The Changelog: Software Development, Open Source - What even is the modern data stack (Interview)

Episode Date: July 17, 2024

Benn Stancil's weekly Substack on data and technology provides a fascinating perspective on the modern data stack & the industry building it. On this episode, Benn joins Jerod to dissect a few of his essays, discuss opportunities he sees during this slowdown & explain why he thinks maybe we should disband the analytics team.

What up nerds, I'm Jared and this is The Change Log, where each and every week we sit down with the hackers, the leaders, and the innovators of the software world, and we pick their brains. We learn from their mistakes, we get inspired by their accomplishments, and we have a lot of fun along the way. This episode is a good one. I'm joined by Ben Stancil, the co-founder of Mode, a business intelligence platform that was recently acquired by ThoughtSpot. Ben writes a weekly substack on data and technology that I've been reading for years.

His purview is adjacent to ours, so there's a lot of overlap, but some fascinating differences as well. I hope you love this conversation and learn a lot from it. But first, a mention of our partners at Fly.io, the home of changelog.com. Launch your app close to your users.

Fly makes it easy. Find out moreelog.com. Launch your app close to your users. Fly makes it easy. Find out more at fly.io. Okay, Ben Stancil on the changelog. Let's do it. Hey, friends. I'm here with a new friend of mine, Shane Harder, the founder of Chronitor. Check him out, chronitor.io. It lets you keep tabs on your cron jobs, Linux, Kubernetes, Apache Airflow, Sidekick, and more.

With over 12 open source integrations, you can instrument all your jobs no matter where you're running them. So Shane, for me, you know I'm a user of Cronitor. To me, it is the missing link, in my opinion, to Cron. What do you think? How do you explain it? You know, every other software that a developer creates you can watch it work and you can interact with it you run it from a command line or you have an api endpoint and it's you have logs that get produced and it's really easy to add like an apm monitoring into your api where you can start to get a sense of what your application is doing

internally but when it comes to cron jobs that somehow just was never built until Cronator. CronJobs, you would have to run them at the command line, see that they work, and then just fire them off into the ether and let CronTab run them. And the only way you could know if they're working or not is by looking at the database to see if the thing did its job.

Or if it's like, maybe if it's supposed to upload files, maybe you would just go check that the files are uploaded. But that sort of verification doesn't scale. It's hard to write tests, like end-to-end tests that do that in production. Even if you can, then they're bound to break eventually as the cron job breaks. You know, if you're testing a specific bucket for a file, if you're checking that file gets uploaded, you know, soon enough that bucket's going to change or the file's going to change,

and then your test is going to break. Rather than just looking for the side effects to know that it's working, Chronitor is actually watching every crown job execution and reporting back to the cloud service when your job runs, starts, or fails, along with like telemetry, including like the full log output. So when it does fail, you've got like the metrics and the logs that you need to dig in and understand why and debug it and fix it. So I'm using Linux and Linux cron jobs are by far the most popular in my opinion, right? But there's so many other cron like things, Kubernetes,

Airflow, Sidekick. Help me understand the full spectrum of background jobs and cron jobs beyond Linux cron. Yeah, Linux cron jobs are massively popular. They are still 40 years later the tool that most developers will go to first when they need to start scheduling something in the background. But when you get into a team environment or an enterprise environment, there is a lot of other constraints at play and there's other considerations and whether it's simply like redundancy that you're not going to get from crontab itself or you know more like complex orchestration stories like you can get with like airflow. We see companies eventually outgrowing cron and what we wanted to be sure of is that first of all like migrating from cron to anything else is a complicated thing so we wanted to give you tools to help you monitor that transition and make sure your jobs are working good as you as you do that migration you know and then second we wanted to give you a way to unify all these

different job platforms because seldom do you have just like platform a and you migrate cleanly to platform b probably in a in a real world scenario you're running both side by side for a while. You don't want to have different monitoring tools or different monitoring strategies for every different platform that you deploy. So our goal is anywhere you're running a background job, you can use Chronitor. The number one way that we ensured that was possible is by having a really simple API that you can just use with an HTTP request yourself, which is pretty abnormal for monitoring tools, but that works in a lot of cases. But to make it easier then, every popular job platform out there like Linux CronJobs, Kubernetes CronJobs, Windows, Sidekick, Airflow,

you name it, we have a Cronitor SDK that you can install that will run automatically, configure your monitoring, run in the background, and sync all your jobs with Cronitor the same way your Linux Cron jobs will be synced. Okay, friends, join more than 50,000 developers using Cronitor. I'm one of them. You can start for free, and they have a pay-as-you-grow pricing plan. Setup is too easy with more than 20 SDKs. Check them out at cronitor.io.

That's C-R-O-N-I-T-O-R.io. Again, chronitor.io. I am joined today by Ben Stancil, whose sub stack I've been following for quite some time. I know it's been a while now because I had to go look up some of my favorite posts and I had to scroll quite a ways. Ben, you are a writer in the data world. I'm not sure how you refer to it. Talking about the modern data stack a lot. Things that I barely understand, but I really enjoyed your writing. And I thought our audience might enjoy some of your topics as well. So thanks for joining us on the Change Log. Yeah, for sure. Thanks for having me.

So if I got your story right, you were in the data world for a while. You were a hesitant writer, and then you started this company called Mode. And in the early days of Mode, you started writing more for the Mode blog as a co-founder. And I don't know if that's where you found your niche, or somehow you got inspired to write your own thing later after Mode was established. I know you've sold that to somebody else at this point. But tell me about your journey into what is now a weekly authorship. Is that what I'd call it? Maybe it's just a blog.

I don't know. It's a sub stack. You're writing weekly, long essays, really, and pretty consistent with it. Can you tell the story? Sure. So like you mentioned, I started a data company as a BI tool, basically.

It was called Mode about 10 years ago. When we first started it, there were three of us. One person was the CEO who was a good face of the company and out talking to investors and customers and things like that, and was the sort of person that we could proudly stroll out as the external face of what we were building. There was a person who was the technical co-founder, really, who was chained to his desk building a product. And then there was me, who was neither of those things, who neither was an engineer nor was sort of fit for external consumption, I would say.

I didn't have anything to do. Why I was a founder there, who knows? That's a question you have to ask them. My job, basically, my background was in data and as an analyst and things like that. Really, what I was doing was representing the customer in a lot of ways, where the product we were building was for people who were like me. I was, to some degree pm-ing things or helping the person who was the engineer like you know do the here's what we think we should build and testing stuff out but that leaves you with a lot of time and the early days when you're basically moving as fast as one or two engineers can build it and so i had a lot of time to spend on stuff and so i started doing basically writing a blog that we didn't really have a grand plan behind it,

but what it ended up being was kind of like five 38 style analyses of pop culture. It was, we wanted to do something that would get mode some visibility. I kind of just started doing this because I needed something to do and it was kind of entertaining to me. And so the very first blog posts we ever wrote were things about like Miley Cyrus and the VMAs, like the Video Music Awards and various things that were just like interesting things to me going on in the world

from a like data-driven perspective. So it was sort of like, here, let's take a data-driven look at X thing or whatever. People seem to like it. It, I think, worked because this was a time when content marketing and sort of having company blogs was becoming pretty normal. But most of those blogs would be sort of transparently thought leadership with the intent of somebody clicking on the bottom to say, download our white paper or check out our product or whatever, where it would be like, here's five tips to build your engineering team. And then the fifth tip would be like, use our products or whatever. And so this was not that. There was no real call to action for anything related to mode. It was just like, here's a bunch of charts about the baseball playoffs or whatever. And so if you

seem to like it, I enjoyed it. I thought it was kind of fun to do. And the writing part of it was something I'd never really done, but it was like, this is kind of interesting. Eventually, within, I don't know, six to nine months of starting doing that, mode grew, my job expanded, I started doing a lot of other stuff. I started having customers. I basically didn't do it for that long because there came a point where writing a blog about Miley Cyrus is not the most important thing that you can be doing to grow a startup. Honestly, I think actually probably it should have been the thing

that I kept doing. It probably was more important than me answering customer support tickets and somebody else could have answered customer support tickets. But at the time, you certainly don't think that. And so for a long time, this was always a little bit in the back of our minds of like, we thought that was a fairly successful thing. At some point, I'll get back to writing that sort of stuff. And honestly, it took, we thought it would take two years. It took, I don't know, eight. But there was a period much further down the road with Mode where my job at Mode basically bounced around to a bunch of different things. I was kind of like, the internal joke was my title should have been chief interim officer, where I basically filled

the roles of whatever executive we didn't have. So it's like, oh, we currently don't have a head of marketing. Go do that for a bit. Or we currently don't have a head of support or product or whatever. Go do those sorts of things. At some point, we had hired somebody to do all those things, and we sort of built out the executive team, and a lot of the sort of good and much better leadership than I was was in place. And so I was like, all right, I now have a little bit more time. I'm going to go back to doing this blog stuff. The original intention was to do what it was back in the early days of, all right, I'll write these kind of like data-driven things. For whatever reason, that's not exactly what I started doing. Like I had all these rants essentially about the industry that we had been working in.

Right. And so I wrote a handful of those posts. People liked some of them. And at some point you get sort of captured by your audience a little bit, I would say, where you recognize that these are things people like, that you have stuff to say or stuff that you kind of entertain yourself with saying, and then at some point it kind of takes on a life of its own. So there is no sort of grand plan behind it.

It never really became a product marketing thing or an actual marketing funnel for Mode. It just became a thing that people kind of paid attention to, and so I kept doing it, and that's as far as the plan goes. Is there an end game? Do you have an end game to this? Is it just like serve your audience forever until, you know? Yeah, no, there, there is definitely not an end game. Like I don't, that doesn't mean it's like, do I imagine I will do it forever? No,

probably not. Um, I don't know what would be next, but no, there is no, there is no, okay. It's time to initiate step two kind of thing. Like step two is it's Friday to initiate step two kind of thing like step two is it's friday and i guess i'll publish something well i'm often impressed by how reliably you publish on fridays but also how deep you tend to go into your thoughts and your rants i would i would characterize your writing as somewhat irreverent definitely pop culturey sometimes meandering and i say that in a positive sense, although some people don't like meandering,

but then always coming back to the point. I've enjoyed it even being a bit askew from your world. I think our worlds overlap but aren't one-to-one, and so oftentimes I find myself just kind of like with a view into the world of BI and data and whatever they're calling it these days, the data cloud, I've heard recently. You seem to refer to the modern of BI and data and whatever they're calling it these days, the data cloud, I've heard recently.

You seem to refer to the modern data stack a few times. Can you define that? Is that a real thing? Or is it just like everybody who's in the industry knows what it means and nobody else does? It's actually sort of yes to all of those things. It does not have a quite clear definition. The most accepted definition of it, I would say, but are kind of like specific point solutions for solving data warehousing or moving data between certain tools or data visualization or BI

for a particular subset of types of consumers versus sort of technical tools for data analysts and data scientists. Like there's a lot of products for each one of these kind of narrow verticals. And there was a period of time where data was one of the hotter kind of like VC spaces. It was a place that you could raise a lot of money. And so there was a lot of like draw of getting customers or getting people to like found companies in that space. And so a lot of people who were, myself included, the mode sort of predated this fad.

But probably in the 2017 to 2021 phase, if you were a data practitioner, an analyst, or someone who'd worked on an internal data tool, let's say a Facebook or an Airbnb or whatever, it was pretty straightforward to go out and say, we're going to turn this thing into a product and we're going to go raise money and you'd get pretty good valuations and all that sort of stuff. And so, and there was a collective sense of like, we're all like somewhat in the same cohort. The modern data stack roughly refers to that. My view of it is the sort of like jokey, but actually sort of serious definition of it to me is it's data tools that were launched on product hunt. There's the irreverence right there.

I mean, yeah, I get it. Yeah. It's like product hunt was sort of a marker in time. Like there was a point at which that was a big thing. And now I guess it's still there, but people don't really focus as much get it. Yeah, it's like Product Hunt was sort of a marker in time. There was a point at which that was a big thing, and now I guess it's still there, but people don't really focus as much on it. It also is like a particular type of tool gets launched on Product Hunt. Oracle doesn't launch their stuff on Product Hunt.

And the modern data stack typically is not referring to Oracle's latest releases. It's referring to very Silicon Valley-oriented, bottoms-up things that have some ambition to build great user experiences and product-led growth and all that kind of stuff. And all of those ideas are sort of jumbled together. But the modern data set is basically some collection of that set of things. Right.

So another thing that I guess is in my world, but not exactly as a software developer, which I find a lot of parallels, is the cloud and the Kubernetes world. So I don't know if you're familiar with Kubernetes and that whole ecosystem of cloud native things. But when you go to KubeCon and you attend that event with 3,000 to 5,000 people, and that's all open source projects and open source companies and all these things. And you're like, wow, the money is here. This is where the money is in the open source world. This is where the commercial open source companies are.

Everybody else who's not in the Kubernetes land or the cloud native land, we can get some money with our open source companies, or maybe not. And we struggle. But the money is there. And I felt like for a very long time in the world of data and like led by snowflake at least publicly and there's all these companies that are like either ipo'd or like well-funded creating data tools for data analysts and i felt like the money is also there that's the parallel i'm drawing like for a long time the money was there one of your recent, I guess it was this year even, yeah, January, it's time to build,

in which you're referring to a change, a shift in your world. And I think it was a shift of open AI, chat GPT, large language models, and really the hype that has moved into and stayed, at least for now, in the AI world, did that suck a lot of the money and the air out of the modern data stack? Yeah.

Yeah, a lot. And so the Kubernetes parallel, which I don't know, I'm not familiar with Kubernetes, I certainly don't know the vibes of Kubernetes conferences. But there definitely was a period of time, to some extent pre-pandemic and very much so in the 18 months sort of post-pandemic bottoming out in mid-2020 to market turning in whatever that was early 2023 for that one and a half, two year period, there definitely was the same kind of like, this is just a crazy amount of money in the data space, lots of people starting companies, raising obscene valuations, numbers that are, you know, infinite multiples, essentially,

of companies raising at nine and 10 figure valuations on, on a handful of millions in revenue or even less in some cases. And so, yeah, there, there were, there's a particular line. I remember from a conference, there was a conference. I want to say it was the first conference post pandemic that was in person. That was like the first one that really was like, all right, let's go out and do this stuff. I think it was early 2022 where it was a pretty big conference. It was probably 1500, 2000 people. So it's not huge,

but like not some tiny meetup. And somebody asked kind of this question of like, how many $100 million businesses are there to be built in the space? There was a panel of like VCs or whatever. And the answer the VC gave was basically like infinite. It was like everybody in this room could build a $100 million company in the data space. The data space is that big. And that was sort of the attitude was like, this stuff is all huge. It's all going to be like these enormous companies. Snowflake just had this IPO and was like the biggest tech IPO in a long time or ever, depending on how you kind of count it. Then two things really happened that sucked the air out of the room kind of at the same

time. One was the market turned. And so there was just a lot of like, oh, wait, maybe this was a big bubble. Maybe the company that is making $50,000 a year isn't worth half a billion dollars. Like there was some just like, oh, wake up from that fever dream. There was also AI, like suddenly all of the VC interest turned from, oh, data stuff is going to be the future to AI stuff is the future. And so people got kind of hit twice with the market's going to put a lot of pressure on

things, but also it was just like, wasn't cool anymore. It wasn't the, it wasn't the place where VCs aren't hosting dinners for data founders. They're hosting dinners for AI founders. Everybody had to talk about why they're an AI company, not a data company. And so like, it became much more of just like a, okay, this is just one of the pieces of the tech industry the same way that CRMs are or marketing tools or like back-end finance infrastructure. Right. Data is just one of those sets. And so like, fine, that's probably good. It's probably good for us building better things.

As me, as someone who yells on the internet about this stuff, it's not as fun to yell about because it's not, I don't know, like part of the appeal of it is there's energy in it. And so that post ultimately was kind of like, look, I, is it fun to have a blog about CRM software? No, it probably isn't. Well, in that post, you confessed how you had changed even the content of what you've been writing about because, well, you're going to follow what's interesting.

And of course there's parallels, or I guess there's touches between AI and between the data world. You wrote this in that post, which I thought was, for me at least, was the nugget that I thought, okay, this is insightful. Not that the whole thing wasn't insightful, Ben,

but this part, I thought I would read back to you and have you expand on. You said, though, it can be demoralizing for the air to leave the room. There's a lot of opportunity in the slowdown. Startups just need to change their tactics. Don't build something new or go after major incumbents. The wilderness is too hard to tame and the cities are too hard to conquer with a lot of money. The better targets are the helter-skelter frontier towns built by frenzied founders who wanted to stake their claim on any piece of open ground that they could find.

I found that to be insightful, and I'm curious, what are the Helter Skelter frontier towns? I like the wording of that, but I'm not really sure. Are there concrete examples? This is like where people have kind of staked a claim and then they've failed and moved on. Is that the idea? So taking the modern data stack, for instance,

the primary thing that the modern data stack, for instance, the primary thing that the modern data stack sort of did was say, like, let's move a bunch of data tooling

to the cloud. Like, that's probably the biggest thing that it was, its biggest sort of philosophical bent was

cloud-based SaaS software versus some sort of like heavy infrastructure that you buy from Oracle

or Microsoft or whatever. And so, there's a lot of things that people did to make that easier. Data pipelining can be moving from SaaS rather than these big, heavy data pipelines

that you have to write yourself. It's sort of push-button stuff from SaaS products like Salesforce into data warehouses and easier ways to be able to share data back and forth between other tools or share data between how does a data team share their results with a marketing team easily and stuff like that and all those sorts of things. So there's a bunch of stuff here that, like, the way that data teams work kind of changed. They also became, there's this sort of new team, like, prior to 2010, data teams were kind of either capital D data science teams that were doing hard math or they were kind of like business intelligence reporting teams that would be building just like binders of reports for executives there also kind of was this rise of like the the analytics team that was supposed to help people make better decisions by doing a bunch of analysis that wasn't we're going to build some crazy model but was like we want to help you

decide how to be smarter so like the easy analogy for this is like sports teams. Like there's analytics teams and sports franchises now that are scientist-y types, but they're not saying like we're going to build some crazy predictive model that we wouldn't plug into anything. They're saying like, oh, we want to help our coaches make better draft picks. Businesses try to do the same thing. They've got a bunch of tools built for all of those things, for those people, for those different workflows, things like that. They were kind of new and novel approaches to it. But during kind of the frenzy years, those things, one, are all new. And so they're still figuring out how to make it work. They're still figuring out like, what's the best way to do this and move it to the cloud? What's the best like experience for that? All that kind of stuff. And because there's so much money in it, people are trying to move really

quick. They're trying to like have some, they're like, we got to build a giant business. And as a result, you build kind of shoddy products. They're not bad, but they're the frontier. You're figuring out what to do. You don't really know what works. The ground is changing underneath you some. So for instance, Mode got built, started originally in 2013. There's this really popular open source tool called DBT that's like a data transformation tool

that helps you define how to basically like build data pipelines that thing got popular and became pretty ubiquitous 2017 2018 now what mode does would be better off served if it also kind of understood the way that dbt works because a lot of customers are using dbt however mode was built before that and so if we were to rebuild mode today you'd kind of rebuild it knowing that, okay, there's this new set of technologies that most people are using. There's a lot of examples of that sort of thing where products got built and then the ground was still evolving and everything was still changing. And so like, they're not quite built for the world that exists today. And so I think there's, there is an opportunity now to basically say, okay, the ground shifting has settled. Like the,. Like the earthquake is sort of over. There's a bunch

of half built buildings that were built on the ground before it shifted entirely to where it is. And they're a little bit shaky and all that kind of stuff. And actually you can now go back and basically say like, we're just going to build what those things built. Like the ideas were good, but they were early. They were products that were still figuring out how to do it. They were products that were built for a slightly different time. Let's just say we take the really good ideas and rebuild it for the landscape as it exists today. And you can't do that when everything's crazy and there's so much money everywhere. But when things are calmer and more settled, there's a lot of opportunity to do that. Yeah, kind of a second wave of products, having learned the lessons of the first wave.

So are most of them like Mode, where they were acquired and now owned by something else and maybe brought in? Or some of them like Mode where they were acquired and now owned by something else and maybe brought in? Or some of them have small customer bases and they're chugging along? Or are there ones that are actually dead on the side of the road? Is it a whole mixture of all those things? It's kind of all of it. I think that you could have examples of all of it. There are some that got acquired for huge numbers.

There are some that got acquired for less huge numbers. There are some that got acquired for huge numbers. There are some that got acquired for less huge numbers. There are some that got acquired in fire sales. There are some that are dead along the side of the road. There are some that are walking dead that are going to be dead in three years but managed to raise enough money in the good times to keep chugging. There are some that will probably make it through that that are semi-walking dead but will figure out some way to make it work there's probably a lot that are just gonna not ever quite figured out and not be able to grow into the evaluations that they had so i think it's in some ways i think this is just like the silicon valley circle of life that the same is going to happen with ai i'm sure there's thousands of ai companies that one were built for kind of the same thing, built for a world in which AI models were GPT-3,

that was the best they were, or they were built for us assuming we're all going to use RAG. I don't know. Maybe we do, maybe we don't. But it's certainly possible that RAG is a fad, that in two years, actually, there's something way better that makes way more sense. Everything that was built for RAG, we'll have to sort of figure out what to do.

And I think that's basically what happened with the data world, except instead of RAG it was a handful of other paradigms that we evolved our way through. So there'll be a lot of companies there that figure out places to land well. There'll be some that figure out how to survive and thrive in the new world.

There'll be some that'll die. If you're interested in executing on this advice, A, is there still enough money floating around to where you could raise if you had a solid plan or would you have to bootstrap? I guess I'll stop at A and let you answer that before I ask B. Yeah, I think there is. I mean, I think that there's still a lot of money in venture. You might have to tack some nonsense AI pitch on there to really get people excited. That's unfortunate, but yeah. But I think that if you have,

people are generally aware, I would say, that the data industry is full of a lot of tools that were propped up by the good times of 2021, 2022, and probably are vulnerable in a number of ways. They're like businesses that are not really designed for a slower market. They're businesses that were sort of spending as though there was no tomorrow and stuff like that. They've made adjustments and sort of, they're not still doing that necessarily, but it's hard to sort of restructure a business to that degree.

I think there's a lot of understanding of these products. Some of them are good ideas, but they're a space to reimplement them or just build really good versions of it. I think there's a lot of investors that are chasing the notions and linears and figmas of the world that are kind of this polished craftsmanship version of an existing tool, that I think you can pitch that. You can be like, look, we are the same as these other things, but we're just going to do a really good job of it.

We have this great team. Let us show you the quality of our craft. People will always, I think, you can sell that pitch. So no, I don't think you have to bootstrap. I don't think you can raise at the sort of astronomical valuations by any means. But yeah, I think if you come along with something that's like, yeah, this looks exactly the same as the other thing, but our goal is we're just going to make it really good

and we know how to do that and we're great at our craft, I think there's always money to be had in that pitch. Okay, so part B would then be, imagine that you're not you, you're me. And hypothetical me, because I'm not going to go do this, but a software developer who doesn't understand the data world very well, how would you identify a target to actually go and do something like this? Now, you have to take away your own knowledge, which is really hard to do.

What would I do? How would I start? How would I figure it out? Is that possible, or do you have to be in the world already to know? I think that's really tough, but partly, so there are, there are a lot of products in the data world that are traps that seem like things that aren't too bad to build and can be solved. And like, why doesn't somebody just do this? And my God, they are messes. So, so mode is a BI tool basically. And BI is like dashboards, right? It is an endlessly tempting thing to build because it's just charts.

Charts seem pretty simple. There's some cool new open source library that makes that easy. What if instead of asking questions and stuff, we now have AI? We can do natural language. There's all these things about it that seem pretty straightforward. And it's just, it is a product with a thousand edges that everybody wants a slightly different thing that every customer is going to have slightly different preferences about the way they want stuff to work. Charting, for instance, like visualization is,

it is the biggest cookie you could ever give a mouse where like people will want an infinite list of customizations about visualizations. So I think there's a lot of things that like look easy on day one that once you start to doing them and like, we suffer, we thought like, how hard can this be? And it was like 10 years later, like, that's pretty hard that there are versions of the, the, like that famous hacker news comment when someone released Dropbox were like, well, isn't this just like an FF SFTP thing that I could run myself.

Right. I can build it in a weekend. Yeah. There's a lot of data products that you feel like you can build a weekend and you like kind of can, you can build like the basic data products that you feel like you can build in a weekend. And you kind of can. You can build the basic versions of it. But you can't really sell them. And so my advice to an engineer would basically be build something you really know. Because if you only sort of half know it, chances are you're going to find out there's a whole bunch of skeletons in that closet. And we knew BI okay. And we still found a ton of skeletons in that closet.

And so many people who end up in this space, like there's just skeletons everywhere. And, and it's kind of like, you should make sure you really know what you're getting yourself into. Maybe you're fine with that. Maybe like, I just love visualization tools and I'm happy to do that forever. Great. You can, you can build a great visualization tool if you are willing to invest a ton of time in it.

But if you're like, I can do this quick and get rich quick or whatever, it's a slog. Yeah, get rich slow. What were the hardest parts with Mode? Was it the endless customizations or were there more hairier problems that you had to solve? Well, I think it is the hardest. Technically, there's not a ton that's

that challenging. I think it is a technically complex product because you're basically building like an application that needs to do a lot of stuff that there isn't one, there isn't like one thing they're like, if we just do this one thing really fast and really well and really reliably, then we're great. Like it is a very feature rich thing. And so similar to like a marketing automation tool or CRMs or whatever, like those things just have to do a lot of things. And so

it is technically hard to build that and keep it performant and make it like a good UI and make it understandable. And it's just like easy to make a messy product. I think that honestly, the harder part of BI is, and it's somewhat related to that, is it is a very preference oriented thing that people, the way that you think about data and want to interact with it is probably different than the way that I want to interact with it. And the way that someone who is a marketer thinks about it probably wants to interact with it. And so that's, some of that is driven by like our abilities and our backgrounds. Some of that's just driven by personal preference. They're just like, Tableau makes a ton of sense to me. It's just the way I think.

Or some people will be like, Tableau doesn't make any sense. I can only think in spreadsheets. And so you end up, you end up like getting pulled in a ton of different directions where everybody kind of likes something, but like needs it to be a little bit different. And so it's just a, it takes a lot of discipline to build a product that is really good

for a certain group of people. You end up building often something that's like fine for a whole lot of people. And I think it's, it's really hard to always build something that's just like really good for the subset because there's always going to be some adjacent group. That's 90% the same as a subset you're building. I'm like, you only add this one or two things and they'll love it. And you just drift. It's a fractal. Yeah. And so you end up, it's easy

to basically spend a long time building something and five years into building it. You don't know who your customer is anymore because you've built a handful of features for 50 people. What's up, friends? I'm here in the breaks with David Hsu, founder and CEO at Retool. If you didn't know, Retool is the fastest way to build internal software. So, David, we're here to talk about Retool. I love Retool. You know that.

I've been a fan of yours for years, but I'm on the outside and you're clearly on the inside, right? You're on the inside, right? I think so. Yeah, I'd say so. Okay, cool. So given that you're on the inside and I'm not on the inside, who is using Retool and why are they using Retool? Yeah. So the primary reason someone uses Retool is typically they are a backend engineer who's looking to build some sort of internal tool, and it involves the front end. And backend engineers typically don't care too much for the front end. They might not know React, Redux all that well. And they say, hey, I just want a simple button, simple form on top of my database or API. Why is it so hard? And so that's kind of the core concept behind Retool is front-end web development has gotten so difficult in the past

5, 10, 20 years. It's so complicated today. Put together a simple form with a submit button, have that submit to an API. You have to worry, for example, about, oh, you know, when you press the submit button, you got to bounce it or you got to disable it when it's, you know, is fetching is true. And then when it comes back, you got to enable the button again. When there's an error, you got to display the error message. There's so much crap now with building a simple form like that. And Retool takes that all away. And so really, I think the core reason why someone would use Retool is they just don't want to build any more internal tools. They want to save some time. Yeah, clearly the front end has gotten complex. No doubt about that. I think even

front-enders would agree with that sentiment. And then you have back-end folks that already have access to everything, API keys, production database, servers, whatever. But then to just stand up Retool, to me, seems like the next real easy button because you can just remove the entire front-end layer complexity. You're not trying to take it away. You're just trying to augment it. You're trying to give developers a given interface. That's Retool.

Build out your own admin, your own view to a Google Sheet or to the production database. All inside Retool. Let Retool be the front end to the already existing back end. Is that about right? Yeah, that is exactly right. The way we think about it is we want to abstract away things that a developer should not need to focus on, such that the developer can focus on what is truly specific or unique to their business. And so the vision of what we want to build is something like an AWS, actually,

where I think AWS really fundamentally transformed the infrastructure layer. Back in the day, developers spent all their time thinking about how do I go rack servers? How do I go manage cooling, manage power supplies? How do I upgrade my database without it going down? How do I change out the hard drive while still being online? All these problems. And they're not problems anymore because nowadays when you want to upgrade your database, just go to RDS, press a few buttons.

And so what AWS did to the infrastructure layer is what we want to do to the application layer specifically on the front end today. And for me, that's pretty exciting because as a developer myself, I'm not really honestly that interested, for example, in managing infrastructure in a nuts and bolts way. I would much rather be like, hey, I want an S3 bucket, boom, there's an S3 bucket. I want a database, boom, there's a database. And similarly, on the front end or in the application layer, there is so much crap people have to do today when it comes to building a simple CRUD application. It's like, you know, you probably have to install 10, 15, maybe even 20 different libraries. You probably don't know what most libraries do. It's really complicated

to load a simple form. You know, you're probably downloading almost like a megabyte or two of JavaScript. It's so much crap to build a simple form. And so that's kind of the idea behind Retool is could it be a lot simpler? Could we just make it so much faster? Could you go from nothing to a form on top of your database or API in two minutes? We think so. Yeah, I think so too. So listeners, Retool is built for scale.

It's built for enterprise. It's built for everyone. And Retool is built for scale. It's built for enterprise. It's built for everyone. And Retool is built for developers. That's you. You can self-host it. You can run in the cloud. And custom SSO, audit log, SOC 2 Type 2, professional services. Starting with Retool is simple, fast.

And of course, it's free if you want to try it right now. So go to retool.com slash changelog. That's R-E-T-O-O-L dot com slash changelog. All right, let's go up a level now and talk about this other post. Disband the analytics team. So up a level, I mean, not talking about this other post, disband the analytics team. So upper level, I mean, not talking about building the tools,

but is the whole endeavor worthwhile? This post is where I was just like, get out my popcorn. He's going after his own industry, the industry that he's a part of. You kind of liken it to a Ponzi scheme, but you say it's not really a Ponzi scheme, but then also you drew the analogy to a Ponzi scheme. And some of this resonated with me because I'm not a big data guy. Like I, I can see narratives well illustrated with data, you know, like you say a thing and then you show a thing. And I've always enjoyed that. But when it comes to like data oriented decision-making,

I always find it kind of like, I don't know, the cart and the horse or the chicken and the egg. I feel like the tail's wagging the dog. As I try to actually make decisions based on data, that's just my personal experience. And this post here, you're talking about, do analytics actually do what we all say they'll do in practice, which is inform you to make better decisions. This is like the version of the analytics team

you're talking about where they're informing the leadership to make better decisions. And then it's like, is that actually doing what it's supposed to be doing? And you say this in that post, analytics not as an industry or a technology ecosystem, but as a discipline might not work the average company may never be able to make better decisions by hiring a team of average

analysts we can make dashboards and be operational accountants but the fun exploratory valuable work may always be an indulgent empty dessert and never the entree that we want it to be i know you have a follow-up post of that i know know that you're, you're very much analyzing and, and considering your own industry, but quite a question. And one that I was like, dang, I mean, is the whole thing a pipe dream? What do you think? Yeah, kind of like, I mean, it's like individually, yes. Or individually, maybe not collectively. Yes. I think that that, so, so the, the backstory a little bit is, and anybody who's worked around, anybody who's worked like sort of anywhere, but certainly worked around tech and stuff, knows that like data has been for a long time this kind of like promise of smarter organizations. That like data, companies have to be data driven or die. And like, that's to some extent, I think that is back to the sports bit. Like there is some, Oh, look what's happening with sports.

If these people don't have data teams, they're become like terrible sports teams and every company is going to have that same evolution where if you don't money ball like your business, then, then you will be dead. And so analysts for a time and like data scientists kind of,, this was the sexiest job of the 21st century thing. Part of the reason for that was, this was a famous blog post by a couple of people, DJ Patel being one, who's the guy who coined the term data scientist, that data science

is going to be the sexiest job of the 21st century. And generally, there was a mindset a while back that these people are going to change the way everything operates. And just being smart with data was this huge advantage that everybody eventually needed to to be able to take advantage of and it's kind of been like a no true scotsman things like the no true scotsman thing is like you say this person's a scotsman but they don't like whatever and it's like well no true scotsman wouldn't like that and so like you basically just like reject anything that isn't a Scotsman by saying the person that you're talking about actually isn't a real one. Everything with data has sort of been like,

if a data team struggles to get stuff right, or like a company doesn't, tries to do this stuff with data, but it doesn't work. It's like, well, they're not doing it right. That's not a true data team that's like doing this the way that we all know is possible. And I think like this has happened for long enough and enough companies have sort of like put investments in their data infrastructure and are like not that much better at how they operate that I think you have to start wondering if it's worth it. And to me, that isn't necessarily saying like the potential isn't there. It's not necessarily saying like if you could interrogate this data so smartly, you may find things that actually make you better. You may actually be able to find patterns in this that could make you a fundamentally better business. I think like, yeah, maybe that's true.

I don't know. The problem is it may be that it's so hard to get at that most people don't have the ability to do it. And it's just such a hard problem to do that the idea that we all need to do this is like it's just not going to work. It's just like as a general discipline, it's not valuable unless you are like a very, very, very sort of top tier analyst or whatever. And there's not that many of those. And so for most of us who are trying to do it, it's like, eh, we're just not being that useful. Like, we don't really say this about other disciplines. We don't really say like HR is the thing

that every business needs to have an amazing HR team. There probably are some companies that HR truly is a differentiator. That they truly are better businesses because they have great HR leadership that really genuinely does, like the appeal of working here is like, HR has built this and made this amazing place to work. I am sure that is true. But we don't really think of does, like, the appeal of working here is, like, HR has built this and made this amazing place to work.

I am sure that is true. But we don't really think of it just like, okay, but most HR thing is, like, do the job, it's fine. It's possible data is that. It's not a transformational thing. It's not a thing that most of us can do as well as, like, the top, top-tier people. And so we shouldn't try. We should acknowledge that, like, we're just kind of out here doing the kind of

mechanical work, and that's the extent of extent of like the ceiling that we have. And so I don't, I don't know that I fully believe that, but I like think I do. And really, I think it's like an acknowledgement, like, it's more of a question to me of just like, we've had this data is the new oil type of stuff for a long time. And it's like, in reality, maybe data is just a, it's really low octane. It it's like if you're willing to invest a ton of money in it you can actually extract some energy from it and if you're google or you have like a very particular problems you can probably actually make that extraction worth it but if you're a random business like i don't know is this i think the analogy i've used before

is like a peep bog like is peep that useful not really you probably don't want to put that much investment into getting a bunch of peak because it's kind of like hard to get anything real value out of it so yeah i think it's we've talked about the promise of data for so long that we're still kind of waiting for it that at some point you got to wonder if that's ever actually going to materialize yeah that's interesting i guess it's kind of disheartening because because like the story makes total sense right like that. And even the heroic moment in which the causation was, you know, attributed to this thing that was only found

because of this dashboard that changed the course of the business. You know, like those are the stories that we tell ourselves. And like you said, I'm sure those things do happen from time to time. But that is a promise, you know, because there's a lot of money that

goes into it. There's a lot of time. There's a lot of risk. I mean, data as a new oil also been called like data as the new toxic thing that you don't want to have because there's all kinds of drawbacks to holding onto other people's data. Yeah. Is it all worth it? And I mean, if it's, if it's not worth it, then holy cow cow but if it's maybe kind of worth it then it goes back to like well do we need an HR department and then do we need a data solution or do we need

what do you guys call yourselves do we need a modern data stack do we need BI I mean I think maybe the answer is like low hanging fruit for everybody you know because there are things now where it's relatively attractable to get a certain amount of insights that everybody gets for a relative amount. But the super deep,

expensive, I don't know what it looks like, data stack is the one that maybe people in the future will opt out of or I don't know. Yeah, and I don't know that you need, it doesn't mean like data is useless. It's like, yeah, reporting is valuable. You got to know how much money you're making. You got to know how people are using stuff.

Like all that stuff I think is, it is really useful to do that. But a lot of times, and data teams do this themselves, and this is like obviously just something to be self-serving. Reporting is seen as, and I think this is sort of mentioned in the little bit that you read or sort of referenced. Reporting is seen as kind of the prerequisite to doing the important stuff. That a lot of times people will say, the point of things like BI is so that data teams do less reporting and work on more valuable, high-impact work. And this high-impact work is the thing you said. We found the nugget, the bit of insight

that caused the pivot, that changed the business, and all that stuff. And those stories do happen. But so much of what data teams do is, how do we get these prerequisites off our plate so we can work on this valuable stuff? And I think oftentimes they get to the valuable stuff and it's like, there's not that much value there.

They never actually deliver on it. So I get why if you're a data person in this camp, I get why if you're a data person you would want to do that. It's the fun stuff. It is fun to go digging through things and trying to find stuff and finding these stories to tell. That's what I did in the very beginning of Mode was writing blog posts about this with my hires. But I don't know that a lot of times those answers are, they're not trajectory changing things for the business they can be sure but like it's hard i do love those narratives that have data behind them though do you remember the old okay cupid ones oh yeah that was they were yeah that was great great yeah and that's fun like that

that it's it's fun to do that stuff and so we all like there's some appeal to it, but is it that valuable? I don't know. Maybe. Maybe every once in a while. In light of all these two discussions that we've had so far, the first one being the air is sucked out of the room to a certain extent. There's opportunities in the slowdown, but they're not going to be the new hotness necessarily. Everyone's focus is on AI.

And in light of the fact that analytics and BI the new hotness necessarily, and everyone's focus is on AI. And in light of the fact that analytics and BI as a, you call it a practice, as a discipline, that's the word, is maybe not all that it cracked up to be. What are you interested in today here, mid-2024? You're looking forward, you're doing stuff, I'm sure. It looks like you're in an office, so you haven't completely gone to Mojito Island based off of your mode sale or anything. What's interesting to you these days?

To the extent that I'm interested in the data space or the adjacencies around it or whatever, I think it's just the dynamics of a big ecosystem like this are kind of fun. I was talking to somebody about this a couple days ago. Data itself, I am not someone who is attached to data itself. Some people are like, look, I just love SQL compilers. I think they're fun.

SQL compilers are awesome. I'm like, great. Some people love data pipelines and think the idea of just figuring out how to move big things around faster and reliably is cool. I'm like, okay, that's cool. I am neither of those things. I do think like the OKCupid type of blog analysis stuff was kind of the first attraction I had to the whole data world. And that's, that stuff is fun. So like just being an analyst, I think is actually kind of fun, but in like the tech ecosystem side of it, part of the appeal and part of the things that like the reasons I wrote about this stuff, the main reason was because I was working in it for a long time.

But I think the thing that made it interesting to me was it's a big ecosystem with a bunch of products that kind of have weird places to go and we're trying to figure out what to do with them. And it's like you have all these parts that you need to bring together in some way. And there's like a Rubik's cube of sorts to figure out there. And I think that's interesting. And I think to the point of the, like, the air has been sucked out of the room. The gossip of Silicon Valley is interesting. Like, just the various, like, dynamics of such and such raised money at this crazy number. Now there's crazy stuff.

Like, FTX is a fascinating story. Not because crypto is interesting, though it kind of is. But it's like, because it's just a crazy stuff. FTX is a fascinating story, not because crypto is interesting, though it kind of is, but it's because it's just a crazy drama. I think that AI is interesting to me, not so much because it's like, oh my god, this is going to become some super intelligence thing or whatever. Sure, that's kind of cool. It's more that there's a whole bunch of stuff to be like, what do we do with it? What are ways that we imagine this crazy new thing? What kind of other worlds can we think about that might happen?

Or like, you can, you can come up with all sorts of thought experiments that aren't quite thought experiments, but are maybe reality in these sorts of situations. And I think those things are fun to just like, think about. It's also, there's a lot of drama. And again, I am, I am a sucker for the drama. Love a good soap opera. And so I think part of the appeal of Silicon Valley is that it's kind of a soap opera. Well, how might those interests then manifest here over the next 12 to 18 months? Are you just going to continue to write?

Are you currently doing analysis for, I mean, are you going to join the, what's that, 402 journalist people? You know, they're kind of covering things more in a journalistic endeavor. What are you up to, man? Still figuring that out. I don't know. I mean, yeah, probably continue the blog. There's like sort of the obvious adjacencies of what I did of like, go do another startup or go join some data thing or like sell yourself and become a VC. Those things are there.

I was talking to someone about this earlier. The ideal, this is very much just like, what am I doing with my life? The thing actually I think is sort of the ideal is like these banks, like if you go to like an investment bank or whatever, they have like these rotational programs where you end up, okay,

go work in the trading desk for a while, go work in fixed income, go work in like you sort of deal desk M&A stuff or whatever. And you spend like three months each of these things. I like basically I'm like, I'll just go on some like professional rotation program around different things in the world. Because I think like tech is interesting, but there's so many other kind of interesting problems out there that just seeing, even if it's data related, but like doing data stuff for an industry that isn't selling software to other software people. There's lots of interesting things in that that are, you just get exposure to like, oh, this is like a fun problem. Like the person I was talking to,

we're talking about casinos. Casinos are like a little bit of a greasy industry, but like the problem there, I bet is pretty fascinating. Like the things that casinos have to deal with, I bet, are pretty interesting. And so I have some friends who work in various political circles. They do the same thing, but it's like a very different sort of set of problems. So all that kind of stuff, I think, is more to me about, is this like a fun thing to think about? And I say all that, and then probably it's like, all right, fine,

I'll sell my soul and start another company or whatever. But these are the things you dream about before you do the thing that is the boring thing. Right, until you just start your next dashboard company. Yeah, exactly, until you do like, you know what, I figured out BI. I know what the right thing is. I know what people, it's like, oh God. Yeah, from the co-founder of Mode, here comes.

Yeah, BI 2.0, great. Exactly. Hey friends, I'm here with Brian Clark, VP of Product at Neon. You know we use Neon. 2.0. Great. Exactly. Hey, friends. I'm here with Brian Clark, VP of Product at Neon. You know we use Neon. We love Neon. So, Brian, you're both a fan and a listener of the show. So you kind of know what our shows are about, who we reach. And of those folks that listen to our podcasts, what do you think they need to know most about Neon?

I think the thing I found in talking to developers is that they really don't understand, they don't understand database branching. Sometimes they'll say, is this expensive? Or is it slow? Or like, I don't really understand where it fits in. And so we're changing the face of it a bit to like, maybe focus less on branching, because that's the tool and more on like maybe calling it database previews so you can better see how it fits into your development environment the more people can understand oh i get it like hey any changes you make they don't affect production like this is a separate copy the cost of those changes is only the difference between production and whatever changes you made so if you deleted a bunch of things

or added new data, things like that, you're only actually paying the difference because we use copy and write. So I think it's like these sets of things is what I have the team really focused on. Getting people to really grasp database preview environments and then like, what's the advantage?

And like, can I use it in my system? And that's where I'm like, yeah, like you should be taking this system on. Like this will increase your confidence. It doesn't cost a lot. It's super fast. That idea isn't out there. And I think it's because it's not in most products.

Most databases don't have this kind of integration. Okay, so a concern I've heard out there is why not just run Postgres local? Why database branching? Why preview branches? However you want to frame it. A serverless managed in the cloud Postgres may be more latent or slower than a local

copy. It may cost more. There's more storage. Debunk this. Help me understand the true cost, the true speed. Lay it on me. So in a pull request like a preview environment, this system is fast. So neon databases spin up in 500 milliseconds or less.

You're not affecting the speed of your CICD system at all. The copy on write for our storage means that there's no actual operation. It's like a kind of a null operation. When we create a branch, you instantly have access to the production data, but nothing has changed. Only until you start writing do we actually save the differences there. Yeah, you're not paying for extra data. It's not like you're creating a fork

and then you allocate a whole other 200 gigabyte storage system and a whole other separate compute. We attach compute directly to the original storage. Yeah, those things are super fast and that's in the pull request environment. For the most part on your desktop environment, your laptop environment,

you won't notice a slowdown there and you can do reset and things like that. So you can make a bunch of changes. You can use our CLI and do branch reset and it'll just reset with whatever the parent branch was. But I completely understand the need for people to want to have a purely local environment. And I want to get there. So Neon is super fast. Production managed serverless databases that are

basically never idle. They wake up in less than 500 milliseconds. That's fast. It's managed. It it branches what else do you need learn more at neon.tech that's n-e-o-n.tech neon.tech all right well before i let you go i do want to talk about your post you wrote i guess last year around this time which i loved and was one of the only posts i actually shared with our audience here on change log news because i was like a lot of your stuff is adjacent, but this was like right in our wheelhouse. A lot of people giving conference talks, a lot of people making speeches, having to demonstrate their work. And you wrote a gambler's guide, speaking of casinos, a gambler's guide to giving talks. Some posts you can just tell like everything you need to from the subtitle and yours says a bewildered audience is better than a bored one. So I think that like your premise is well known

just from the subtitle, but then you go on to back that up with some argumentation. Can you unpack the short version of that? Of course, we'll link it up for people to read the whole thing. Basically say if you give talks, there's a lot of canned advice out there for how to give talks. A lot of it is things like don't have too many slides and talk slow and repeat yourself and all these sorts of things where it's like the point is to be very deliberate in your communication and expressive and all those sorts of things. Okay, great. I am sure that is good advice. I am sure that if you implement that well, then you will give good talks and people will say you're giving good talks. I personally hate it. I struggle to do it that way. I also find those talks typically pretty boring. And so like I had to give a bunch of talks inside

of mode, just like sort of company all hands types of things. And one of the things, partly because of the blog is like, there's like a, I don't know, a data conference circuit kind of thing that people end up on. And so I've got a number of talks with those things. And sort of somewhat accidentally, somewhat because I once, when I was in college, saw someone give a talk with this style and was like, couldn't turn away from it, developed a style that was like the opposite of that. And so would basically, it's like an outline of how I think about giving talks, which is essentially counter to all the advice that you typically get. No, it's not totally.

There are some people who have the same sort of style that say this is the way to do it. So this isn't like some totally novel thing. But basically, it's like it's okay to talk fast. It's slides. The main thing is you just have an astronomical number of slides. I probably averaged 15 slides a minute. And so it's like a talk that is structured in that sort of way. And there's some other stuff in there that's a little bit more to your point

of the title and the subtitle. I view talks as your primary enemy, as just people getting bored and tuning out. And so it's basically like if you talk loud enough and fast enough and flip slides fast enough, then people won't get bored. And then you've won 80% of the battle. Yes. I first saw this in practice by a fella whose name I believe is Giles.

It could be Giles. Who knows? Boquette. Could be Boquette. I don't know. He was in the Ruby community and worked on some interesting open source library called Archaeopteryx. Now talk about things that are memorable, like just that word. And it was like a MIDI library that he was into. And he would give talks at Ruby meetups and conferences. And exactly what you described with regard to the

slides per capita was just completely insane. And it was the first time I'd ever seen anybody do that, and I couldn't forget it. I was like, this guy has, he's just, everything he, like every sentence, there's like some sort of reference, and he's not talking about the slides at all. They're literally just an adjunct, or a sprinkle to what he's saying.

But he has it all timed out, to where like every time, similar to what you'll see on the Daily Show, or comedic things like saturday night live's uh weekend update where like as the punchline hits you know the background updates and someone's job is to time that sucker out you know sometimes you're watching it and it's live and so it'll be a little bit slow and they'll wait for the uh for the punchline to land because like the the slide hasn't but giles had it completely timed out and it was like 45 minutes of pure action i don't know what he was talking about i think

it was archaeopteryx but this is probably 15 years ago and i still remember the talk it was amazing and whereas i've watched probably dozens if not scores of other talks throughout the years and it's like yeah i'm sure it was good but not gonna change my life you know yeah and i i would love to see this because that that's basically the exact way that i try to do it is yeah the slide is sort of its own to me i view slides is basically like there is a second conversation going on in the slides right that there is a talk but the slides in some cases you have to have slides that are like okay here's a diagram and it's going to help me explain it that's fine but a lot of the slides are references to they're like indirect references to whatever it is that you're saying.

And so, you know, if you're trying to say something about like, in the future, we're not sure we're going to continue to need the same infrastructure we have today. You show like a back to the future thing that has like in the future, we need no roads. And like, you don't really explain the joke. You don't really, you just like, if people get it, they get it. Great. And people, I think, appreciate the sort of inside knee jokeness of it. If they don't get it,

they'll be like, oh, and then they'll be bewildered. But they probably keep paying attention because they might want to see what the next slide is. And then they're on to the next slide anyways, right? And the other thing is, I think this is like the, to your point of flipping slides,

you can't do this unless you flip your own slides and you do kind of know what's coming. You don't have to memorize it or whatever. And it's bad, I think, to memorize it outright. But you do have to know how to time. You don't have to memorize it or whatever. And it's bad, I think, to memorize it outright. But you do have to know how to time it because there's this bit in this blog post

about rhyming off the beat. To me, good rap songs are the songs that aren't sing-songy. Back in the early days of rap, it was very sing-songy where they always rhymed into the line. And it gets really boring. And so now it's like the rhymes are all interspliced within the bars and like they're off the beat and it's like you you can't you have to pay attention because it's

like you're gonna there's all these sort of rhymes that come at different times and i think like jokes basically have to be there's a lot of people who will be like i'm gonna deliver a joke new slide here's the meme slide and now i make the joke about the meme and you all are like it's just like clunky it's like yeah if you flip it at the right like right as you're delivering the punch line then it's like then the joke can land as opposed to this like and now let me tell you a joke and so yeah i think there's there's a tendency to think like i break up my talk based on slides where i flip a slide i talk to the slide i make my point i flip the slide talk about the slide i think it's much

better to basically make 20 of the point in the previous slide and like when you hit the punch line on the point flip the slide if that about the slide. I think it's much better to basically make 20% of the point in the previous slide and when you hit the punchline on the point, flip the slide. If that punchline is an actual punchline or that punchline is something else, it's just like that. Knowing that the slide flip is going to happen at an unknown point, I also think keeps people much more engaged, where if I turn away, I'm going to miss the transition. Yeah. No, I love that style,

and I definitely wanted to just expose our audience to that idea. Now, that being said, that particular conference talk or talk style is just one way that you can be entertaining and risky and try to be memorable. That's one way of doing it that I've seen to be very successful. And obviously, Ben, you appreciate that as well. There's also other ways you can go. Kelsey Hightower, who has been on the show a few times and is like, you know, keynoters for all kinds of things. He goes no slides, no anything. Like the guy's up there just talking and he can get an audience, you know, engaged

and stay with him throughout the 45 minutes to an hour just based on the storytelling. So there's other ways of doing it. And the things that he does, sometimes live demos, are considered risky. Like most people wouldn't even have the guts to try the stuff and i think like the overarching thought that i appreciated from that particular post is like take the chance like take the risk because anybody can be like average or slightly above average and you'll get the pat on the back or whatever and you'll feel good about

yourself but you don't have very many opportunities to capture people and, and, and do something that everyone's going to remember. And so maybe like step out on the, on the ledge a little bit and see what happens. Yeah. I mean, that's my most conference talk, like to your point of like, you have seen talks for forever and you remember basically one. It's like, that's, that's basically what happens is I don't remember any of these things. You go to a conference and you're like, the thing is in one ear and out the other and it's like oh it's fine and even the ones that are super practiced and super polished and like you have no feedback on that you can't be like well that was bad because they messed this up what i was like it was all good and yet you remember none of it and so like

i think it's yeah i i would rather go to a conference where I remember the thing and like to remember it, you got to be a little bit too. Yeah. It doesn't have to be this particular thing by any means, but, and I am not nearly charismatic enough to hold a room without like flipping through slides and basically doing it with volume and speed. Most of us aren't. I have to have my cheeks, but, but, but I think that like, yeah, the goal is more of

like, how do I make sure people don't forget this thing? Because that's really, again, the enemy you're fighting is attention and just memory and being a total void to most people instead of something that's, oh yeah, I remember that. That was interesting.

In the software world, oftentimes our topics don't help us out very much because they are dry and detailed and very specific oftentimes. And so it's a struggle to make a talk both educational and actually entertaining and off the wall and like all the things that would become memorable. And so it certainly is a challenge. I don't decry anybody for not trying and going the traditional route

but I would encourage folks to given the next opportunity go out there and step out and take the gambler's guide to giving talks from Ben's sub stack and see how it works out for you and the last thing I'd say also on that is this is maybe

not good advice and this is true for the blog too. Would rather people enjoy the time they spent reading it than walk away feeling like they've learned something. Like, I think, and to me, I very much would approach talks that way, where like, I'd rather you just be like, that was a fun 20 minutes,

than be like, that was smart. It's really hard, I think, if you just have a smart talk, but it's boring, it's not going to matter. And it's like, I'd rather be a talk with no point that at least keeps people entertained. Obviously, you can do both, great, but that's tough. But I prioritize basically, this will keep people awake more than this is smart.

Why do you prioritize it that way? Partly because I don't think smart, well, there's a few reasons. One, I don't think people remember smart. Again, you still can't win people's attention even with brilliant ideas. All the smart things that people remember, I think, overwhelmingly come from people who they are biased to believe are going to say something smart to begin with. If you think about the smart things you remember from a podcast or from whatever else, oftentimes those are like, I went in knowing that person. It was going to be, it was Jeff Bezos.

I'm going to listen to Jeff Bezos and I'm going to like say the thing he says is smart because he's Jeff Bezos. And I'm like paying closer attention. But if like you go to a random conference and see a person that you don't know giving a talk, I think it's really hard for people to be like, that was really memorable and smart. Unless you are like memorable in the style that you give it to. Right. The other thing is like, I don't know, like there's a million things out there that are especially like teaching you stuff. I'd rather people just be like, that was enjoyable. Like enjoy your time. Like, I don't know.

How many conference talks do you go to where you learn something that you actually, even if you thought it was smart, you actually do anything with? It's like, hey, you take notes, you take pictures of slides and then you forget they ever exist. Like, I don't, I have never like implemented something from a conference. It's like, yeah, you have like a little nugget. And so I don't know why, why chase that, I guess. Yeah, so that has turned into my conference strategy, which is to go to zero talks and hang out in the hallway

and meet people and talk to them and have fun conversations. Because while I know there is valuable and useful information in those talks, like you said, I've never left a conference talk and been like, I'm going to go implement this in my business or in my software today. I know there's people who have done that, it's just I'm not one of them. And so I think we're kindred spirits in that way.

So also hang out in the hallways, man. That's where the action is. And that's like the thing I would want to fight against is people thinking like, oh, I missed something. I'd rather be in the hallway. Yeah, it's like, can you make it so that people thinking like, oh, I missed something. I'd rather be in the hallway. Yeah. It's like, can you make it so that people be like, oh, I missed out on something fun not being in that talk, as opposed to, oh, I missed out on that smart talk,

but whatever, I can get a picture of the slides or see the deck and whatever supposedly useful thing is in there, I'll get in five minutes. I don't know. I'd rather be like, oh, I have a friend who works in TV, and he does broadcastss for major league baseball and he says his goal is to make people who attend the games feel like they're missing out by not seeing the broadcast it's like he wants the people at the game to be like i am missing a better show on tv than i'm seeing in person and i think like there's a little bit of that to me of like yeah the real value of conferences

is going to be like the hanging out and stuff with people afterwards i want people to feel like they're missing the the better show by not doing the talks that are usually boring yeah i think good commentary can do that i know i've been at the um road of the final four uh because it came here through omaha this last spring uh round one and two were here in omaha and so i'm at the games you know and you're seeing it live but you're wondering what the commentators are saying about it, especially when like, you couldn't really see that particular play. Like, did he actually travel or not? And you just wish you had the commentary in your ear. You still want to be in the, in the stands versus at home, unless you have a very nice setup at home, but missing that.

So I think, and I know there's people that go to baseball games and they'll still turn on the local radio station because they want to hear their and they'll still turn on the local radio station because they want to hear the commentary from the biased version, the local, because that's fun. But yeah. Yeah. So it's the ambition anyway. Yeah.

Love it. Love it. All right. Well, Ben, thank you so much for joining me today. The website is ben.substack.com. That's Ben with two N's. I held off and didn't ask you why there's two N's in your name. I just figured people

ask you that all the time, even though I'm deadly curious. Ben, why are there two N's at the end of your name? Is that from your parents? It is. My name is not Benjamin, it's Bennett. Bennett. And so do you opt into the second N or did your parents opt you in or how did it work out?

My parents, I guess. I have never, never not done it. And so like, when I was learning what my name was and how to write it, that's what I learned.

I don't know. I don't know if they were like, it's been with one N. I'm like, no, I have two. And I was, you know, just able to talk. I'm not sure who was the originator of that,

but I can never remember writing it any other way. So we're going to go with it. Awesome. So benn.substack.com. Of course, all the links to all the things will be in the show notes, including the three essays that we talked about today.

Anything else, Ben, that I didn't ask you or that you want to say before we call it a show? No, I think that's good. I appreciate you having me on. I enjoyed it. I enjoy your writing. Keep on doing it. Keep entertaining me, even if I learn nothing. Just keep them coming every Friday and entertain me along the way. Do my best.

All right, that's our show. We'll talk to you all on the next one. All right, that is our interview for this week. If you're wondering where Adam was on this one, the storms in Texas had knocked out his internet, which is a huge bummer, but we decided that the show must go on. Speaking of the show, did you dig it? What are your thoughts on disbanding the analytics team or the opportunities in the slowdown?

Let us know in the comments. We love hearing from you. Thanks again to Ben for joining me. To our awesome sponsors of this episode, shout out to Chronitor, Retool, and Neon. Please support them. They support us. And of course, to our longtime partners at Fly.io. You've tried Fly, haven't you? They provide a scalable, full stack without the cortisol. Learn more at fly.io. Changelog's beats are lovingly crafted by the one and only Breakmaster Cylinder. If you dig them, do yourself a favor and search for Changelog Beats in Spotify, Apple Music, and other streaming platforms. Thank me later.

That's all for now. We'll come back on Friday for a fun conversation with our friend, Nick Janitakis, all about text-based UIs and terminal tools. All right, that's all for me, but I'll talk to you again on Friday. Thank you. Outro Music