Podcasts about snowflakes

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM   This episode features a conversation with Daniel Cohen‑Dumani on why many organisations feel stuck on AI despite rapid advances. The discussion focuses on agentic AI, the growing gap between consumer and business adoption, and why strategy matters more than experimentation. You will hear practical guidance on narrowing AI efforts to real business problems, building organisational memory for reliable agents, and avoiding paralysis caused by hype and fear. The conversation also challenges traditional systems like CRM and reframes AI as a tool to learn, not shortcut, building sustainable capability inside organisations.

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

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.After years of remote‑first work built on swift trust, companies are asking a harder question: what does a organization really stand for when people rarely show up together? As AI accelerates change, leaders are rethinking presence, team design, and collaboration to fuel trust, innovation, and growth. This week, Dave, Esmee, and Rob are joined by Dr. Tim Currie, disruptor, author, innovator, and advisor, to examine transformation versus trust, the role of AI, and whether organisations can truly build culture without deeper human connection. TLDR00:42– Introduction01:10 –  Hang out: New film releases07:17 – Dig in: The trust gap in remote work17:57 – Conversation with Dr. Tim Currie54:07 – The Wizard of Oz at the Sphere in Las Vegas and staying connected GuestDr. Tim Currie: https://www.linkedin.com/in/dr-tim-currie-37756a/Book Swift Trust: https://swifttrustbook.com/HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

For some time now, I had been meaning to share the profound experiences that took place in relation to my father's death. It feels tender and vulnerable to offer these stories but I own the decision to do that. I hope that hearing about these experiences might help someone else who is moving through a death or grief process…or they might simply provide another way to view these moments which inevitably take place at some time or another in all our lives. It's been quiet on the Pod lately as I'm in the last few weeks of writing my book, ‘Modern Fairy Sightings: Personal Encounters in Extraordinary Times.' I'll be back to my normal fortnightly release from April onwards. We will return to fairy experiences then. These topics around life and death feel relevant - particularly in these times. To me, they suggest that the separation between our realm and Other realms may not be as fixed as we hold it to be. I'd love to hear your thoughts and feelings on all of this. ⭐️ JOIN THE MODERN FAIRY SIGHTINGS COMMUNITY ⭐️ ⁠⁠⁠⁠⁠https://www.patreon.com/c/themodernfairysightingspodcast/membership⁠⁠⁠⁠⁠ If you're looking for exclusive bonus material, monthly zoom chats with like-minded folks, access to the Discord chat channels, quiet meditation gatherings and meeting other members, join us at: ⁠⁠⁠⁠⁠ https://www.patreon.com/c/themodernfairysightingspodcast/membership⁠⁠⁠⁠⁠ S U P P O R T If you'd prefer to support the Modern Fairy Sightings with a one off donation, you can ‘buy me a coffee' and I'd be very grateful

В гостях Руслан Ибрагимов — Kotlin-разработчик и архитектор. Обсуждаем эволюцию языка, замену шардированных баз на Snowflake и модернизацию легаси с помощью ИИ. 00:00 — Интро 03:00 — Путь Руслана в Kotlin 06:00 — Преимущества Kotlin перед Java 11:00 — Новая обработка ошибок 16:00 — Проблемы Kotlin Multiplatform 18:00 — Тренды в Kotlin-сообществе 22:00 — Выбор бэкенд-фреймворков 31:00 — Обзор новых библиотек 36:00 — Работа со Snowflake 45:00 — Модернизация легаси через ИИ 51:00 — Автоматизация и Argo Workflows 01:24:00 — Поломка обратной совместимости Ссылки: Ktor Snowflake Argo Workflows Temporal jOOQ Koin Awesome Kotlin Kotlin MCP @kotlin_lang @TheDailyKotlin Ссылки на подкаст: Сайт - https://javaswag.github.io/ Телеграм - https://t.me/javaswag Youtube - https://www.youtube.com/@javaswag Linkedin - https://www.linkedin.com/in/volyihin/ X - https://x.com/javaswagpodcast

En esta tertulia de Itnig arrancamos con una conversación a fondo sobre Snowflake, su evolución de plataforma de datos a compañía claramente posicionada en torno a la IA, y cómo compite en un mercado cada vez más duro frente a gigantes como Databricks, Microsoft o Google. Hablamos de producto, multicloud, go-to-market enterprise, consumo vs contratos cerrados y de cómo una compañía de este tamaño vende, crece y se adapta cuando todo el mercado gira alrededor de la inteligencia artificial. A partir de ahí, la conversación se abre al gran tablero de la IA global, con el pulso entre OpenAI y Anthropic, el papel de la ética, la relación con el gobierno de Estados Unidos y la velocidad a la que están creciendo estas compañías. También debatimos sobre especialización, distribución, consumo energético, centros de datos y por qué esta nueva ola tecnológica no solo está redefiniendo el software, sino también quién captura el valor en esta nueva etapa. En la segunda parte entramos en una discusión especialmente interesante sobre el futuro del trabajo, el software y la productividad, intentando separar el ruido de la realidad. ¿La IA destruirá empleo o multiplicará la capacidad de las empresas y de las personas? ¿Estamos ante otra revolución tecnológica más o ante un cambio de paradigma mucho más rápido y profundo? La tertulia mezcla visión de negocio, mercado e inversión con una mirada mucho más filosófica sobre cómo puede cambiar nuestra relación con el trabajo en los próximos años. Y además, como siempre en Itnig, hay espacio para abrir otros melones: el enfoque de Apple frente a la carrera del CAPEX en IA, el posible futuro del hardware alrededor de nuevos dispositivos inteligentes, y las dudas reales que tienen hoy los emprendedores sobre compliance, regulación europea y uso de modelos como OpenAI. Una tertulia especialmente completa para entender hacia dónde se mueve el ecosistema tecnológico y qué implicaciones puede tener todo esto para startups, corporates e inversores. Sigue a los "tertulianos" en Twitter/LinkedIn:• Bernat Farrero: @bernatfarrero• Jordi Romero: @jordiromero• Marcel Queralt: https://www.linkedin.com/in/marcelqueralt/SOBRE ITNIG

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Business messaging is transforming customer engagement by enabling brands to move conversations into familiar, always‑on messaging platforms. The result for customers is greater convenience, quicker resolutions, and more meaningful, personalized interactions. This week, Dave, Esmee, and Rob are joined by Kathleen Tandy, Global Director and Head of Business Messaging Marketing and WhatsApp for Business at Meta , to explore how companies are using messaging platforms to engage customers, what customers expect from these experiences, and the challenges of scaling messaging in tech.TLDR00:35 – Introduction01:00 –  Hang out: The new Remarkable05:25 – Dig in: Using messaging to enhance customer experiences20:49 – Conversation with Kathleen Tandy55:26 – The passion for college football and championship weekend!GuestKathleen Tandy: https://www.linkedin.com/in/kptandy/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

What does it really take to sell an AI-native product into the Fortune 500? In this episode of Founded & Funded, Madrona Managing Director Matt McIlwain sits down with two founders deep in the trenches of enterprise AI adoption, Esha Joshi (Co-founder, Yoodli⁩) and Anup Chamrajnagar (Co-founder, Gradial.) Their companies are selling into some of the world's most complex organizations, like Google, SAP, Snowflake, Databricks, and more. And they break down what founders often underestimate about enterprise AI sales. They dive into: Why most AI pilots fail and how to prevent it The "three-legged stool" of enterprise sales How AI review boards are reshaping buying cycles Securing long-term contracts Pricing AI: seats vs. usage vs. outcomes Navigating non-deterministic AI failures with customers Building champions who accelerate their careers with AI If you're building an AI-native company and selling into enterprises, this is for you. Full Transcript: https://www.madrona.com/this-is-how-fortune-500-companies-are-buying-ai-today Chapters: (00:00) – Introduction (03:37) – Early AI Pilots: What Worked (and What Didn't) (05:01) – Sell Pain, Not Features (06:25) – Why Enterprise Expectations Are Higher Now (07:48) – Moving From "Wow" Factor to Durable Outcomes (09:17) – How to Structure a Pilot That Converts (10:35) – Expanding Beyond the Initial Wedge (13:41) – Turning Pilots Into 12-Month Contracts (14:47) – Navigating Procurement & AI Governance Boards (16:02) – What's Changed (and What Hasn't) in Enterprise Sales (16:45) – How to Increase Deal Velocity (19:39) – Using AI to Improve Your Own Sales Ops (20:20) – Are You Replacing Jobs with AI? (23:14) – Building Career-Accelerating Champions (23:46) – When AI Outputs Go Wrong (Real Stories) (25:23) – Why the Pilot Never Stops (29:04) – Pricing AI: Seats vs. Usage vs. Outcomes (34:48) – Go-To-Market Partnerships That Unlock Enterprise (37:25) – The Role of Forward-Deployed Engineers (38:44) – Final Advice for AI Founders Selling to Enterprise

AI funding rounds are getting bigger. Infrastructure bets are getting steeper. And the SaaS model is back under pressure. On episode 294 of The Six Five Pod, Patrick Moorhead and Daniel Newman break down the $110B OpenAI raise, Amazon's expanded role, AMD's $100B Meta deal, sovereign cloud momentum, and whether or not the SaaS premium is being permanently eroded. The handpicked topics for this week are: OpenAI's $110B Funding Round & Amazon's $50B Commitment: OpenAI secured a $110B round backed by Amazon, NVIDIA, and SoftBank. Amazon committed $50B over eight years, including Tranium capacity, co-development, Bedrock integration, and custom model initiatives. Microsoft remains the exclusive API cloud provider, but the competitive cloud dynamics are shifting. Anthropic, the Pentagon & the AI Safety Line: Anthropic risks a $200M DoD contract over refusing to drop safety restrictions related to mass surveillance and automated weapons. Pat and Dan explore the ethics and competitive positioning of this, and what happens if another lab steps in. Model Distillation & IP Risk: Anthropic cited 24,000 fraudulent accounts generating 16 million interactions to distill model capabilities. The episode examines IP theft, enforcement gaps, and global competition. DeepSeek & NVIDIA Blackwell Reports: Recent reports suggest DeepSeek leveraged NVIDIA Blackwell chips. The hosts discuss export controls, enforcement realities, and whether this was ever realistically in doubt. Microsoft Sovereign Cloud Goes GA: Microsoft introduced full-stack Azure sovereign cloud capabilities with support for disconnected operations. Sovereignty, regulatory compliance, and latency management are becoming core enterprise and government requirements. AMD's $100B Meta AI Infrastructure Deal: AMD secured a massive multi-gigawatt inference-focused deal with Meta using MI450. The discussion centers on competitive dynamics with NVIDIA, scale-up architecture, and whether AMD can materially shift market share. Intel & SambaNova Alignment: Intel Capital invested in SambaNova's Series E. The hosts examine inference strategy, CPU resurgence, and how Intel rounds out its AI positioning while advancing its GPU roadmap. The Flip: Is SaaS Permanently Repriced? Are enterprise SaaS multiples structurally resetting due to AI agents and consumption models, or is the market misreading enterprise AI adoption speed? Nuance emerges around consolidation, consumption pricing, and the durability of complex enterprise platforms. Bulls & Bears: NVIDIA, Salesforce, Synopsys, Dell, Snowflake, IBM, Everpure, HP Strong earnings across several big tech companies met with mixed market reactions. Terminal value concerns, consumption transitions, stock-based compensation, and memory constraints shape sentiment more than raw performance. For a deeper dive into each topic, subscribe to The Six Five Pod so you never miss an episode.

Robert DeNiro said something about Trump on MS-NOW that Bill O'Reilly didn't like so now he thinks DeNiro should catch a charge

Carl Quintanilla, Jim Cramer and David Faber delved into Nvidia's blowout quarter and upbeat guidance fueled by the AI boom — plus why the stock swung into negative territory at the opening bell. It was a different story for Salesforce, which posted better-than-expected Q4 results and erased its pre-market losses at the open. The CEOs of both companies spoke to CNBC: Nvidia's Jensen Huang on what the market got "wrong" — and Salesforce's Marc Benioff on the "SaaS-pocalypse" that has sent shares of the company and its software rivals tumbling. Also in focus: Snowflake heats up, the earnings chapter in the battle for Warner Bros. Discovery, the automaker that posted its first-ever annual loss, robots in China. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Chris Degnan was the first sales hire at Snowflake and spent 11 years scaling the company from zero to $3.5 billion in revenue as its CRO, working alongside four different CEOs and learning from each one. In this episode, Chris breaks down what it actually takes to scale an enterprise sales organization, why MEDDIC is the methodology every founder should know, and what working under Frank Slootman taught him about firing fast, taking feedback and finding the fakers in your team. In today's episode, we discuss: What the CRO job looks like at $10M vs. $1B+ Why sales leaders must know how to sell the product themselves The MEDDIC methodology and why it's a founder's best insurance policy How to find the fakers, manage-uppers and passengers in your org What Frank Slootman got right — and wrong — about scaling Snowflake Why most AI companies will face a go-to-market reckoning References: Amazon: https://www.amazon.com/ Bob Muglia: https://www.linkedin.com/in/bob-muglia-714ba592/ Carl Eschenbach: https://www.linkedin.com/in/carl-eschenbach-980543/ Christian Kleinerman: https://www.linkedin.com/in/christian-kleinerman-a973102/ Denise Persson: https://www.linkedin.com/in/denisepersson/ Dell: https://www.dell.com/ Frank Slootman: https://www.linkedin.com/in/frankslootman/ John McMahon: https://www.linkedin.com/in/johnmcmahon1/ Michael Scarpelli: https://www.linkedin.com/in/michael-scarpelli-1b289b9/ Microsoft: https://www.microsoft.com/ Oracle: https://www.oracle.com/ Salesforce: https://www.salesforce.com/ Snowflake: https://www.snowflake.com/ Sridhar Ramaswamy: https://www.linkedin.com/in/sridhar-ramaswamy/ Stanford Graduate School of Business: https://www.gsb.stanford.edu/ Where to find Chris: LinkedIn: https://www.linkedin.com/in/chris-degnan/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 What is the job of a CRO? 01:12 What excellence looks like at different revenue stages 02:59 Sales leaders need to know how to sell the product 04:52 The hardest skill leaders have to learn 08:17 You need to stay open to feedback - at all levels 14:01 Sales, segmentation, and international expansion 16:17 Why MEDDIC is the foundation for every sales org 20:32 The metrics that actually matter 22:56 A week in the life of a CRO at scale 28:32 Navigating compensation at a GTM organization 31:45 What technical CEOs get wrong about GTM 36:01 The role of hunger in great sales leaders 40:35 What makes an exceptional IC sales rep 46:41 Dysfunctional vs. high-performing executive teams 48:01 Chris' most impactful decisions at Snowflake 49:53 "When there's doubt, there's no doubt" 54:49 Learning from world-class leaders

Snowflake (SNOW) and Salesforce (CRM) are signaling a shift in software economics as AI moves from hype to real revenue impact. Stephanie Walter explains how consumption-based and subscription models are giving way to pricing tied to digital workload scale and data activation. The advantage is consolidating around platforms that can turn clean data into automated workflows, not just store it.======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Snowflake expects fiscal 2027 product revenue of $5.7 billion, above analysts’ average estimate of $5.5 billion compiled by LSEG, driven by rising AI demand. CEO Sridhar Ramaswamy said the company signed its largest deal ever, over $400 million, without naming the client. He speaks with Bloomberg's Ed Ludlow and Caroline Hyde. See omnystudio.com/listener for privacy information.

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry, and tech. Energy transportation is a deeply local business, safely delivering gas and electricity, more and more from renewable sources, directly to the communities it serves. Technology and AI help make that possible by strengthening safety, bringing companies closer to customers, and enabling teams to build the future together. This week, Dave, Esmee, and Rob are joined by John Koerwer, CIO of UGI Corporation, to explore explore why “the business” and tech still struggle to speak the same language, nd what helps close the gap.TLDR00:35 – Introduction01:17 – Hang out: new toys and coffee07:55 – Dig in: the business - tech divide21:07 – Conversation with John Koerwer59:40 – The amazing AI technology in The Sphere's version of The Wizard of OzGuestJohn Koerwer: https://www.linkedin.com/in/john-koerwer-46102127/HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:  https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

The Information's Sri Muppidi talks with TITV Host Akash Pasricha about Amazon's potential $50 billion OpenAI deal and its AGI-triggered terms. We also talk with Wedbush Managing Director Matt Bryson about Nvidia's blowout quarter, stock selloff, China export risks and margins, and reporter Anita Ramaswamy about how AI is reshaping Salesforce and Snowflake's growth and how Alphabet, Amazon and Meta are using debt to fund AI capex. Lastly, we get into autonomous warships and defense investing with Deputy Bureau Chief of Finance Cory Weinberg and the new data infrastructure stack for humanoid robots with Encord Co-CEOs Ulrik Stig Hansen and Eric Landau.Articles discussed on this episode: https://www.theinformation.com/articles/amazons-50-billion-investment-openai-hinge-ipo-agihttps://www.theinformation.com/articles/alphabet-big-tech-borrow-hundreds-billionshttps://www.theinformation.com/articles/autonomous-warship-startup-saronic-raising-7-5-billion-valuationhttps://www.theinformation.com/newsletters/ai-agenda/robot-data-startup-raises-60-millionSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Scott Wapner and the Investment Committee debate the tech sector as Nvidia, Salesforce and Snowflake all reporting earnings tonight. CNBC's Kristina Partsinevelos joins us with the latest from Nvidia. Plus, the Committee share their latest portfolio moves. And later, we get to the Setup on some key Committee names reporting earnings tonight and tomorrow.  Investment Committee Disclosures Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Los Angeles man arrested for serving alcohol to a hawk. How much cocaine is in the Nantucket sewage? New York City Police investigating after officers hit with snowballs during a snowball fight in the park. Weird AF News is the only daily weird news podcast in the world. Weird news 5 days/week and on Friday it's only Floridaman. SUPPORT by joining the Weird AF News Patreon http://patreon.com/weirdafnews - OR buy Jonesy a coffee at http://buymeacoffee.com/funnyjones Buy MERCH: https://weirdafnews.merchmake.com/ - Check out the official website https://WeirdAFnews.com and FOLLOW host Jonesy at http://instagram.com/funnyjones - wants Jonesy to come perform standup comedy in your city? Fill out the form: https://docs.google.com/forms/d/e/1FAIpQLSfvYbm8Wgz3Oc2KSDg0-C6EtSlx369bvi7xdUpx_7UNGA_fIw/viewform

The future of the tech trade is on the line tonight as Nvidia, Salesforce and Snowflake report. We discuss with our mega-panel: Star Bernstein analyst Stacy Rasgon, Capital Area Planning's Malcolm Ethridge, Requisite Capital's Bryn Talkington, CNBC's Kristina Partsinevelos and Seema Mody. Plus, private credit concerns are front and center again today. We discuss these new developments with our Leslie Picker. And, the battle between Anthropic and the government is heating up. We break down all the details – and what's at stake for the big AI battle. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Software faces it latest test with results from Workday. A look ahead to earnings from Salesforce and Snowflake. Plus, the CEO of Cava with his first reaction to earnings. The stock up more than 20% after the company says they are bridging the K-shaped economy. And the Department of Defense pressing anthropic for full access to its AI tools. The company's response and why it may not be so straight forward. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

APAC stocks traded higher as the region took impetus from the rebound on Wall Street after Anthropic's presentation helped soothe some AI/software concerns, and with tech also bolstered by the USD 60bln Meta-AMD chip deal; Euro Stoxx 50 futures up 0.2% after the cash market closed flat on Tuesday.US President Trump talked up the economy in his State of the Union Address, saying that the nation is back, bigger, better and stronger than before, while he added that we've seen nothing yet.Regarding tariffs, Trump said the Supreme Court decision on tariffs is very unfortunate but added that tariffs will remain in place and nearly all countries want to keep the trade deals.Trump also commented on Iran, which he claimed is working on missiles that could soon reach the US, and noted Iran wants to make a deal but hasn't yet said that it won't pursue nuclear weapons.Antipodeans were firmer amid the positive risk appetite, and with AUD/USD leading the advances following firmer-than-expected monthly CPI data from Australia.Looking ahead, highlights include German GfK (Mar), GDP Final (Q4), Swiss Sentiment (Feb), EZ HICP Final (Jan). Speakers include RBA's Bullock, Fed's Musalem, Barkin & Schmid. Supply from Germany & US. Earnings from NVIDIA, Salesforce, Snowflake, TJX Companies, Lowe's, Synopsys & Bayer.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

US President Trump talked up the economy in his State of the Union Address, saying that the nation is back, bigger, better and stronger than before, while he added that we've seen nothing yet.Regarding tariffs, Trump said the Supreme Court decision on tariffs is very unfortunate but added that tariffs will remain in place and nearly all countries want to keep the trade deals.European bourses firmer as HSBC lifts the banking sector; US equity futures hold onto Anthropic- driven gains. DXY flat, Aussie outpaces peers post CPI while JPY lags in continuation of recent weakness.JGBs underperform on Takaichi's "reflationist" BoJ candidates; USTs await Fed speak & NVDA.Crude prices rangebound; Spot XAU holds above USD 5200/oz. Looking ahead, highlights include Fed's Musalem, Barkin & Schmid. Supply from the US. Earnings from NVIDIA, Salesforce, Snowflake, TJX Companies, Lowe's & Synopsys.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Ep. 35 - Snow Day! - Music with Miss Jen - An Early Childhood Music Class PodcastWelcome to the Music with Miss Jen podcast, an engaging early childhood music class full of playful songs, imaginative lyrics, and music that will make your child want to clap, dance, and sing along! While designed especially for the younger listener, this music class is one the whole family can enjoy, with simple instrumentation and a focus on high-quality music selections.In this episode, we are singing winter songs for our snow days this week, including:"Let's Sing Hello Together" - words © 2000 Music with Miss Jen, traditional music“Snow on the Rooftops” - music © Kathy Reid-Naiman from Sing the Cold Winter Away“Move to the Beat” - © Stephanie Leavell (www.musicforkiddos.com)“If All the Snowflakes” - traditional, additional words © 2025 Music with Miss Jen"Winter Weather” - words © 2024 Music with Miss Jen, accompaniment music licensed from Pixabay“Bluesy Shaker Song” - words and music © 2025 Music with Miss Jen“Windshield Wiper” - traditional first verse, additional words © 2025 Music with Miss Jen“Chubby Little Snowman” - traditional words, music © 2025 Music with Miss JenFind my Chubby Little Snowman video here: https://youtu.be/cVxV7A1gJ5s“S is for Snowman” - words © 2024 Music with Miss Jen, accompaniment music licensed from PixabayFind my S is for Snowman video here: https://youtu.be/xAlDxSh5N28“Goodbye, My Friends” - - words and music © 2025 Music with Miss JenVisit my website for printable song pages to go along with some of today's songs: https://www.musicwithmissjen.com/podcast/ep-35You can find more songs in my digital products available in my Teachers Pay Teachers store or on Etsy.Where to find more Music with Miss Jen:Website: https://www.musicwithmissjen.comYouTube: https://www.youtube.com/@musicwithmissjenInstagram: https://www.instagram.com/musicmissjen/About Miss Jen -Miss Jen has been making music with young children for over 25 years. While she has taught all ages, early childhood has been her area of expertise for her entire teaching career. She has taught in both public and independent schools in a number of urban, suburban, and rural settings in 3 states. For the past 20 years, she has taught music outreach programs in preschools and day care centers, as well as conservatory-based music programs for infants up through fourth grade. She still actively teaches in multiple preschools and daycare centers, working with nearly 300 students and 45 teachers each year.

Nvidia, Salesforce, Lowe´s, HP, Snowflake...bajo la lupa de Javier Aledo, analista de Afi.

We're coming to the cottage and we want you to join us! Our much-anticipated Heated Rivalry episode is finally here. This conversation is formatted a little differently than usual, as we recorded our thoughts after watching each episode to give you a watch-along experience. This will also make it easy to avoid spoilers if you somehow haven't seen Heated Rivalry yet. Just a heads up that since this was a less formal recording set up, we used different mics, so the sound quality is a little different from our usual episodes.Let us know what you think! How many times have you reheated? We can't wait to hear from you.Check out our Patreon for some of our favorite HR related videos and internet content! ⁠https://www.patreon.com/thebipod⁠⁠We also wanted to share some resources for protecting your communities from ICE:You can learn more or get involved with Órale (local to Long Beach, CA) here: https://www.orale.org/Immigrant Defense Project has toolkits for defending against ICE raids and community arrests: https://www.immigrantdefenseproject.org/raids-toolkit/

In this episode of The Effortless Podcast, Dheeraj Pandey speaks with Dr. Abhishek Bhowmick about how quantum mechanics reshaped our understanding of determinism and why that shift matters for AI today.  From the Einstein–Bohr debates to the idea that nature is fundamentally probabilistic, they explore how the collapse of “if-then” thinking began nearly a century ago. The discussion draws parallels between quantum superposition and modern LLM behavior. At its core, the episode reframes AI as a rediscovery of how reality computes. The conversation then moves from physics to computing architecture, tracing the evolution from scalar CPUs to GPUs, TPUs, tensors, and eventually quantum computing. They examine why probabilistic systems and vector math feel more natural than purely deterministic software. Hybrid computing models show that classical systems still matter. The episode also unpacks what quantum computers are truly good at, especially in cryptography and simulation. Ultimately, it reflects on whether the future of computing lies in embracing probability rather than resisting it. Key Topics & Timestamps 00:00 – Welcome, context, and how Dheeraj & Abhishek met 04:00 – Abhishek's journey: IIT, Princeton, Apple, Snowflake 08:00 – The 1927 Solvay Conference and physics at a crossroads 12:00 – Einstein vs. Bohr: determinism vs. probability 16:00 – Superposition and the collapse of the wave function 20:00 – Fields vs. particles: what is an electron really? 25:00 – Matter particles, force particles, and the Standard Model 30:00 – Transistors, voltage, and the rise of deterministic computing 35:00 – From scalar CPUs to vectors and matrices 40:00 – Tensors, linear algebra, and modern AI systems 45:00 – Principle of Least Action and gradient descent parallels 50:00 – Hallucinations, probability mass, and LLM behavior 55:00 – Vector databases, embeddings, and KNN search 59:00 – GPUs vs. TPUs: matrix vs. tensor architectures 1:05:00 – What quantum computers are actually good at 1:10:00 – Post-quantum cryptography and the future of computing Host -  Dheeraj Pandey Co-founder & CEO at DevRev. Former Co-founder & CEO of Nutanix. A systems thinker and product visionary focused on AI, software architecture, and the future of work. Guest -  Dr Abhishek Bhowmick                                                                                                                                                                                                                Co-Founder and CTO of Samooha, a secure data collaboration platform acquired by Snowflake. He previously worked at Apple as Head of ML Privacy and Cryptography, System Intelligence, and Machine Learning, and earlier at Goldman Sachs. He attended Princeton University and was awarded IIT Kanpur's Young Alumnus Award in 2024. Follow the Host and Guest - Dheeraj Pandey: LinkedIn - https://www.linkedin.com/in/dpandey Twitter - https://x.com/dheeraj Abhishek Bhowmik  LinkedIn –  https://www.linkedin.com/in/ab-abhishek-bhowmick Twitter/X – https://x.com/bhowmick_ab Share Your Thoughts Have questions, comments, or ideas for future episodes?

Fluent Fiction - Swedish: Lost in Snowflakes: An Architect's Journey Back to Joy Find the full episode transcript, vocabulary words, and more:fluentfiction.com/sv/episode/2026-02-21-08-38-20-sv Story Transcript:Sv: Utan förvarning började snöflingorna tumla över de kullerstensbelagda gatorna i Gamla Stan.En: Without warning, snowflakes began to tumble over the cobblestone streets of Gamla Stan.Sv: Inne i den lilla kaféet spred sig aromen av färskt kaffe i den varma och hemtrevliga atmosfären.En: Inside the little café, the aroma of fresh coffee spread in the warm and cozy atmosphere.Sv: Kaféet var fyllt av mjukt ljus som reflekterades i de gamla träbalkarna.En: The café was filled with soft light that reflected off the old wooden beams.Sv: Elin satt vid ett hörnbord och väntade nervöst.En: Elin sat at a corner table, waiting nervously.Sv: Elin hade alltid varit fokuserad på sin karriär.En: Elin had always been focused on her career.Sv: Som arkitekt tillbringade hon timmar på kontoret.En: As an architect, she spent hours at the office.Sv: Men ibland, när snön föll och staden blev en idyllisk snöglob, längtade Elin efter ett enklare liv.En: But sometimes, when the snow fell and the city became an idyllic snow globe, Elin longed for a simpler life.Sv: Hon tänkte ofta på sin barndomsvän Magnus.En: She often thought about her childhood friend, Magnus.Sv: Magnus hade valt en annan väg i livet.En: Magnus had chosen a different path in life.Sv: Han drev en liten bokhandel, precis runt hörnet.En: He ran a small bookstore just around the corner.Sv: De hade känt varandra sedan de var barn och nu, efter alla dessa år, hade de bestämt sig för att träffas igen.En: They had known each other since they were children, and now, after all these years, they had decided to meet again.Sv: Magnus anlände med ett leende.En: Magnus arrived with a smile.Sv: Hans kindrosor vittnade om den kalla vintervinden.En: His rosy cheeks testified to the cold winter wind.Sv: De beställde kaffe och någonstans mellan första och andra klunken kaffe, började prata.En: They ordered coffee, and somewhere between the first and second sip, they began to talk.Sv: De delade minnen och skratt, men inombords kämpade Elin.En: They shared memories and laughter, but inside, Elin was struggling.Sv: Hon ville berätta om sina känslor, sin stress och sitt behov av förändring.En: She wanted to talk about her feelings, her stress, and her need for change.Sv: Men vad skulle Magnus tycka?En: But what would Magnus think?Sv: Elins hjärta bultade när Magnus plötsligt ställde en fråga som träffade henne mitt i hjärtat: "Hur mår du egentligen, Elin?"En: Elin's heart pounded when Magnus suddenly asked a question that hit her right in the heart: "How are you really doing, Elin?"Sv: Hon tvekade, men de var ju vänner.En: She hesitated, but they were friends after all.Sv: "Magnus, jag... ibland känns det som om jag har tappat bort mig själv i allt jobb."En: "Magnus, I... sometimes it feels like I've lost myself in all the work."Sv: Magnus nickade förstående och lutade sig lite närmare.En: Magnus nodded understandingly and leaned in a little closer.Sv: "Du behöver tid för det som gör dig glad, Elin."En: "You need time for what makes you happy, Elin."Sv: Elin kände sig plötsligt lättare.En: Elin suddenly felt lighter.Sv: Det var som om en börda hade lyfts från hennes axlar.En: It was as if a burden had been lifted from her shoulders.Sv: Magnus talade vidare om att följa sin passion och inte glömma bort livets små glädjeämnen.En: Magnus continued talking about following one's passion and not forgetting the little joys of life.Sv: Kaféets varma ljus och känslan av en gammal väns förståelse trängde undan vinterkylan utanför.En: The warm light of the café and the feeling of an old friend's understanding pushed away the winter cold outside.Sv: Elin log, tacksam för Magnus uppriktighet.En: Elin smiled, thankful for Magnus' honesty.Sv: De pratade vidare och innan de skildes åt, hade Elin bestämt sig: hon skulle fokusera mer på det som gjorde henne lycklig.En: They talked further, and before they parted ways, Elin had made up her mind: she would focus more on what made her happy.Sv: När de lämnade kaféet och snöflingorna fortsatte sin dans genom luften, visste Elin att det var början på en ny balans i hennes liv.En: As they left the café and the snowflakes continued their dance through the air, Elin knew that it was the beginning of a new balance in her life.Sv: Hon såg inte längre på framtiden med samma bekymrade blick.En: She no longer looked at the future with the same worried gaze.Sv: Istället följde hon snöflingornas dans med ett nyfunnet lugn.En: Instead, she followed the dance of the snowflakes with a newfound calm.Sv: Och Magnus, han visste att han hade hjälpt en gammal vän att hitta vägen till sig själv.En: And Magnus, he knew he had helped an old friend find her way back to herself. Vocabulary Words:tumble: tumlacobblestone: kullerstensbelagdaaroma: aromencozy: hemtrevligbeam: träbalkarnervously: nervöstidyllic: idyllisklonged: längtadetestified: vittnadehesitated: tvekadeburden: bördashoulders: axlarpassion: passionjoys: glädjeämnenunderstanding: förståelsethankful: tacksamhonesty: uppriktighetbalance: balansgaze: blickcalm: lugnspread: spredfocused: fokuseradcareer: karriärsip: klunkenstruggling: kämpadepounded: bultadeleaned: lutademakes: görlifted: lyftsparted: skildes

Fluent Fiction - Danish: Secrets, Snowflakes, and Twinkling Lights at Tivoli Gardens Find the full episode transcript, vocabulary words, and more:fluentfiction.com/da/episode/2026-02-20-08-38-20-da Story Transcript:Da: Tivoli Gardens var klædt i vinterens smukke lys.En: Tivoli Gardens was adorned with the beautiful lights of winter.Da: Sneen dalede blidt fra himlen, og luften var fyldt med duften af brændte mandler og varm kakao.En: Snow fell gently from the sky, and the air was filled with the scent of roasted almonds and hot cocoa.Da: Magnus gik hurtigt gennem menneskemængden.En: Magnus hurried through the crowd.Da: Han var bekymret.En: He was worried.Da: Han havde mistet et brev.En: He had lost a letter.Da: Et vigtigt brev.En: An important letter.Da: Brevet indeholdt en hemmelighed om en nær ven.En: The letter contained a secret about a close friend.Da: Hvis nogen fandt det, kunne det bringe stor skam.En: If someone found it, it could bring great shame.Da: Magnus kunne ikke lade det ske.En: Magnus could not let that happen.Da: Han måtte finde det hurtigst muligt.En: He had to find it as quickly as possible.Da: Pludselig hørte han en velkendt stemme.En: Suddenly, he heard a familiar voice.Da: "Magnus?En: "Magnus?Da: Hvad laver du her?"En: What are you doing here?"Da: Det var Stine, hans kollega fra arbejdet.En: It was Stine, his colleague from work.Da: Hun var der tilfældigt, nyde de smukke lys.En: She was there by chance, enjoying the beautiful lights.Da: Magnus tøvede et øjeblik, men besluttede at fortælle hende alt.En: Magnus hesitated for a moment but decided to tell her everything.Da: "Jeg har brug for din hjælp," sagde han.En: "I need your help," he said.Da: Stine forstod straks alvoren.En: Stine immediately understood the gravity of the situation.Da: "Selvfølgelig, lad os lede sammen," sagde hun og tog fat i hans arm.En: "Of course, let's search together," she said, taking hold of his arm.Da: Sammen bevægede de sig gennem folkemængden som et målrettet hold.En: Together, they maneuvered through the crowd like a determined team.Da: De kiggede under bænke og langs stierne, mens musikken spillede stille i baggrunden.En: They looked under benches and along the paths while music played softly in the background.Da: Da de nærmede sig den centrale sø, hvor lyshjerter var sat op på vandet, så Magnus noget ud af øjenkrogen.En: As they approached the central lake, where light hearts were set up on the water, Magnus spotted something out of the corner of his eye.Da: "Der!"En: "There!"Da: råbte han.En: he shouted.Da: En fremmed stod med brevet i hånden, klar til at åbne det.En: A stranger stood with the letter in hand, ready to open it.Da: Magnus' hjerte bankede.En: Magnus' heart pounded.Da: Han kunne konfrontere personen direkte, men det kunne skabe en scene.En: He could confront the person directly, but it could create a scene.Da: Stine lagde en hånd på hans skulder.En: Stine placed a hand on his shoulder.Da: "Vi laver en distraktion," hviskede hun.En: "We'll create a distraction," she whispered.Da: Mens Magnus gik tættere på, sørgede Stine for at vælte en lille taske ved et kiksebageri.En: As Magnus moved closer, Stine made sure to knock over a small bag at a cookie bakery.Da: Folk vendte sig om for at se på det lille optrin.En: People turned to look at the small commotion.Da: I det øjeblik snuppede Magnus brevet, stadig uåbnet, fra den fremmede forskrækket over tumulten.En: At that moment, Magnus snatched the letter, still unopened, from the stranger startled by the brouhaha.Da: Han satte hurtigt hen til Stine.En: He quickly went back to Stine.Da: "Lad os gå," sagde hun smilende.En: "Let's go," she said, smiling.Da: De gik begge mod udgangen fra de lysende haver, solen gik ned, og lyset blev stærkere omkring dem.En: They both walked towards the exit from the illuminated gardens, the sun setting, and the lights growing stronger around them.Da: "Tak, Stine," sagde Magnus hengivent.En: "Thank you, Stine," said Magnus gratefully.Da: Han indså, at han ikke altid behøvede at gøre alting selv.En: He realized that he didn't always have to do everything himself.Da: Under sneen og de blinkende lys fik deres venskab ny styrke.En: Beneath the snow and the twinkling lights, their friendship gained new strength.Da: Kristian, en gammel ven af Magnus, gik pludselig forbi dem.En: Kristian, an old friend of Magnus, suddenly walked past them.Da: Han vinkede og smilede varmt.En: He waved and smiled warmly.Da: Måske betød dette tilfældige møde, at en ny begyndelse var mulig.En: Perhaps this random meeting signaled that a new beginning was possible.Da: Magnus følte håb.En: Magnus felt hopeful.Da: Tivoli Gardens, med sine lys, sne og varme venskaber, føltes som en perfekt baggrund for Magnus' nye indsigt.En: Tivoli Gardens, with its lights, snow, and warm friendships, felt like a perfect backdrop for Magnus' new insight.Da: Han var ikke alene.En: He was not alone.Da: Han havde venner, og de var der for ham, når det virkelig galdt.En: He had friends, and they were there for him when it truly mattered.Da: Dette var kun begyndelsen på mange flere gode øjeblikke.En: This was only the beginning of many more good moments. Vocabulary Words:adorned: klædtscent: duftenroasted: brændteworried: bekymretcontained: indeholdtsecret: hemmelighedshame: skamhesitated: tøvedeabsolute: alvormaneuvered: bevægededetermined: målrettetbenches: bænkepaths: stiernespotted: såstranger: fremmedconfront: konfronterescene: scenecommotion: optrinstartled: forskrækketsnatched: snuppederealized: indsåinsight: indsigtilluminated: lysendehopeful: håbbackdrop: baggrundhappened: sketepounded: bankededistraction: distraktionknocked: væltedetumult: tumulten

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

#331 | Dave is joined by a group of marketing leaders from Ramp, Snowflake, and Hightouch for a discussion about ABM and their plans for 2026. Casey Patterson (Director of ABM, Snowflake), Drew Pinta (Director of Growth Data Science, Ramp), and Brian Kotlyar (CMO, Hightouch) break down what ABM actually looks like in 2026 and what's working right now inside of their companies. They share how they're picking target accounts, aligning with sales, and building programs that go way beyond running ads. The group also digs into measurement, personalization, and how teams are using better data and AI to scale ABM without wasting budget. If you need a deeper dive on ABM tactics right now, this is the episode for youTimestamps(00:00) - - Why ABM is still a top topic in 2026 (04:31) - - Intros: Snowflake, Ramp, and Hightouch (07:51) - - Defining ABM (and why sales alignment is everything) (14:01) - - The “stop list”: ABM tactics they've killed (16:31) - - Why paid social “ABM awareness” is overrated (19:51) - - Shifting ABM to in-person and physical plays (24:01) - - Budgeting for ABM and how to start small (28:59) - - Why ABM measurement is different than traditional demand gen (34:19) - - How they build ABM audiences using data + signals (39:39) - - Scaling personalization without making it manual (45:19) - - Final takeaways and wrap-up Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Knak - A no-code, campaign creation platform that lets you go from idea to on-brand email and landing pages in minutes, using AI where it actually matters. Learn more at knak.com/exitfive.Optimizely - An AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Get a free, personalized 45-minute AI workshop to help you identify the best AI use cases for your marketing team and map out where agents can save you time at optimizely.com/exitfive (PS - you'll get a FREE pair of Meta Ray Bans if you do). Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.  ***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, technology, and society. Hosts Dave Chapman, Esmee van de Giessen, and Rob Kernahan unpack 2026's defining trends, from AI and sovereignty to adaptability and automation, offering fresh insight, candid reflections, and forward‑looking conversations shaping the year ahead. TLDR00:20 – Introduction of Realities Remixed02:30 – Why the show evolved?04:50 – Dig in with the team: Predictions for 202606:40 – Macro trends13:00 – Sovereignty 17:40 – Agentic AI22:17 – Human–AI interaction26:06 – Cloud trends30:42 – AI scaling, domain‑specific models35:03 – Adoption lag39:34 – Physical AI43:47 – Quantum computing48:21 – Hardware acceleration50:30 – Cybersecurity52:38 – Season outlook HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

What if your data platform could power both critical business decisions and real-time product features at scale? In this episode, host Benjamin sits down with Magnus Dahlbäck, Senior Director of Data and Platform at Voi, to explore how a metrics-first approach and semantic layers transform data accessibility, why traditional ML and LLMs require different strategies for different problems, and how to balance FinOps costs while processing billions of IoT events daily. Whether you're building data infrastructure for a high-growth company or rethinking how your organization consumes data, this conversation is packed with practical strategies for unlocking data value and preparing your platform for AI. Tune in to discover how Voi ditched traditional BI tools and revolutionized their approach to enterprise analytics.

Fluent Fiction - Danish: From Snowflakes to Spotlight: Mikkel's Winter Festival Triumph Find the full episode transcript, vocabulary words, and more:fluentfiction.com/da/episode/2026-02-17-08-38-20-da Story Transcript:Da: Mikkel stod ved indgangen til Tivoli og så ud over den sneklædte park.En: Mikkel stood at the entrance to Tivoli and looked out over the snow-covered park.Da: Tidlig morgen, og snefnuggene dalede stille ned.En: It was early morning, and snowflakes were silently falling.Da: Han havde altid drømt om at lave den perfekte festival.En: He had always dreamed of creating the perfect festival.Da: En festival, der lokkede folk til på trods af vinterens kolde greb.En: A festival that would attract people despite the cold grip of winter.Da: "Vi skal tænke stort, Mikkel," sagde Freja, da hun kom gående forbi med en lang to-do-liste i hånden.En: "We need to think big, Mikkel," said Freja as she walked by with a long to-do list in hand.Da: "Folk er ikke vant til festivaler om vinteren."En: "People aren't used to festivals in winter."Da: "Jeg ved det," svarede Mikkel med et smil.En: "I know," replied Mikkel with a smile.Da: "Men tænk på, hvad vi kan gøre.En: "But think about what we can do.Da: Lysinstallationer, interaktive kunstværker, lokale kunstnere.En: Light installations, interactive artworks, local artists.Da: Alt indendørs, hvis vi skal, for vejret kan være lunefuldt."En: Everything indoors, if we must, because the weather can be unpredictable."Da: Lars ankom kort efter, pænt pakket ind i hans store frakke.En: Lars arrived shortly after, neatly bundled in his large coat.Da: "Husk, vi har ikke råd til at gå over budget," sagde han med et bekymret blik.En: "Remember, we can't afford to go over budget," he said with a worried look.Da: "En storm kan nemt koste os dyrt."En: "A storm can easily cost us dearly."Da: Mikkel nikkede forstående.En: Mikkel nodded understandingly.Da: Han var under pres.En: He was under pressure.Da: Han måtte finde en balance mellem sine drømme og realiteterne.En: He had to find a balance between his dreams and reality.Da: Han præsenterede sin plan for Freja og Lars med entusiasme.En: He presented his plan to Freja and Lars with enthusiasm.Da: Freja så skeptisk ud, men Mikkels detaljerede udlægning og vilje til at lytte vandt hende over.En: Freja looked skeptical, but Mikkel's detailed explanation and willingness to listen won her over.Da: Lars, der fokuserede på budgettet, gav et nik og en lille, acceptabel smil.En: Lars, focused on the budget, gave a nod and a small, acceptable smile.Da: Dagene gik hurtigt, og festivalen nærmede sig.En: The days went by quickly, and the festival approached.Da: Parken blev langsomt forvandlet til et vintereventyr, med lys i hver krog og varm chokolade klar.En: The park was slowly transformed into a winter wonderland, with lights in every corner and hot chocolate ready.Da: Selvom sneen fortsat dalede, var stemningen magisk.En: Although the snow continued to fall, the atmosphere was magical.Da: Den store dag kom.En: The big day arrived.Da: Men morgenen bød på en overraskelse: en voldsom snestorm.En: But the morning brought a surprise: a severe snowstorm.Da: Mikkel så bekymret på Freja og Lars.En: Mikkel looked worriedly at Freja and Lars.Da: "Plan B?"En: "Plan B?"Da: spurgte Freja kort.En: Freja asked curtly.Da: Mikkel reagerede hurtigt.En: Mikkel reacted quickly.Da: "Vi rykker det hele indenfor.En: "We'll move everything indoors.Da: Vi har forberedt os, lad os gøre det."En: We've prepared for this, let's do it."Da: Personalet arbejdede hurtigt med at flytte aktiviteterne ind i parkens eventhaller.En: The staff worked quickly to move activities into the park's event halls.Da: Besøgende, der kæmpede mod sneen, fandt vej ind og blev betaget af de kreative indendørs oplevelser.En: Visitors, who battled against the snow, found their way inside and were captivated by the creative indoor experiences.Da: Interaktive installationer, kunstudstillinger fra lokale talenter, og varme drinks bragte smil til de besøgendes ansigter.En: Interactive installations, art exhibitions from local talents, and warm drinks brought smiles to the visitors' faces.Da: Mikkel så ud over mængden, lettet.En: Mikkel looked out over the crowd, relieved.Da: Sneen kunne ikke knuse hans drømme, og festivalen summede af liv.En: The snow couldn't crush his dreams, and the festival buzzed with life.Da: Lars kom og klappede ham på skulderen.En: Lars came over and patted him on the shoulder.Da: "Godt arbejde, Mikkel," sagde han med et oprigtigt smil.En: "Great job, Mikkel," he said with a sincere smile.Da: Freja tilføjede: "Jeg troede måske ikke helt på det først, men din plan holdt."En: Freja added, "I may not have fully believed in it at first, but your plan held."Da: Mikkel smilte tilbage.En: Mikkel smiled back.Da: Han havde lært værdien af at være fleksibel og samarbejde med sine kollegaer.En: He had learned the value of being flexible and collaborating with his colleagues.Da: Tivoli Gardens blændede stadig i vinterlandskabet, men indendørs var hjerter varme og kreativiteten i fuldt flor.En: Tivoli Gardens still dazzled in the winter landscape, but indoors, hearts were warm and creativity flourished.Da: Festivalen blev en succes.En: The festival was a success.Da: En symfoni af lys, kunst og glæde.En: A symphony of lights, art, and joy.Da: Og for Mikkel, Freja og Lars, var det starten på noget nyt og lovende i Tivoli.En: And for Mikkel, Freja, and Lars, it was the beginning of something new and promising in Tivoli. Vocabulary Words:entrance: indgangsnowflakes: snefnuggeneattract: lokkedespite: på trods afinstallations: installationerinteractive: interaktiveartworks: kunstværkerunpredictable: lunefuldtbudget: budgetcost dearly: koste dyrtunder pressure: under presbalance: balanceenthusiasm: entusiasmeskeptical: skeptisktransformed: forvandletmagical: magisksevere: voldsomreacted quickly: reagerede hurtigtevent halls: eventhallerbattled: kæmpedecaptivated: betagetexhibitions: udstillingerrelieved: lettetcrush: knusesincere smile: oprigtigt smilflexible: fleksibelcollaborating: samarbejdedazzled: blændedeflourished: i fuldt florpromising: lovende

Tonia Krügers „Love Songs in London“-Reihe hat mich durch meine Trennung begleitet. Dann habe ich „Kisses in the Snow“* und „Snow Flakes and Heartbeats“* von ihr in Zusammenarbeit mit Leonie Lastella und Valentina Fast gehört und mich gefragt: Wer hat was geschrieben? Wie geht das, die Arbeit von drei Autorinnen unter einen Hut zu bekommen, sodass auch alles wie aus einem Guss erscheint? Wie kam es zu dem Projekt, und was ist noch geplant? All das, aber auch alles über die individuellen Schreibroutinen der drei, über ihre Anfänge als Autorinnen, und auch zu ihrem eigenen Podcast mit Schreibfokus erfahrt ihr in diesem Interview. Dabei erwähnen wir: „I give you my body: How I write sex scenes“ von Diana Gabaldon „Somewhere in summer“ von Tonia Krüger „Two steps away“ von Valentina Fast Die „Outer Banks“-Saga von Emma Cole und Joanne St. Lucas bzw. Leonie Lastella und Jana Lukas „Das Licht von tausend Sternen“ von Leonie Lastella „Ein Leben aus Glas“ von Valentina Fast „All I (don't) want for Christmas“ von Tonia Krüger „Found“ aus der „Lake of Lies“-Reihe von Leonie Lastella Die „Secret Academy“-Reihe von Valentina Fast Die „Royal“-Reihe von Valentina Fast Die „Meereswelten“-Saga von Valentina Fast „Mate“ und „Problematic Summer Romance“ von Ali Hazelwood „Miss Moons höchst geheimer Club für ungewöhnliche Hexen“ von Sangu Mandanna „Regretting you/All das Ungesagte zwischen uns“ und „Verity“ von Colleen Hoover „Three words unspoken“  und „Four nights together“  aus der „London Hearts“-Reihe von Valentina Fast und Lorena Schäfer Ein audible-Original von Leonie Lastella „Bitten“ von Jordan Stephanie Gray „Heartless Hunter“ von Kristen Ciccarelli „A duet of fear and trust“ von Jenny Krone Den „Bookish Delight Bookclub“ Viel Spaß beim Hören des Interviews mit Leonie Lastella, Tonia Krüger & Valentina Fast! Wenn ihr mehr über die Autorinnen erfahren wollt, hört unbedingt auch mal in ihren Podcast „The Book Hangover“ rein. Eure Ilana Entschuldigt bitte, dass die Tonqualität leider nicht auf dem üblichen Niveau ist, da das Interview digital geführt und aufgenommen wurde. *Das Buch wurde mir als Rezensionsexemplar vom Verlag oder dem Autor/der Autorin zur Verfügung gestellt. Ich benutze teilweise Affiliate Links von Amazon.de. Näheres siehe "Impressum und Rechtliches".

Kennst du diese Situation im Team: Jemand sagt "das skaliert nicht", und plötzlich steht der Datenbankwechsel schneller im Raum als die eigentliche Frage nach dem Warum? Genau da packen wir an. Denn in vielen Systemen entscheidet nicht das nächste hippe Tool von Hacker News, sondern etwas viel Grundsätzlicheres: Datenlayout und Zugriffsmuster.In dieser Episode gehen wir einmal tief runter in den Storage-Stack. Wir schauen uns an, warum Row-Oriented-Datastores der Standard für klassische OLTP-Workloads sind und warum "SELECT id" trotzdem oft fast genauso teuer ist wie "SELECT *". Danach drehen wir die Tabelle um 90 Grad: Column Stores für OLAP, Aggregationen über viele Zeilen, Spalten-Pruning, Kompression, SIMD und warum ClickHouse, BigQuery, Snowflake oder Redshift bei Analytics so absurd schnell werden können.Und dann wird es file-basiert: CSV bekommt sein verdientes Fett weg, Apache Parquet seinen Hype, inklusive Row Groups, Metadaten im Footer und warum das für Streaming und Object Storage so gut passt. Mit Apache Iceberg setzen wir noch eine Management-Schicht oben drauf: Snapshots, Time Travel, paralleles Schreiben und das ganze Data-Lake-Feeling. Zum Schluss landen wir da, wo es richtig weh tut, beziehungsweise richtig Geld spart: Storage und Compute trennen, Tiered Storage, Kafka Connect bis Prometheus und Observability-Kosten.Wenn du beim nächsten "das skaliert nicht" nicht direkt die Datenbank tauschen willst, sondern erst mal die richtigen Fragen stellen möchtest, ist das deine Folge.Bonus: DuckDB als kleines Taschenmesser für CSV, JSON und SQL kann dein nächstes Wochenend-Experiment werden.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Fluent Fiction - Hebrew: Snowflakes & Secrets: Love Amidst the City Grind Find the full episode transcript, vocabulary words, and more:fluentfiction.com/he/episode/2026-02-15-08-38-20-he Story Transcript:He: בחורף, כשהשלג כיסה את העיר הלבנה, משרד גבוה ומודרני במרכז העיר עמד מאורגן ויפה.En: In the winter, when the snow covered the white city, a tall and modern office in the city center stood organized and beautiful.He: בכניסה קרצו קישוטי ולנטיין עדינים, מוסיפים אווירה חמה באמצע הקרירות.En: At the entrance, delicate Valentine's decorations winked, adding a warm atmosphere in the midst of the chill.He: לוי, עובד חרוץ ושאפתן, עמד מול המחשב וסידר את המצגת שלו.En: Levi, a diligent and ambitious worker, stood in front of the computer arranging his presentation.He: המבט שלו נדד מדי פעם לרינה, הקולגה שלו, שהיא לא רק יפה אלא גם מקצוענית אמיתית.En: His gaze occasionally drifted to Rina, his colleague, who was not only beautiful but also a true professional.He: לוי היה מאוהב ברינה בסתר, אבל היום הוא היה חייב להתרכז במשימה: מצגת חשובה ללקוח גדול.En: Levi was secretly in love with Rina, but today he had to focus on the task: an important presentation for a major client.He: אבי, המנהל, היה שם לפקח.En: Avi, the manager, was there to supervise.He: הוא עמד בצד ודיבר עם רינה, מנסה להרגיע אותה.En: He stood to the side talking with Rina, trying to calm her.He: לוי הרגיש נקיפת קנאה קטנה, אך התנער מכך במהירות.En: Levi felt a slight twinge of jealousy but shook it off quickly.He: הייתה לו מטרה: להרשים את הלקוח, את רינה, ואת אבי.En: He had a goal: to impress the client, Rina, and Avi.He: כאשר הגיע הזמן לפגישה, לוי נשם עמוק ופנה לעבר חדר הישיבות.En: When the time for the meeting arrived, Levi took a deep breath and headed towards the conference room.He: רינה חייכה אליו בעידוד, ואבי הנהן בראשו באישור.En: Rina smiled at him in encouragement, and Avi nodded his head in approval.He: המצגת החלה.En: The presentation began.He: לוי דיבר בביטחון, אבל אז, לקוח שאל שאלה מפתיעה.En: Levi spoke confidently, but then a client asked a surprising question.He: לרגע, לוי הרגיש את הלחץ עולה, אך הוא נזכר בדבר אחד – הכנה.En: For a moment, Levi felt the pressure rising, but he remembered one thing—preparation.He: עם חיוך קל, הוא ענה על השאלה בצורה ברורה ומשכנעת.En: With a slight smile, he answered the question clearly and convincingly.He: בסוף המצגת, החדר היה מלא במחיאות כפיים.En: At the end of the presentation, the room was filled with applause.He: רינה פנתה ללוי, עיניה בורקות, "עשית עבודה נהדרת!En: Rina turned to Levi, her eyes sparkling, "You did a great job!"He: "לוי חייך, הקשיים נמסו כשלג באביב.En: Levi smiled, the struggles melted away like snow in the spring.He: הוא הבין שהיכולות המקצועיות שלו הן אלו שבולטות באמת.En: He realized that his professional abilities were what truly stood out.He: אולי, חשב, יש כאן יותר מרק עבודה.En: Maybe, he thought, there is more here than just work.He: לוי יצא מהמשרד עם רינה, השלג ירד שוב וגרם לעיר לנצוץ.En: Levi left the office with Rina, the snow fell again, causing the city to sparkle.He: שני הלבבות שמו את המקצועיות במרכז, אך אולי גם מקום לרגשות.En: The two hearts put professionalism in the center, but maybe there was also room for feelings. Vocabulary Words:diligent: חרוץambitious: שאפתןcolleague: קולגהprofessional: מקצועניתsecretly: בסתרsupervise: לפקחtwinge: נקיפהjealousy: קנאהencouragement: עידודapproval: אישורconfidently: בביטחוןpreparation: הכנהconvincingly: משכנעתapplause: מחיאות כפייםsparkling: בורקותstruggles: קשייםmelted: נמסוabilities: יכולותtruly: באמתrealized: הביןhearts: לבבותcover: כיסהdelicate: עדיניםatmosphere: אווירהchill: קרירותpresentation: מצגתgoal: מטרהpressure: לחץclear: ברורהtask: משימהBecome a supporter of this podcast: https://www.spreaker.com/podcast/fluent-fiction-hebrew--5818690/support.

BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake In this episode, we talk about what it really takes to scale go-to-market from zero to billions. We interview Chris Degnan, a builder of one of the most iconic revenue engines in enterprise software at Snowflake. This conversation is grounded in the transformation described in his book Make It Snow—the journey from early-stage chaos to durable, aligned growth. Embedding Sales with Engineering While Still in Stealth "I don't expect you to sell anything for 2 years. What I really want you to do is get a ton of feedback and get customers to use the product so that when we come out of stealth mode, we have this world-class product."   Chris joined Snowflake when there were zero customers and the company was still in stealth mode. The counterintuitive move of embedding sales next to engineering so early wasn't about driving immediate revenue, it was about understanding product-market fit. Chris's job was to get customers to try the product, use it for free, and break it. And break it they did. This early feedback led to material changes in the product before general availability. The approach helped shape their ideal customer profile (ICP) and gave the engineering team real-world validation that shaped Snowflake's technical direction. In a world where startups are pressured to show revenue immediately, Snowflake's investors took the opposite approach: focus on building a product people cannot live without first. Why Sales and Marketing Alignment Is Existential "If we're not driving revenue, if the revenue is not growing, then how are we going to be successful? Revenue was king."   When Denise Persson joined as CMO, she shifted the conversation from marketing qualified leads (MQLs) to qualified meetings for the sales team. This simple reframe eliminated the typical friction between sales and marketing. Both leaders shared challenges openly and held each other accountable. When someone in either organization wasn't being respectful to the other team, they addressed it directly. Chris warns founders against creating artificial friction between sales and marketing: "A lot of founders who are engineers think that they want to create this friction between sales and marketing. And that's the opposite instinct you should have." The key insight is treating sales and marketing as a symbiotic system where revenue is the shared north star. Coaching Leaders Through Hypergrowth "If there's a problem in one of our organizations, if someone comes with a mentality that is not great for us, we're gonna give direct feedback to those people."   Chris and Denise maintained tight alignment at the top level of their organizations through four CEO transitions. Their partnership created a culture of accountability that cascaded through both teams. When either hired senior people who didn't fit the culture, they investigated and addressed it. The coaching approach wasn't about winning by authority—it was about maintaining partnership and shared accountability for results. This required unlearning traditional management approaches that pit departments against each other and instead fostering genuine collaboration. Cultural Behaviors That Scale (And Those That Don't) "We got dumb and lazy. We forgot about it. And then we decided, hey, we're gonna go get a little bit more fit, and figure out how to go get the new logos again."   Chris describes himself as a "velocity salesperson" with a hyper-focus on new customer acquisition. This focus worked brilliantly during Snowflake's growth phase—land customers, and the high net retention rate would drive expansion. However, as Snowflake prepared to go public, they took their foot off the gas on new logo acquisition, believing not all new logos were equal. This turned out to be a mistake. In his final year at Snowflake, working with CEO Sridhar Ramaswamy, they redesigned the sales team to reinvigorate the new logo acquisition machine. The lesson: the cultural behaviors that fuel early success must be consciously maintained and sometimes redesigned as you scale. Keeping the Message Narrow Before Going Platform "Eventually, I know you want to be a platform. But having a targeted market when you're initially launching the company, that people are spending money on, makes it easier for your sales team."   Snowflake intentionally positioned itself in the enterprise data warehousing market—a $10-12 billion annual market with 5,000-7,000 enterprise customers—rather than trying to sound "bigger" as a platform play. The strategic advantage was accessing existing budgets. When selling to large enterprises that go through annual planning processes, fitting into an existing budget means sales cycles of 3-6 months instead of 9-18 months. Yes, competition eventually tried to corner Snowflake as "just a cute data warehouse," but by then they had captured significant market share and could stretch their wings into the broader data cloud opportunity. Selling Consumption-Based Products to Fixed-Budget Buyers "Don't believe anything I say, try it."   One of Snowflake's hardest challenges was explaining their elastic, consumption-based architecture to procurement and legal teams accustomed to fixed budgets. In 2013-2015, many CIOs still believed data would stay in their data centers. Snowflake's model—where customers could spin up a thousand servers for 4 hours, load data, while analysts ran queries without performance impact—seemed impossible. Chris's approach was simple: set up proof of concepts and pilots. Let the technology speak for itself. The shift from fixed resources to elastic architecture required changing not just technology but entire mindsets about how data infrastructure could work.   About Chris Degnan Chris Degnan is a builder of one of the most iconic revenue engines in enterprise software. As the first sales hire at Snowflake, he helped scale the company from zero customers to billions in revenue. Chris co-authored Make It Snow: From Zero to Billions with Denise Persson, documenting their journey of building Snowflake's go-to-market organization. Today, Chris advises early-stage startups on building their go-to-market strategies and works with Iconiq Capital, the venture firm that led Snowflake's Series D round.   You can link with Chris Degnan on LinkedIn and learn more about the book at MakeItSnowBook.com.

Church of England vicars with a difference Jamie Franklin and Daniel French talk about the big stories in Church and State. This time:Keir Starmer is clinging to power as folly of Epstein-linked Mandelson decision (and many other decisions) is being made increasingly apparent.It's the Church of England's General Synod and the new Archbishop Sarah Mullally promises to support the local and deprioritise central initiatives. But will she follow through on this promise and why is she still supporting £100 million slavery reparations initiative Project Spire?Project Spire itself takes a battering in question and answer session at Synod and should be renamed "Project Snowflake" as those working on the scheme are said to need special support because they can't handle questions and criticism.And the Living in Love and Faith gay relationships project is officially cancelled after years of fruitless toil...and then restarted again with a new "working group" to look at the same issues.We answer some questions on talking Bibles and the link between Lockdown and the Quiet Revival, plus a few other things as always.All that and much more as always. Please enjoy!You make this podcast possible. Support us and get episodes early, bonus Uncollared audio podcasts, monthly epic chats between Jamie and Nick Dixon and more!On Patreon - https://www.patreon.com/irreverendOn Substack - https://irreverendpod.substack.com/Buy Me a Coffee - https://www.buymeacoffee.com/irreverend To make a direct donation or to get in touch with questions or comments please email irreverendpod@gmail.com!For the Clergy Post at Holy Trinity, Stroud Green make enquiries with the Bishop of Fulham's office fulham.chaplain@london.anglican.org or phone 020 7932 1130.Notices:Join our Irreverend Telegram group: https://t.me/irreverendpodFollow us on Twitter: https://x.com/IrreverendPodBuy Jamie's Book! THE GREAT RETURNDaniel French Substack: https://undergroundchurch.substack.com/Jamie Franklin's "Good Things" Substack: https://jamiefranklin.substack.comIrreverend Substack: https://irreverendpod.substack.comFind me a church: https://irreverendpod.com/church-finder/Support the show

Sridhar Ramaswamy is the CEO of Snowflake. Ramaswamy joins Big Technology Podcast to break down the competitive dynamics in the AI race today, drawing from his experience working at Google and competing with it. We also cover the future of software, looking at whether AI will turn established software companies into "dumb backends." In the second half, we discuss “shadow AI” driving enterprise adoption from the bottom up, the risk of becoming a feature in someone else's platform, and why Chinese open-source models might actually be a net positive for the US. Hit play for a sharp, deeply informed conversation about where AI competition, enterprise software, and the future of work are heading. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/bigtech.  Try it risk-free now with a 30-day money-back guarantee! Learn more about your ad choices. Visit megaphone.fm/adchoices

NFLSuper Bowl LX recapNews/NotesWinter OlympicsCurrent medal countLindsey Vonn updateCollege BasketballMens and Women's Week 15 AP Top 10Scores from the weekTop 25 schedule for the weekNews/NotesShow music by DJ Cam One: Twitter/Instagram/SpotifyDJ Cam One's label: Mysteryismusic.comCover art by Xclusive Packaging & Design: InstagramEmail: x.pac.design@gmail.com Your host Uncle Dub: Bluesky/Twitter/InstagramPodcast Instagram and YouTubeUntappd (Beer Check-in app): ItsUncle_DubEmail: sportswagonpodcast@gmail.comCheck out the Bald Head Logic podcast co-hosted with DJ Cam OnePlease consider supporting the podcast: Buy Me a CoffeeSend a voicemail, subscribe, rate and tell a friend about the show!Thank you for listening!

I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from StreamKap to catch up on where the world of streaming data is heading—and things have changed fast since we last spoke.We dive into the concept of "vibe coding" and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they're flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and StreamKap's upcoming move into the Snowflake native app ecosystem.Streamkap: https://streamkap.com/

Collate is building a semantic intelligence platform that unifies fragmented metadata tooling across the modern data stack. With 12,000+ community members, 3,000+ open source deployments, and 400+ code contributors, the company has proven that open source can be a systematic GTM engine, not just a distribution tactic. In this episode of BUILDERS, I sat down with Suresh Srinivas, Co-Founder & CEO of Collate, to explore his journey from the Hadoop core team at Yahoo, through founding Hortonworks, to architecting data systems processing 4 trillion events daily at Uber—and why that experience led him to rebuild metadata infrastructure from scratch. Topics Discussed: Why platform builders at Yahoo and Hortonworks struggled to drive business value despite powerful technology The metadata fragmentation problem: how siloed tools lack unified vocabularies and end-to-end context Collate's contrarian decision to build Open Metadata from zero rather than spinning out Uber's internal tooling Engineering an open core GTM model that generates nearly 100% inbound sales from technical practitioners Scaling community contribution: moving from feedback loops to 400+ code contributors Hiring a CMO to translate technical value into business-leader messaging without losing practitioner trust The convergence thesis: structured data, knowledge graphs, and semantic layers as the foundation for reliable AI GTM Lessons For B2B Founders: Architect your open source for GTM leverage, not just distribution: Suresh built Open Metadata as a unified platform consolidating data discovery, observability, and governance—previously fragmented across multiple tools. This architectural decision created natural upgrade paths to Collate's managed offering. The lesson: open source architecture should solve a complete job-to-be-done that reveals commercial value through usage, not just demonstrate technical capability. 100+ daily practitioner conversations beats any user research: Collate maintains ongoing dialogue with their community across Snowflake, Databricks, and other integrations. Suresh called this "a product manager's dream"—immediate feedback on what breaks, what's missing, and what workflow improvements matter. For infrastructure startups, this beat rate of validated learning is nearly impossible to replicate through traditional customer development. High-velocity releases build credibility faster than pedigree: Starting from scratch without Yahoo or Uber's brand meant proving commitment through shipping cadence. Collate's strategy: demonstrate you'll be around and responsive before asking for production deployments. This matters more in open source than closed-source where sales cycles force commitment conversations earlier. Separate technical-buyer and business-buyer GTM motions explicitly: Collate's founding team spoke fluently to data engineers and architects who lived the metadata problem daily. Their CMO hire (after establishing product-market fit) brought expertise in articulating business impact—ROI on data initiatives, compliance risk reduction, AI readiness—without the founders faking business-speak. The timing matters: hire for the motion you're entering, not the one you're in. Play the long game with builder-culture companies: At Uber, internal tools were 2-3 years ahead of vendor solutions but became technical debt as teams moved to new problems. Suresh's advice: "Keep in touch with these larger companies. Your technology will improve and you will have better conversation with larger technical companies." The wedge is timing—catch them when maintenance burden outweighs building pride, typically 24-36 months post-launch. Design for all company scales from day one: Unlike Uber's internal metadata platform built for massive scale with corresponding complexity, Open Metadata works for small teams through enterprises. This wasn't just good design—it was GTM expansion strategy. Building only for scale locks you into enterprise-only sales. Building only for simplicity caps your ACV. The middle path requires architectural discipline upfront. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

The Information's Ann Gehan talks with TITV Host Akash Pasricha about OpenAI's rollout of ads and the massive tax challenges facing its future shopping ambitions. We also talk with Catherine Perloff about Amazon's secret plans to launch a content marketplace for publishers to sell data to AI companies, and Rishi Jaluria of RBC Capital Markets about the carnage in software stocks and the wave of CEO changes at companies like Workday and Snowflake. Lastly, we get into the technical mechanics of how OpenClaw agents learn new skills with our reporter Rocket Drew.Articles discussed on this episode: https://www.theinformation.com/articles/chatgpt-shopping-get-complicated-fasthttps://www.theinformation.com/articles/amazon-discusses-ai-content-marketplace-publishershttps://www.theinformation.com/newsletters/ai-agenda/openclaw-learns-new-thingshttps://www.theinformation.com/newsletters/the-briefing/workdays-ceo-shuffle-sign-things-comeSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Alex breaks down why Bad Bunny's Super Bowl halftime show was more than just a performance—it was a cultural statement that had MAGA conservatives whining about reggaetón, pronouns, and inclusivity. He contrasts the positive, unifying energy of the halftime show with TPUSA's low-turnout, divisive counter-event, unpacking what this says about America's political and cultural divides.

In this episode of Run the Numbers, CJ Gustafson sits down with Dan Miller, CFO at RightRev. They unpack why leasing is underused in software, how RevTech emerged, and why revenue recognition may be the next AI battleground. Dan also shares how he evaluates durable growth vs. hypergrowth.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS: Dan on LinkedIn: https://www.linkedin.com/in/danmillercpa/RightRev: https://www.rightrev.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:00:00:00 Preview and Intro00:02:41 Why Operating Experience Matters for CFOs00:04:08 Defining Durable Growth00:06:06 Snowflake and Consumption Revenue Complexity00:10:17 Forecasting in Consumption Models00:11:29 AI's Role in Revenue Forecasting00:12:14 Sponsors — Rillet | Tabs | Abacus AI00:15:39 Comping Sales in Usage-Based Models00:18:15 Leasing as a Software Monetization Tool00:20:47 The CFO's Role in Sales and GTM00:22:29 How CFOs Help Close Deals00:24:14 Rev Tech vs RevOps00:26:20 Sponsors — Brex | Metronome | RightRev00:29:40 Where AI Actually Helps Rev Rec00:31:55 Deterministic vs Probabilistic AI00:33:05 Why Enterprises Hesitate on AI Agents00:34:18 Startups vs Incumbents in the AI Race00:35:13 FOMO, Overfunding, and Market Distortions00:38:13 CFO Playbooks Without Hypergrowth00:39:38 Finding PMF as a CFO00:41:15 Career Advice: Growth vs Shiny Objects00:42:00 Building the CEO–CFO Relationship00:42:49 Learning Beyond the Back Office00:43:22 Lightning Round00:44:28 Advice to My Younger Self00:45:09 Finance Tech Stack00:46:36 Credits

Maxima is building AI agents that automate enterprise accounting while maintaining the auditability and control standards finance teams require. In a recent episode of BUILDERS, we sat down with Yogi Goel, CEO and Co-Founder of Maxima, to explore his eight-year journey at Rubrik from Series C through IPO, and how those lessons shaped his approach to solving the 70-80% of finance time currently wasted on manual work. Topics Discussed: Why Rubrik's approach—entering stagnant markets with first-principles thinking—became Maxima's blueprint Securing $3K-$5K POC commitments from Figma mockups before writing code Why Scale AI and Rippling rejected a point solution and demanded 3-4 modules from day one The compound startup model: building multiple products simultaneously to meet buyer expectations How 17% of CFOs are adopting AI tools today (vs 51% in software development) Why finance teams view AI agents as "digital college freshmen" who need proof of work Hiring from YouTube Studios, Apple, and Robinhood instead of legacy finance software companies How NetSuite World conference booth sizes revealed the data integration infrastructure gap The $3K-$5K validation threshold that proved finance pain was urgent enough to pay pre-product GTM Lessons For B2B Founders: Demand generation unlocks engineering potential: Yogi learned from his Rubrik mentors: "focus on demand and if you have great engineers then they will solve the problems." Maxima built products in 2-3 months they didn't initially know were technically feasible—because customer demand pulled the engineering team forward. For founders with strong technical teams, customer demand should drive the roadmap, not engineering's comfort zone. Trust your engineers to solve hard problems when customers are waiting. $3K-$5K is the pre-product validation threshold: Before writing any code, Yogi secured POC commitments at this price point based solely on Figma mockups. This isn't about revenue—it's about proving urgency. Verbal interest means nothing. Small pilot commitments mean "we'll try it someday." But $3K-$5K pre-product means "this problem is urgent enough to pay before seeing a working solution." Use this threshold to separate real pain from polite interest. Sophisticated buyers will reject your narrow MVP: Scale AI and Rippling told Maxima explicitly: "If you will only build this one thing, we will not buy. You have to commit to building three, four modules." Conventional wisdom says start narrow, but enterprise buyers with complex workflows won't adopt point solutions that create new integration headaches. When sophisticated buyers articulate their real buying criteria, ignore the startup playbook. Yogi built a "compound startup" with 4-5 modules from day one because that's what the market demanded. Target acute pain over easy access: Early-stage companies (10-30 people) were easier to reach but finance wasn't urgent enough. At that scale, it's "build product, ship product"—finance operations aren't broken enough to warrant urgent attention. Companies at 500-1,000+ employees have finance teams drowning in manual work that prevents strategic contribution. Target where pain justifies urgent action and budget exists, not where calendar access is easiest. Hire intensity and first-principles thinking over domain knowledge: Maxima deliberately hired zero engineers from legacy finance software companies. Their frontend engineer came from YouTube Studios. Others came from Apple, Robinhood, Netflix—none with financial product experience. Yogi's three hiring criteria: "incredible intensity, huge confidence in themselves, and fast thinking mode." Domain expertise creates pattern-matching to old solutions. First-principles thinking creates breakthrough products. One team member didn't finish high school but is "one of the best out there." Make AI explainable or finance teams won't adopt: Finance teams adopted faster than expected because Maxima showed every calculation step. "If they can prove by looking at the Math, you know, 18 plus 88 plus 36 is X. And I can see the step of the work, they are willing to give it to them." This isn't about fancy UX—it's about auditor-grade proof of work. Finance professionals won't trust black box outputs. Build transparency into the product architecture, not as an afterthought. This explainability became Maxima's competitive moat. Conference booth sizes reveal infrastructure gaps: At NetSuite World, the largest booths weren't ERP vendors or payment processors—they were data integration companies. This single observation validated that enterprises are desperately solving data fragmentation problems. Companies manually download from Stripe, Snowflake, Salesforce weekly to build Excel pivots. Maxima invested in upstream integrations as core infrastructure from day one. Use industry conferences to validate where companies are spending money on workarounds—that's where infrastructure gaps exist. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Today's minisode features Chris Degnan, former CRO of Snowflake. In this clip, Chris explains what it really takes to grow with a company as it scales, and why earning your role does not stop once the title changes. He shares how treating every quarter like a 90-day contract, staying open to feedback, and knowing when to shift from grinding in the business to building leaders helped him navigate board pressure and scale through hypergrowth.If you're a sales leader navigating rapid growth, or questioning how to evolve without losing your edge, this is a perspective worth hearing.Chris Degnan is the former Chief Revenue Officer of Snowflake, where he helped build the company from zero to more than $1B in consumption revenue. He is known for his expertise in scaling go-to-market organizations through early-stage ambiguity, enterprise expansion, and consumption-based selling models.Connect with Chris:LinkedInFrom Zero to Billions: How Snowflake Scaled its Go-to-Market Organization by Denise Persson & Chris DegnanResources mentioned:Multiple Myeloma Research Foundation Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Building a company from the ground up is rarely clean, fast, or glamorous. It requires leaders who are willing to earn their role repeatedly, adapt faster than the business evolves, and stay grounded in customer reality even as pressure to scale intensifies. In this replay of one of our favorite Revenue Builders Podcast conversations, Chris Degnan shares what it actually took to help build Snowflake from pre-product uncertainty into a billion-dollar revenue engine. Drawing on his experience joining the company two years before general availability, Chris breaks down the stages of growth, the discipline required to identify real product-market fit, and the leadership mindset needed to scale teams, go-to-market motion, and accountability without losing velocity or culture.Chris Degnan is the former Chief Revenue Officer of Snowflake, where he helped build the company from zero to more than $1B in consumption revenue. He is known for his expertise in scaling go-to-market organizations through early-stage ambiguity, enterprise expansion, and consumption-based selling models.Connect with Chris:LinkedInFrom Zero to Billions: How Snowflake Scaled its Go-to-Market Organization by Denise Persson & Chris DegnanResources mentioned:Multiple Myeloma Research FoundationIf you're responsible for scaling a go-to-market organization, drive predictability at scale with Force Management's Predictable Revenue Framework. Get the free guide: https://hubs.li/Q03-T6NH0Key takeaways from this episode:05:10 – Why joining an early-stage company means earning your role every quarter, not relying on past success or title10:25 – How defining a narrow and honest ideal customer profile creates momentum, while chasing outliers quietly destroys focus and capital16:45 – Why velocity and enterprise selling must coexist, and how overcommitting to one creates instability as companies scale20:05 – How coachability and adaptability determine whether leaders grow with the company or get replaced as scale increases21:55 – Why consumption-based selling demands accountability beyond the deal, and how reps must own customer success to earn full value26:30 – Why resisting the urge to replace leaders too early preserves institutional knowledge and strengthens culture during scale Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Part two of my chat with Faun.

Hosts Spencer Neuharth, Janis Putelis, and Seth Morris speak with Sue Richardson about her great-great uncle, Wilson "Snowflake" Bentley, and his groundbreaking nature photography, talk some gear, share Top 3s, and chat with Peace River K9 Search and Rescue's Michael Hadsell about the world's only search-and-rescue otter, Splash. Watch the live stream on the MeatEater Podcast Network YouTube channel. Subscribe to The MeatEater Podcast Network MeatEater on Instagram, Facebook, Twitter, and YouTubeSee omnystudio.com/listener for privacy information.