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Relying solely on programmatic CTV auctions locks brands out of premium live sports and high-value cultural moments while racking up heavy middleware fees. This deep dive reveals how direct-to-ad-server tech and large language models are restructuring streaming media execution to protect brand safety, automate delivery, and enforce flawless frequency capping. Key Highlights

YouTube has officially outgrown traditional media taxonomies to become a unique, multi-surface ecosystem spanning streaming, social, and commerce. This week, Tinuiti's Sean Odlum explains how brands can leverage first-party CRM data and the Ads Data Hub, to bypass mobile-heavy biases and measure true impact across every screen. Key Highlights

Medical cannabis producers face costly licensing delays from overlooked GMP standards in lab design. Cleanroom engineering failures, poor ventilation, and documentation gaps are the pharmaceutical-grade pitfalls that derail facilities — and proper planning from day one prevents them. To learn more, visit https://hempirelabs.com/ Hempire Labs S.L. City: Sotogrande Address: C.C., Mar y Sol Local 3.9 Website: https://hempirelabs.com

We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y

Ed is a clean room production technician at New York Creates in Albany, New York. Ed describes his work as a technician including data collection, machine operation, and process streamlining. Ed shares his career progression from a level 1 operator to a clean room production technician in just over two years. He emphasizes the importance of soft skills and communication in the semiconductor industry. Ed also shares his non-traditional career path, starting in construction and customer service, and how his role at New York Creates has provided him with stability and independence.MNT-EC: https://micronanoeducation.org/Talking Technicians on the Web: https://micronanoeducation.org/students-parents/talking-technicians-podcast/NY Creates Workforce Development: https://ny-creates.org/workforce-development/

Klik je týždenný komentovaný prehľad technologických správ, o udalostiach, ktoré sa udiali vo svete IT, médií a sociálnych sietí. Moderátori: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ondrej Podstupka⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Martin Hodás Discord diskusný server nájdete tu: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/dAUW4PCaEh Linky: Google IO Spark https://www.techmeme.com/260519/p39#a260519p39 Omni https://www.techmeme.com/260519/p40#a260519p40 Vyhľadávanie https://www.techmeme.com/260519/p37#a260519p37 Karpathy https://www.axios.com/2026/05/19/anthropic-openai-karpathy-andrej-claude QR platby https://www.sme.sk/index/c/online-platby-na-mileticke-zaplatit-za-ceresne-trva-dve-minuty-terminaly-su-vynimkou Clean Room v Košiciach https://www.sav.sk/?lang=sk&doc=services-news&source_no=20&news_no=13630 Space Talk playlist https://www.youtube.com/playlist?list=PLNAJsgS6RlziDSV4GNWq8UXmSk81ETubA Spravili sme chybu, máte pripomienku? Napíšte nám na ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠klik@sme.sk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Kapitoly 00:00 Úvod01:14 Google AI novinky26:18 Karpathy v Anthropicu36:25 Microsoft Surface novinky41:00 Bezhotovostné platby pokračovanie45:50 Trump phone únik dát47:46 Starship štartuje52:04 ZáverSee omnystudio.com/listener for privacy information.

On today's award-winning episode of Turtle Time - "The Dark Side of Danny Drunko " - we begin by talking about all of the latest news like Ciara Miller correcting TMZ's false reporting, James Kennedy getting a baby, and other stuff. (00:00 - 25:30) As Turtle Time continues, we discuss this week's episode The Valley (season 3, episode 7) - "Please Give Him Lala". (25:30) And then, we give RHORI season 1, episode 7 - "Waterslides for Thee" - a little pop in. (01:21:20) And then finally, we discuss RHOA season 17, episode 6 - "A Clean Room" - for a short amount of time. (01:40:40) If you enjoyed this episode and need more Turtle Time in your life, join the ⁠⁠⁠⁠⁠Turtle Time Patreon⁠⁠⁠⁠⁠ and become a Villa Rosa VIP to hear exclusive bonus content! We're recapping the Vanderpump Rules series from the beginning each week. And if you need even more Turtle Time in your life, follow us on ⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠. And please, if you want to watch some of the fun things we do, subscribe on ⁠⁠⁠⁠YouTube⁠⁠⁠⁠. Show less Learn more about your ad choices. Visit megaphone.fm/adchoices

Die sogenannten Cleanroom-Gespräche der NOW bieten regelmäßig den deutschen Autobauern Gelegenheit, in vertraulicher Atmosphäre realistische Einschätzungen zur Marktentwicklung von Fahrzeugen mit alternativen Antrieben abzugeben. Die meisten Prognosen aus den Gesprächen 2020 und 2023 wurden in der neuen Runde ganz schön zurechtgestutzt. Und speziell für Brennstoffzellen-Fahrzeuge gilt: Nichts ist härter als die Wahrheit. Selbst die bereits 2023 gehörig rasierten Erwartungen wurden in der Realität um Größenordnungen unterboten. Wie also geht's weiter mit Batterie- und Wasserstoff-Fahrzeugen in der deutschen Autoindustrie?

Епізод присвячений аналізу сучасного стану розвитку штучного інтелекту та його практичного застосування. Ми розбираємо витік похідного коду компанії Anthropic, який дозволив дослідникам проаналізувати внутрішні механізми побудови AI-систем. Аналізуємо технічні причини інциденту, зокрема використання Source Map та баг у bundler Bun, а також підхід до побудови pipeline і промптів, що виявився значно простішим, ніж очікувалося. Також говоримо про: — обмеження локального запуску LLM — стартап Malus з його сумнівним підходом до open-source ліцензування — зміну позиції Microsoft щодо Copilot — проблему накопичення когнітивного боргу при використанні AI-агентів — практичне застосування військової системи Maven 00:41 — витік Claude Code     08:27 — стартап Malus: clean room as a service 13:17 — кодинг-агенти проти SaaS     16:20 — Cursor, Opus vs GPT   18:32 — критика Claude від AMD   20:40 — доцільність локального інференсу 22:53 — нова ліцензія Microsoft Copilot 26:40 — cognitive debt: втрата розуміння коду через AI 28:13 — менеджери оптимізують те, що легко виміряти 34:07 — Карпаті про еволюцію LLM: від чату до агентів 36:42 — сайдноут: історія з Сільпо 39:09 — Пентагон і Maven: ситуаційна обізнаність та демо «все видно» 44:23 — дослідження камер безпеки 51:07 — АІ-фейки в музиці

Equipment and Answers Buy Stars Without Number here! We have a Patreon! What to support us? Click HERE! The Cast: GM - Chris Hayden Hardcrow - Tyler Santander Clemente - Jake You can find us on: Instagram Bluesky Youtube You can also email us at chaosenginepod@gmail.com We have a Discord now! Feel free to stop by if that interests you! Check out our friends: Pretending to be People! Stories & Lies Sorry, Honey I have to Take This Tabletop Talk Wilderspace Gaming Doomed to Repeat The Great Old Ones Gaming Negative Modifier Chaos Springs Eternal The Black Flare Podcast 9mm Retirement Radio Suffer Not

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b

Topics covered in this episode: chardet ,AI, and licensing refined-github pgdog: PostgreSQL connection pooler, load balancer and database sharder Agentic Engineering Patterns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: chardet ,AI, and licensing Thanks Ian Lessing Wow, where to start? A bit of legal precedence research. Chardet dispute shows how AI will kill software licensing, argues Bruce Perens on the Register Also see this GitHub issue. Dan Blanchard, maintainer of a Python character encoding detection library called chardet, released a new version of the library under a new software license. (LGPL → MIT) Dan is allowed to make this change because v7 is a complete “clean room” rewrite using AI BTW, v7 is WAY better: The result is a 48x increase in detection speed for a project that lives in the hot loops of many projects. That will lead to noticeable performance increases for literally millions of users (the package gets ~130M downloads per month). It paves a path towards inclusion in the standard library (assuming they don't institute policies against using AI tools). Thread-safe detect() and detect_all() with no measurable overhead; scales on free-threaded Python 3.13t+ An individual claiming to be Mark Pilgrim, the original creator of the library, opened an issue in the project's GitHub repo arguing that Blanchard had no right to change the software license, citing the LPGL requirement that the license remain unchanged. A 'complete rewrite' is irrelevant, since they had ample exposure to the originally licensed code (i.e. this is not a 'clean room' implementation). Blanchard disagreed, citing how version 7.0.0 and 6.0.0 compare when subjected to JPlag, a library for detecting plagiarism. Blanchard told The Register he had wanted to get chardet added to the Python standard library for more than a decade since it's a core dependency to most Python projects. Brian #2: refined-github Suggested by Matthias Schöttle A browser plugin that improves the GitHub experience A sampling Adds a build/CI status icon next to the repo's name. Adds a link back to the PR that ran the workflow. Enables tab and shift tab for indentation in comment fields. Auto-resizes comment fields to fit their content and no longer show scroll bars. Highlights the most useful comment in issues. Changes the default sort order of issues/PRs to Recently updated. But really, it's a huge list of improvements Michael #3: pgdog: PostgreSQL connection pooler, load balancer and database sharder PgDog is a proxy for scaling PostgreSQL. It supports connection pooling, load balancing queries and sharding entire databases. Written in Rust, PgDog is fast, secure and can manage thousands of connections on commodity hardware. Features PgDog is an application layer load balancer for PostgreSQL Health Checks: PgDog maintains a real-time list of healthy hosts. When a database fails a health check, it's removed from the active rotation and queries are re-routed to other replicas Single Endpoint: PgDog can detect writes (e.g. INSERT, UPDATE, CREATE TABLE, etc.) and send them to the primary, leaving the replicas to serve reads Failover: PgDog monitors Postgres replication state and can automatically redirect writes to a different database if a replica is promoted Sharding: PgDog is able to manage databases with multiple shards Brian #4: Agentic Engineering Patterns Simon Willison So much great stuff here, especially Anti-patterns: things to avoid And 3 sections on testing Red/green TDD First run the test Agentic manual testing Extras Brian: uv python upgrade will upgrade all versions of Python installed with uv to latest patch release suggested by John Hagen Coding After Coders: The End of Computer Programming as We Know It NY Times Article Suggested by Christopher Best quote: “Pushing code that fails pytest is unacceptable and embarrassing.” Michael: Talk Python Training users get a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source, article + video My Always activate the venv is now a zsh-plugin, sorta. Joke: Ergonomic keyboard Also pretty good and related: Claude Code Mandated Links legal precedence research Chardet dispute shows how AI will kill software licensing, argues Bruce Perens this GitHub issue citing JPlag refined-github Agentic Engineering Patterns Anti-patterns: things to avoid Red/green TDD First run the test Agentic manual testing uv python upgrade Coding After Coders: The End of Computer Programming as We Know It Suggested by Christopher a better account dashboard Package Managers Need to Cool Down Will AI Kill Open Source Always activate the venv now a zsh-plugin Ergonomic keyboard Claude Code Mandated claude-mandated.png blobs.pythonbytes.fm/keyboard-joke.jpeg?cache_id=a6026b

This is a recap of the top 10 posts on Hacker News on March 12, 2026. This podcast was generated by wondercraft.ai (00:30): Malus – Clean Room as a ServiceOriginal post: https://news.ycombinator.com/item?id=47350424&utm_source=wondercraft_ai(01:56): Shall I implement it? NoOriginal post: https://news.ycombinator.com/item?id=47357042&utm_source=wondercraft_ai(03:22): Innocent woman jailed after being misidentified using AI facial recognitionOriginal post: https://news.ycombinator.com/item?id=47356968&utm_source=wondercraft_ai(04:48): Show HN: s@: decentralized social networking over static sitesOriginal post: https://news.ycombinator.com/item?id=47344548&utm_source=wondercraft_ai(06:14): Asian governments roll out 4-day weeks, WFH to solve fuel crisis caused by warOriginal post: https://news.ycombinator.com/item?id=47352215&utm_source=wondercraft_ai(07:40): ATMs didn't kill bank teller jobs, but the iPhone didOriginal post: https://news.ycombinator.com/item?id=47351371&utm_source=wondercraft_ai(09:06): Returning to Rails in 2026Original post: https://news.ycombinator.com/item?id=47347064&utm_source=wondercraft_ai(10:32): Big data on the cheapest MacBookOriginal post: https://news.ycombinator.com/item?id=47349277&utm_source=wondercraft_ai(11:58): Dolphin Progress Release 2603Original post: https://news.ycombinator.com/item?id=47348304&utm_source=wondercraft_ai(13:24): US private credit defaults hit record 9.2% in 2025, Fitch saysOriginal post: https://news.ycombinator.com/item?id=47349806&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

​ @restishistorypod  How the Iranian Revolution Was Hijacked | EP 2 https://youtu.be/VojWVIF3HSg?si=wV0fqImpWd62Kbc1  https://paulvanderklay.substack.com/p/what-do-doug-wilson-and-the-ayatollahs   @InterestingTimesNYT  No, Young Men Are Not Returning To Church. | Interesting Times with Ross Douthat https://youtu.be/JOoYdVlTIzw?si=0drnVSSwkaM64jpK   @InterestingTimesNYT  Christian Nationalism vs Clown World | Interesting Times with Ross Douthat https://youtu.be/WAYWbbSeIhE?si=jNjVg5E5UzUHcPAg  https://www.livingstonescrc.com/give Register for the Estuary/Cleanup Weekend https://lscrc.elvanto.net/form/94f5e542-facc-4764-9883-442f982df447 Paul Vander Klay clips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg https://www.meetup.com/sacramento-estuary/ My Substack https://paulvanderklay.substack.com/ Bridges of meaning https://discord.gg/WA2RmWx2 Estuary Hub Link https://www.estuaryhub.com/ There is a video version of this podcast on YouTube at http://www.youtube.com/paulvanderklay To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays me a small commission at no additional cost to you if you buy through one of the product links here. This is is one (free to you) way to support my videos.  https://paypal.me/paulvanderklay Blockchain backup on Lbry https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640 https://www.livingstonescrc.com/give  

TSMC: extreem goede cijfers. Samsung: explosieve verwachtingen. Intel: kan niet aan de vraag voldoen. Kortom, het lijkt in de sterren geschreven te staan dat ook het laatste kwartaal van afgelopen jaar garant staat zéér goede cijfers van de chipmachinemaker uit Veldhoven. Ook analisten zijn enthousiast. Ze verhogen stuk voor stuk hun koersdoel voor het aandeel. Het lijkt er dus op dat je superlatieven te kort komt. Woensdag weten we ook of dat terecht is, want dan komt ASML met hun cijfers. Bob Homan van ING Investment Office vertelt je in hoeverre ASML nog teleur kan stellen. En naar welk cijfertje je woensdagochtend op zoek moet. Over de podcast: In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro.See omnystudio.com/listener for privacy information.

TSMC: extreem goede cijfers. Samsung: explosieve verwachtingen. Intel: kan niet aan de vraag voldoen. Kortom, het lijkt in de sterren geschreven te staan dat ook het laatste kwartaal van afgelopen jaar garant staat zéér goede cijfers van de chipmachinemaker uit Veldhoven. Ook analisten zijn enthousiast. Ze verhogen stuk voor stuk hun koersdoel voor het aandeel. Het lijkt er dus op dat je superlatieven te kort komt. Woensdag weten we ook of dat terecht is, want dan komt ASML met hun cijfers. Bob Homan van ING Investment Office vertelt je in hoeverre ASML nog teleur kan stellen. En naar welk cijfertje je woensdagochtend op zoek moet. Over de podcast: In Beurs in Zicht stomen we je klaar voor de beursweek die je tegemoet gaat. Want soms zie je door de beursbomen het beursbos niet meer. Dat is verleden tijd! Iedere week vertelt een vriend van de show waar jouw focus moet liggen. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro.See omnystudio.com/listener for privacy information.

I had a chance to attend and cover SHIFT by Commvault in New York last week. I just spoke to Tim Zonca, VP of Product Marketing at Commvault, to talk ResOps, Keynote takeaways and more.What we covered• ResOps in one line and why it matters now• How the Unity Platform turns resilience into daily practice across cloud, SaaS, and on-prem• Synthetic Recovery and what it does to RTO and incident playbooks• Identity resilience for AD with real-time detection and safe rollback• Cloud-native protection with cost and energy views that drive smarter policiesLeaders want AI speed with clean recovery and tighter identity control. Tim breaks down how Unity brings these pieces together so teams can move fast and stay safe. Simple language. Clear outcomes. No hype.My takeSynthetic Recovery plus Cleanroom is a real shift. The AD rollback story closes a gap I see in many incident reviews. The cost and energy view belongs in every daily dashboard!!!!The interview is live now. I will share more clips and floor notes from SHIFT as sessions go up.Check out all the announcement links in the comments!#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

Dive into the dynamic world of SwiftUI, SwiftData, and Apple Intelligence in this episode, where we explore how these technologies are transforming development. Join us as we discuss Frank Kruger's innovative work on the Clean Room application, which showcases the elegance of macOS UI design. Discover how AI-driven tools like Apple Intelligence can enhance your Mac's capabilities, offering powerful APIs and translation features that simplify complex tasks. We also delve into the benefits and challenges of using VS Code for Swift development, sharing insights on optimizing Swift projects and leveraging AI for content creation. Perfect for developers and tech enthusiasts, this episode provides actionable takeaways and thought-provoking discussions that will inspire your next project. Tune in to uncover the future of development and productivity! Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us (https://itunes.apple.com/us/podcast/merge-conflict/id1133064277?mt=2&ls=1) ⭐⭐ Machine transcription available on http://mergeconflict.fm

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Cosa si intende per Cyber Resiliency? Da cosa differisce dal Disaster Recovery più tradizionale? Cos'è un Data Vault? E cosa si intende per Clean Room? Quali sono i servizi che AWS mette a disposizione? Oggi ne parliamo con Antonio Aga Rossi, Principal Solutions Architect di AWSLink utili:- Proactive strategies for cyber resilience and business continuity on AWS- Building cyber resiliency with AWS Backup logically air-gapped vault

In this episode, recorded live at Shoptalk, Sarah Hofstetter sits down with Louise Cotterill, Global Senior Director of Insights and Intelligence at Kellanova, for a wide-ranging conversation on how to turn complex data into business results and build cross-functional trust along the way.Louise shares how her team built a proprietary clean room solution to drive more precise audience targeting, tailor creative to shifting shopper behavior, and ultimately deliver a 36 percent sales lift for Special K in the UK. She also explains how marketers can move from one-size-fits-all campaigns to dynamic, behavior-based segmentation, even without first-party data.Beyond the tech, Louise emphasizes the importance of soft skills. From speaking the language of the CFO to running test-and-learn pilots that bring skeptical teams along gradually, she shows how marketers can lead with both credibility and curiosity. In a standout personal moment, she reveals the bravest thing she's ever done: spending a summer living with nomadic eagle hunters in Mongolia.Key takeaways:Insights only matter if they drive action. Clean rooms enable smarter targeting and measurement, but results come from applying those insights across creative and media strategy.Buy-in is built through transparency. Louise outlines how gradual testing, third-party validation, and a shared focus on sales help teams embrace new tools with confidence.Transformation is both technical and cultural. Success depends on aligning stakeholders, evolving incentives, and creating a safe space to test, learn, and adapt. Hosted on Acast. See acast.com/privacy for more information.

From CAR-T therapies to viral vectors, cell and gene treatments are redefining the boundaries of pharmacy practice—but with innovation comes complexity. Host Carolyn Liptak welcomes Dr. Mark Wiencek, Principal Microbiologist with the Technical Services Group at Contec, and Dr. Amanda Frick, Senior Clinical Manager of Market Intelligence at Vizient, to break down the challenges of compounding these advanced therapies.   Listen in as they discuss real-world risk assessments, biosafety considerations, and how hospital pharmacies can safely manage these groundbreaking yet high-risk treatments.   Guest speakers:  Mark Wiencek, PhD Principal Microbiologist, Technical Services Group Contec Amanda Frick, PharmD, BCPS Senior Clinical Manager, Market Intelligence Vizient   Host:  Carolyn Liptak, MBA, RPh  Pharmacy Executive Director  Vizient   Show Notes:  [01:02-01:51] Mark shares his background and experience in microbiology [01:52-04:04] Overview of the types of cell and gene therapies (CGT) currently used in clinical practice [04:05-05:14] Which CGT therapies are most applicable to pharmacy compounding and why [05:15-10:29] Things not on the NIOSH list and the risks [10:30-12:03] Evaluating whether viral vectors can penetrate intact skin and the true occupational exposure risks [12:04-13:18] If hazards are not defined by the NIOSH list, how should these CGT hazards be classified [13:19-15:03] Determining the safest environment for compounding CGT therapies [15:04-20:14] Best practices for decontamination, disinfection, and viral vector handling [20:15-20:59] Do you need a dedicated biosafety cabinet for CGT therapies [21:00-22:55] Recommended resources for further learning   Links | Resources:   Blind and colleagues (Nationwide): Click here Wang and colleagues (Stanford): Click here CONTEC HEALTHCARE WEBINAR Using Bugs as Drugs: Compounding Viral Vectors in Cell & Gene Therapy for Hospital Pharmacies, Mark Wiencek, May 13, 2025: Click here Blind, J.E., Ghosh, S., Niese, T.D., Gardner, J.C., Stack-Simone, S., Dean, A. and Washam, M., 2024. A comprehensive literature scoping review of infection prevention and control methods for viral-mediated gene therapies. Antimicrobial Stewardship & Healthcare Epidemiology, 4(1), p.e15. Click here Deramoudt, L., Pinturaud, M., Bouquet, P., Goffard, A., Simon, N. and Odou, P., 2024. Method for the detection and quantification of viral contamination during the preparation of gene therapy drugs in a hospital pharmacy. Occupational and Environmental Medicine, 81(12), pp.615-621. Click here Korte, J., Mienert, J., Hennigs, J.K. and Körbelin, J., 2021. Inactivation of adeno-associated viral vectors by oxidant-based disinfectants. Human Gene Therapy, 32(13-14), pp.771-781. Click here (abstract only; full article available for purchase) Martino, J.G., McConnell, K., Greathouse, L., Rosario, B.D. and Jaskowiak, J.M., 2024. Cellular therapy site-preparedness: Inpatient pharmacy implementation at a large academic medical center. Journal of Oncology Pharmacy Practice, 30(8), pp.1442-1449. Click here Penzien, C., 2023. Safe handling of BioSafety drugs and live virus vaccines. Pharm Purch Prod, 20(4), p.12. Click here Petrich, J., Marchese, D., Jenkins, C., Storey, M. and Blind, J., 2020. Gene replacement therapy: a primer for the health-system pharmacist. Journal of Pharmacy Practice, 33(6), pp.846-855. Click here Wang, A., Ngo, Z., Yu, S.J. and MacDonald, E.A., 2025. Implementing standard practices in the safe handling of gene therapy and biohazardous drugs in a health-system setting. American Journal of Health-System Pharmacy, p.zxaf026. Click here   VerifiedRx Listener Feedback Survey: We would love to hear from you - Please click here   Subscribe Today! Apple Podcasts Spotify YouTube RSS Feed

Kellanova's pilot offers a blueprint for using data clean rooms to bridge gaps between brands and retailers, unlock deeper shopper insights and rebuild brand value amid inflationary pressure

Effective air filtration in cleanrooms prevents contamination and protects workers. Understanding bag-in/bag-out systems helps facilities maintain safety standards. Go to https://www.pfpspokane.com for more information. Pure Filtration Products, Inc City: Spokane Address: 4008 East Broadway Avenue Website: http://www.pfpspokane.com

Elis is a company that provides the garments and gowns to prevent any human matter from getting into the clean room for giants such as Intel. His garments don't shed nor have any loose fibres and are cleaned after each use. Bernard Flynn of Elis, Cleanroom and he is the Repak Green Business Person Of The Month for September.

While working inside a high-tech cleanroom and wearing a suit covering most of his body, Zach falls in love with a mysterious woman. Short story with text and audio. The post Love in a Cleanroom appeared first on 500 Ironic Stories.

In today's MadTech Daily, we cover TripleLift laying off staff amid cuts, xAI seeking a $200bn valuation in its next fundraise, and Hearst taking over the Dallas Morning News after 140 years.

The newfound respectability of medicinal cannabis offers an opportunity for aspiring growers, but many lab businesses have folded due to a lack of knowledge. Today, we look at the benefits of working with a cannabis cultivation consultancy. More details at https://hempirelabs.eu Hempire Labs S.L. City: Marbella Address: POLIGONO NUEVA CAMPANA, 93 Website: https://hempirelabs.eu

¿Hemos encontrado la fórmula ganadora para el aprovechamiento de los datos “de primera parte” (1st-party data)? ¿Qué desafíos presentan los Data Clean Rooms? ¿Qué es la DCRA? Henry Velasquez es Global DPO para el sur de Europa y LATAM en el Grupo Publicis, abogado especializado en tecnología y protección de datos, y miembro del Consejo Asesor de la IAPP. A todo ello suma además ahora ser co-fundador de la Data Clean Room Alliance de la que hablaremos hoy. También es profesor asociado en varias instituciones como la Universidad Complutense de Madrid, la IAPP, la IAB, el Data Privacy Institute, ISMS Forum, el Colegio de Abogados de Madrid y otros, además de colaborador en publicaciones especializadas.  Referencias: Henry Velasquez en LinkedIn Data Clean Room Alliance Henry Velasquez: Retail Media y Data Clean Rooms en acción (Masters of Privacy, 2023) Nicola Newitt: The legal case for Data Clean Rooms (Masters of Privacy) EDPB Guidelines on targeting of social media users Enrique Extremera: Aspectos legales de los Data Clean Rooms (Masters of Privacy, 2022) Silvia Ruiz y Henry Velásquez: el rol del DPO en la agencia de medios (Masters of Privacy, 2021)  

Jake and Anthony are joined by Dante Lauretta, Principal Investigator of NASA's OSIRIS-REx mission, and Professor of Planetary Science at the University of Arizona, to talk about—take a guess!—returning and studying pristine samples from Bennu.TopicsOff-Nominal - YouTubeEpisode 195 - Dirtiest Thing in the Cleanroom (with Dante Lauretta) - YouTubeDante Lauretta | Lunar and Planetary Laboratory & Department of Planetary Sciences | The University of ArizonaNASA's OSIRIS-REx Mission to Asteroid BennuAbundant ammonia and nitrogen-rich soluble organic matter in samples from asteroid (101955) Bennu | Nature AstronomyAn evaporite sequence from ancient brine recorded in Bennu samples | NatureContamination monitoring of the OSIRIS-REx ISO5 asteroid sample cleanroom - NASA Technical Reports Server (NTRS)NASA's Asteroid Bennu Sample Reveals Mix of Life's Ingredients - NASASurprising Phosphate Finding in NASA's OSIRIS-REx Asteroid Sample - NASANASA's Bennu Asteroid Sample Contains Carbon, Water - NASANASA's First Asteroid Sample Has Landed, Now Secure in Clean Room - NASAOSIRIS-APEX - NASA ScienceNASA Asteroid Sampling Mission Renamed OSIRIS-APEX for New Journey - NASAFollow Off-NominalSubscribe to the show! - Off-NominalSupport the show, join the DiscordOff-Nominal (@offnom) / TwitterOff-Nominal (@offnom@spacey.space) - Spacey SpaceFollow JakeWeMartians Podcast - Follow Humanity's Journey to MarsWeMartians Podcast (@We_Martians) | TwitterJake Robins (@JakeOnOrbit) | TwitterJake Robins (@JakeOnOrbit@spacey.space) - Spacey SpaceFollow AnthonyMain Engine Cut OffMain Engine Cut Off (@WeHaveMECO) | TwitterMain Engine Cut Off (@meco@spacey.space) - Spacey SpaceAnthony Colangelo (@acolangelo) | TwitterAnthony Colangelo (@acolangelo@jawns.club) - jawns.club

The risen Christ has folded Death up and put it in its place. Now Christ invites you to trust in Him and find place and purpose in His new creation.

Tariff panic, Adalytics fallout, the fate of the open web, and TikTok weirdness.Takeaways:Tariffs & Ad World Uncertainty

SpinLaunch has received a $12 million investment from Kongsberg, and exclusively contracts NanoAvionics for 280 satellites to be part of the Meridian constellation. Portal Space Systems raises $17.5 million in an oversubscribed seed round. ispace-US has formed a US Lunar Science Advisory Board, chaired by Dr. Alan Stern, and more. Remember to leave us a 5-star rating and review in your favorite podcast app. Be sure to follow T-Minus on LinkedIn and Instagram. T-Minus Guest Our guest today is Steven Jordan Tomaszewski, AIA Vice President of Space Systems. You can connect with Steve on LinkedIn, and learn more about AIA 2025 Space Priorities on their website. Selected Reading SpinLaunch Announces $12M Strategic Investment from Kongsberg, Unveiling Revolutionary LEO Satcom Constellation NanoAvionics Secures €122.5M Contract to Build 280 Satellites for Meridian Space, SpinLaunch's Global Broadband Constellation Portal Space Systems Raises $17.5M in Oversubscribed Seed Round to Build Multi-Role, Trans-Orbital Spacecraft Powered by Solar Thermal Propulsion In-space mobility with Portal Space Systems. ispace-U.S. Establishes U.S. Lunar Science Advisory Board to Propel Lunar Exploration and Business Development PLD Space and D-Orbit Sign Launch Agreement for Equatorial Missions ISS National Lab Launches Orbital Edge Accelerator Program to Propel Startups Into the Growing Space Economy Karman Space & Defense Inaugurates New ISO 8 Clean Room for Spacecraft Assembly and Integration: Advancing Lunar and Space Missions with Flight-Proven Subsystems Turion Space Investment Dhruva Space ties up with Japan's Infostellar Space Force Conducts First War Game For Commercial Reserve With Satellite Comm Providers - Defense Daily Fermenting miso in orbit reveals how space can affect a food's taste T-Minus Crew Survey We want to hear from you! Please complete our 4 question survey. It'll help us get better and deliver you the most mission-critical space intel every day. Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at space@n2k.com to request more info. Want to join us for an interview? Please send your pitch to space-editor@n2k.com and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Is your cleanroom air filter due for maintenance? Pure Filtration Products performs accredited bag-in/bag-out (BIBO) installations and change-outs. With their expert services, you'll get a safe, efficient, and compliant filter replacement with minimal disruptions to your facility's operations. Call 509-315-8460 or visit https://www.pfpspokane.com Pure Filtration Products, Inc City: Spokane Address: 4008 East Broadway Avenue Website: http://www.pfpspokane.com Phone: +15093158460 Email: lynn@pfpspokane.com

I was very excited to meet today's guest, Leslie Pasternack. Leslie has a Ph.D. in Theatre History from UT Austin and spent an intensive year at the Dell'Arte International School of Physical Theatre (sometimes referred to as “clown school”). She has won a couple of Spotlight Awards for her solo show, CLEAN ROOM. She has directed and acted at a variety of places, including The Players' Ring, New Hampshire Theatre Project and The Firehouse Center for the Arts and teaches acting workshops a couple times a year at Riverwoods Durham. She's about to start rehearsing as an actor in AS SMALL AS STARS, by Will Murdock, which opens at the Ring on January 31st. It's being directed by Kelli Connors. Her website is www.Lemonpunchtheatrelab.com.

In need of HVAC filtration for your Northwestern cleanroom facility? Pure Filtration Products is certified by the National Air Filtration Association to offer bag-in/bag-out maintenance, saving you the costs of training your staff and ensuring your facility stays compliant. Call 509-315-8460, or go to: https://www.pfpspokane.com/ Pure Filtration Products, Inc City: Spokane Address: 4008 East Broadway Avenue Website: http://www.pfpspokane.com Phone: +15093158460 Email: lynn@pfpspokane.com

On this episode of the Spot Radio Podcast Charlie Webb CPPL speaks with Cord Burnham CKF about his observations of both healthy and unhealthy cleanroom cultures. Over the past 10 years, Cord has visited hundreds of cleanrooms throughout the United States and abroad.About Cord Burnham: As a Van der Stahl Scientific's ISO 17025 laboratory manager, Cord oversees all of the functions of the calibration and their empirical Laboratory. Cord is certified as a CKF (certified kaizen facilitator) and he co-authored the quality system manual for Van der Stahl Scientific. Cord is hands-on with all of the machinery that Van der Stahl Scientific offers and is also an SME for regulatory processes surrounding the ISO 17025 standardsEmail: Cord@vanderstahl.comWebsite: www.vanderstahl.com

While some of us knew a good bit about mRNA prior to 2020, we all got a crash course on mRNA technology and its prophylactic and therapeutic potential as a result of the COVID pandemic and subsequent SARS CoV-2 vaccine development. In fact, most of us have now received at least one mRNA vaccine at this point. Our guest for this episode, Dr. Christian Cobaugh, Co-founder and CEO of Vernal Biosciences, was a passionate believer in mRNA medicines well before the pandemic. Join us to hear his story and his passion for this technology. He walks us through the molecular methods by which high-purity mRNAs are now made and purified, as well as going into the lipid nanoparticle technology by which they're commonly delivered. As a contract development and manufacturing provider, we get to learn about the state of the market and what clients of their care about today. As a seasoned expert in this space, Christian talks about the future potential of mRNA technology for applications such as personalized cancer vaccines. If you enjoy hearing smart people talk about interesting topics with a passion, you won't want to miss this episode! Subscribe to get future episodes as they drop and if you like what you're hearing we hope you'll share a review or recommend the series to a colleague.  Download Transcripts: Speaking of Mol Bio Podcast | Thermo Fisher Scientific - US Visit the Invitrogen School of Molecular Biology to access helpful molecular biology resources and educational content, and please share this resource with anyone you know working in molecular biology.

Symitri is a company that provides a real-time privacy firewall and data clean room solution for the open internet, called Symitri Prism. The platform leverages a partnership with Akamai to allow advertisers and publishers to securely manage and match first-party data without sharing it directly, enabling real-time deterministic addressability and measurement. The company evolved from a media exchange service called Trust X and now focuses on privacy-safe, real-time data solutions for programmatic advertising.For more in-depth discussion of these topics and links to the news we discuss, subscribe to the Marketecture newsletter at https://news.marketecture.tvCopyright (C) 2024 Marketecture Media, Inc.

CW Today with Loretta Walker is heard each weekday at 12:05 Central Time on Faith Music Radio. Learn more about Loretta, her family and their ministries at ChristianWomanhood.org. Follow Loretta on Facebook - https://www.facebook.com/ChristianWomanhood

SummaryWhile Matt is out of town for work, Kevin, Casey, James, and Dave Van Epps discuss Dave's business, Sonderwerks, which specializes in upholstery, complete restorations, and motor work for Porsche cars. Dave shares how he got into the business and the vision behind Sonderwerks. They discuss the importance of custom upholstery for a perfect fit and the craftsmanship involved. Dave also talks about the challenges of finding skilled upholsterers and the dying art of upholstery. They touch on the customization of seats, including bolstering and foam adjustments. The conversation also covers the complete restoration services offered by Sonderwerks and the size of their facility. They conclude by discussing motor work and the clean room for engineering builds. Sonderwerks is a specialized Porsche restoration shop that focuses on air-cooled Porsches. They offer a range of services, from stock rebuilds to custom modifications. The shop is known for its attention to detail and high-quality workmanship. They also buy and sell cars, often looking for vehicles that need some love and restoration. Sonderwerks has built a strong reputation in the Porsche community and is recognized for their expertise and passion for the brand. The conversation concludes with a discussion about personalizing Porsche cars and the future of Sunderworks. The hosts talk about their own experiences customizing their cars and the flexibility that Dave Van Epps offers in allowing customers to make their own choices. They also mention the Rucklos Rally in West Virginia, where they enjoyed driving on beautiful roads and experiencing the hospitality of the locals. Dave mentions that Sunderworks is developing an improved air conditioning system for air-cooled Porsches. The conversation ends with plans for future episodes and the possibility of featuring a joint project with Dave.

Join us as we dissect the University of Barcelona's intense encounter with ransomware. Learn how adherence to Zero Trust principles can be the difference between a cyber catastrophe and a resilient recovery.

Welcome to the Engineers HVAC Podcast! In this episode, host Tony Mormino sits down with Bo Coffman, Director of New Business Development at Nortek CleanSpace and Nortek Air Solutions, to discuss the fascinating world of cleanroom technology. Cleanrooms are essential for industries ranging from semiconductor manufacturing to pharmaceuticals. Bo Coffman shares his extensive knowledge on the principles of cleanroom design, the importance of maintaining controlled environments, and the advanced air filtration and flow technologies that keep these spaces contaminant-free. Listeners will learn about the various classification standards for cleanrooms, the materials and construction methods used, and the unique contamination control strategies beyond air filtration. This episode also explores how cleanroom designs vary across different industries and the regulatory standards that ensure their efficacy and safety. Bo provides insights into the future trends in cleanroom technology and the innovative solutions offered by Nortek, including their modular and stick-built cleanroom systems. Whether you're an engineer, contractor, or simply curious about the technology behind cleanrooms, this episode is packed with valuable information and expert advice. Tune in to discover the intricacies of cleanroom environments and how Nortek is leading the way in this specialized field. For more HVAC content, you can visit our YouTube channel here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@HVAC-TV⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Engineers HVAC Podcast: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://anchor.fm/engineers-hvac-podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Insight Partners (Commercial HVAC Products and Controls in NC, SC, GA): Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.insightusa.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Hobbs & Associates, Inc. (Commercial HVAC Products and Controls in VA, TN, MD, AL): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.hobbsassociates.com⁠

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We talk about why having a clean room and a dirty car is the best combo. Rob gets in a few uber altercations and we talk about how no one eats bologna anymore. Give it a listen and enjoy... or elseFOLLOW US:YOUTUBE: /@wetjeanspodcast   ROB:insta: robbywguccitwitter: @heatdaddy69420ANDY:insta: andychampstwitter: @ChampagneAnyone POD:insta: wetjeanspodcasttwitter: wetjeanspodLucy.co code: WETJEANSManscaped code: WETJEANSSupport the show

In this episode, Jed Clevenger, VP of Growth and Analytics at Indeed, shares with us how he bridges the gap between business and technology, how cross-functional teams can and should work together, and the way Indeed is partnering with companies like Hulu and Disney for secure data sharing through the use of clean room technology.---Dive deep into the world of apps and generative AI at Snowflake BUILD 2023. This December, developers, data scientists, engineers, and startups will have access to exclusive sessions focused on building apps, data pipelines, and machine learning workflows in the age of generative AI and LLMs. Discover how to use the latest Snowflake innovations through product deep dives, demos, and hands-on-labs.Learn more and register for a BUILD event in your region.

Use code VFP100 for $100 off on the Rejuvenate Indoor Garden and 3 pod packs for freeUnlock the future of modern agriculture with Nick Bateman, Executive Chairman at GrowPura. Nick, a seasoned veteran in the high tech business sphere, uncovers the exciting blend of clean room technology, robotics, and artificial intelligence that's shaping vertical farming. He illustrates how this burgeoning field is not only responding to the shifting crop demands of customers, but also bolstering sustainability and food security solutions. This conversation takes an intriguing turn as we navigate the unique challenges of the vertical farming industry, notably around the crucial aspect of funding. You'll discover how their groundbreaking technology is making strides, thanks to its cost-saving benefits, two years ahead of other vertical farming companies. Hear from Nick about the importance of forging the right partnerships and the role they play in our journey towards a more sustainable future. Finally, Nick shares his enthusiasm for the potential of vertical farming and how you can connect with GrowPura. Tune in to this enlightening discussion and stay on the cutting edge with technological advancements that are reshaping our world.Thanks to Our SponsorsCultivatd – https://cultivatd.com/Indoor AgCon '22 - https://indoor.ag/Use promo code 'VFP' to receive 20% your registrationAgTech Marketing TeamFarmAnywhere Horti Agri Next MEAKey Takeaways00:00 - Exploring Vertical Farming With Nick Bateman 19:04 - Technology and Crops in Vertical Farming 30:37 - Global Vertical Farming Solutions Provider 40:24 - Partnering With the Right Funding Backers 44:47 - Sustainable Funding in Vertical Farming 51:15 - Getting ConnectedTweetable Quotes"Vertical farming is definitely really important. The existing businesses, the existing farmers, growers, they can't increase production to that extent. This is a growth industry." “Large corporations are all thinking about global changes. A lot of farmers are starting to flex their business around energy pricing. You've got to really work harder and find a solution that enables you to use the energy all day and all night, because the capital cost of the facilities are so large."Resources MentionedNick's Website - https://growpura.com/ Nick's Linkedin - www.linkedin.com/company/growpura/Nick's Twitter - https://twitter.com/GrowPura Nick's Email - nick@growpura.comConnect With UsVFP - LinkedIn - https://www.linkedin.com/company/verticalfarmingpodcastVFP Twitter - https://twitter.com/VerticalFarmPodVFP Instagram - https://www.instagram.com/direct/inbox/VFP Facebook -

Levi struggles with maintaining a clean room, but with chore day at hand, he is determined to tidy up. However, his search for a new spot to stash his toys leads him on a thrilling escapade with his talkative playthings. Along the way, Levi learns a valuable lesson about the significance of valuing and taking care of our possessions. Welcome to Lively Lewis Stories!! You may know us from The Lively Lewis Show!! This is our podcast where siblings Levi and Ivy go on incredible adventures, where they learn and model positive life lessons. These imaginative stories of Levi's energetic personality and Ivy's spunky silliness will keep you engaged, laughing and learning episode after episode! Our goal is to create a safe environment for kids to listen and learn about strong values, big imaginations, humorous pretend play, and healthy family dynamics. Our stories are great for bedtime stories, car rides, or just for fun! Thanks for listening!! Lively Lewis Show: https://www.youtube.com/@LivelyLewisShow Lively Lewis Vibes: https://www.youtube.com/@LivelyLewisVibes LinkTree: https://linktr.ee/livelylewis

A petulant piece-of-shit child kills his mother because she makes him clean his room.  Sub and follow our show wherever you get podcasts: https://availableon.com/truecrimetodayatruecrimepodcast Get AD-FREE advance & bonus content when you sub via apple podcasts! Try it for 3 days free now! https://podcasts.apple.com/us/podcast/1504280230