Podcasts about S3

This week, we drop the second part of the S1, Chapter 4 Reimagining, and I've written another chapter outline of S3, which is wild cause things have been crazzzzyyyyyyy All this and more, but first a message to our Heroes —--------------------- Want more 7th Valkyrie? Check out our Patreon to become a Hero of Edara, where you can shape the future of the series, decide on merch drops and incentives, get early access to new episodes, enjoy bonus features and content, and help us hit the major checkpoints on the Path of Heroes!  https://www.patreon.com/7thvalkyrie  

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

The PHP Podcast streams live, typically every Thursday at 3 PM PT. Come join us and subscribe to our YouTube channel. Another fun episode of the PHP Podcast! Here’s what we covered: Internet Woes & Technical Difficulties Eric continued his saga with connectivity issues, dropping multiple times on Zoom calls and even during the podcast. After trying everything from coax cable converters to different network setups, he’s considering just running a new network cable to his office. The Wi-Fi experiment during the show… didn’t go great. First Waymo Experience John shared his first ride in a Waymo self-driving car! While the wife wasn’t thrilled about having to walk to a specific pickup spot, the experience was pretty impressive. One weird moment: the car got confused by a bus at a 45-degree angle and started creeping into the left lane. Overall verdict: comfortable, cheaper than Uber, and no awkward small talk required. Eric’s Coding Adventure In a rare “Eric writes code” moment, he debugged a POC project by littering the codebase with 15+ write-to-log statements (because who needs X debug?). The culprit? A renamed variable he forgot to update elsewhere. Classic. John was horrified to learn there’s no static analysis running. The demo went well… until someone asked to see the customer interface. MySQL 8.0 → 8.4 Upgrade Planning John’s been preparing for the MySQL 8.0 to 8.4 upgrade (8.0 is end of life). The previous team left amazing documentation, but there’s one major issue: the DBA rejected converting from utf8mb3 to utf8mb4 character set because the tables are so massive it would lock them for way too long. That’s a problem for future John. AWS S3 Cleanup – 75 Million Files! John tackled a years-old problem: phone call recordings stored as both WAV and MP3 files in S3. The cleanup script identified 75 million WAV files to delete, which took a day and a half to process. Potential savings: $100/day. Joe asked about intelligent tiering, which… yeah, probably should look into that. PHP Tek 2026 – 68 Days Away! The conference schedule is live! Four tracks (three PHP Tek + one JS Tek), hotel rooms at the discounted rate are going fast, and Eric admitted he skipped Scale this year because he was just too exhausted. Focus is on PHP Tek now! Laravel 13 Dropping March 17 Laravel 13 is dropping on Tuesday with a focus on moving from protected properties to attributes. According to the article, there are no breaking changes (we’ll see about that). Overall, it’s a light upgrade with some new features but nothing earth-shattering. March Friday the 13th Anniversary Eric and Beck’s dating anniversary! They started dating on March Friday the 13th, 1987, when Eric picked her up at 5 PM for a midnight showing of a terrible Burt Reynolds movie called “Heat” (which apparently doesn’t exist according to IMDB). The whole show tried to help figure out what movie it actually was. Spoiler, it was called HEAT PHPUnit 13 Released Sebastian Bergmann appeared on PHP Alive & Kicking to talk about PHPUnit 13. The big change: array of assertions. The show also features a hard deprecation of some older methods. Check out the release for all the details. OpenClaw/Archie AI Success Eric’s thrilled with how the team is using the OpenClaw AI agent for daily standups. Team members are not only doing their morning standups but updating it throughout the day and even asking it to check for security alerts. The engagement has been way beyond expectations. Now Eric’s fighting the temptation to buy a Mac Mini to run it properly and get it back on Ollama, saving on API costs. Links from the show: PHP Tek 2026 – The Premier PHP Conference WiFi Mapping User Guide – Turn your router into a see-through-walls device WiFi Mapping Demo on X Laravel 13 drops March 17 — here’s every new feature with code examples X: https://x.com/phparch Mastodon: https://phparch.social/@phparch Bluesky: https://bsky.app/profile/phparch.com Discord: https://discord.phparch.com Subscribe to our magazine: https://www.phparch.com/subscribe/ Host: Eric Van Johnson X: @shocm Mastodon: @eric@phparch.social Bluesky: @ericvanjohnson.bsky.social PHPArch.me: @eric John Congdon X: @johncongdon Mastodon: @john@phparch.social Bluesky: @johncongdon.bsky.social PHPArch.me: @john Streams: Youtube Channel Twitch Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. Music Provided by Epidemic Sound https://www.epidemicsound.com/ The post The PHP Podcast 2026.03.12 appeared first on PHP Architect.

S3-37 Intelligent Reformer Class Programming with Elizabeth Larkam Elizabeth Larkam has been teaching and evolving Pilates for 40 years. She developed the original group Reformer program when Balanced Body's Pilates Allegro was launched in 1999. Since then Elizabeth has explored many advances in movement science, and integrated them into movement practice in the Pilates environment. She has now come full circle and is teaching 15 group Reformer classes each week. Listen to our fascinating discussion of how Elizabeth incorporates her many insights into the group Reformer environment.    Elizabeth's Socials and Offers: Pilates Anytime classes 2026: Reformer Cardio - Leg and Arm Jumps Creating New, Strong Connections -   Reformer with Box https://www.pilatesanytime.com/instructor-bio/76/Elizabeth-Larkam-Pilates-Teacher Books: 1. Fascia in Motion (Handspring 2017)2. Pilates Applications for Health Conditions Two Volume Set Co-editors Madeline Black and Elizabeth Larkam Handspring Publishing, May 2025   Elizabeth teaches online  classes every day. A different prop every day of the week as well as Chair with rotator discs, Reformer with jump board, Reformer with box, Reformer with cords, loops, infinity footbar.  http://www.bewellstaywell.net/elizabethlarkam  Pilates Anytime classes 2026: Reformer Cardio - Leg and Arm Jumps Creating New, Strong Connections -   Reformer with Box 

Debbie Millman, designer, author, educator, curator, artist, and pioneering podcast host, joins Designers On Film to talk about Arrival (2016), a movie that has all the ingredients to keep you engaged and make you curious about life on this planet, or life beyond this planet. Amy Adams is Louise Banks, Jeremy Renner is Ian Donnelly, and together they're brought into a government operation to understand, analyze, and hopefully communicate with visitors from another planet. In addition to sharing everything about the movie that she loves, Debbie also talks about how science has been an integral part of her own life, why she believes in alien lifeforms, and ponders big questions about language, love, and time.-Debbie Millman is host of the pioneering podcast Design Matters. Fast Company called her "one of the most creative people in business" and Graphic Design USA called her "one of the most influential designers working today." She's a "woman of influence" as Success Magazine has said, building a career at the intersection of design, storytelling, and cultural commentary. As the founder and host of Design Matters, one of the first and longest-running podcasts in the world, she's interviewed more than 700 of the world's most creative thinkers and makers, having earned the Cooper Hewitt National Design Award, multiple Webby awards and Ambie nominations, and numerous accolades from Apple Podcasts who named Design Matters one of their "All-Time Favorites" three times. Debbie worked on the concept and design of the vault plate that's aboard NASA's Europa Clipper mission to Jupiter's moon. Her work has also appeared in The New York Times, The Washington Post, Philadelphia Inquirer, New York Magazine, The Baffler, The New York Review of Books, and Fast Company. The author of two books of illustrated essays, plus author of eight books, she's also Editorial Director of PrintMag.com which she co-owns, Debbie and her business partners rescued the publication from bankruptcy in 2019, preserving its 80-year legacy. Debbie and her wife, best-selling author Roxane Gay, recently acquired The Rumpus. Debbie lives in New York City and Los Angeles with her beautiful wife, two lovable cats and a very charismatic dog.https://www.printmag.com/author/debbie-millman/https://www.instagram.com/debbiemillman/https://designmattersmedia.com/https://apple.co/designmattershttps://debbiemillman.com/https://therumpus.net/-Zipeng Zhu is a Chinese-born artist, designer, educator, and founder of the award-winning creative studio Dazzle in New York City. He wants to make every day a razzle-dazzle musical and has collaborated with iconic brands such as Apple, Adidas, Adobe, Coca-Cola, Instagram, MTV, Microsoft, Netflix, The New York Times, The New Yorker magazine, Samsung and Uber. His work has been exhibited at major museums and institutions in cities all over the world, including New York, Barcelona, Dubai, Shanghai, Beijing, and Mumbai. Zipeng dedicates his days running both the Dazzle Studio and merch shop Dazzle Supply, bringing his dazzling design to clients and fans around the globe.https://dazzle.studio/-Arrival (2016)https://www.imdb.com/title/tt2543164/‍ ‍https://www.imdb.com/name/nm5384213/‍ ‍Stories of Your Life and Others by Ted Chianghttps://amzn.to/4rfSiBk‍ ‍-Other movies, shows, and books discussed:Close Encounters of the Third Kind (1977)Contact (1997)Interstellar (2014) The Twilight Zone, S3.E24: To Serve Man (1962)

Проверяем знания кандидата на позицию Senior DevOps инженера в прямом эфире. В этом выпуске: архитектурные паттерны в AWS, вечный спор Terraform против CloudFormation, глубокое погружение в Kubernetes (Karpenter, скейлинг) и Live-траблшутинг сломанного Helm-чарта. О ЧЁМ ВЫПУСК: • Архитектура и облака: Как выбрать между EKS и ECS/Fargate и настроить безопасное хранение бэкапов в S3.  • IaC войны: Честное сравнение Terraform и CloudFormation — где заканчивается удобство и начинается боль.  • Kubernetes под капотом: Разбираем Control Plane, работу контроллеров и нюансы обновления on-prem кластеров.  • Live Debug: Реальная задача по починке упавшего пода (CrashLoopBackOff) — работа с пробами, портами и Helm.  • CI/CD стратегии: Строим идеальный пайплайн с GitHub Actions и ArgoCD. ГОСТЬ: Максим — DevOps-инженер (5 лет опыта DevOps, 10 лет SysAdmin). Стек: AWS, Terraform, Kubernetes, Ansible, Monitoring. ССЫЛКИ

Andrey and Mattias share a fast re:Invent roundup focused on AWS security. What do VPC Encryption Controls, post-quantum TLS, and org-level S3 block public access change for you? Which features should you switch on now, like ECR image signing, JWT checks at ALB, and air-gapped AWS Backup? Want simple wins you can use today?  We are always happy to answer any questions, hear suggestions for new episodes, or hear from you, our listeners. DevSecOps Talks podcast LinkedIn page DevSecOps Talks podcast website DevSecOps Talks podcast YouTube channel

S3.68 - The Chicago Table - Crack the Sky by Wererat Studios

S3, Ep : 30. Spain's Aperitivo Ritual: Sip, Savor, Socialize.Hola amigos, Jorge and Fran here from Spanish Loops… and this week we're talking about something that is not just food, not just a drink, but a lifestyle. El aperitivo in Spain.Because in Spain, we don't just eat. We prepare to eat.The aperitivo usually happens between 12:30 and 2:30 PM, right before lunch. It's that golden moment when the sun is high, the streets are alive, and friends gather around a high table at the bar. It's not rushed. It's not formal. It's social glue.In Madrid, you might see vermouth on tap. Yes, vermouth straight from the barrel,  served with ice and a slice of orange. In Andalusia, especially in Seville or Cádiz, a chilled fino or manzanilla sherry takes the spotlight. Head north to the Basque Country and you'll find txakoli, slightly sparkling and poured from a height, paired with gildas, those iconic skewers of olives, anchovies, and peppers.Summer? Expect cold beer, tinto de verano, olives, anchovies, boquerones, ensaladilla rusa, or a simple bag of crispy patatas fritas. Winter? Maybe a small glass of red wine, a slice of tortilla española, or a warm tapa to keep the chill away.But here's the key: the aperitivo is not about getting full. It's about awakening the appetite. It's about conversation before commitment.You stand, you chat, you laugh. Sometimes you move from one bar to another. It's spontaneous. It's democratic. It's beautifully Spanish.And depending on the region, what starts as “just one” can easily stretch into lunch itself.So in this episode, we explore the origins of el aperitivo, the regional variations, the unspoken rules, and why understanding this ritual means understanding Spain.Because if you truly want to feel Spanish culture… start before lunch.

S3, Ep : 30. Spain's Aperitivo Ritual: Sip, Savor, Socialize.Hola amigos, Jorge and Fran here from Spanish Loops… and this week we're talking about something that is not just food, not just a drink, but a lifestyle. El aperitivo in Spain.Because in Spain, we don't just eat. We prepare to eat.The aperitivo usually happens between 12:30 and 2:30 PM, right before lunch. It's that golden moment when the sun is high, the streets are alive, and friends gather around a high table at the bar. It's not rushed. It's not formal. It's social glue.In Madrid, you might see vermouth on tap. Yes, vermouth straight from the barrel,  served with ice and a slice of orange. In Andalusia, especially in Seville or Cádiz, a chilled fino or manzanilla sherry takes the spotlight. Head north to the Basque Country and you'll find txakoli, slightly sparkling and poured from a height, paired with gildas, those iconic skewers of olives, anchovies, and peppers.Summer? Expect cold beer, tinto de verano, olives, anchovies, boquerones, ensaladilla rusa, or a simple bag of crispy patatas fritas. Winter? Maybe a small glass of red wine, a slice of tortilla española, or a warm tapa to keep the chill away.But here's the key: the aperitivo is not about getting full. It's about awakening the appetite. It's about conversation before commitment.You stand, you chat, you laugh. Sometimes you move from one bar to another. It's spontaneous. It's democratic. It's beautifully Spanish.And depending on the region, what starts as “just one” can easily stretch into lunch itself.So in this episode, we explore the origins of el aperitivo, the regional variations, the unspoken rules, and why understanding this ritual means understanding Spain.Because if you truly want to feel Spanish culture… start before lunch.

In dieser Folge widmen wir uns gemeinsam mit unserem Gast Prof. Dr. Jeannine Schübel dem Thema Leitlinien - einem entscheidenden Element für die Qualität der medizinischen Versorgung. Als Expertin zu dem Thema zeigt sie die Relevanz von Leitlinien in der Allgemeinmedizin auf. Sie erklärt zudem die verschiedenen Arten von Leitlinien, gibt uns Einblicke in den Entstehungsprozess sowie den Transfer in die hausärztliche Praxis. Darüber hinaus erzählt sie, wie sich Interessierte in die Leitlinienarbeit einbringen können, welche Leitlinienthemen als nächstes anstehen und gibt Tipps, wie man up to date bleiben kann.Sendet Feedback gerne an: kontakt@kwhessen.de Shownotes:Umfrage zum PodcastInfos zu Prof. Dr. Jeannine SchübelSektion Leitlinien & Qualitätsförderung der DEGAMArbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e.V. (AWMF)AWMF-RegelwerkÜberblick über DEGAM-LeitlinienInstitut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG)Leitlinien-Telegramm von AMBOSSDeximed newsletterDoccheckUnser Seminarprogrammmehr Informationen zu unserem Angebot (Seminare, Mentoring, Fallkonferenzen, Beratung) und uns als Kompetenzzentrum Weiterbildung Hessen wir als Kompetenzzentrum Weiterbildung auf Instagramwir als Kompetenzzentrum Weiterbildung auf Facebookwir als Kompetenzzentrum Weiterbildung auf LinkedInUnser Standort an der Universität in Frankfurt am Institut für AllgemeinmedizinUnser Standort an der Universität in Gießen am Institut für hausärztliche MedizinUnser Standort an der Universität in Marburg am Institut für Allgemeinmedizinmehr Infos zum Hessischen Ministerium für Familie, Senioren, Sport, Gesundheit und Pflege, den Förderern des Projekts Moderation: Dr. Sandra Herkelmann & Dr. Katharina DippellKonzeption & Redaktion: Ida LotterProduktion: Philip Schunke und Christian Köbke, YAPOLA Der Podcast wird vom Hessischen Ministerium für Familie, Senioren, Sport, Gesundheit und Pflege (HMFG) gefördert.

S3.67 - The Chicago Table - A Feast of Furry Friends by Wererat Studios

Le D.E.V. de la semaine est Simon Parisot, CEO et cofondateur de Blank. Simon a fait un pari, un peu fou, au début de l’aventure Blank : avoir un environnement 100% serverless ! Lambda, DynamoDB, S3, … il connait tous les services AWS, mais n’utilise pas une seule EC2 !! Il vient nous raconter comment il a construit cette plateforme, et surtout pourquoi ! Il nous explique aussi les changements que cela a sur le travail des dev (le dev en local est compllqué), les impératifs de qualité du code que cela implique et aussi comment le recrutement doit s’adapter à ce choix technique.Liens évoqués pendant l’émissionIFTTD avec Olivier Dupuis - Faites entrer le hackeurFramework serverless🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Rec Deck™️ Welcome to the Third Romulans Bearing Gifts Cutbits aka REC DECK episode! Cutbits originated on the mother stream, Mostly Harmless Cutaway—little bits of audio tape that ended up on the cutting room floor, now packaged into a nice little program. In this Cutbit, we join the Romulans Bearing Gifts crew (Carl and Eric) as they loosely discuss Star Trek V, The Book of Boba Fett and the Skyline series.  This is a lost cut bits episode from 2023.  WARNING: This discussion contains miscellaneous SPOILERS pertaining to the film(s) discussed and Doctor Who! If you are 100% spoilerphobic to films not yet seen, do not complain to us. The commentary is littered with EXPLICIT terms, concepts, and as always expect strokes of innuendo throughout. DISCLAIMER: Note: These Cutbits conversations are completely impromptu audio tapings and should be treated as such. This Cutbit was recorded Live on Feb. 24th, 2023, during the recording session for The Next Generation & Disengage [PCD, S3]. COMING SOON: RBG #89   LLAP |VL   Caleb @CalebAlexader Cat @fancyfembot Carl @Robominister Eric @BullittWHO   Prognosis Negative @ProgNeg Email: guidetothewhoverse ~at~ gmail ~dot~com Website: prognosisnegative.libsyn.com Patreon: patreon.com/progneg Facebook: facebook.com/progneg Produced by E.A. Escamilla

Nicolas Deshais-Fernandez est déjà venu dans Com d'Archi (numéros S3 #1 & #2). En écho des interview Europan, il vient de nouveau nous parler de son expertise du monde végétal.L'on débute cette interview sur le paysage en parlant safran et menthe, puis nous entrons rapidement dans l'actualité des projets : celui au long court, au Luxembourg, adapté aux normes, des projets arrêtés dont celui à Fréfossé sur la légende d'Arsène Lupin, de nouveaux projets autour de la botanique et de la désimperméabilisation. Citons les jardins publics des bassins à flots de Bordeaux porté par le Grand Port Autonome, projet laboratoire, ou un autre projet à Reims qui met en exergue le dialogue entre les arbres remarquables et le patrimoine et où il est question de garder les jardins pleine terre. Cette prise de conscience de la terre née pendant le Covid nous raconte-t-il...Image teaser DR © Atelier NDFIngénierie son : Bastien Michel____Si le podcast COM D'ARCHI vous plaît n'hésitez pas :. à vous abonner pour ne pas rater les prochains épisodes,. à nous laisser des étoiles et un commentaire, :-),. à nous suivre sur Instagram @comdarchipodcast pour retrouver de belles images, toujours choisies avec soin, de manière à enrichir votre regard sur le sujet.Bonne semaine à tous! Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

The Joint Readiness Training Center is pleased to present the one-hundredth-and-thirty-third episode to air on ‘The Crucible - The JRTC Experience.' Hosted by MAJ Marc Howle, the Brigade Senior Engineer / Protection Observer-Coach-Trainer, and MAJ Michael Stewart, BDE S-3 Operations OCT, from Brigade Command & Control (BDE HQ) on behalf of the Commander of Ops Group (COG). Today's guests are subject matter experts from the Brigade Command & Control task force: CPT Lowell Gothard is the Air Defense Support Element / Air-Ground Integration Element OCT (formerly the Air Defense Airspace Management / Brigade Aviation Element OCT), MAJ Edward Pecoraro the BDE S-2 Intelligence Officer OCT, CW2 Luis Alicea the Senior BDE Electronic Warfare Targeting Officer OCT, and CSM Bryan Jaragoske acting Command Sergeant Major of Operations Group (formerly BC2 CSM).   This episode examines how infantry brigade combat teams must reclaim reconnaissance and security as core competencies following the loss of cavalry squadrons. A central theme is that while the structure has changed, the requirement has not—brigades still must answer PIRs, develop NAIs, and shape the fight before committing combat power. Without a dedicated squadron headquarters to plan and synchronize reconnaissance, those responsibilities now sit squarely with the brigade staff. The discussion highlights friction points in intelligence architecture, reporting pathways, and the synchronization of collection assets, stressing that reconnaissance is no longer “someone else's problem.” Infantry battalions, multi-purpose companies (MPCs), and multi-functional reconnaissance companies (MFRCs) must all contribute to the reconnaissance fight, requiring commanders and staffs to deliberately task, synchronize, and integrate ground patrols, UAS, and other sensing capabilities.    The conversation also underscores the need to return to fundamentals—patrolling, reporting discipline, and combined arms integration across warfighting functions. Leaders emphasize that reconnaissance is not limited to scout formations; any element with the capability and proximity can be tasked to collect and report, provided it understands the task and purpose. Effective reconnaissance now demands tighter integration between S2, S3, aviation planners, and electronic warfare sections to sequence sensors, manage airspace, and fuse reporting into actionable intelligence. The key takeaway is clear: brigades must deliberately plan reconnaissance during MDMP, publish detailed reconnaissance guidance, and train these skills at home station. Without that discipline, formations risk fighting blind in LSCO.       Part of S13 “Hip Pocket Training” series.   For additional information and insights from this episode, please check-out our Instagram page @the_jrtc_crucible_podcast   Be sure to follow us on social media to keep up with the latest warfighting TTPs learned through the crucible that is the Joint Readiness Training Center.   Follow us by going to: https://linktr.ee/jrtc and then selecting your preferred podcast format.   Again, we'd like to thank our guests for participating. Don't forget to like, subscribe, and review us wherever you listen or watch your podcasts — and be sure to stay tuned for more in the near future.   “The Crucible – The JRTC Experience” is a product of the Joint Readiness Training Center.

Как справляться с потоком информации, не забывать детали рабочих созвонов и превращать хаос в структуру? Виктор делится своей системой Personal Knowledge Management (PKM) в Obsidian. В этом выпуске разбираем теорию «Второго мозга»: от пирамиды знаний до метода Zettelkasten и системы организации папок Johnny Decimal. Саша скептически ищет практическую пользу, а Виктор показывает свой граф заметок. Также внутри — анонс нашей книги про Kubernetes интервью и список мастхэв плагинов. О чём выпуск: - Пирамида DIKW: Чем данные отличаются от мудрости и как это процессить. - Методологии: Zettelkasten (связи) и Johnny Decimal (структура папок). - AI и Obsidian: Как сделать RAG по своим заметкам с помощью Copilot и локальных моделей. - Синхронизация: Git, S3, WebDAV или платные сервисы — что выбрать. - Плагины: Обзор базового набора (Dataview, Excalidraw, Templater и др.). - Анонс книги: Как мы 2 года писали «Cracking the Kubernetes Interview».   ССЫЛКИ

What a time to be an investor! Markets are moving quickly as investors consider disruption risk and re-price moats for asset-lite companies.In this episode we are joined by Hirsh Jain of Ananda Strategy - we make predictions for 2026 on Cannabis & General Investing.In this episode we discuss:- S3 in a midterm year- New investment ideas- New States? FL, PA & VA- Hemp as a disruptive force- What lessons cannabis teaches us about the AI Infrastructure build outThanks to Hirsh as always for a candid conversation about the future of the industryConnect with Hirsh:LinkedIn - ⁠⁠⁠https://www.linkedin.com/in/hirsh-jain/⁠⁠⁠Twitter - ⁠⁠⁠https://twitter.com/anandastrategy⁠⁠⁠Hirsh's Website - ⁠⁠⁠https://anandastrategy.com/

Pol, Kirsten, and Bud catch up on THREE WEEKS of missed pods due to scheduling difficulties, and somehow managed NOT to make it a three hour pod! Three hour pod! Bud’s Weekly Geek-out 10:06 – Samsung privacy screen tech / Discord Coming Soon 13:49 – The Mandalorian and Grogu (in theatres May 22, Super Bowl :36) 17:47 – The Super Mario Galaxy Movie (Super Bowl :30, in theatres April 1) 18v01 – Spider-Noir (Sony/Marvel series, Prime Video, May 27, colour or black & white) (Zoner Mary) 19:09 – Project Hail Mary: trailer, article explaining the revealed plot elements (in theatres March 20) _____ – Disclosure Day (Super Bowl :60, in theatres June 12) 24:06 – The Devil Wears Prada 2 (in theatres May 1) 24:28 – Stranger Things: Tales from ’85 (conveniently-timed Netflix animated series, April 23) 26:09 – Monarch: Legacy of Monsters (Apple TV series, S2, February 27) 27:48 – [STROBE LIGHT WARNING AGAIN UGH] Scream 7 (February 27) 29:53 – Michael: trailer, 2m first look (in theatres April 24) 30:04 – Rooster (10-ep miniseries, Steve Carell, John C. McGinley, HBO Max, March 8) 31:07 – In The Blink of An Eye (dir: Andrew Stanton, starring Kate McKinnon, Rashida Jones, Daveed Diggs, basically Cloud Atlas?, Disney+, film February 27) 32:25 – War Machine (Netflix film, March 6) 33:18 – Lucky (Apple TV series, Anya Taylor-Joy, Annette Bening, Timothy Olyphant, July 16) 34:30 – Exit 8 (in theatres April 10) related: Bud’s micro-R&R of The Exit 8 and Platform 8 (games) 36:16 – Supergirl (Super Bowl :30, in theatres June 26) 37:09 – What’s Your Favorite? (Pokémon Super Bowl :60, Trevor Noah, Lady Gaga, Blackpink’s Jisoo, Formula One driver Charles Leclerc, Spanish professional footballer Lamine Yamal, Canadian actor/voice actor Maitreyi Ramakrishnan, and Puerto Rican rapper, singer, songwriter and former footballer Young Miko) 40:31 – The Adventures of Cliff Booth (written by Quentin Tarantino, Directed by David Fincher, Netflix film, also in theatres sometime this year) related: Quentin Tarantino’s new Beverly Cinema goes back to its porno roots this month also related: Paul Dano reacts to Quentin Tarantino dissing him: ‘Incredibly grateful the world spoke up for me so i didn’t have to’ 44:06 – White Lotus season 4 casts Helena Bonham Carter, Chris Messina, Marissa Long (HBO series, preproduction, location reveal) 45:05 – Ted Lasso to return for fourth season this summer (Apple TV series) 45:34 – Baldur’s Gate Series in the works at HBO (creator/writer/EP/showrunner: Craig Mazin) related: The Last of Us to film S3 in BC March 2–November 27 Geek News Proper 48:30 – The Grammys 49:05 – Gone too soon: Catherine O’Hara, 71, James Van Der Beek of Dawson’s Creek, 48, Jesse Jackson, 84, and Robert Duvall, 95 59:28 – John Lithgow on playing Dumbledore in Harry Potter series and J.K. Rowling’s trans-exclusionary views Reviews and Kirstemendayshes 1:02:24 – IT: Welcome to Derry (Kirsten & Pol) 1:05:16 – The Muppet Show (Kirsten & Pol) 1:07:22 – Wonder Man (Bud) 1:09:10 – The Secret Agent (Kirsten) 1:11:22 – Fallout S2 (Kirsten & Pol) 1:13:48 – Heated Rivalry (Kirsten) 1:20:28 – Send Help (Pol) 1:22:24 – Wuthering Heights (Pol) Other stuff that didn’t make the pod – Resident Evil Requiem – Evil Has Always Had A Name: short film, article w/ background – Unable to stop AI, SAG-AFTRA mulls a studio tax on digital performers Join The Geek-out Podcast’s Facebook page (where we’ll release new episodes, and where you can talk with us) and Facebook group (where fans of the podcast can gather and talk geeky stuff)! Questions? Comments? Corrections? Suggestions? e-mail geekout@TheZone.fm Subscribe to The Zone’s Geek-out Podcast on Apple Podcasts. And, get more Zone podcasty goodness at TheZone.fm/podcast

S3.66 - The Chicago Table - Wintery Invaders by Wererat Studios

If you've ever wondered how Oracle Database really works inside AWS, this episode will finally turn the lights on.   Join Senior Principal OCI Instructor Susan Jang as she explains the two database services available (Exadata Database Service and Autonomous Database), how Oracle and AWS share responsibilities behind the scenes, and which essential tasks still land on your plate after deployment.   You'll discover how automation, scaling, and security actually work, and which model best fits your needs, whether you want hands-off simplicity or deeper control.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26   Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University.  Nikita: Hi everyone! In our last episode, we began the discussion on Oracle Database@AWS. Today, we're diving deeper into the database services that are available in this environment. Susan Jang, our Senior Principal OCI Instructor, joins us once again.  00:56 Lois: Hi Susan! Thanks for being here today. In our last conversation, we compared Oracle Autonomous Database and Exadata Database Service. Can you elaborate on the fundamental differences between these two services?     Susan: Now, the primary difference is between the service is really the management model. The Autonomous is fully-managed by Oracle, while the Exadata provides flexibility for you to have the ability to customize your database environment while still having the infrastructure be managed by Oracle.   01:30 Nikita: When it comes to running Oracle Database@AWS, how do Oracle and AWS each chip in? Could you break down what each provider is responsible for in this setup?  Susan: Oracle Database@AWS is a collaboration between Oracle, as well as AWS. It allows the customer to deploy and run Oracle Database services, including the Oracle Autonomous Database and the Oracle Exadata Database Service directly in AWS data centers.   Oracle provides the ability of having the Oracle Exadata Database Service on a dedicated infrastructure. This service delivers full capabilities of Oracle Exadata Database on the Oracle Exadata hardware. It offers high performance and high security for demanding workloads. It has cloud automation, resource scaling, and performance optimization to simplify the management of the service.  Oracle Autonomous Database on the dedicated Exadata infrastructure provides a fully Autonomous Database on this dedicated infrastructure within AWS. It automates the database management tasks, including patching, backups, as well as tuning, and have built-in AI capabilities for developing AI-powered applications and interacting with data using natural language. The Oracle Database@AWS integrates those core database services with various AWS services for a comprehensive unified experience.  AWS provides the ability of having a cloud-based object storage, and that would be the Amazon S3. You also have the ability to have other services, such as the Amazon CloudWatch. It monitors the database metrics, as well as performance. You also have Amazon Bedrock. It provides a development environment for a generative AI application.   And last but not the least, amongst the many other services, you also have the SageMaker. This is a cloud-based platform for development of machine learning models, a wonderful integration with our AI application development needs.  03:54 Lois: How has the work involved in setting up and managing databases changed over time?  Susan: When we take a look at the evolution of how things have changed through the years in our systems, we realize that transfer responsibility has now been migrated more from customer or human interaction to services. As the database technology evolves from the traditional on-premise system to the Exadata engineered system, and finally to the Autonomous Database, certain services previously requiring significant manual intervention has become increasingly automated, as well as optimized.  04:34 Lois: How so?  Susan: When we take a look at the more traditional database environment, it requires manual configuration of hardware, operating system, as well as the software of the database, along with initial database creation. As we evolve into the Exadata environment, the Exadata Database, specifically the Exadata cloud service, simplifies provisioning through web-based wizard, making it faster and easier to deploy the Oracle Database in an optimized hardware.     But when we move it to an Autonomous environment, it automates the entire provisioning process, allowing users to rapidly deploy mission-critical databases without manual intervention, or DBA involvement. So as customers move toward Autonomous Database through Exadata, we have fewer components that the customer needs to manage in the database stack, which gives them more time to focus more on important parts of the business.  With the Exadata Database, it provides a co-management of backup, restore, patches and upgrade, monitoring, and tuning. And it allows the administrator the ability to customize the configuration to meet their very specific business needs. With Autonomous Database, it's now fully automated and it's a greater responsibility is shift toward the service. With Autonomous Database on dedicated infrastructure, it provides that fine-grained tuning more for Oracle to help you perform that task.  06:15 Nikita: If we narrow it down just to Oracle and AWS for a moment, which parts of the infrastructure or day-to-day ops are handled by each company behind the scenes?  Susan: When we take a look at Oracle Database@AWS, it operates under a shared responsibility model, dividing the service responsibilities between AWS, as well as Oracle, as well as you, the customer.   The AWS has the data center. Remember, this is where everything is running. The Oracle Database@AWS, the Oracle Database infrastructure may be managed by Oracle and run in OCI, but is physically located within the AWS regions, as well as the availability zones and the AWS data centers.  The AWS infrastructure, in this case, is AWS's responsibility to secure the environment, including the physical security of the data center, the network infrastructure, and the foundational services like the compute, the storage, and the networking, all within AWS.  The next thing of who's responsible for the shared responsibility, it's Oracle. And that would be the hardware. We provide the hardware. While the hardware may physically reside in the AWS data center, Oracle's Cloud Infrastructure operational team will be the one managing this infrastructure, including software patching, infrastructure update, and other operations through a connection to OCI. This means Oracle handles the provisioning, as well as the maintenance of any of the underlying Exadata infrastructure hardware.  When we take a look at the next thing that it manages, it is also responsible besides the infrastructure of the Exadata. It is also the ability to manage the hardware, the environment of that hardware through the database control plane. So Oracle manages the administration and the operational for the Oracle Database@AWS service, which resides in OCI. So this includes the capabilities for management, upgrade, and operational features.  08:37 Nikita: And what are the key things that still remain on the customer's plate?   Susan: If you are in an Exadata environment or in an Autonomous environment, it is you, the customer, who is responsible for most of the database administration operation, as well as managing the users and the privileges of the user to access the database. No one knows the database and who should be accessing the data better than you.  You will be responsible for securing the applications, the data of the database, which now allows you to define who has access to it, control the data encryption, and securing the application that interacts with the Oracle Database@AWS.  09:29 Lois: Susan, we've talked about both Autonomous Database and Exadata Database Service being available on Oracle Database@AWS, but what's different about how each works in this environment, and why might someone pick one over the other?  Susan: Both databases, even though they run on the same Exadata Cloud Infrastructure, both can be deployed on both public cloud, as well as the customer data center, which is Oracle Cloud@Customer.  The Autonomous Database is a fully managed, completely automated environment. And this provides a capability of having a fully Autonomous Database Service running on a dedicated Oracle Exadata Infrastructure within your AWS data center.  The Exadata is a service that is provided and managed by Oracle and is physically running in the AWS data center, but is designed for mission critical workload and includes RAC environment, Real Application Cluster, offering a high performance availability and full feature capability that is similar to other Exadata environment, such as those running in our customers' data center.  The primary difference is really between the two services. When you take a look at the Exadata, the customer only pays for the compute resources that is used. Autoscaling can be used for a variety or variable resources, the workload, to automatically scale to the compute resources up or down when required.  The Autonomous Database also has automatic optimization for data warehousing, transaction processing, as well as JSON workload. The Exadata service, the customer again, also pays for the compute resources that they allocate. But that's the key thing. The customer can initiate the scaling because it's very specific to the workload that is needed.  So when you take a look at the two database services, one gives the ability to let Oracle fully manage it, including the scaling capability. The other, the Exadata, provides you the capability of having the environment that it's running on the infrastructure be managed by Oracle that adds a database administrator. You may wish to have a little bit more granular control of how you want the database to not only be scaling, but how you wish to customize how the database will be running.  12:10 Nikita: Focusing on Autonomous Database for a moment, what should teams know about how it actually runs within AWS?   Susan: The Autonomous Database on the Oracle Database@AWS brings the power of the Oracle's self-managing, self-securing, and self-repairing database into your AWS environment.  It provides the capability of the database automatically, automates many of the traditional, complex, and time-consuming database management tasks, such as the provisioning of the database, the patching, the backing up, and the scaling, and the performance tuning, reducing the need for any manual intervention by the database administrator.  Running the Autonomous Database in your AWS region enables low latency access for your AWS applications and services that is deployed within AWS, thus improving performance and response time. With the Autonomous Database, it automates many of the traditional things that is now automatically done by Oracle. It also supports integration with various AWS services, such as the ability of the not in addition to AIM, but the cloud formation, the CloudWatch for monitoring and the S3 for the storage.  You can easily migrate existing Exadata workload, including those running on Oracle RAC to AWS with minimum or no change to any of your databases or applications. In addition, there's a really powerful capability and feature of the database is called zero ETL, and that's zero extract, transformation, and load.  It's an integration capability with services like your Amazon Redshift, enabling near real time analytics and machine learning on your transactional database that is stored within the Autonomous Database on in your AWS environment. So with the Autonomous Database, it checks off many of the boxes for automatic capability, securing, tuning, as well as scaling the database.  With the Autonomous Database in the Dedicated Exadata Infrastructure, the Exadata Cloud Infrastructure resource represents the physical system, which can be expanded with storage, as well as compute services, the compute host. This now provides the ability to have an isolated zone for the highest protection from other tenants. The data is stored on a dedicated server only for one customer. That would be you.  14:56 Lois: Could you explain the role of Autonomous VM? What are its primary benefits?  Susan: The virtual machine or as we refer to them as the cluster, includes the grid infrastructure and provides a private network isolation. This provides you the capability of having custom memory, core, and storage allocation.  The Oracle Grid Infrastructure includes the Oracle Clusterware, which manages the cluster, as well as the servers, and ensure that the database can failover to another server in case of any failure.  15:34 Be a part of something big by joining the Oracle University Learning Community! Connect with over 3 million members, including Oracle experts and fellow learners. Engage in topical forums, share your knowledge, and celebrate your achievements together. Discover the community today at mylearn.oracle.com.  15:55 Nikita: Welcome back! Susan, what is the Autonomous Container Database?  Susan: With the Autonomous Container Database, and you need that if you're going to create an Autonomous Database, you need to provision that within your Autonomous Exadata VM Cluster. It serves as a container to hold or to house one or more Autonomous Databases.  This allows multiple Autonomous Databases to coexist in the same infrastructure while still being logically separated. And this allows for the separation of databases based on their intended use. Think of a database for production. Think of a database for development. Think of a database for testing. You may have different database versions within the same infrastructure.  This isolation makes it easier for you to be able to meet your SLA, your Service Level Agreement, any long-term backups you may have, very specific encryption key needs to prevent issues from one database impacting another. So, the ability to have everything be isolated and secure is still grouping it in a manner that will meet your business needs.  17:08 Lois: Looking at Exadata Database Service specifically, what are some standout advantages for customers who deploy it on Oracle Database@AWS? Is there anything in particular they should get excited about in terms of performance or integration with AWS?  Susan: The Exadata Database Service is running on a dedicated Exadata Infrastructure that's deployed within your AWS data center. It delivers the same Exadata service experience in cloud control planes as the Oracle Cloud Infrastructure, allowing you to leverage existing skills and processing across your multi-cloud environment.  It addresses the data resiliency, or residency rather. And that's the scenario where many of our customers has the need. You have a need because of your security compliance to have the data local to you. By having the Exadata Database in your Oracle Database@AWS, it is running in your data center. So, this addresses that very important need, data residency, to have it close to you.  It also allows for seamless integration with other AWS services and applications. So now you have a capability of a hybrid cloud architecture leveraging the benefit of both Oracle Exadata and your AWS system. It has built-in high availability, the RAC application cluster, as well as Data Guard, a capability of addressing disaster recovery capability.  This also provides the ability for you to scale your compute, as well as your storage and your I/O resources independently. So as mentioned with Exadata, you have flexibility of how you want your database to be running individually. So just like the Autonomous, the Exadata Database checks off many of the boxes for running a mission-critical with high availability, highly redundant hardware and software features, along with extreme performance, scalability, and reliability.  This now allows you to run your AI environment, your online transaction processing, your analytic workload on any scale on the Exadata Infrastructure running in the Oracle Cloud. And in this case, running in your data center.  19:45 Nikita: If a business suddenly needs more capacity, how does scaling work with Exadata Database Service versus Autonomous Database on Oracle Database@AWS?   Susan: So with the Exadata scaling, you now can scale to meet expected demands so you know at certain point I will need more. I will then ask it to scale at that point when I will assign it-- and I'm using an example, I will assign it three computer cores all the time. But there may be demands. Think of your end of the quarter, end of the year processing that you may need more. So, you are enabling the compute cores to scale at the time you need it.  And what's cool is it will then, when it's no longer needed, it will then scale back down to the original three cores that you assign. So, you only pay for the enabled cores. But what's very cool about the Autonomous is that it is real-time scaling. So, with Autonomous, now you have the capability using Autonomous Database since it is self-tuning, self-monitoring, the Autonomous Database actually monitors the workload requirement and scales to match the workload demand.  Once the minimum level of the compute is defined and enabled, the automatic scaling is set. Autonomous Database will adjust to the consumption when it's needed, and it will scale back down when it's not. So though the Exadata is pretty cool, it will scale up and down on the workload demand.  This is with the Autonomous is even more powerful. It is real-time scaling based on that usage at that moment. Built-in automatic increase to meet the workload demands when it spikes and it automatically scales back when it's not needed.  A very powerful capability with all of our Oracle databases, the ability, even with traditional, to allow you to define what you may need with Exadata scaling for peak demands, as well as Autonomous scaling for real-time consumption and scaling when needed.  When you look at all of our options, one of the key things to bear in mind is a phrase that we use: performance scale as more servers are added. And what this is really saying is Oracle's automated scaling ability for the database, it basically has the ability to maintain or improve its performance under increased workload by automatically adding computational resources when needed.  This process is also known as horizontal scaling. It involves adding more servers, compute instances, to a cluster to share the processing load. And it has that capability automatically.  22:53 Nikita: There's so much more we can discuss about Oracle Database@AWS, but let's pause here for today! Thank you so much Susan for joining us.  Lois: Yeah, it's been really great to have you, Susan. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston…  Nikita: And Nikita Abraham, signing off!  23:23 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In the spirit of GORDON PARK and PAUL ROBESON, J. David Shanks prides himself on being the contemporary renaissance man. He's a writer, producer, and actor with aspirations of adding to his hyphenated list of industry skill sets, Director for feature films and television series.David is a graduate of the University of Southern California's School of Cinematic Arts. He received his MFA in Producing for Film, Television, and New Media from the world-renowned Peter Stark Producing Program. His writing credits include NBC's SHADES OF BLUE, FOX Television's SHOTS FIRED, SHOWTIME's THE CHI and the critically acclaimed NETFLIX SERIES SEVEN SECONDS where his episode, MATTERS OF LIFE AND DEATH won the 2019 NAACP IMAGE AWARD for Outstanding Writing in a Television Drama.Presently, David is working under an overall deal with AMC Network. He is currently the Executive Producer/ Co-Showrunner for the AMC SERIES 61st STREET and EP on CROSS S2 & S3.Another don't miss episode y'all!

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

It's just Audree and Vic for Episode 4 of S3 of A Sam Girl Retrospective! They get right into it by discussing "Red Sky at Morning" & "Fresh Blood!" Check out the episode links for this one because there are a TON. Don't forget to check out the visuals for this episode on Instagram, drop comments of your thoughts on our takes, and rate/like and subscribe! Episode Links: Shug's Bagels The Good Place giraffe scene The best of Joey Tribiani  Supernatural Exec: "We Won't Be One Tree Hill with Monsters!" The Bela Effect Meta Analysis  The Boys Creator Talks About His Aim To Avoid "Edgelord Material For Incels"  Angel and Cordelia (Cangel) - an in-depth analysis of their relationship Buffy Meta Miss Havisham   A literal candy cane cuteness tag  Convention: Jared and Jensen's favorite dynamics in the story Women in the show   Milo and Sterling fanboy over Jared Sterling K Brown loved working on Spn  Sterling K Brown Awards and Nominations Destination X hosted by JDM TV Series: True Blood  Like/Rate/Review & Subscribe to the show on: Apple Spotify Youtube Patreon And anywhere else you listen to podcasts! Email us at: samgirlretrospectivepod@gmail.com Follow us on IG for visuals and updates. Follow us on Tiktok for clips and memes. Images from Supernatural Archive.

En el anterior episodio hablamos largo y tendido sobre los "homelabs" o laboratorios de prueba informáticos que muchos tenemos en casa. Hemos recibido muchísimos comentarios y hoy repasamos qué tenéis cada uno en casa, y aprendemos juntos sobre muchísimas de estas herramientas. Además, os dejamos una lista de enlaces de todas estas herramientas y hardware para que podáis empezar a montar vuestra propia versión para aprender y probar cosas nuevas: Herramientas Guía de Iban para una transición a alternativas europeas Home Assistant (domótica libre) Kopia (copias de seguridad) Tailscale (VPN entre tus dispositivos, open-source con headscale) authentik (proveedor de identidad privado) immich (gestor de fotos) Komga (gestor de cómics, libros) plex (gestor multimedia de pago) Jellyfin (gestor multimedia) Omoide (gestor multimedia) TeslaMate (gestión de tu Tesla) Heimdall (landing page) Syncthing (sincronización de ficheros) Proxmox (virtualización) Adguard (bloqueo de publicidad) Pi-hole (DNS con bloqueo de publicidad u otras categorías) Unbound (DNS local) Mealie (gestor de recetas de cocina) Obsidian (gestor de notas) K3S (Kubernetes liviano) WireGuard (VPN) podman (contenedores) Docker (contenedores) Harbor (repositorio de contenedores) Verdaccio (registro NPM) Forgejo (repositorios Git) Gitea (repositorios Git) RustFS (servidor S3) cert-manager (certificados TLS en Kubernetes) step-ca (Let's Encrypt local) TrueNAS (SO para NAS) Kiwix (copia local de wikipedia y otras wikis) Prometheus (métricas y monitorización) Grafana (gráficos de métricas) ArgoCD (CI/CD) FluxCD (CI/CD) vLLM (IA generativa local compatible con API de OpenAI) Open WebUI (interfaz web para IA generativa) Hardware Switchbot (domótica) Shelly (relés y domótica) Aqara (domótica) Eve (domótica) Inels Wireless (domótica) Reolink (cámaras de seguridad) GMKtec (mini-PCs) EliteDesk (mini-PCs) QNAP (NAS) Synology (NAS) Raspberry Pi (mini-PCs) Noticias IKEA lanza 21 nuevos productos para un hogar inteligente Sánchez anuncia que España prohibirá acceder a las redes sociales a los menores de 16 años El fundador de Telegram carga contra Pedro Sánchez y alerta a España con un mensaje masivo Música del episodio Introducción: Safe and Warm in Hunter's Arms - Roller Genoa Cierre: Inspiring Course Of Life - Alex Che Puedes encontrarnos en Mastodon y apoyarnos escuchando nuestro podcast en Podimo o haciéndote fan en iVoox. Si quieres un mes gratis en iVoox Premium, haz click aquí.

S3.65 - The Chicago Table - Into the Emerald Forest by Wererat Studios

Cloud bills are climbing, AI pipelines are exploding, and storage is quietly becoming the bottleneck nobody wants to own. Ugur Tigli, CTO at MinIO, breaks down what actually changes when AI workloads hit your infrastructure, and how teams can keep performance high without letting costs spiral. In this conversation, we get practical about object storage, S3 as the modern standard, what open source really means for security and speed, and why “cloud” is more of an operating model than a place. Key takeaways• AI multiplies data, not just compute, training and inference create more checkpoints, more versions, more storage pressure • Object storage and S3 are simplifying the persistence layer, even as the layers above it get more complex • Open source can improve security feedback loops because the community surfaces regressions fast, the real risk is running unsupported, outdated versions • Public cloud costs are often less about storage and more about variable charges like egress, many teams move data on prem to regain predictability • The bar for infrastructure teams is rising, Kubernetes, modern storage, and AI workflow literacy are becoming table stakes Timestamped highlights00:00 Why cloud and AI workloads force a fresh look at storage, operating models, and cost control 00:00 What MinIO is, and why high performance object storage sits at the center of modern data platforms 01:23 Why MinIO chose open source, and how they balance freedom with commercial reality 04:08 Open source and security, why faster feedback beats the closed source perception, plus the real risk factor 09:44 Cloud cost realities, egress, replication, and why “fixed costs” drive many teams back inside their own walls 15:04 The persistence layer is getting simpler, S3 becomes the standard, while the upper stack gets messier 18:00 Skills gap, why teams need DevOps plus AIOps thinking to run modern storage at scale 20:22 What happens to AI costs next, competition, software ecosystem maturity, and why data growth still wins A line worth keeping“Cloud is not a destination for us, it's more of an operating model.” Pro tips for builders and tech leaders• If your AI initiative is still a pilot, track egress and data movement early, that is where “surprise” costs tend to show up • Standardize around containerized deployment where possible, it reduces the gap between public and private environments, but plan for integration friction like identity and key management • Treat storage as a performance system, not a procurement line item, the right persistence layer can unblock training, inference, and downstream pipelines What's next:If you're building with AI, running data platforms, or trying to get your cloud costs under control, follow the show and subscribe so you do not miss upcoming episodes. Share this one with a teammate who owns infrastructure, data, or platform engineering.

In this episode of Cybersecurity Today, host Jim Love discusses the latest advancements in AI-driven cyber attacks and their implications for security infrastructure. The episode covers a variety of topics, including the vulnerabilities in OpenClaw Marketplace, a rapid AI-assisted AWS attack, and data breaches linked to the Shiny Hunters group targeting Harvard and the University of Pennsylvania.  From discussing the porous architecture of AI agents to exploring how attackers exploited AWS credentials in unsecured S3 buckets, this episode sheds light on the accelerated risks posed by AI in cybersecurity. Additionally, Jim Love speaks about the critical need for proactive measures and the inadequacies in current security frameworks. Hashtag Trending would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt 00:00 Introduction and Sponsor Message 00:20 Open Clause Marketplace and AI Threats 00:46 AI Agents and Security Risks 01:09 OpenClaw's Vulnerabilities 02:06 Malicious Skills in OpenClaw 03:37 Strategies for CIOs 04:38 AWS Breach Accelerated by AI 08:27 Shiny Hunters and University Data Breaches 10:48 Conclusion and Sponsor Message

🧭 REBEL Rundown 📌 Key Points 💨 HFNC met criteria for non-inferiority to BPAP for preventing intubation or death within 7 days in four of the five ARF subgroups.🧪 Bayesian dynamic borrowing increased power across subgroups but created variable certainty, especially in smaller groups such as COPD.🫁 The immunocompromised hypoxemia subgroup did not meet non-inferiority, leading to early trial stopping for futility.️ Rescue BPAP use, subgroup-specific exclusion criteria, and non-standardized BPAP delivery are important contextual factors that influence how subgroup results should be interpreted. Click here for Direct Download of the Podcast. 📝 Introduction Bilevel Positive Airway Pressure (BPAP) has long been a foundational modality in the management of acute respiratory failure (ARF), particularly in COPD exacerbations and cardiogenic pulmonary edema, where it can rapidly reduce work of breathing and improve gas exchange. It remains a core tool in our respiratory support arsenal.High-flow nasal cannula (HFNC), however, has expanded what we can offer patients by delivering many of the same physiologic benefits through a far more comfortable interface. With high flows, modest PEEP, and effective dead-space washout, HFNC can improve oxygenation and decrease work of breathing while preserving the ability to talk, cough, eat, and interact with staff and family. This combination of physiologic support and tolerability makes HFNC especially attractive in patients where comfort, anxiety, or cardiovascular stability are key considerations, and in settings where prolonged noninvasive support may be needed. Rather than competing with BPAP, HFNC broadens our options in ARF and allows us to better match the modality to the patient and their underlying disease process.The RENOVATE trial set out to answer a high-impact question across five distinct etiologic groups: Is HFNC non-inferior to BPAP (NIV) for preventing intubation or death in acute respiratory failure? 🧾 Paper Azoulay É, et al. High-Flow Nasal Oxygen vs Noninvasive Ventilation in Patients With Acute Respiratory Failure: The RENOVATE Randomized Clinical Trial. JAMA. 2025 PMID: 39657981 🔙Previously Covered On REBEL: HFNC: Part 1 – How It WorksHFNC: Part 2 – Adult and Pediatric IndicationsFLORALI and AVOID TrialFLORALI-2: NIV vs HFNC as Pre-Oxygenation Prior to IntubationThe Pre-AeRATE Trial – HFNC vs NC for RSI ️ What They Did CLINICAL QUESTION Is HFNC non-inferior to BPAP for rate of endotracheal intubation or death at 7 days in patients with acute respiratory failure due to a variety of causes? STUDY DESIGN Multicenter, randomized non-inferiority trial33 Brazilian hospitalsNov 2019 – Nov 2023Adaptive Bayesian hierarchical modeling with dynamic borrowingOpen label, outcome adjudicators blindedPatients were classified into 5 subgroups SUBGROUPS 1. Non-immunocompromised hypoxemiaSpO₂ < 90% on room air orPaO₂ < 60 mm Hg on room air plusIncreased respiratory effort (accessory muscle use, paradoxical breathing, thoracoabdominal asynchrony) orRespiratory rate > 25 breaths/min2. Immunocompromised hypoxemiaDefined as:Use of immunosuppressive drugs for >3 monthsOR high-dose steroids >0.5 mg/kg/dayOR solid organ transplantOR solid tumors or hematologic malignancies (past 5 years)OR HIV with AIDS / primary immunodeficiency3. COPD exacerbation with acidosisHigh clinical suspicion of COPD as primary diagnosisRR >25 with accessory muscle use, paradoxical breathing, and/or thoracoabdominal asynchronyABG: pH 454. Acute cardiogenic pulmonary edema (ACPE)Sudden onset dyspnea and rales± S3 heart soundNo evidence of aspiration, infection, or pulmonary fibrosisCXR consistent with pulmonary edema5. Hypoxemic COVID-19 (added June 2023)Added due to deviations between expected and observed outcome proportionsAny patient across the other 4 groups with PCR-confirmed SARS-CoV-2 infection in any of the above groups POPULATION Inclusion Criteria:≥18 yrs with ARF* in one of 5 pre-defined subgroups excluding COPD was defined by the following:Hypoxemia with SpO₂

How is fellowship really lived out at Watermark? Is there hope for the younger generation? In today's episode, Emma Dotter sits down with Watermark's Shoreline Director, Will McIlroy, to share powerful stories of genuine fellowship in action. Will highlights how Watermark students are actively living out their faith through gathering for worship, devotion to God's Word, and meaningful community with one another. This conversation offers a glimpse into what it looks like when faith is practiced together—not just taught. // ADDITIONAL VERSES MENTIONED: 1 John 1:1-4  // RESOURCES FOR FURTHER STUDY:  If you have students in 6-12 grade, check out DTown happening February 6-7, 2026: https://www.watermark.org/events/6834-dtown-2026 See what God is doing in our students' lives on Shoreline's Instagram: https://www.instagram.com/shorelinedallas/?hl=en // RELATED JOIN THE JOURNEY EPISODES:  S4:263 – Introduction to the book of Acts” (https://podcasts.apple.com/us/podcast...) S3:035 – Ecclesiastes 7 with Craig Wenning (https://podcasts.apple.com/us/podcast/s3-035-ecclesiastes-7/id1600151923?i=1000645562308)  // WHAT IS JOIN THE JOURNEY? Join The Journey is a realistic daily Bible reading plan that helps followers of Jesus at Watermark Community Church and beyond enjoy abiding in Jesus together. Join The Journey Jr. is designed to help parents guide their kids in Bible reading through interactive and age-specific lessons. In 2026, we're studying the book of Acts—one passage per week. For another year, teaching on Sunday will align with each week's passage. Then, for the next six days, we'll return to the same passage with fresh focus, exploring insights about who God is and how we can enjoy him more deeply. Monday through Saturday, we'll approach the same passage from a different perspective each day—whether observation, interpretation, prayer, or another spiritual practice—to gain a deeper understanding and appreciation for God's Word. Then, watch or listen to the video podcast to tackle the week's toughest verses and discover key historical, theological, and practical insights. Daily Bible lessons for adults: https://jointhejourney.com Daily Bible lessons for parents and families: https://jointhejourney.com/jr Weekly Bible podcast for kids: https://podcasts.apple.com/us/podcast...  // MORE RESOURCES FROM JOIN THE JOURNEY:  Digital Bible study resources: https://jointhejourney.com/resources Previous years' print curriculum: https://www.amazon.com/stores/Waterma... Contact the Join The Journey team: jointhejourney@watermark.org 

S3.64 - The Chicago Table - The Purple Cloud and the Cubic Gate by Wererat Studios

We've got it all! S3 update, social media "re-brand", new shows, timeline, and what's next.TRANSCRIPT: https://docs.google.com/document/d/12KStP4jwaBGfvcKsjFxDWjHtsVWyW0JCOrTeY1mZtrA/edit?usp=sharingFollow us @EthicsTownProd for updates.InstagramTikTokBlueskyYouTubeTumblrTwitter is so beyond dead to me, but I will be posting exclusively episode announcements there once S3 is completed.Visit our website: https://ethicstown.weebly.com/Support us on Ko-fi: https://ko-fi.com/ethicstown

Fabric CTO Ankush Goyal reveals how AI Search is transforming commerce discovery and why merchants need AI agents to compete effectively.Topics Include:Fabric builds AI agents for commerce, solving merchant visibility challengesCommerce shifting due to AI Search channels and smaller retail teamsProduct Agent monitors and improves product visibility across AI channelsPetMeds uses Fabric to optimize AI Search and automate SKU onboardingFabric evolved from commerce platform company to AI agent solutionsAgentic and generative AI work together to optimize product catalogsFabric uses AWS EKS, Bedrock, S3, and Nova models heavilyAWS partnership connects Fabric with industry leaders and growth opportunitiesAWS services enable reliable, cost-effective, and performant enterprise AI agentsPrototyping agents is easy, but enterprise-grade reliability is extremely challengingFour key learnings: workflow reliability, context engineering, cost effectiveness, feedback loopsCTOs should define agent goals, guardrails, context, and evaluations earlyLong-running workflow durability and snapshots prevent costly repeated work failuresFuture innovations focus on specialized models, retrieval frameworks, automated evaluationsMerchants can evaluate AI Search performance at fabric.inc or LinkedInParticipants:Ankush Goyal – Chief Technology Officer, FabricSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Anne-Marie and Peter continue their journey through Babylon 5 S1 covering Grail. The Babylon 5 theme was written by Christopher Franke. All music included is for illustrative purposes only, and no copyright infringement is intended. The artwork for BablyOrg 5 was by Quinn Organ. On Tuesday 3rd Feb the Orgs look at Eyes. Over on their Borgcast feed, on Wednesday the 28th, they cover Strange New World's S3's Wedding Bell Blues. Feel free to send your thoughts in (just keep the feedback to less than 5 minutes please). Borgcast@gmail.com

Welcome back to The Snack – a lighter serving of Girls Gotta Eat. This week, we're talking about: Kristi and Desmond Scott's divorce and scandal Kyle and Amanda from Summer House divorce announcement Brooklyn Beckham airing his family's dirty laundry  Breaking down the 2016 trend and reminiscing  Tell Me Lies – review of S3 so far and the real life couples on the show Headlines: Euphoria trailer breaks records, Indiana University wins football championship, Luda pulls out of MAGA Fest  Follow us on Instagram @girlsgottaeatpodcast, Ashley @ashhess, and Rayna @rayna.greenberg. Visit girlsgottaeat.com for more. Thank you to Shopify: If 2026 is your year, go to shopify.com/gge and make your move. Download the Kitchen Sink app here.

S3, Ep : 24. Paradores of Spain: Sleeping in History, Chasing the EclipseIn this new episode of Spanish Loops, we travel through one of Spain's most unique and quietly ambitious success stories: Paradores de Turismo. More than just a hotel chain, Paradores is a living idea: one born from the need to protect Spain's historical heritage while opening the country to travelers in a thoughtful, dignified way.The concept emerged in the early 20th century, when Spain realized that its castles, monasteries, palaces, and fortresses could have a second life. Instead of becoming ruins or museums frozen in time, they could welcome guests. The very first Parador opened in 1928 in the Sierra de Gredos, setting the tone for a model that blended hospitality, preservation, and national identity. From that moment on, sleeping in history became part of the journey.But Paradores are not just about beautiful rooms with thick stone walls and dramatic views. Over the decades, they have played a key role in boosting local economies, supporting rural areas, creating stable jobs, and protecting regional traditions. Their kitchens became ambassadors of local gastronomy,turning traditional recipes into something to be proud of, refined without losing their soul. Staying in a Parador often means tasting the region as much as seeing it.Today, Paradores stand as a symbol of Spain's public hospitality model: elegant, rooted in history, and deeply connected to place. They represent a national style of hostelry that many countries admire and few have managed to replicate. And in this episode, there is a big surprise.Jorge reveals a project he has been carefully developing for over a year: an extraordinary 2027 eclipse tour. During the longest total solar eclipse visible on Earth in the next 200 years, Ceuta (Spanish territory in Africa) will sit right at the center of the path of totality. We'll experience the eclipse fromthe Parador of Ceuta, with the rest of the journey unfolding in Southern Spain near Málaga and finishing in the historic city of Cádiz.It will be summer, but don't be fooled by the heat. We'll be by the water constantly and the Mediterranean and Atlantic breeze cools the temperature down. And yes, we'll cross the Strait of Gibraltar by helicopter. (There is an option B, just in case.)Seats are limited. If you want to be part of this once-in-a-lifetime experience, write directly to Jorge at travelingwithjorge.web@gmail.com and get on the list.History, travel, and the sky itself. This episode brings it all together!

Hunter Leath, CEO of Archil, spent 8 years building Amazon's EFS file storage system, learning exactly why making cloud storage act like a hard drive always fails. Old programs need hard drives, but cloud storage doesn't work like hard drives—a problem that's existed for 20 years.Now Hunter's building Archil, which puts super-fast storage between programs and S3 so they can finally work together. Your programs think they're talking to a regular disk while your data lives safely in the cloud.Hunter explains how they're doing what others couldn't, why it costs less than Amazon's own solutions, and why file systems suddenly matter again in the AI era.Show Highlights:(01:37) What Archil Does and Why It Exists(02:26) Why Mounting S3 as a File System Has Always Failed(03:07) What Building EFS Taught Hunter(06:55) Using Fast SSDs as a Cache Layer for S3(09:45) Attaching Archil to Your Existing S3 Buckets(15:08) Why Archil Costs Less Than EBS When You Do the Math(17:56) What Happens If Amazon Builds This Feature(19:20) Competing With EBS Performance on GP3 Volumes(21:43) Raising $6.7 Million Without an AI Pitch(23:46) What Customers Get Wrong About Archil(28:07) Accessing Data Stored in Glacier Deep Archive(29:24) The Plan to Get Into the Linux Kernel (30:51) Where to Find HunterAbout Hunter Leath: Hunter is the founder and CEO of Archil, which transforms S3 buckets into infinite, local file systems that provide instant access to massive data sets. Prior to Archill, Hunter spent the last ten years in the cloud storage industry, including 8 years building Amazon's Elastic File System product and one year on Netflix's core storage team.Links:Hunter Leath on LinkedIn: https://www.linkedin.com/in/hleath/Hunter Leath on X: https://x.com/jhleath/Archil's Website: https://archil.comSponsored by: duckbillhq.com

SPRING TOUR TICKETS > www.barstoolsports.com/events/bestshowonearthtour. Noah's 'The Book of Mormon' Broadway review (00:00-20:03). Harry Styles announces new album! (21:11-36:03). ‘Marty Supreme' originally had a vampire ending (36:04-42:41). ‘Euphoria' S3 trailer released (42:42-49:43). Jacob Elordi & Olivia Jade spotted out together (49:44-55:24). Pete Davidson launching Netflix podcast + Alix Earle launches new YouTube show (55:25-1:10:26). ‘The Real Housewives: Ultimate Road Trip: Roaring 20th' cast revealed (1:10:25-1:13:28). ‘Tell Me Lies' S3 E 1-3 recap (1:13:29-1:32:15). Interview with ‘Tell Me Lies' Spencer House - talking season 3, how he got cast + more! (1:33:16-2:04:27). Beat Ria & Fran game 203 with Kristie & Jeneé (2:04:28-2:25:52). CITO LINKS > barstool.link/chicks-in-the-office.You can find every episode of this show on Apple Podcasts, Spotify or YouTube. Prime Members can listen ad-free on Amazon Music. For more, visit barstool.link/chicks-in-the-office

S3.63 - The Chicago Table - Storm the Bridge by Wererat Studios

R. Tyler Croy, a principal engineer at Scribd, joins Corey Quinn to explain what happens when simple tasks cost $100,000. Checking if files are damaged? $100K. Using newer S3 tools? Way too expensive. Normal solutions don't work anymore. Tyler shares how with this much data, you can't just throw money at the problem, but rather you have to engineer your way out.About R. Tyler: R. Tyler Croy leads infrastructure architecture at Scribd and has been an open source developer for over 14 years. His work spans the FreeBSD, Python, Ruby, Puppet, Jenkins, and Delta Lake communities. Under his leadership, Scribd's Infrastructure Engineering team built Delta Lake for Rust to support a wide variety of high performance data processing systems. That experience led to Tyler developing the next big iteration of storage architecture to power large-scale fulltext compute challenges facing the organization.Show Highlights:01:48 Scribd's 18-Year History04:00 One Document Becomes Billions of Files05:47 When Normal Physics Stop Working08:02 Why S3 Metadata Costs Too Much10:50 How AI Made Old Documents Valuable13:30 From 100 Billion to 100 Million Objects15:05 The Curse of Retail Pricing 19:17 How Data Scientists Create Growth21:18 De-Normalizing Data Problems25:29 Evolving Old Systems27:45 Billions Added Since Summer29:29 Underused S3 Features31:48 Where to Find TylerLinks: Scribd: https://tech.scribd.comMastodon:  https://hacky.town/@rtylerGitHub: https://github.com/rtylerSponsored by: duckbillhq.com

Anne-Marie and Peter continue their journey through Babylon 5 S1 covering TKO. The Babylon 5 theme was written by Christopher Franke. All music included is for illustrative purposes only, and no copyright infringement is intended. The artwork for BablyOrg 5 was by Quinn Organ. On 22ND the Orgs look at Grail. Over on their Borgcast feed, on the 15th, they cover Strange New World's S3's Hegemony part 2. Feel free to send your thoughts in (just keep the feedback to less than 5 minutes please). Borgcast@gmail.com  

Nase zu, Geldbeutel auf! Bei Erkältung landet oft irgendwas aus der Apotheke im Einkaufskorb. Aber können Erkältungsmittel wie Gelomyrtol, ACC-Akut, Sinupret oder Dolo-Dobendan die Erkältung wirklich bekämpfen? Oder haben wir es hier hauptsächlich mit dem Placeboeffekt zu tun? Ein Blick in die Studienlage zeigt: Für viele Mittel ist die wissenschaftliche Evidenz überraschend dünn. Falls ihr in dieser Folge Umckaloabo, Meditonsin, Aspirin Complex und Wick MediNait vermisst – die haben wir uns in Folge #48 angeschaut: https://www.quarks.de/podcast/quarks-science-cops-folge-48-erkaeltungsmittel-wissenschaft-oder-kruemeltee/ Aus der Quarks-Redaktion empfehlen wir den Quarks-Podcast Mal Angenommen. Erste Folge: Mal angenommen, Deutschland wäre jung. Den gibt's hier: https://1.ard.de/quarks_mal_angenommen_audiothek und hier: https://1.ard.de/quarks_mal_angenommen_spotify Hier sind unsere wichtigsten Quellen für diese Folge (alle findet ihr auf https://www.quarks.de/podcast/erkaeltungsmittel-2-quarks-science-cops): Cardot, J.-M. et al.: Validated correlation of mass loss and drug release [...] cetylpyridinium chloride (CPC) and benzocaine (1.4 mg/10 mg) lozenges […] (Journal of Drug Delivery Science and Technology, 2022) https://www.sciencedirect.com/science/article/pii/S177322472200733X Jund, R. et al. Clinical efficacy of a dry extract of five herbal drugs in acute viral rhinosinusitis (Rhinology, 2012) https://www.rhinologyjournal.com/Rhinology_issues/1120.pdf S3-Leitlinie der DEGAM für Halsschmerzen von 2020 (aktuellste Fassung, wird aktuell überarbeitet) https://register.awmf.org/assets/guidelines/053-010l-S3_Halsschmerzen_2021-12-abgelaufen.pdf Jund, R. et al.: Herbal drug BNO 1016 is safe and effective in the treatment of acute viral rhinosinusitis. (Acta Oto-Laryngologica, 2014) https://pubmed.ncbi.nlm.nih.gov/25496178/ Gillissen, A. et al.: A Multi-centre, Randomised, Double-blind, Placebo-controlled Clinical Trial on the Efficacy and Tolerability of GeloMyrtol® forte in Acute Bronchitis (Drug Research, 2013) https://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0032-1331182 S3-Leitlinie DEGAM Akuter und chronischer Husten 2021 https://register.awmf.org/assets/guidelines/053-013l_S3_akuter-und-chronischer-Husten_2025-11.pdf Sheffner, A. L.: The Reduction in vitro in viscosity of mucoprotein solutions by a new mucolytic agent, N-Acetyl-Cysteine (Annals oft he New York Academy of Sciences, 1963) https://nyaspubs.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1749-6632.1963.tb16647.x Video- und Ton-Ausschnitte, die in dieser Episode verwendet wurden: Werbespot GeloMyrtol® forte https://www.youtube.com/watch?v=HABJPkjpQVk Werbespot ACC https://www.youtube.com/watch?v=5IYpoJc1MCk Werbespot ACC Akut mit Iris Berben https://www.youtube.com/watch?v=DYow3EZLIDQ Werbespot Dolo Dobendan https://www.facebook.com/DobendanDeutschland/videos/1945925302566218 Werbespot Sinupret eXtract https://www.youtube.com/watch?v=UYyKfa2rx2w Donald Trump speech from 2024 election night https://www.youtube.com/watch?v=vPOdsmGXUAM Von Maximilian Doeckel und Jonathan Focke.

Este podcast es, básicamente, vuestra carta a los Reyes Magos anticipada. En el mundo de los aficionados al motor, a menudo nos atormenta una pregunta: "¿He llegado tarde?". Vemos las subastas millonarias, vemos cómo se disparan los precios de los GTI de los 80 y pensamos que todo lo que tiene alma ya es inaccesible. Pero eso es un error. El mercado es un organismo vivo y nunca duerme. Lo que hoy consideramos un "coche viejo" de quinta mano, mañana será una pieza de colección. Pasó con los japoneses de los 90, pasó con los BMW pre-Bangle y está pasando ahora mismo delante de tus narices con los coches que vamos a analizar hoy. En esta guía de inversión 2025 bajamos al mundo real. Nada de Paganis ni coches de museo. Hemos seleccionado 10 joyas ocultas que la mayoría ignora, pero que reúnen los ingredientes sagrados para revalorizarse: tacto analógico, cambios manuales y carácter. Aquí tienes la lista ordenada por presupuesto, desde la opción más accesible hasta la inversión seria: 1. Mazda MX-5 (NB) 1.8 Sport (7.000 € - 11.000 €) Olvídate del básico. La inversión está en el motor 1.8 de 146 CV, caja de 6 velocidades y, lo más importante: el Diferencial Autoblocante Torsen. Es la escuela de conducción perfecta y su configuración de motor atmosférico y peso pluma está en peligro de extinción. 2. Audi TT Mk1 1.8T Quattro 225 CV (8.000 € - 12.000 €) La Bauhaus con Turbo. Un icono de diseño con una calidad interior que humilla a los coches modernos. La clave es buscar la unidad de 225 CV (mismo motor que el S3, turbo K04) y tracción Quattro. Si encuentras uno con tapicería "Mocassin", no lo dejes escapar. 3. Toyota MR2 W30 (8.000 € - 13.000 €) El "Lotus Elise" japonés. Un incomprendido genial con motor central-trasero y menos de 1.000 kg. Fracasó por no tener maletero, pero hoy es un juguete puro. Busca unidades post-2003 para evitar problemas de precatalizadores. 4. Renault Clio Sport 182 (9.000 € - 14.000 €) El último samurái de los compactos. Motor 2.0 atmosférico rabioso y un chasis vivo que se insinúa. El "Santo Grial" es la unidad con "Chassis Cup" o Pack Racing y asientos Recaro. Incómodo, ruidoso y maravilloso. 5. BMW E46 330Ci (10.000 € - 16.000 €) El M3 del hombre sensato. El motor M54B30 (3.0 litros, 6 cilindros, 231 CV) es pura seda y fiabilidad. Es el equilibrio perfecto. Imprescindible manual y carrocería Coupé. El último BMW clásico antes de la digitalización total. 6. Honda Civic Type R EP3 (11.000 € - 17.000 €) No te dejes engañar por su forma de monovolumen. Esconde el motor K20A2, uno de los mejores 4 cilindros de la historia, capaz de aullar a 8.000 vueltas. Encontrar una unidad de estricta serie es cada vez más difícil, y eso se paga. 7. Alfa Romeo GTV (916) 3.0 V6 24v (12.000 € - 18.000 €) "La macchina più bella". Aunque sea tracción delantera, su eje trasero direccional y, sobre todo, el motor V6 "Busso", lo convierten en arte. Posiblemente el mejor sonido V6 de la historia. 8. Mercedes-Benz CLK 55 AMG W208 (15.000 € - 22.000 €) El "tapado" de la lista. Parece el coche de un jubilado, pero bajo el capó lleva un V8 atmosférico de 5.4 litros y 347 CV. Un muscle car fabricado en Stuttgart que ha tocado suelo en su depreciación. 9. Porsche Boxster S 986 (16.000 € - 22.000 €) El coche que salvó a Porsche. Dinámicamente más equilibrado que el 911 gracias a su motor central. Busca el "S" (3.2 litros, 252 CV) manual. No dejes que el pánico al IMS te frene; la mayoría ya están solucionados. 10. Ford Focus RS Mk1 (22.000 € - 30.000 €) El unicornio. Solo 4.501 unidades fabricadas. Un coche de homologación con diferencial autoblocante Quaife que tira hacia el interior de las curvas con violencia. Su curva de valor es vertical: es el caballo ganador absoluto. Conclusión: Invertir en estos coches no es especular, es comprar "valor" para disfrutarlo. El mercado pagará caro mañana lo que hoy te hace sonreír al volante. Y recuerda la regla de oro: compra siempre la mejor unidad que puedas permitirte, porque aquí lo barato sale muy caro.

S3.62 - The Chicago Table - Ghosts of Home by Wererat Studios

LAST EP OF THE SZN AND THE YEAR!! In this weeks episode.. I discuss the latest tv/film updates, events I've been too and divide It moments! -Tell me lies S3 release date-Jonas Brothers Christmas movie -Camp Rock 3 teaser-Artful dodger Trailer s2 -Rivals first look -Paradise s2 teaser -People we meet on vacation trailer -Bridgerton first look photos+ 12 days of bridgerton -Emily in Paris trailer -Stranger things Vol 1+Vol 2 trailer-A24's The Drama teaser -Tubi's How to lose a popularity contest -Tell me Softly Prime video-Heated Rivalry Follow Divideit: IG: https://www.instagram.com/divideitwithgill/Tiktok: https://www.tiktok.com/@divideitwithgill?lang=en

Think your cloud backups will save you from a ransomware attack? Think again. In this episode, Matt Castriotta (Field CTO at Rubrik) explains why the traditional "I have backups" mindset is dangerous. He distinguishes between Disaster Recovery (business continuity for operational errors) and Cyber Resilience (recovering from a malicious attack where data and identity are untrusted) .Matt speaks about the "dirty secrets" of cloud-native recovery, explaining why S3 versioning and replication are not valid cyber recovery strategies . The conversation shifts to the critical, often overlooked aspect of Identity Recovery. If your Active Directory or Entra ID is compromised, it's "ground zero” and you can't access anything. Matt argues that identity must be treated as the new perimeter and backed up just like any other critical data source .We also explore the impact of AI agents on data integrity, how do you "rewind" an AI agent that hallucinated and corrupted your data? Plus, practical advice on DORA compliance, multi-cloud resiliency, and the "people and process" side of surviving a breach.Guest Socials - ⁠Matt's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions:(00:00) Introduction(02:20) Who is Matt Castriotta?(03:20) Defining Cyber Resilience: The Ability to Say "No" to Ransomware(05:00) Why "I Have Backups" is Not Enough(06:45) The Difference Between Disaster Recovery and Cyber Recovery(10:20) Cloud Native Risks: Versioning and Replication Are Not Backups(12:50) DORA Compliance: Multi-Cloud Resiliency & Egress Costs(15:10) The "Shared Responsibility Model" Trap in Cloud(17:45) Identity is the New Perimeter: Why You Must Back It Up(22:30) Identity Recovery: Can You Restore Your Active Directory in Minutes?(25:40) AI and Data: The New "Oil" and "Crown Jewels"(27:20) Rubrik Agent Cloud: Rewinding AI Agent Actions(29:40) Top 3 Priorities for a 2026 Resiliency Program(33:10) Fun Questions: Guitar, Family, and Italian Food

We unpack Seattle's latest transit surge, from the Federal Way light rail extension to the cross‑lake Two Line testing across the Lake Washington Floating Bridge. We close with the express‑lane BRT buildout and how highway stitches, feeder routes, and frequent service can reshape daily trips.• Federal Way extension scope, stations, and timeline• Federal Way retail core and TOD potential toward Tacoma• Pros and cons of building along I‑5• Cross‑lake Two Line testing on the floating bridge• Mercer Island median station and highway stitches• Judkins Park station access and trail links• Combined four‑minute headways on the shared trunk• Reliability risks with limited bypasses• Ridership growth and network effects• BRT S1, S2, S3 using express lanes and median platforms• Service spans and 10–15 minute headways• Feeder buses and suburban connectivity• Using highway ROW to accelerate deliveryIf you enjoyed this episode, please feel free to support us in any way that you can. The biggest and best way that you can do that is by liking and subscribing to the channel, sharing these videos with your friends, family, bus drivers, train drivers, whoever you think is going to enjoy them. We also have our merch store where you can buy t‑shirts and hats and a couple other things, as well as utilizing our Buy Me a Coffee link or joining our Patreon where we try to get episodes out earlySend us a textSupport the show

Linktree: ⁠https://linktr.ee/Analytic⁠Join The Normandy For Additional Bonus Audio And Visual Content For All Things Nme+! Join Here: ⁠https://ow.ly/msoH50WCu0K⁠Dive into Segment of Notorious Mass Effect with Analytic Dreamz for the complete ultra-compact breakdown of Hazbin Hotel Season 2—soundtrack, story, critical reception, sales, and Season 3 setup.Analytic Dreamz unpacks the explosive 20-track soundtrack (Nov 19, 2025, Atlantic/A24) by Sam Haft & Andrew Underberg: pop-jazz-rock anthems featuring Erika Henningsen, Christian Borle, Alex Brightman, Jessica Vosk, Jeremy Jordan, Darren Criss, and more. Fan faves like "Gravity" (Vosk/Brightman vocal powerhouse), "VOX POPULI" (Jordan/Borle media satire), and "Losin' Streak" (Blake Roman bop) drive 125M+ global streams in 2 weeks (70% Spotify/Apple), outpacing S1 by 30%. Debut #70 Billboard 200 (13K units), Week 2 #8 Top 10 breakthrough; 35K+ TikTok/IG creates fuel 15% weekly growth; physicals (5 vinyls, CD, cassette) sold out via merch bundles.Story arc (Oct 29–Nov 19, 8 eps on Prime Video): Charlie rebuilds post-Extermination, battles Vox's Heaven-infiltrating weapon & Vees' smear campaign. Alastor breaks Vox deal; Angel Dust's hypnosis betrayal leads to Husk uncertainty & Vees return; Heaven/Hell unite vs. explosion. Emotional peaks: Lilith's call to Charlie. Critics rave (ScreenRant: "outdoes S1 emotional resonance"; Radio Times: "fluid soundtrack"; Album of the Year 78/100; ComicBook.com: Heaven/Hell insights)—despite minor rushed fusions.S3 tease: Haft's "different vibe" with fan duets (Alastor/Charlie, Lucifer/Lute), non-singers debut, Lilith focus, Morningstar arcs; music done, 2026 window. Full tracklist, stats verified—Hazbin's raunchier, deeper musical Hellaverse evolution. Notorious Mass Effect, powered by Analytic Dreamz.Support this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/donationsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, Cliff Crosland, CEO & co-founder of Scanner.dev, shares his candid journey of trying (and initially failing) to build an in-house security data lake to replace an expensive traditional SIEM.Cliff explains the economic breaking point where scaling a SIEM became "more expensive than the entire budget for the engineering team". He details the technical challenges of moving terabytes of logs to S3 and the painful realization that querying them with Amazon Athena was slow and costly for security use cases .This episode is a deep dive into the evolution of logging architecture, from SQL-based legacy tools to the modern "messy" data lake that embraces full-text search on unstructured data. We discuss the "data engineering lift" required to build your own, the promise (and limitations) of Amazon Security Lake, and how AI agents are starting to automate detection engineering and schema management.Guest Socials -⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cliff's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:25) Who is Cliff Crosford?(03:00) Why Teams Are Switching from SIEMs to Data Lakes(06:00) The "Black Hole" of S3 Logs: Cliff's First Failed Data Lake(07:30) The Engineering Lift: Do You Need a Data Engineer to Build a Lake?(11:00) Why Amazon Athena Failed for Security Investigations(14:20) The Danger of Dropping Logs to Save Costs(17:00) Misconceptions About Building Your Own Data Lake(19:00) The Evolution of Logging: From SQL to Full-Text Search(21:30) Is Amazon Security Lake the Answer? (OCSF & Custom Logs)(24:40) The Nightmare of Log Normalization & Custom Schemas(28:00) Why Future Tools Must Embrace "Messy" Logs(29:55) How AI Agents Are Automating Detection Engineering(35:45) Using AI to Monitor Schema Changes at Scale(39:45) Build vs. Buy: Does Your Security Team Need Data Engineers?(43:15) Fun Questions: Physics Simulations & Pumpkin Pie

In a filing to Judge Subramanian in United States v. Combs, S3 24 Cr. 542 (AS), the Government requests permission to admit limited additional testimony from expert witness Dr. Dawn Hughes. This request comes in response to what prosecutors describe as "forceful and repeated" arguments made by the defense during their cross-examination of the witness known as Mia. The defense, the Government argues, presented misleading implications about Mia's behavior and credibility—specifically regarding how victims of abuse are expected to act. Prosecutors contend that this line of questioning has "opened the door" for rebuttal testimony addressing misconceptions about trauma responses.The Government seeks to have Dr. Hughes offer expert insight drawn from her original notice, focused solely on clarifying how victims of abuse often exhibit behaviors that may seem counterintuitive to jurors unfamiliar with trauma psychology—such as delayed reporting, continued contact with abusers, or minimized disclosure. This testimony, they assert, is necessary to correct the jury's potential misinterpretation created by the defense's narrative. The request is framed as narrow in scope and designed not to go beyond the boundaries previously set by the Court, but rather to preserve the integrity of the witness's testimony in light of the defense's strategy.to contact me:bobbycapucci@protonmail.comsource:gov.uscourts.nysd.628425.376.0_1.pdf