and create like a product category and then fill in the items. So we should just be one request away from getting that solved.

Got it. And, you know, instead of sitting here and waiting, Jackie, I'm presuming Farmer Jackie would have to go in here and one by one sort of enter, enter this data in and make sure it's right and all that kind of stuff.

Uh, one of the like really underappreciated, uh, things about LLMs is like how much they function as like data duct tape. You know, it's like it doesn't quite fit. You can do something to just kind of make it all mush together.

Yep.

And that's where a lot of the day-to-day data work is, is trying to match all those things.

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And this is something like I could follow up with this and say like, okay, grab images or even generate images and like upload them all.

Yeah, that's what I was gonna ask. Yeah. I wanna, I wanna AI generated apple image.

Exactly. Yeah. And the, um, I'll skip it for the, the sake of time, but like there's so much you can do to just like navigate through all of this. And this is something where like we're watching it live for the sake of the demo, but what really the best version of this is like Hand it the CSV and walk away and come back, and then you're gonna just see that this is done. And so that kind of like background task thing is the, I think the best mode for Goose in a lot of ways. But yeah, it like goes and figures this out and this is like a real account. So what's cool about this is that this isn't just like a little website showing that I have prices for apples. It's something I can take payments with. So I can now say, um, okay, let's make a payment link for 3 pumpkins. And we'll let that run for a little bit too.

This is not, this is not the topic I thought I was gonna get from you all today. You know, I had a lot of things in mind, but using AI to generate farm stand payment links for gourds was just not on the top of my list this, uh, this afternoon, but I'm excited to see it. Okay. So I think, you know, one of the other things that I think, and I know this is running locally and we won't get into like sort of the local mobile debate right now. But I just think that the ability to do this stuff via text on the go is so powerful. Imagine somebody wants to buy the 3 pumpkins off the back of your truck and you want to be able to do this really easily. This sort of like ability to just type in a short command to get a really custom software solution backed by, you know, either a platform or not, I think is pretty cool for people.

I, I think there's like so many little details too that again, like while you're watching it, it's a little slow, but, but it just figures this out for you without any intervention. Like, oh, it's, it has to decide which location I'm selling these from.

Mm-hmm.

For the payment link. And it's just, it just like saw that it needed it and, and like went and looked up what locations I've got here. And, uh, like just, it just unblocks itself, which is really nice. So you can just wait for this to go. And so, okay, so I actually have a real payment And this is just gonna load up and it uses my items, has a price.

Look at that.

Yeah. And so we could just throw in a real credit card right now and this would work and I, and we would collect on that. And, and so this is like, okay, so this is a bit of a tour of an existing MCP, like how you connect these agents to real products. And, but then I think what's like part of my job is really like, okay, what's, what's the next step and how do we make the agent Do the next step. And, and so for me, I think a thing we could switch to from here is like maybe we go give it another MCP so that it can send these payment links in an email. And so I have a test email account here that's totally empty. And I think we can work with that and then just try to show you how you can literally like extend the existing conversation. So I'm going to leave this open and we'll come back to it. I'm going to make a new window that we're going to use to make the, like, vibecoding half of this. So we'll just go vibecode NCP together. I'm also gonna open up a terminal window so that we can follow along. And I did do one thing in advance, which is, um, I went and grabbed an API key, uh, here in an env file. And, and I set that up just in advance so we didn't have to show the API key on the screen. Um, and so we're gonna use that to, um, Set up an MCP server with Mailgun.

And I wanna just pause here because we've heard this a lot on this podcast, which is people are always saying, you know, you can, you can do the output. I'm sure you could take this link and send the email, but anytime you're about to create an output, instead think about how can I create a tool or a prompt or an input that could do this for me? So instead of saying, great, I'm gonna email this link, you say, great, I'm gonna build an MCP to send emails.

Exactly.

Yeah.

Like how do you take that whole end-to-end workflow and solve every part? Yeah. Okay. So let's, let's talk it into, um, vibe coding in MCP for us. So the way I like to do this usually is I'll start with the scaffolding and making sure that everything kind of connects and works. So I won't even say MCP at first. I'm going to just say like, let's try to send an email. Um, so I'll tell it like I have a scaffolded Python package here using UB. I'll tell it about that .env file I mentioned.

Okay.

And I'll tell it what's in there because I don't want it to actually read this. It's like, you know, I don't want the model to know about my API key. Um, and so then I'll say, can you make a Python, uh, Python script in this directory, uh, that sends a test email? And this is like the vast majority of the work, like it going from a blank slate to sending a test email. That's the hard part. The, the MCP half is really easy. easy. So yeah, I always start that way. Like, let's just prove that everything works.

That you can do the thing and then we can figure out how to invoke the tool.

Yeah.

And, uh, this is actually another feature of Goose that I'm kind of glad it accidentally triggered is that it, it tried to read the .env file, um, because it was like, oh, what am I working with? Um, and we denied that actually because it's like a security concern. So there's a bunch of stuff that we try to catch. Um, And so it can't see it, but it's, it's going to have to just trust me that I, what I said is in there and then use it. And so, okay. And let me follow along over here. So I'm going to just, uh, we have a new file now, send test email that should be using this. Um, and it just wrote that. And if I wanted to like follow along in detail, I could do, I can, I could read it in an editor or something like that. But for the most part, I'm just going to trust it and hope it works. And so let's say, okay, can you, um, and I'm gonna give it that demos email.

Got it. So now it's written the script and you're just gonna double check this thing works and that I'm gonna get an email outta the script.

And so it figured out what I meant by the .env.

Yeah.

So it's like, okay, I found a thing and it's got this hardcoded recipient that it's gonna go edit. And you can see like, uh, if I like just re— yeah, there, it like just fixed that for it now that I told it who it's gonna send it to. And it's figuring out all this virtualenv stuff.

Okay. So, so just AI, just like us, figuring out its virtual environment.

Yes.

That is the theme of, of today's episode. Yeah, every time.

Okay. And then let me go over here and see if we got it. And I'm sure it's gonna end up in spam or something. Um, yep.

Oh, there, there it is.

And so we got, a test email sent with Python. And it goes to spam because obviously this is all demo stuff. That's not surprising.

And whenever I, you know, I get engineers asking me a lot, they're like, Mailgun, it's well documented. I can write the thing. But I think about the number of characters you wrote in Goose and the, just the number, the, like the typing that had to happen. Yeah. In the file. And I just think it has just saved you 15 minutes of time of like looking at the docs, copying it over, you know, checking that it works. And we did it in probably 2 minutes.

Yeah, exactly. And like, I just, I've, I've never built an email sending service. So it's like, I don't know this API at all. It didn't have to ask me for that. It's like, oh, it knew the endpoint name and everything that's all trained in, which is just fantastic. Um, okay. So, so now that this work is working, let's, let's turn it into an MCP. And then this is an example of like a little bit of model knowledge going a long way. So this model. isn't trained recently enough to know about the MCP SDKs. So that's, that's a thing where I'm gonna actually go get it that context so that it can use it. And so I'll just go find like the, um, yeah, MCP Python SDK, and we'll just grab it from the README. And so at this point I could actually tell Goose the URL and like it could download this README and it would work, but I, I'd rather just like copy and paste honestly because it's easier.

I'm, I'm the same as you. I'm just like, let's just drag the whole page and paste it in.

Yeah, exactly. And it's like, okay, great. Like it's already got these links. All I need is just this one example and that should be enough. And so I'll say, um, you know, okay, be nice to your LLMs, of course.

I agree. Be nice to your LLMs.

Um, so I'm gonna say now we're gonna make, um, an MCP server outta this. Uh, here's reference code. And then I'll say, uh, can you use that example to make an NCP web-based send email tool? And we'll see how far it gets with no other context, actually. We might need to tweak stuff, but hopefully that's like a good start for it.

And while it's, it's thinking, are you doing, does Goose auto-select models? Do you have a default? Do you have a favorite?

We, we do. We currently don't auto-select models because they're just— people bring so many different models to Goose. And so for us, um, we kind of switch context by context, like Jackie was mentioning. So like for writing code and using these tools, currently I'm using Claude Sonnet, which is great. Um, but would I, you know, if I wanted to like go write, um, a, a design doc, I might actually switch to like one of the reasoner models from OpenAI. Or I could even like try out like a local only model and run QN. Okay. So let's see what it's doing. I'll check in again and I'm, I'm just gonna like take a look. Okay. So it's got a new file and it's combining like what we were doing before and it's following that example pretty well, it looks like. And that's actually good code. I'm gonna, I'm gonna interrupt it honestly, because I, I, I know what it's going It's going down like a slightly wrong path cuz it doesn't know how I'm gonna integrate it. So I'm just gonna pause that and I'll make—

I like the, I like the, the judgment.

Yeah. The code.

It's like definitely the right idea. I, I know I'm not gonna like read line by line cuz I kind of trust it at this point. But what I am gonna do is I'll make my one-line contribution to this project right now, which is I know I want it to be running, um, not as a, a remote URL. I want it to just be a, a standard IOMCP. If you, if you work in it, that's the difference here. So I'm just gonna make it, uh, standard I/O. And then, um, I'm gonna ask it like a weird question to help me set this up in Goose. So I'm gonna say, to help me do that, can you get the path in this virtualenv? So really I'm just trying to make sure that I have a portable command that I can run in Goose. And so yeah, I'm gonna ask it to figure out that virtualenv. So we'll send that. And that should just take like 1 or 2 tool calls. from it.

And is this your just general flow for making MCPs all the time? Build me the, build me the function, then here's the code to turn it into an MCP. I'm going to make my one-line contribution. I'm going to ask it a, this very specific question that I know works in my system, and then you're going to get it, get it going.

Yes, exactly. And so once you have that, like a core functionality working, then you give it this example and turn it into the MCP.

Exactly that.

Yeah. And so, okay, so it got me this, like, this is the path of the virtualenv Python, which is what I needed. And then I'll ask it, like, also, what's the abs path of the Python file? And that's gonna be the 2 ingredients that I need to go make this work. And it's just, you know, running shell commands to do this. So I've already got it there. So let's do this. And I'm gonna go into that, like, existing settings, and I'm just gonna immediately add a new custom extension that we just built. So I'm gonna call this email. We're gonna do that path to the Python file. Oops. And I didn't paste the other one. So let me just go to the terminal and grab this. Um, and there, and we're gonna do, we're just gonna run that server we just created. And that should be everything. Oops, I got an error. Let's see what that is. I'm just gonna paste the output. Um, oh, so there's actually a code error.

Oh, well, there you go.

And I'm going to—

It wasn't good code.

And so, um, I'm gonna tell it this, um, I'm gonna tell it this error and see if it can just fix it.

So I like that you actually say, I got an error, can you help me? Because I just go straight to just paste the error in.

Yeah, it's probably smarter. I, I like spend a little too much time like having a conversation honestly with the LLMs. And, uh, and so like hopefully it can figure this out without me thinking too hard about what's in the file. We can like watch it work, of course. Um, and honestly, I think the thing that it did here is this resource. So it's fixing that, but, um, Yeah. Okay. Okay. So it figured it out at about the same time I did. Yep.

Okay. So this is gonna be the new podcast. It's human developer, AI developer. Who figures it out? Who spots it first?

I, uh, the, uh, from, uh, the debugging flow is like something that I thought was gonna be what kept me employed with the AI writing all these code, but I'm like, oh, it's actually pretty good. It's pretty fast. Um, yeah, so we're gonna try that again now that we've edited that file. I'm gonna interrupt it cuz again, I think it's doing some extra stuff we don't need. And I'm just gonna, um, toggle that. Where did it end up in the list? I'm gonna toggle that on again. Oops. Okay. New error. Sorry about that. Normally this works in one shot actually.

Do you find as, as an engineer that you have shifted your debugging strategy to this, which is just, I'd rather loop with the, the agent and just get it to figure it out than sit here and use my eyeballs and my fingers to solve this thing. I'm just curious how that's happened internally to yourself.

I think I basically, I almost always let the LLM try once. Like if it, if it knows what to do to fix it, I'm like, great, that's faster than me figuring out. And then the thing that, um, I think I am better at than it is, is like just being tenacious. So if it's like, if it's not figuring it out, it's like, okay, I will take over. And, uh, so this version is like, I, I'll take a look at like whatever idea, um, it's coming up with and if it like seems like it's in the right direction, I'll let it finish.

We did it.

Hooray. Um, great. So now that we've got the, like, env vars loading, and we actually, so what we just did is we, like, turned that thing we just built on in the existing chat.

Okay.

So it's, it's an ex— it actually should now see it. So I'm gonna say, like, great, we just enabled that. Can you see the new tools? Um, try sending a test email and let's see if that works. So now we've at least, like, got it running and yeah, it sees the tools, right? So, and it's, and it's trying this like get email status, which doesn't, we don't need that. It, but it's like, great. It sees that everything is ready to go. And then it's like sending me an SMTP email test. Sending you a test email.

Yeah.

And it is success. And so it's working. And so we kind of built it, plugged it into Goose, and then immediately tested it out. And let me go check my inevitable spam folder. Yep, there it is.

You did it!

Great. So despite all the spam checks, uh, we've got that working. And then, and then now we can kind of finish that off by bringing it back to where we started, right? So we know that works. I'm gonna go over to this existing chat. And this, these are 2 separate conversations, right? So like this, this LLM knew all about everything we just did. I'm gonna close that out. This one has no idea that that just happened. So I'm just gonna go in and toggle that on and off real quick. And we're gonna go back here and I'm gonna say, I just enabled an email tool. Can you send that payment link to my demo email address? So, it doesn't have any of that, like, it doesn't have to— its memory doesn't have to worry about it.

It doesn't know the functions or any of that stuff. So, this is the real test.

Yes. So it has no idea other than that this thing just got created and it's doing the same thing that the other one did, which is like, okay, it's, it's like checking and knows where it's coming from. And yeah, hopefully we'll get it to send something. At this point it's like writing more content, right? 'Cause it's like, I'm sure it's gonna try to be cute about the, the like body.

Are we gonna get a, are we gonna get a pumpkin emoji in the—

Right. Yeah. That's just, that's what I'm hoping for. Um, okay. And we should have something in spam. Yep. And so we've got produce store payments. We've got our payment link, um, order details, all of that stuff. And you know, this same link should bring us back.

So there are 2 levels of faces I make on this podcast. This, this is one of them where I go, ah, and then this is the other one where something works. So you've gotten the, the raised hands. Podcast reaction. So just to recap everything Goose did for us today, I'm like still still processing. Jackie, we got your CSV. We took some like dirty produce data, got some insights on how to make it better. Brad, you took that and populated a store with your produce items, then created a way for people to. buy those items through a nice little friendly link. Then you vibe coded an MCP live. Thrilling. As somebody who's avoided building an MCP for no less than 6 weeks, now I feel like I have no, no excuse other than to just go do it. And then you use that MCP to send somebody a pumpkin purchase email.

That's right.

This is so great. Okay. This has been, uh, incredibly fun. I have to ask you both as part of our lightning round questions. Jackie, what's your favorite MCP tool in your stack right now?

I hate to say it, but it's probably, uh, Google Drive. That is, as a manager where I live, and I'm just so happy to be able to not have to copy and paste and click and save me a ton of time.

Okay, Brad, what's your favorite?

Definitely Shell. I just, you know, I've lost, I've lost all my memory of the arguments to various Shell commands. It's just 100% filling them in for me at this point.

Okay. And then if you could make a pitch to people inside an organization that are hesitant about getting started with AI, or even people that are outside of technology that just say, I don't know, how could this apply to me? Why would I use it? Seems really complicated. What would you, what would you say to them?

Find the thing that you don't like doing and automate that. Like, don't, don't, if you love programming, don't try to automate your programming. That's like fun for you. You maybe figure out when, when you wanna make that trade-off, but it's like, oh, I hate writing the unit tests. That's the thing I'm gonna have the AI do.

Okay. Get rid of toil. Spoken like a true, true engineer. Jackie, what about you?

I think really treating it as a learning experience and realizing that this is the worst the models are ever gonna be. And so even if it's not something that transforms your daily life right now, even by keeping up with the tools and seeing where it fails, seeing where it fails is like just as useful as seeing where it succeeds and still really treating it as an experiment and a, a way to learn versus, you know, a magic bullet that's gonna magically solve everything.

Okay. And then Brad, we saw how polite you are. We have to ask both of you our favorite question, which is when AI is not doing What you want and you're starting, maybe you get exasperated. I don't know. What's your tactic? You seem very polite. So every, I saw every line started with great, but you really screwed that up. So Brad, what's your, what's your strategy here?

I, so I thought this was kind, but I realize now it's even more brutal is, is that like the moment it's not good, I just, I just throw out the session. I throw out the whole convo and I start over. And it's like, I, cuz I want, like, it's so much work to convince it to do the thing you want. It's so easier to start, so much easier to start over. And then I'm like, oh, I guess that, that like that thread with that model, it's just done forever now. So it's kind of sad, but.

Can you imagine if we did this at work where somebody just started to say something that was totally wrong? You just like shut the door in their face and said, come back and just start, start totally, totally over. Okay. Uh, Jackie, what about you?

When it's not functioning right, definitely start a new, uh, session. But what I like to do is, uh, have it, I'll give it like really keywords that I want if I want the style of something to change. So, you know, if something comes out like too NBA Like a lot of times I'll say, write it like you're a hacker. Um, another thing I like to do is have it like summarize everything that it did, the good parts, and then like take out the bad parts and start a new session based on those instructions.

Great. I, uh, have learned so much myself. I'm gonna go download Goose and build that MCP that I've been avoiding. Where can we find more information about this? Where can we find you two and how can we be helpful?

Yeah, definitely check out block.github.io/goose. That's like the homepage, all the install instructions, and it should lead you also to our like GitHub GitHub repo where we love contributions. Um, and if you want to chat with us about it, we run a Discord for Block Open Source. So get in there and like all of us are in there and we, we like debug live and all that kind of stuff. So a lot of activity for sure.

Amazing. Well, thank you so much for sharing all this on the show.

Thanks for having me.

This was fun.

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