Podcasts about webscale

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Live from Shoptoberfest presented by Shopware! Join our show partner Brent Peterson and his fantastic podcast,Talk Commerce and Scott Ohsman as we record live from a beer hall in Brooklyn, NY. This was our 2nd Shoptoberfest and it was a blast. Learn from Adrian Luna with Webscale and Adam Fabian with Crimson Agility about the 2A world, regulated categories and how these two companies are helping brands with their B2B solutions that bring ecommerce to very challenging and heavily regulated types of products. If you are interested in commerce infrastructure and systems that make it all happen, you will love this episode. Enjoy Always Off Brand is always a Laugh & Learn!    Adrian Luna  LinkedIn: https://www.linkedin.com/in/adrianlunajr/ Webscale: https://www.webscale.com/   Adam Fabian  LinkedIn: https://www.linkedin.com/in/adam-fabian/ Crimson Agility - https://crimsonagility.us/    FEEDSPOT TOP 10 Retail Podcast! https://podcast.feedspot.com/retail_podcasts/?feedid=5770554&_src=f2_featured_email QUICKFIRE Info:   Website: https://www.quickfirenow.com/ Email the Show: info@quickfirenow.com  Talk to us on Social: Facebook: https://www.facebook.com/quickfireproductions Instagram: https://www.instagram.com/quickfire__/ TikTok: https://www.tiktok.com/@quickfiremarketing LinkedIn : https://www.linkedin.com/company/quickfire-productions-llc/about/ Sports podcast Scott has been doing since 2017, Scott & Tim Sports Show part of Somethin About Nothin:  https://podcasts.apple.com/us/podcast/somethin-about-nothin/id1306950451 HOSTS: Summer Jubelirer has been in digital commerce and marketing for over 17 years. After spending many years working for digital and ecommerce agencies working with multi-million dollar brands and running teams of Account Managers, she is now the Amazon Manager at OLLY PBC.   LinkedIn https://www.linkedin.com/in/summerjubelirer/   Scott Ohsman has been working with brands for over 30 years in retail, online and has launched over 200 brands on Amazon. Mr. Ohsman has been managing brands on Amazon for 19yrs. Owning his own sales and marketing agency in the Pacific NW, is now VP of Digital Commerce for Quickfire LLC. Producer and Co-Host for the top 5 retail podcast, Always Off Brand. He also produces the Brain Driven Brands Podcast featuring leading Consumer Behaviorist Sarah Levinger. Scott has been a featured speaker at national trade shows and has developed distribution strategies for many top brands. LinkedIn https://www.linkedin.com/in/scott-ohsman-861196a6/   Hayley Brucker has been working in retail and with Amazon for years. Hayley has extensive experience in digital advertising, both seller and vendor central on Amazon. Hayley lives in North Carolina.  LinkedIn -https://www.linkedin.com/in/hayley-brucker-1945bb229/   Huge thanks to Cytrus our show theme music “Office Party” available wherever you get your music. Check them out here: Facebook https://www.facebook.com/cytrusmusic Instagram https://www.instagram.com/cytrusmusic/ Twitter https://twitter.com/cytrusmusic SPOTIFY: https://open.spotify.com/artist/6VrNLN6Thj1iUMsiL4Yt5q?si=MeRsjqYfQiafl0f021kHwg APPLE MUSIC https://music.apple.com/us/artist/cytrus/1462321449   “Always Off Brand” is part of the Quickfire Podcast Network and produced by Quickfire LLC.  

Cookies are out, context is in. People Inc.'s Jonathan Roberts joins The Big Impression to talk about how America's biggest publisher is using AI to reinvent contextual advertising with real-time intent.From Game of Thrones maps to the open web, Roberts believes content is king in the AI economy. Episode TranscriptPlease note, this transcript  may contain minor inconsistencies compared to the episode audio.Damian Fowler (00:00):I'm Damian Fowler, and welcome to this edition of The Big Impression. Today we're looking at how publishers are using AI to reinvent contextual advertising and why it's becoming an important and powerful alternative to identity-based targeting. My guest is Jonathan Roberts, chief Innovation Officer at People Inc. America's largest publisher, formerly known as Meredith. He's leading the charge with decipher an AI platform that helps advertisers reach audiences based on real time intent across all of People Inc. Site and the Open Web. We're going to break down how it works, what it means for advertisers in a privacy first world and why Jonathan's side hustle. Creating maps for Game of Thrones has something for teachers about building smarter ad tech. So let's get into it. One note, this episode was recorded before the company changed its name. After the Meredith merger, you had some challenges getting the business going again. What made you realize that sort of rethinking targeting with decipher could be the way to go?Jonathan Roberts (01:17):We had a really strong belief and always have had a strong belief in the power of great content and also great content that helps people do things. Notably and Meredith are both in the olden times, you would call them service journalism. They help people do things, they inspire people. It's not news, it's not sports. If you go to Better Homes and Gardens to understand how to refresh your living room for spring, you're going to go into purchase a lot of stuff for your living room. If you're planting seeds for a great garden, you're also going to buy garden furniture. If you're going to health.com, you're there because you're managing a condition. If you're going to all recipes, you're shopping for dinner. These are all places where the publisher and the content is a critical path on the purchase to doing something like an economically valuable something. And so putting these two businesses together to build the largest publisher in the US and one of the largest in the world was a real privilege. All combinations are hard. When we acquired Meredith, it is a big, big business. We became the largest print publisher overnight.(02:23):What we see now, because we've been growing strongly for many, many quarters, and that growth is continuing, we're public. You can see our numbers, the performance is there, the premium is there, and you can always sell anything once. The trick is will people renew when they come back? And now we're in a world where our advertising revenue, which is the majority of our digital revenue, is stable and growing, deeply reliable and just really large. And we underpin that with decipher. Decipher simply is a belief that what you're reading right now tells a lot more about who you are and what you are going to do than a cookie signal, which is two days late and not relevant. What you did yesterday is less relevant to what you need to do than what you're doing right now. And so using content as a real time predictive signal is very, very performant. It's a hundred percent addressable, right? Everyone's reading content when we target to, they're on our content and we guaranteed it would outperform cookies, and we run a huge amount of ad revenue and we've never had to pay it in a guarantee.Damian Fowler (03:34):It's interesting that you're talking about contextual, but you're talking about contextual in real time, which seems to be the difference. I mean, because some people hear contextually, they go, oh, well, that's what you used to do, place an ad next to a piece of content in the garden supplement or the lifestyle supplement, but this is different.Jonathan Roberts (03:53):Yes. Yeah. I mean, ensemble say it's 2001 called and once it's at Targeting strategy back, but all things are new again, and I think they're newly fresh and newly relevant, newly accurate because it can do things now that we were never able to do before. So one of the huge strengths of Meredith as a platform is because we own People magazine, we dominate entertainment, we have better homes and gardens and spruce, we really cover home. We have all recipes. We literally have all the recipes plus cereal, seeds plus food and wine. So we cover food. We also do tech, travel, finance and health, and you could run those as a hazard brands, and they're all great in their own, but there's no network effect. What we discovered was because I know we have a pet site and we also have real simple, and we know that if you are getting a puppy or you have an aging dog, which we know from the pet site, we know you massively over index for interest in cleaning products and cleaning ideas on real simple, right?Damian Fowler (04:55):Yeah.Jonathan Roberts (04:55):This doesn't seem like a shocking conclusion to have, but the fact that we have both tells us both, which also means that if you take a health site where we're helping people with their chronic conditions, we can see all the signals of exactly what help you need with your diet. Huge overlaps. So we have all the recipe content and we know exactly how that cross correlates with chronic conditions. We also know how those health conditions correlate into skincare because we have Brody, which deals with makeup and beauty, but also all the skincare conditions and finance, right? Health is a financial situation as much as it is a health situation, particularly in the us. And so by tying these together, because most of these situations are whole lifestyle questions, we can understand that if you're thinking about planning a cruise in the Mediterranean, you're a good target for Vanguard to market mutual funds to. Whereas if we didn't have both investipedia and travel leisure, we couldn't do that. And so there's nothing on that cruise page, on the page in the words that allows you to do keyword targeting for mutual funds.(05:55):But we're using the fact that we know that cruise is a predictor of a mutual fund purchase so that we can actually market to anyone in market per cruise. We know they've got disposable income, they're likely low risk, long-term buy andhold investors with value investing needs. And we know that because we have these assets now, we have about 1500 different topics that we track across all of DDM across 1.5 million articles, tens of millions of visits a day, billions a year. If you just look at the possible correlations between any of those taxonomies that's over a million, or if we go a level deeper, over a hundred million connected data points, you can score. We've scored all of them with billions of visits, and so we have that full map of all consumers.Damian Fowler (06:42):I wanted to ask you, of course, and you always get this question I'm sure, but you have a pretty unusual background for ad tech theoretical physics as you mentioned, and researcher at CERN and Mapmaker as well for Game of Thrones, but this isn't standard publisher experience, but how did all that scientific background play into the way you approached building this innovation?Jonathan Roberts (07:03):Yeah, I think when I first joined the company, which was a long time ago now, and one of the original bits of this company was about.com, one of the internet oh 0.1 OG sites, and there was daily data on human interest going back to January 1st, 2000 across over a thousand different topics. And in that case, tens of millions of articles. And the team said, is this useful? Is there anything here that's interesting? I was like, oh my god, you don't know what you've got because if you treat as a physicist coming in, I looked at this and was like, this is a, it's like a telescope recording all of human interest. Each piece of content is like a single pixel of your telescope. And so if somebody comes and visit, you're like, oh, I'm recording the interest of this person in this topic, and you've got this incredibly fine grained understanding of the world because you've got all these people coming to us telling us what they want every day.(08:05):If I'm a classic news publisher, I look at my data and I find out what headlines I broke, I look at my data and I learn more about my own editorial strategy than I do about the world. We do not as much tell the world what to think about. The world tells us what they care about. And so that if you treat that as just a pure experimental framework where this incredible lens into an understanding of the world, lots of things are very stable. Many questions that people ask, they always ask, but you understand why do they ask them today? What's causing the to what are the correlations between what they are understanding around our finance business through the financial crash, our health business, I ran directly through COVID. So you see this kind of real time change of the world reacting to big shocks and it allows you to predict what comes next, right? Data's lovely, but unless you can do something with it, it's useless.Damian Fowler (08:59):It's interesting to hear you talk about that consistency, the sort of predictability in some ways of, I guess intense signals or should we just say human behavior, but now we've got AI further, deeper into the mix.Jonathan Roberts (09:13):So we were the first US publisher to do a deal with open ai, and that comes in three parts. They paid for training on our content. They also agreed within the contract to source and cite our content when it was used. And the third part, the particularly interesting part, is co-development of new things. So we've been involved with them as they've been building out their search product. They've been involved with us as we've been evolving decipher, one of the pieces of decipher is saying, can I understand which content is related to which other content? And in old fashioned pre AI days when it was just machine learning and natural language processing, you would just look at words and word occurrence and important words, and you'd correlate them that way. With ai, you go from the word to the concept to the reasoning behind it to a latent understanding of these kind of deeper, deeper connections.(10:09):And so when we changed over literally like, is this content related to that content? Is this article similar in what it's treating to that article? If they didn't use the same words but they were talking about the same topic, the previous system would've missed it. This system gets deeper. It's like, oh, this is the same concept. This is the same user need. These are the same intentions. And so when we overhauled this kind of multimillion point to point connection calculation, we drastically changed about 30% of those connections and significantly improved them, gives a much reacher, much deeper understanding of our content. What we've also done is said, and this is a year thing that we launched it at the beginning of the year, we have decipher, which runs on site. We launched Decipher Plus Inventively named right? I like it. We debated Max or Max Plus, but we went with Plus.(10:59):And what this says is we understand the user intent on our sites. We know when somebody's reading content, we have a very strong predictor model of what that person's going to need to do next. And we said, well, we're not the only people with intent driven content and intent driven audiences. So we know that if you're reading about newborn health topics, you are three and a half times more likely than average to be in market for a stroller. We're not the only people that write about newborn health. So we can find the individual pages on the rest of the web that do talk about newborn health, and we can unlock that very strong prediction that this purchase intent there. And so then we can have a premium service that buy those ads and delivers that value to our clients. Now we do that mapping and we've indexed hundreds of premium domains with opening eyes vector, embedding architecture to build that logic.Damian Fowler (11:56):That's fascinating. So in lots of ways, you're helping other publishers beyond your owned and operated properties.Jonathan Roberts (12:02):We believed that there was a premium in publishing that hadn't been tapped. We proved that to be true. Our numbers support it. We bet 2.7 billion on that bet, and it worked. So we really put our money where our mouth is. We know there's a premium outside of our walls that isn't being unlocked, and we have an information advantage so we can bring more premium to the publishers who have that quality content.Damian Fowler (12:24):I've got lots of questions about that, but one of them is, alright. I guess the first one is why have publishers been so slow out of the starting blocks to get this right when on the media buying side you have all of this ad tech that's going on, DSPs, et cetera.Jonathan Roberts (12:42):I think partly it's because publishers have always been a participant in the ad tech market off to one side. I put this back to the original sin of Ad Tech, which is coming in and saying, don't worry about it, publishers, we know your audience better than you ever will. That wasn't true then, and it's not true today, but Ad Tech pivoted the market to that position and that meant the publishers were dependent upon ad Tech's understanding of their audience. Now, if you've got a cookie-based understanding of an audience, how does a publisher make that cookie-based audience more valuable? Well, they don't because you're valuing the cookie, not the real time signal. And there is no such thing as cookie targeting. It's all retargeting. All the cookie signal is yesterday Signal. It's only what they did before they came to your site, dead star like or something, right? The publisher definitionally isn't influencing the value of that cookie. So an ad tech is valuing the cookie. The only thing the publisher can do to make more money is add scale, which is either generate clickbait because that's the cheapest way to get audience scale or run more ads on the page.(13:57):Cookies as a currency for advertising and targeting is the reason we currently have the internet We deserve, not the internet we want because the incentive is to cheap scale. If instead you can prove that the content is driving the value, the content is driving the decision and the content is driving the outcome, then you invest in more premium content. If you're a publisher, the second world is the one you want. But we had a 20 year distraction from understanding the value of content. And we're only now coming back to, I think one thing I'm very really happy to see is since we launched a cipher two years ago, there are now multiple publishers coming out with similarly inspired targeting architecture or ideas about how to reach quality, which is just a sign that the market has moved, right? Or the market moving and retargeting still works. Cookies are good currency, they do drive performance. If they didn't, it would never worked in the first place. But the ability to understand and classify premium content at web scale, which is what decipher Plus is a map for all intent across the entire open web is the thing that's required for quality content to be competitive with cookies as targeting mechanism and to beat it atDamian Fowler (15:15):Scale. You mentioned how this helps you reach all these third party sites beyond your properties. How do you ensure that there's still quality in the, there's quality content that match the kind of signals that makes decipher work?Jonathan Roberts (15:32):Tell me, not all content on the internet is beautiful, clean and wonderful. Not allDamian Fowler (15:36):Premium is it?Jonathan Roberts (15:36):I know there's a lot of made for arbitrage out there. Look, we, we've been a publisher for a long time. We've acquired a lot of publishers over the years, and every time we have bought a publisher, we have had to clean up the content because cheap content for scale is a siren call of publishing. Like, oh, I can get these eyeballs cheaper. Oh, wonderful. I know I just do that. And everyone gives it on some level to that, right? So we have consistently cleaned up content libraries every time we've acquired publishers. Look at the very beginning about had maybe 10 to 15 million euros. By the time we launched these artists and these individual vertical sites were down to 250,000 pages of content. It was a bigger business and it was a better business. The other side is the actual ad layout has to be good,Damian Fowler (16:29):ButJonathan Roberts (16:29):Every time we've picked up a publisher, we've removed ads from the site. Increase, yeah, experience quality,Damian Fowler (16:33):Right?Jonathan Roberts (16:36):Because we've audited multiple publishers for the cleanup, we have an incredibly detailed understanding of what quality content is. We have lots of, this is our special skill as a publisher. We can go into a publisher, identify the content and see what's good.Damian Fowler (16:54):Is that part of your pitch as it were, to people who advertisers?Jonathan Roberts (16:58):We work lots of advertisers. We're a huge part of the advertising market because we cover all the verticals. We have endemics in every space. If you're trying to do targeting based on identity, we have tens of millions of people a day. It'll work. You will find them with us, we reach the entire country every month. We are a platform scale publisher. So at no point do we saying don't do that, obviously do that, right? But what we're saying is there's a whole bunch of people who you can't identify, either they don't have cookies or IDs or because the useful data doesn't exist yet. It's not attached to those IDs. So incremental, supplementary and additional to reach the people in the moment with a hundred percent addressability, full national reach, complete privacy compliance, just the content, total brand safety. And we will put these two things side by side and we will guarantee that the decipher targeting will outperform the cookie targeting, which isn't say don't do cookie targeting, obviously do it. It works, it's successful. This is incremental and also will outperform. And then it just depends on the client, right? Some people want brand lift and brand consideration. They want big flashy things. We run People Magazine, we host the Grammy after party. We can do all the things you need from a large partner more than just media, but also we can get you right down to, for some partners with big deals, we guarantee incremental roas,Damian Fowler (18:26):ActualJonathan Roberts (18:26):In-store sales, incremental lift.Damian Fowler (18:29):So let's talk about roas. What's driving advertisers to lean in so heavily?Jonathan Roberts (18:34):Well, I think everybody's seen this over the last couple of years. In a high interest or environment, the CMOs getting asked, what's the return on my ad spend? So whereas previously you might've just been able to do a big flashy execution or activation. Now everybody wants some level of that media spend to be attributable to lift to dollars, to return to performance, because every single person who comes through our sites is going to do something after they come. We're never the last stop in that journey, and we don't sell you those garden seeds. We do not sell you the diabetes medication directly. We are going to have to hand you off to a partner who is going to be the place you take the economic action. So we are in the path to purchase for every single purchase on Earth.(19:19):And what we've proven with decipher is not only that we can be in that pathway and put the message in the path of that person who is going to make a decision, has not made one yet. But when we put the messaging in front of it of that person at the time, it changes their decisions, which is why it's not just roas, which could just be handing out coupons in the line to the pizza store. It's incremental to us, if you did not do this, you would have made less money. When you do this, you'll make more money. And having got to a point where we've now got multiple large campaigns, both for online action and brick and mortar stores that prove that when we advertise the person at this moment, they change their decision and they make their brand more money. Turns out that's not the hardest conversation to have with marketers. Truly, truly, if you catch people at the right moment, you will change their mind.Damian Fowler (20:10):They'll happily go back to their CFO and say, look at this. This is workingJonathan Roberts (20:15):No controversially at can. During the festival of advertising that we have as a publisher, we may be the most confident to say, you know what? Advertising works.Damian Fowler (20:27):You recently brought in a dedicated president to leadJonathan Roberts (20:30):Decipher,Damian Fowler (20:30):Right? So how does that help you take what started out as this in-house innovation that you've been working on and turn it into something even bigger?Jonathan Roberts (20:39):Yeah, I think my background is physics. I was a theoretical physicist for a decade. Theoretical physicists have some good and bad traits. A good trait is a belief that everything can be solved. Because my previous job was wake up in the morning and figure out how the universe began and like, well, today I'll figure it out. And nobody else has, right? There's a level of, let's call it intellectual confidence or arrogance in that approach. How hard can it be? The answer is very, but it also means you're a little bit of a diante, right? You're coming like, oh, it's ad tech. How hard can it be? And the just vary, right? So there's a benefit. I mean, I've done a lot of work in ad tech over the last couple of years. Jim Lawson, our president of Decipher, ran a publicly listed DSP, right? He was a public company, CEO, he knows this stuff inside a and back to front, Lindsay Van Kirk on the Cipher team launched the ADN Nexus, DSP, Patrick McCarthy, who runs all of our open web and a lot of our trade desk partnerships and the execution of all of the ways we connect into the entire ecosystem.(21:38):Ran product for AppNexus. Sam Selgin on the data science team wrote that Nexus bitter. I've got a good idea where we're going with this and where we should go with this and the direction we should be pointed in. But we have seasoned multi-decade experience pros doing the work because if you don't, you can have a good idea and bad execution, then you didn't do anything. Unless you can execute to the highest level, it won't actually work. And so we've had to bring in, I'm very glad we have brought in and love having them on the team. These people who can really take the beginnings of what we have and really take this to the scale that needs to be. Decipher. Plus is a framework for understanding user intent at Webscale and getting performance for our clients and unlocking a premium at Webscale. That is a huge project to go after and pull off. We have so many case studies proving that it will work, but we have a long way to go between where we are and where this thing naturally gets to. And that takes a lot of people with a lot of professional skills to go to.Damian Fowler (22:43):What's one thing right now that you're obsessed with figuring outJonathan Roberts (22:46):To take a complete left turn, but it is the topic up and down the Cosette this summer. There isn't currently any viable model for information economy in an AI future. There's lots of ideas of what it would be, but there isn't a subtle marketplace for this. We've got a very big two-sided marketplace for information. It's called Google and search. That's obviously changing. We haven't got to a point to understand what that future is. But if AI is powered by chips, power and content, if you're a chip investor, you're in a good place. If you're investing energy, you're in a good place of the three picks and shovels investments, content is probably the most undervalued at the moment. Lots of people are starting to realize that and building under the hood what that could look like. How that evolves in the next year is going to really determine what kind of information gets created because markets align to their incentives. If you build the marketplace well, you're going to end up with great content, great journalism, great creativity. If you build it wrong, you're going to have a bunch of cheap slop getting flooded the marketplace. And we are not going to fund great journalism. So that's at a moment in time where that future is getting determined and we have a very strong set of opinions on the publishing side, what that should look like. And I am very keen to make sure it gets done. You soundDamian Fowler (24:17):Optimistic.Jonathan Roberts (24:19):A year ago, the VCs and the technologists believed if you just slammed enough information into an AI system, you'd never need content ever again. And that the brain itself was the moat. Then deep seek proved that the brain wasn't a moat. That reasoning is a commodity because we found out that China could do it cheaper and faster, and we were shocked, shocked that China could do it cheaper and faster. And then the open source community rebuilt deep to in 48 hours, which was the real killer. So if reasoning is a commodity, which it is now, then content is king, right? Because reasoning on its own is free, but if you're grounding it in quality content, your answer's better. But the market dynamics have not caught up to that reality. But that is the reality. So I am optimistic that content goes back to our premium position in this. Now we just have to do all the boring stuff of figuring out what a viable marketplace looks like, how people get paid, all of this, all the hard work, but there's now a future model to align to.Damian Fowler (25:23):I love that. Alright, I've got to ask you this question. It's the last one, but I was going to ask it. You spent time building maps, visualizing data, and I've looked at your site, it's brilliant. Is there anything from that side of your creativity that helped you think differently about building say something like decipher?Jonathan Roberts (25:42):Yeah. So I think it won't surprise anyone to find out that I'm a massive nerd, right? I used to play d and d, I still do. We have my old high school group still convenes on Sunday afternoons, and we play d and d over Discord. Fantasy maps have been an obsession of mine for a long time. I did the fantasy maps of Game of Thrones. I'm George r Martin's cartographer. I published the book Lands of Ice and Fire with him. Maps are infographics. A map is a way of taking a complex system that you cannot visualize and bringing it to a world in which you can reason about it. I spent a lot of my life taking complex systems that nobody can visualize and building models and frameworks that help people reason about 'em and make decisions in a shared way. At this moment, as you're walking up and down the cosette, there is no map for the future. Nobody has a map, nobody has a plan. Not Google, not Microsoft, not Amazon, not our friends at OpenAI. Nobody knows what's coming. And so even just getting, but lots of people have ideas and opinions and thoughts and directions. So taking all that input and rationalize again to like, okay, if we lay it out like this, what breaks? Being able to logically reason about those virtual scenario. It is exactly the same process, that mental model as Matt.Damian Fowler (27:12):And that's it for this edition of The Big Impression. This show is produced by Molten Hart. Our theme is by loving caliber, and our associate producer is Sydney Cairns. And remember,Jonathan Roberts (27:22):We do not as much tell the world what to think about. The world tells us what they care about. Data's lovely, but unless you do something with it, it's useless.Damian Fowler (27:31):I'm Damian, and we'll see you next time.

During the earnings call, Cisco Systems acknowledged the competitive landscape in cybersecurity and observability, as evidenced by Palo Alto Networks' acquisition of Exabeam. However, Cisco highlighted its strategic strengths in these areas, emphasizing the value of an integrated, unified platform for end-to-end security and insightful solutions.The company stated its focus on the immediate integration of its XDR (Extended Detection and Response) solution with Splunk Enterprise Security, showcasing its commitment to harnessing the combined strengths of Cisco and Splunk. This integration represents progress in developing seamless product alliances, innovative solutions, and robust go-to-market strategies.Furthermore, Cisco has integrated AI capabilities into its cybersecurity offerings, such as Cisco Hypershield, to differentiate itself from competitors relying on standalone products. The company asserted that embedding security within the network fabric provides a unique and significant market differentiation.Cisco's strategic emphasis on integration, AI capabilities, and unified platforms in cybersecurity and observability positions the company to leverage market opportunities and address evolving industry challenges effectively.Navigating Macroeconomic Challenges and Sector-Specific DynamicsWhile Cisco experienced revenue declines in its core networking business due to inventory implementations, its security and observability segments saw growth driven by innovations and the integration of Splunk. The company acknowledged the ongoing macroeconomic challenges, particularly in the telco and cable segments, although some stabilization was noted in the Webscale sector.Cisco's CEO, Chuck Robbins, stated, "So from a macro perspective, what I would say is that ironically, we saw the quarter actually slow -- showed slight improvement as we move through the quarter." The company's strong cash flow and strategic investments in AI, security, and the Splunk integration position it well for future growth, despite these headwinds.Balancing Growth Opportunities and Competitive PressuresCisco Systems reported mixed financial results, with revenues for Q3 down 13% year-over-year at $12.7 billion, primarily due to reduced product revenue. However, service revenue saw a 6% uptick, and the recent acquisition of Splunk added $413 million post-close, boosting annualized recurring revenue to $29.2 billion. Gross margins remained strong at 68.3%, and operating margins stayed steady.While the company faced declines in its core networking business, key customer sectors like data center and campus switching, security, and collaboration witnessed order increases. Capital returns to shareholders amounted to a robust $2.9 billion in Q3.Moving forward, Cisco Systems must navigate the competitive waters while capitalizing on growth opportunities in cybersecurity and observability. The company's strategic focus on integration, AI capabilities, and unified platforms positions it to address evolving industry challenges and leverage market opportunities effectively. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theearningscall.substack.com

The 91st episode of dToks features Sonal Puri, a Board Director at Magento Asociation and Ex CEO of Webscale.

Webscale is the Cloud Platform for Modern Commerce, offering security, scalability, performance, and automation for global brands. The Webscale SaaS platform leverages automation and DevOps protocols to simplify deploying, managing and maintaining infrastructure in multi-cloud environments, including Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Webscale powers Fortune 1000 brands and thousands of other B2C, B2B, and B2E ecommerce storefronts across 12 countries and has offices in Santa Clara, CA, Boulder, CO, San Antonio, TX, Bangalore, India, and London, UK. https://www.webscale.com

Adrian Luna from Webscale and Lindsey Murray and Rick Buczynski from Blue Acorn iCi discuss the why, what, and how of peak traffic preparedness for your website infrastructure, customer experience, and marketing initiatives.

Adrian Luna from Webscale and Lindsey Murray and Rick Buczynski from Blue Acorn iCi discuss the why, what, and how of peak traffic preparedness for your website infrastructure, customer experience, and marketing initiatives.

Skit raised $23M in Series B funding from WestBridge Capital and others to expand in markets like the US and enhance its voice technology. The company provides a suite of speech and language solutions that enable enterprises to automate their call center operations.Constant Contact completed the acquisition of SharpSpring, initially announced in June this year and met yesterday with the shareholders' approval. It provides a SaaS solution for marketing automation to help SMBs.Google Cloud and C3 AI enter partnership to help organizations spanning industries such as financial, healthcare, manufacturing, supply chain and telecommunications, solve real-world challenges.Crownpeak, a digital experience platform, has formed a strategic agreement with Webscale, a multi-cloud SaaS solution. Webscale will become Crownpeak's preferred cloud delivery engine for its global brands due to the partnership.Corelight, an open network detection and response (NDR) platform, has raised $75M in a Series D round from Energy Impact Partners (EIP). With the latest round of funding, Corelight will be able to expand its global market presence and develop new data and cloud services.Humane Inc., a platform that produces and sells consumer hardware, software, and services, has received a $100M investment from Tiger Global Management. Humane will be able to scale its operations while pursuing the next step in human-computer interaction.Stravito, a knowledge management platform, has raised €12.4 million (about $14.6 million) from Endeit Capital in a Series B round. Stravito will use the new funds to speed up product development and grow globally, emphasizing the US market.

In this episode, Frank and Graham are joined by CEO of Webscale, Sonal Puri, to talk about e-commerce security, scalability, performance, headless applications, and the story behind Webscale.

Welcome to Talk Commerce. Where we explore how merchants, agencies, and developers experience commerce and the ecosystems, and communities they work and live in. This week we interview Sonal Puri, CEO at Webscale. We discuss the meaning of “Modern Commerce '', how online retailers should be thinking about new technology (the shiny new thing is sometimes the best), and how Core Web Vitals is something that requires every merchant's attention (yesterday). We also discuss Jay Smiths' “Edgy” open letter, as well as how Webscale prides itself on always pushing the market in terms of features and capabilities. Sonal also shares how important she feels it is that we mentor young people today, helping them complete their education and finish college. This episode was recorded on June 21st, 2021

This episode, Frank is joined by Adrian Luna of Webscale to talk about how critical hosting is in modern day e-commerce. From scaling to cope with unexpected traffic spikes, to safeguarding against bad actors, your hosting shouldn't be an afterthought.

This episode, Frank is joined by Adrian Luna of Webscale to talk about how critical hosting is in modern day e-commerce. From scaling to cope with unexpected traffic spikes, to safeguarding against bad actors, your hosting shouldn't be an afterthought.

Sonal Puri is the CEO of Webscale Networks, a software company that hosts and manages a wide range of digital commerce and web applications. Sonal has almost 20 years of experience working with Internet infrastructure in sales, marketing, and corporate and business development.  Before her work at Webscale, Sonal was the Chief Marketing Officer at Aryaka Networks. She has also served as a valued advisor for a variety of technology companies and held key management roles at Akamai Technologies, Speedera Networks, and more.  In this episode… Are you looking for a top web hosting platform to help you grow your ecommerce business? Do you want enhanced visibility and control over your web applications, as well as key strategies for scaling your company? If so, this episode of the Ecommerce Wizards Podcast is for you! Sonal Puri is an expert when it comes to software. As the CEO of Webscale Networks, Sonal not only has the inside scoop on all things web hosting, but also knows the secrets to building and managing an effective, customer-focused team of tech gurus. Today, Sonal is here to share her experience and advice on ecommerce hosting solutions, successful business tactics, and more! In this episode of the Ecommerce Wizards Podcast, Guillaume Le Tual sits down with Sonal Puri, the CEO of Webscale Networks, to discuss the ins and outs of web hosting. Listen in as Sonal shares her strategies for delivering a better, faster, and cheaper service, the structure behind Webscale's speedy SLA, and how the platform is helping clients manage the transition from Magento 1 to Magento 2. Stay tuned!

I had a chance to moderate a panel discussion this week with some interesting people in the B2B space. Hope you will learn too.Panelists on this session:Ryan Van Hoozer, VP of Operations, Marysville Marine OperationsKenn Glenn, Marketing Director, Marysville Marine OperationsAdrian Luna, Channel Leader, Webscale NetworksDevon Plopper, Sr. Account Executive, ShipperHQGowtham Ram, Account Manager, DCKAPWhat you’ll learnWhat role a hosting environment play in an online store?What does ShipperHQ do?Where does ShipperHQ fit in the eCommerce ecosystem to drive conversions?The experience of migrating from Magento 1 to 2.Launching a website on large scale vs small size corporations.How was the experience in launching a website through a 100% remote team?Approaching the design of B2B vs B2C websites.Shipping best practices.Process of setting up ShipperHQ.Why do you need to consider middleware implementation as a part of your core project?Some common mistakes distributors make during the initial discovery or vendor selection process.Is the holiday season sale going to be different this year?Show Links and ReferencesMarysville Marine DistributorsClorasShipperHQWebscale NetworksVideo version of this episodeShiva Kumaar on LinkedinDriven: Ecommerce at Work Home

Andi Grabner, our man-on-the-street, gets the scoop on:-Going web-scale with cross-environment features, globally distributed high availability and more​ - with Guido Deinhammer-The role of OpenTelemetry in Dynatrace​ with Daniel Khan and Sonja Chevre-Build resiliency into your continuous delivery pipeline​ with Michael Villiger

Andi Grabner, our man-on-the-street, gets the scoop on:-Going web-scale with cross-environment features, globally distributed high availability and more​ - with Guido Deinhammer-The role of OpenTelemetry in Dynatrace​ with Daniel Khan and Sonja Chevre-Build resiliency into your continuous delivery pipeline​ with Michael Villiger

Sonal Puri is the CEO of Webscale – her 4th B2B SaaS startup. Webscale is disrupting the multi billion dollar market for cloud hosting & cloud services. Sonal talks about: Using deep tech to turn the cloud into a utility Replacing her talented, humble & self-aware founding CEO The potential dangers of pursuing a freemium pricing model How she learns each & every day from other startup CEOs & mentors Webscale's impact on $billion clients like Puma, Unilever & Tommy Hilfiger For more insights into Webscale check out https://www.webscale.com & for advice on hiring world class talent for B2B software scale-ups check out http://alpinasearch.com  

CEO Sonal Puri of Webscale shares how her company helps B2C and B2B sellers of all sizes manage their cloud infrastructure globally, helping them take advantage of the cloud’s almost infinite scalability while optimizing costs, security, and performance. In this episode, we discuss: As a startup, how Webscale is disrupting the digital infrastructure space as “the digital cloud company” for more than 1000 online stores globally Webscale’s ability to manage applications in the public cloud on behalf of customers across all the “hyperscale” cloud providers, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Alibaba Cloud, and others The company’s experience with online commerce is key to helping clients manage their infrastructure for maximum benefit with minimal cost Large hyperscale providers are providing very reliable, scalable cloud infrastructure/hosting on demand, while Webscale runs its own services on top:  security, predictive autoscaling, performance, caching, content optimization, bot management, etc. Webscale provides an easy-to-use interface/portal for customers It also ensures cloud computing consumption is always right-sized so customers don’t pay for more than they use Infrastructure as a commodity or utility, like electricity, allowing retailers/brands to pay only for what they need, when they need it How today’s technology and cloud offerings allow smaller retailers to access the same tools as leading global brands, leveling the playing field Why it makes sense to use a vendor like Webscale to manage your cloud infrastructure Migrating applications to the cloud from traditional hosting models can be confusing and challenging Costs start to add up quickly if you don’t manage your infrastructure actively Security concerns are only escalating and require constant monitoring 24x7 global support includes expertise across multiple clouds Why even the data layer no longer presents scalability issues if the infrastructure is set up correctly, even for retailers who see 20x or 30x traffic spikes during peak seasons Migrating infrastructure to the cloud typically takes anywhere from 1-2 weeks up to 2 months, depending on complexity and the amount of testing required With good business requirements, Webscale quickly figures out the right solution using available providers Why Sonal believes the infrastructure market will undergo further commoditization and look more and more like a utility, with probably three hyperscale providers and perhaps a small group of very targeted cloud providers (e.g. a health cloud or database cloud)

SHOW: 396DESCRIPTION: Aaron and Brian talk with Renaud Boutet (@boutetren, VP Product Management @datadoghq) about logging, monitoring, observability, and the challenges of balancing the collection of the right data with the costs of all the data.SHOW SPONSOR LINKS:Snowflake HomepageGet started with Snowflake at snowflake.com/cloudcastDigital Ocean HomepageGet Started Now and Get a free $100 Credit on Digital OceanGet 20% off VelocityConf passes using discount code CLOUDCLOUD NEWS OF THE WEEK:A Cloud Guru raises $33M in funding to expand courses and labsMicrosoft Azure adds VMware CloudVMware Cloud on Dell SHOW INTERVIEW LINKS:Datadog Homepage - Modern Monitoring and AnalyticsSHOW NOTES:Topic 1 - Welcome to the show. Tell us about some of your background prior to joining Datadog, and about your focus areas today. Topic 2 - Let’s start with some conceptual buckets - how do you sort out the differences when people say “monitoring” vs. “logging” vs. “observability”? Topic 3 - Logging has the inherent tradeoff between the desire to “log everything” and the limitation of costs to log (and retain everything). What are some of the trends to potentially make this tradeoff more manageable?Topic 4 - At some point, the tradeoff between sending logs, filtering logs, storing logs all boils down to a financial trade-off of immediate costs vs potential costs associated with failure. How do you see those conversations playing out in real life? Any suggestions on a framework for doing those types of analysis? Topic 5 - What role do you see AI playing in the future of Logging/Observability? It seems like that needs to become the next big step if the industry solves the challenges of logging/storage more and more. FEEDBACK?Email: show at thecloudcast dot netTwitter: @thecloudcastnet and @ServerlessCast

This week on the podcast, we chat about the latest release of NetApp’s object storage solution – StorageGRID Webscale 11.2! Join us as we ask StorageGRID software director Duncan Moore and Global Solutions Architect Luke Mun all about StorageGRID and object storage.

As the importance of our data continues to grow, so does our need to scale its use, be it for performance, resilience or reasons of data locality, the need to architect solutions and find technologies to support this demand for data at scale is increasingly important. If we look at the way cloud giants use their data, it's clear that "traditional" database methods are not going to be suitable and of course it's not just "cloud giants" who need their data at scale, today, enterprises of all types need to be able to present their data with the same scale, flexibility and resilience. One widely adopted way of doing this is using Apache Cassandra as a database technology. But why? and how does Cassandra differ from our traditional on-prem solutions such as SQL and Oracle? That is the topic of this week's Tech Interviews as Patrick Callaghan, Solutions Architect at Datastax joins me to provide an intro to Cassandra as a database technology, how it works and why it's becoming the database of choice in the modern webscale world. In this episode we discuss; *Cassandra's beginning at Facebook * Why you may need a scalable, distributed database * What challenges does it bring * Why automation at your busiest times is perhaps not the answer * Webscale database use cases * Where Datastax can help Patrick is a great guest and provided a fantastic intro to the world of Cassandra and what it can mean for the way we handle data in this distributed, webscale world. Next week we start a brief series talking with Cloud Architects and Migration specialists about making a success of Public Cloud Projects, too make sure you catch that show then please subscribe in all of the usual podcast places and until next time, Thanks for listening. Full show notes are here: https://wp.me/p4IvtA-1DZ

So you may have thought about using NoSQL or a Document Database for taking care of you needs. But do you know why that might be not be a good (or a pretty bad idea?). Or you may have a Database that have been running fine, but it seems that you can't work with it anymore? (Is it time to move to NoSql? Would it help?). We dive into the "Why" would you choose Databases vs NoSQL Data Stores, or when to ditch your MongoDB and actually come back to MySQL. In our current time of "WebScale" and "CloudReady" we get bombarded by choices! (Mongo, Dynamo, MariaDB, ElasticSearch) and while some of the offerings are great, it might not mean that is the Right choice for what we need to store. So take a listen as we explore normalization and the strength and weaknesses of relational data vs unstructured data. We thank DataDogHQ for sponsoring this podcast episode Don't forget to SUBSCRIBE to our cool NewsCast! Java Off Heap Database Normalization SQL vs NoSQL (StackOverflow) Sql Vs NoSql (TheGeekStuff) Max # of Rows MySQL Do you like the episodes? Want more? Help us out! Buy us a beer! And Follow us! @javapubhouse and @fguime and @bobpaulin  

MageMojo and Webscale Discuss Magento in the Cloud by Kalen Jordan

DCD Webscale in SAN FRANCISCO June 2018- I talked hyperscale data center growth with Compass Datacenters' Nancy Novak, Turner Construction's Ben Kaplan, ABB's Ciaran Flanagan, and 1547 Managing Director Corey Welp. We dove in to how this type of data center development has and will continue to change the industry.

Peggy and Chuck Byers, principal engineer and platform architect, Cisco, discuss interoperability and he questions how we are going to achieve interoperability if there are 900 different platforms. Interoperability—and security—are big challenges that face the industry. He believes quantum computing is a threat to IoT (Internet of Things) security, but the arms race is going to be won by the good guys. At the same time, having one player in the market is an impossible task, as no one can do everything, he explains. When discussing bringing companies into the OpenFog Consortium, he says the big Web-scale companies have been invited to join, but politely refuse. Byers speculates that it is because the companies believe they have the power to influence the industry all by themselves and they are smart because they know where the share owners want them to go, but it might not be best for the industry.

Peggy and Chuck Byers, principal engineer and platform architect, Cisco, discuss interoperability and he questions how we are going to achieve interoperability if there are 900 different platforms. Interoperability—and security—are big challenges that face the industry. He believes quantum computing is a threat to IoT (Internet of Things) security, but the arms race is going to be won by the good guys. At the same time, having one player in the market is an impossible task, as no one can do everything, he explains. When discussing bringing companies into the OpenFog Consortium, he says the big Web-scale companies have been invited to join, but politely refuse. Byers speculates that it is because the companies believe they have the power to influence the industry all by themselves and they are smart because they know where the share owners want them to go, but it might not be best for the industry.

Die aktuelle Softwarelösung unterstützt IT-Organisationen dabei, auch komplexe Dateninhalte und das rasche Wachstum von unstrukturierten Daten im Unternehmen zu kontrollieren sowie kosteneffizient zu verwalten...

This week on the podcast, we welcome Duncan Moore, Director of StorageGRID at NetApp, in to chat about the latest and greatest StorageGRID WebScale release, which comes out today! As a bonus, we also discuss StorageGRID 10.3, which we somehow overlooked on the podcast. Tune in to find out what’s new in the world of S3!

When Michael Phelps bought a ring for his girlfriend at a small retail shop then shared it online, thousands of his more than one million followers flooded the website of this small jewelry shop. That massive increase in traffic could’ve caused the site to crash. But as a customer of WebScale, the site was ale to adapt. Businesses dream of having their website flooded with orders. But for some, when it happens, it’s a nightmare! Everything comes to a stop in the surge of traffic. Why? Because they don’t have the infrastructure to support the number of people creating transactions on their site. Webscale CEO Sonal Puri talks about how her company is helping businesses of all sizes leverage the cloud to avoid crashes. In our “HealthTech” series, Stroll Co-founder and COO Matt Maurer talks about how he’s helping doctors guide their patients on the best, most cost-effective options for medical tests. If you have to get an MRI or an ultrasound, prices can vary by as much as $10,000 within a few miles. His technology provides that information to your doctor before you even leave their office. Plus, UpRamp Managing Director Scott Brown shares four startups that have the potential for disrupting the Cable industry.

StorageGRID Webscale is NetApp’s object data storage system. Recently version 10.2 was released and added a number of new and interesting features. To enlighten us, we asked none other than Duncan Moore, Director for StorageGRID at NetApp, to visit us and give us a breakdown. As always, Duncan does a great job extolling the benefits of object storage, so if you’re curious about object storage and it’s use cases, give this episode a listen!

Aaron & Brian talk with Manoj Chaudhary (CTO & VP of Engineering @loggly) about building and managing massively scalable SaaS applications. Music credit: Nine Inch Nails (nin.com)

Aaron and Brian talk with Dave Lester (@davelester, Open Source Advocate at Twitter. Friend of @ApacheMesos, @ApacheAurora) about how Twitter manages large-scale infrastructure, an introduction to Apache Mesos and how projects like Kubernetes, Docker, Aurora are helping to define the next-generation of web-scale infrastructure management. Music Credit: Nine Inch Nails (www.nin.com)

Brian talks with Eric Bowman (@ebowman, VP of Architecture at Gilt, @gilttech) about the evolution from monolithic apps to scaleable micro-services. They discuss how to manage scale, how they manage deployments across internal, managed and public clouds. They discuss when to open-source a project and how to engage with open-source communities. Music Credit: Nine Inch Nails (www.nin.com)

Brian talks with JR Rivers (@JRCumulus, CEO - Cumulus Networks) about the launch of the company and Cumulus Linux. They explore hardware-acceleration of Linux, integration with Chef/Puppet/Ansible, and the evolution of network operatiing systems and hardware supply chains.