Podcasts about S3

After a discussion of the recently released Narnia images (which reveal a lot!), Corey and Maggie return to think about what we expect in S3 of Rings of Power. Other Minds and Hands: An Open and Friendly Discussion of Tolkien Adaptation, Episode 135, recorded on September 21, 2026. Join us on the Signum University Discord server: / discord Join us on Mondays at 4:30 PM ET, on this SignumU Youtube channel / signumuniversity and SignumU Twitch channel / signumu Check the schedule here. / schedule For more information https://mythgard.org/miscellany/ You can watch or listen to the recordings here. • Other Minds and Hands: An Open and Friendl... https://tolkienprof.fireside.fm/subsc... Hosts: Dr. Corey Olsen the Tolkien Professor https://signumuniversity.org/director... Dr. Maggie Parke https://signumuniversity.org/director... For more information about Signum Studios and Signum Collaboratory, visit Signum Studios https://studios.signumuniversity.org/ Signum Collaboratory https://collaboratory.signumuniversit... For more information about Signum University https://signumuniversity.org/

Three months ago Dwarkesh, who has been posting incredible blogs and episodes about RL, posted a framing question for his video essay on RLVR which upset a lot of Computer Use folks:We are no strangers to learning in public and are no strangers to the stress of getting things wrong when you have a big platform. However, we were at Anthropic for the Computer Use launch, there for Claude Cowork with the first big podcast on it, organized the first Computer Use track at AIE presenting the state of the art, and were close to the OpenAI-Sky Software acquisition that now powers the complete domination of computer use that Codex enjoys today. This is why we're excited to bring you today's first guest, Ari Weinstein, cofounder of Sky and now leading all the amazing CUA progress that casuals might miss:Ari explains why Computer Use is now “180 degrees different” from where it was months ago, how agents are learning to debug and recover from failures, why combining screenshots with accessibility data, the DOM, Playwright, and generated code changes the speed equation, and why the next frontier is making agents literally superhuman at using software.OpenAI clones JevIn the second half, Nikunj Handa from OpenAI's API team breaks down the new developer stack: async tool calling, mid-turn steering, WebSockets, UltraFast inference, the Decisions API, prompt caching, pre-warming, compaction, and the Agents API. Given that we were the first Jev podcast, we particularly focus on the unusually fast sprint on the Decisions API:And why it is just a Luna wrapper for now but the team is motivated and egoless enough to clone what they consider to be good patterns.We discuss:* Why OpenAI thinks Computer Use has changed dramatically in just the last few months* Dots and what changes when every agent gets its own Linux computer* Why Computer Use can now complete some tasks faster than the average human* The path from human-level to “literally superhuman” computer use* Why modern agents are much better at debugging and recovering from failure* How screenshots, accessibility trees, the DOM, Playwright, and generated JavaScript work together* App Shots and why they give models much richer context than ordinary screenshots* Why Computer Use can close the loop between writing software and testing it* Trust, permissions, and safety when agents can make payments and operate websites* Async function calling and why models no longer need to stop reasoning while tools run* Mid-turn steering, WebSockets, and the architecture behind more responsive agents* UltraFast inference and how OpenAI is pushing frontier models toward much lower latency* The rapid internal story behind the Decisions API* Why Decisions API is more than structured outputs at low latency* GPT Live, fast tool calling, and real-time computer control* How OpenAI is already using Decisions API for support classification and internal workflows* Longer prompt caching, cache pre-warming, and cache-aware applications* Server-side compaction vs manual compaction for long-running agent threads* What should live inside an Agents API versus a developer's own harness* OpenAI as an “AI cloud” and the search for higher-level primitives beyond raw model APIsAri Weinstein* Product & Engineering, Computer Use at OpenAI* X: https://x.com/AriX* LinkedIn: https://www.linkedin.com/in/weinsteinari/Nikunj Handa* Product, API at OpenAI* X: https://x.com/nikunjhanda* LinkedIn: https://www.linkedin.com/in/nikunjhanda/Timestamps00:00:00 OpenAI DevDay: Dots, GPT-6.1, Agents API, and Decisions API00:02:52 Dots and Personal Cloud Computers00:04:59 Why Computer Use Is “180 Degrees Different”00:06:04 From Sky to Self-Debugging Computer Use Agents00:09:24 How Computer Use Sees and Operates Software00:12:09 From Faster Than Humans to Superhuman Computer Use00:16:03 Agents API: Trust, Permissions, and Safety00:17:31 Computer Use for Coding, Testing, and QA00:19:14 GPT-6 APIs, Async Tool Calling, and UltraFast Inference00:23:21 The Rapid Story Behind Decisions API00:25:32 What Decisions API Is and How It Works00:30:24 What OpenAI Is Building With the New APIs00:32:23 Prompt Caching, Pre-Warming, and API Performance00:35:20 Context Compaction for Long-Running Agents00:37:13 Memory, Higher-Level APIs, and the AI CloudTranscriptIntroduction: OpenAI DevDay and the New Agent StackVibhu [00:00:00]: Okay. We're very excited to be here. Today is OpenAI DevDay. Special podcastSwyx [00:00:08]: We're the first podcast after your livestream.Vibhu [00:00:10]: First podcast. We have Ari here, who leads the product and engineering team for Computer Use agents. Before we kick in and dive deep on Computer Use, you wanna give a quick recap? What was announced? What's the quick slew of announcements you guys had today?Ari Weinstein [00:00:24]: Yeah. yeah, it was a super exciting day. we just got out of the keynote. It was really sick. there were a bunch of Computer Use announcements that I think are worth thinking about. We have, Dots, which is the new, sort of personal assistant product, and, that has some really exciting Computer Use features. There's GPT-6.1 Sol, which is this amazing new model, that I think is particularly great for Computer Use ‘cause of, sort of the cost and speed, advantages. I think, I think we shared that it's, a fifth of the cost of Astra and a seventh of the cost if you're looking at Computer Use specifically, which is really amazing. sorry, there were so many things. I'm trying to sort through it.Swyx [00:01:02]: And the API.Ari Weinstein [00:01:03]: Agents API, which now has Computer Use in it, which is really cool, ‘cause now developers can build on the same Computer Use, that is part of Codex, and ChatGPT. and then there were some demos of our existing Computer Use features, like app shots, where you can take the context of something you're doing on your computer and bring it into Codex and ChatGPT really fast. And then, like, native Computer Use on your Mac, where Roman had it taking screenshots of his app, automatically, and he could do other things on his computer while Computer Use was using his applications. so yeah, really exciting keynote.Swyx [00:01:35]: And not to mention the Decisions API.Ari Weinstein [00:01:37]: Decisions API.Swyx [00:01:38]: Off the bat, are they all the same model? Like, this is. Or the same dataset distilled to different models?Swyx [00:01:44]: Like, basically, like, is Computer Use using Decisions API, or are they, like, kinda separate?Ari Weinstein [00:01:49]: So what's really cool about the Decisions API is it, you know, it has all these new capabilities. It does inference in parallel. it doesn't have reasoning. It's a smaller model, than the ones we use for Computer Use. and so those capabilities make it really fast.Dots and Delegating Work to a Cloud ComputerSwyx [00:02:07]: Yeah.Ari Weinstein [00:02:07]: They also make it a little bit less good at doing, like, long horizon, sort of sophisticated tasks. And so I think I would say it's still an open area of research for how we, like, bring those approaches together. But, yeah, I'm really excited to see what people build with the Decisions API.Vibhu [00:02:24]: One of the interesting things is Dots now have attached personal computers.Ari Weinstein [00:02:28]: Yeah.Vibhu [00:02:28]: So it seems like they're very much more persistent. You've been using them for a while. How should people push the bounds? Like, what should people aim for? What should they try? Personally, right now I use it for a lot of customer service. LikeAri Weinstein [00:02:41]: CoolVibhu [00:02:41]: “Oh, this was wrong. I don't wanna sign in. I don't wanna authenticate.” Find whatever and just get it fixed.Ari Weinstein [00:02:45]: Yeah.Vibhu [00:02:46]: How should we push further? What should people try?Ari Weinstein [00:02:50]: Dots Are a really cool product because each Dot has access to its own Linux virtual computer in the cloud, which is different from our other products. you know, traditionally, we've have access to a browser in the cloud, or it has access to your own computer, but now you get your own entire Linux computer in the cloud. And so it can run full desktop applications, and it can also use a web browser. And so, yeah, you know, I think the powerful thing about Computer Use and the reason why I think it's so, exciting is because it makes it so that the agent can do anything you as a, as a person can do, because all the software in the world was designed for humans, and now agents can use that same software, and you can delegate to the agent. So, yeah, like, anything that you would do on a computer, you can ask a Dot to do. Yeah, I think what particularly is useful is gonna really depend on who the end user is and what- what's valuable in their life. but yeah, I would just start by thinking about, like, one of the things that you spend time on and how could you delegate those to an agent.Swyx [00:03:47]: Yeah, a lot of flight booking and shopping and honestly even, like, playing a game or whatever, right?Ari Weinstein [00:03:52]: Totally.Swyx [00:03:52]: Yeah.Ari Weinstein [00:03:53]: Yeah, I don't know. For me, something I did recently, I've been working on. I've, subscribed to a meal prep service ‘cause I was trying to, like, eat healthy, you know? And I really like this meal prep service I found because it lets me customize the meals I order to, like, a high degree of granularity. So I can say, like, “I want this many grams of chicken and this many grams of rice.” but it was so complicated. It took me two hours to do an order, and I found that I could ask Computer Use to do it for me, and it did it in 15 minutes. so I actually saved two hours. it both did it eight times faster than I could, and it saved me two hours on GPT-6.1 Sol.Swyx [00:04:32]: Yeah.Ari Weinstein [00:04:32]: So those are the kinds of tasks that I feel like, are really powerful.Swyx [00:04:36]: As a creator, I can tell you automatically, immediately, my number one use case is automating YouTube.Ari Weinstein [00:04:40]: Nice.Swyx [00:04:40]: Because, YouTube doesn't expose a lot of things via API.Ari Weinstein [00:04:43]: Yeah.Swyx [00:04:43]: And you have to just put it in a VM and just, like, run it, for, like, let's say, let's say their AB testing feature or making community posts. None of this is available by API ‘cause they hate developers.Swyx [00:04:53]: Anyway, so,Ari Weinstein [00:04:55]: I've heard that from our developer experience team too. They use it with YouTube a lot. Yeah. It's really awesome.Swyx [00:04:59]: So I wanna draw for, you know. let's say, I wanna get a little bit spicy. One of our, the leading AI podcasts, our friends, is famous for saying that Computer Use hasn't advanced in the last two years.How Computer Use Has Changed in the Last YearAri Weinstein [00:05:12]: Yeah.Swyx [00:05:13]: Which is a very interesting statement, and I think you're one of the best people in the world to talk about this, like, how have things have progressed, right?Ari Weinstein [00:05:20]: Yeah. You know, they said that a few months ago, I think, and I hope they have a different perspective now because Computer Use is, like, 180 degrees different than it was.Swyx [00:05:26]: He's a, he's a tough guy to impress.Ari Weinstein [00:05:27]: Yeah, okay. well, we're working on it.Swyx [00:05:30]: But, you know, you worked on. You've, like, basically spent your whole career working on, like, some kind of computer automation, right?Ari Weinstein [00:05:34]: Yeah.Swyx [00:05:34]: Like shortcutsAri Weinstein [00:05:35]: YeahSwyx [00:05:35]: At Apple, and then Sky, and then, and then joining OpenAI. Can you draw, like, what your through line is for, like, what is driving you and what- you, what wasn't possible back then maybeAri Weinstein [00:05:47]: Yeah.Swyx [00:05:48]: And, like, what your sort of milestones were.Vibhu [00:05:49]: I guess to add on to that as a follow-up question, what's the major change from using Codex Computer Use from, like, last weekAri Weinstein [00:05:57]: YeahVibhu [00:05:57]: Through to today? Is it model? Is it dots? Is it harness? So all the history plus what really just changed in today's announcements?Ari Weinstein [00:06:04]: Yeah. On the through line, I guess I've always been excited about automation and helping people automate tasks because then you can, like, save time in your life and focus on things that are more important to you than, like, operating a computer very intricately. And so, yeah, that was why we worked on some of those products. I was at Apple before. we made a company called Sky. we ended up joining OpenAI, which is really exciting. and I think something that wasSwyx [00:06:27]: And almost like you have to hack around Apple until Apple was like, “Fine, like, we'll just hire you and you can just work on the inside,” right? Like.Ari Weinstein [00:06:35]: It was, it was a cool place to get to work. what was really interesting looking back at Sky is we were, we were working on Computer Use there as well, and the models were so much less capable. And now the models, just in the last one year, have become extraordinarily capable at Computer Use. I think the biggest delta that I see is before they could, like, reliably start tasks, but then they would run into problems, and now they're really good at debugging. They're really good at trying again, introspecting what is and isn't working. and I think we've also brought the Computer Use the Computer Use field itself has moved forward. I think we're using more techniques. now Computer Use, often writes code. So if you actually look at it in Codex and you expand the tool calls manually, you can see that it's not just doing one action at a time. It's actually writing JavaScript code that it executes, that the computer executes to perform sometimes many actions at once, which is a great, you know, speed up and great capability. We use more accessibility, sort of multimodal interfaces. So, the model may use screenshots, it may use accessibility, it may use Playwright. it can use a lot of different mechanisms, based on the task at hand. and then, yeah, the model acceleration has been, has been just amazing. So, yeah, what's different today? I think we're making computers better all the time, so I think just, like, one day's difference, is probably a little bit less consequential than, like, even the past month or the past two months. but, yeah, I think the Computer Use in Dot is really exciting as well as, the new model that we came out with.Measuring Computer Use and Improving the HarnessVibhu [00:08:03]: On the keynote, Tejal was mentioning 7x improvements in Computer Use speed, a lot better on a few benchmarks. How do you guys think about measuring it? Computer Use is one of those things where, as you say, you know, it's improvements over time.Ari Weinstein [00:08:20]: Yeah.Vibhu [00:08:20]: Is it harness? Is it model? Is it post-training?Ari Weinstein [00:08:22]: Right.Vibhu [00:08:22]: How do you guys look at it internally about measuring how good it is, and what were the changes with the new model?Ari Weinstein [00:08:29]: We actually have a bunch of different ways of measuring it, some of which are on different permutations and configurations of the harness. It's a bit of a complicated story because, you know, our production products have, you know, some more safety checks, and, you know, those are configured differently based on the needs of the, of the task at hand. So there's a lot of ways to measure it, but I think regardless of how we measure it, we find pretty consistent gains. and those gains are, sometimes in the harness and sometimes in the model. and yeah, I was really excited by this result that GPT-6.1 is even more cost-effective for Computer Use than its baseline cost improvement as compared to Astra. It's, like, really cool to see.Swyx [00:09:10]: Yeah. I mean, one of the visuals I really liked from the livestream was that, you're sort of improving the Pareto frontier of, your, curve, and there was a lot of talking about how you're improving it together with the harness.Ari Weinstein [00:09:24]: Yeah.Swyx [00:09:24]: Can you give some examples of aha moments that you had, whether it's on, like, model driving the harness driving the model, whatever?Ari Weinstein [00:09:32]: I don't mean to repeat myself, but I think, like, introducing more modalities has been really powerful.Swyx [00:09:36]: Okay.Ari Weinstein [00:09:36]: One more specific example of that is, in the past, I think we saw a lot of Computer Use, products had to spend a lot of time, like, scrolling, you know? So it would, like, take a screenshot. It would try to do something. It would be like, “Oh, I gotta, like, scroll down to the next page of results,” and then it would take a screenshot, and then it would try to do something. It would scroll down again. And so I think, with accessibility and other. and, direct access to the DOM and other things like that, now the language model can actually see, like, an entire page or an entire application. It can write code that can do multiple steps at once. And so I think those have been probably the biggest single aha moments. There's, like, a lot of tiny ones that are less exciting in comparison, but actually we do find also that a lot of speed improvements are driven by, like, a lot of little paper cuts that we gotta go in and introspect.App Shots, Accessibility, and Better Computer ContextSwyx [00:10:21]: Yeah. A lot of really hard engineering.Ari Weinstein [00:10:23]: Yeah.Swyx [00:10:23]: I mean, app shots in general, right? Like, I think people don't quite get the difference if. because there's, like, a nice visual in Codex when itAri Weinstein [00:10:30]: YeahSwyx [00:10:30]: When you take an app shot, but they don't maybe they get the difference that, you are able to actually drive each button and you have the, you have each text, in a very optimal representation.Ari Weinstein [00:10:40]: Yeah. Exactly. Yeah. It's kind of fun actually. If you wanna be, like, really nerdy about it, you can go into Codex, take an app shot by hitting the two command keys. So you grab the content from whatever app you're working with, bring it into the, Codex or ChatGPT chat. And then the. if you click on the attachment and you click on this, like, little tiny button in the top right, you can see the raw text and you see the raw accessibility representation. And yeah, we've put a lot of work into, puttingSwyx [00:11:04]: Just dumping everything out. Yeah.Ari Weinstein [00:11:05]: Dumping it out, but also making it token-efficient, doing it efficiently. There's, like, a bit of an art to it. And, you know, it turns out that the same technology that was invented for humans, you know, who maybe have accessibility needs, who wanna use a screen reader technology, that technology is really helpful for them to be able to use computers. It's also really helpful for LLMs to be able to use computers. So that's been, like, really fun to get to work on.Vibhu [00:11:27]: For context, I feel like a lot of people don't understand app shots. They don't even know it's a feature.Ari Weinstein [00:11:30]: Yeah.Vibhu [00:11:31]: It's when you double hit command, it pulls in what looks like a screenshotAri Weinstein [00:11:34]: RightVibhu [00:11:34]: And you're like, “Oh, why have I opened up just a screenshot and thrown it in?” No, it's actually pulling all the metadata, all the code, everything.Ari Weinstein [00:11:40]: Yeah, exactly. Yeah. So it's like, you know, if you take a screenshot of a webpage that has a link- The screenshot doesn't include where the link goes. It doesn't include, you know, maybe you take a screenshot of your calendar, the ca- event ti- titles are truncated, you know? But when you take an app shot, it gives, like, the language model, like, full context about everything and, that lets it, just sort of, like, do much more.Swyx [00:12:02]: Yeah. For those who wanna see more, Jason Liu, I invited him to do a full workshop on this, at AI Engineer.Ari Weinstein [00:12:07]: Amazing.Swyx [00:12:08]: Did a great job.Vibhu [00:12:09]: I have a broader vision questionToward Superhuman Computer UseAri Weinstein [00:12:11]: YeahVibhu [00:12:11]: On Computer Use agents. So your example of take a screenshot, scroll page, take a screenshot is where we were.Ari Weinstein [00:12:17]: Right.Vibhu [00:12:17]: Today, they can automate a lot. what are the bottlenecks? Is it models? Is it harnesses? What. Where do you see it going in, like, two years? Do you see it just running for hours? How do we get there? Any predictions on where Computer Use goes?Ari Weinstein [00:12:32]: Yeah. I mean, I think what's really crazy that I think, You know, the team's accomplished over the past couple of months is that now Computer Use is, like, faster at accomplishing tasks than, like, the average human probably in most cases. and I think that the next frontier is to have Computer Use be, like, literally superhuman in its performance where it actually is as fast or faster at using software than, like, expert Computer Users like us. and I think that'll be really consequential and exciting when that happens because I think we'll be able to all of a sudden build products, that, provide just much more real-time experiences. And I think it'll also. lowering the barrier to entry of, or the activation energy, I suppose, of using Computer Use I think will make us start to default to doing certain things in agents that we've become accustomed to doing manually. And I think that's exciting also ‘cause it'll save us a ton of time. and I think there's a, you know, there are a lot of different little paper cuts and bottlenecks that are sort of standing in the way of that. I think that there's, yeah, there's things on the model side, there's things on the inference side, there's things on the harness side, there's things in the, in the representation. You know, we find that as Computer Use gets faster, we're increasingly bottlenecked by just, like, the speed of doing an operation. Like, for example, you know, a non-trivial amount of time in our benchmarks of Computer Use tasks is actually, like, let's say you're automating a task on doordash.com. Like, a lot of the time is actually waiting for doordash.com itself to load, you know?Swyx [00:14:04]: Yeah, then you just write a wait and then you execute the wait.Ari Weinstein [00:14:07]: Yeah, totally. And you wanna get. Yeah, actually, it's actually really important that you get that de- like, you want as little delay as possible between when it finally finishes loading and when you go andSwyx [00:14:16]: YeahAri Weinstein [00:14:16]: Trigger the LLM to do the next action, which is actually- itself a statistical science.Swyx [00:14:20]: Like an event-driven way maybe to do that.Ari Weinstein [00:14:22]: When possible, you want it to be event-driven.Swyx [00:14:24]: JavaScript has some load events.Ari Weinstein [00:14:25]: And JavaScript has load events for. or the web browser has load events for web navigation, but there's other types of events that actually really can't be event-driven. So there's a lot of complexityVibhu [00:14:34]: The one that comes to mind is, like, chatting with customer service.Ari Weinstein [00:14:37]: Yeah.Vibhu [00:14:37]: Replies could take 30 seconds, could take three minutes.Ari Weinstein [00:14:39]: Oh, right.Swyx [00:14:41]: I have dealt with so many bots with Codex. it's great, but I also wonder if the other side knows that they're talking to a bot ‘cause I'm, like, answering in complete sentences. Like, I'm capitalized correctly.Ari Weinstein [00:14:50]: That's hilarious.Swyx [00:14:51]: Like, I'm giving full num- full reference numbers and everything. Like, it's too. it's clearly too good. I don't care. Like Like, I'm just, like, trying to get my support case.Vibhu [00:14:58]: I've prompted it to, like, you know, “Don't pretend you're a bot. Be very annoyed human.”Vibhu [00:15:02]: Short one-liners, likeSwyx [00:15:04]: YeahVibhu [00:15:04]: Push it, do all this. I also tell it, “While you're waiting for responses, like, use subagents to research better ways to figure out what we need.”Ari Weinstein [00:15:12]: Nice.Vibhu [00:15:12]: It's just, like, human little intervention.Ari Weinstein [00:15:14]: That's awesome. I also feel like half the time it's a bot on the other end, so now youVibhu [00:15:17]: YeahAri Weinstein [00:15:17]: Got the bots talking to each other.Swyx [00:15:18]: Yeah. I will also say, you know, like, you know, one milestone of Computer Use that we are, we're at now is, you know, three, four years ago, we were scared of hooking up LLMs to the, to the web and toAri Weinstein [00:15:31]: YeahSwyx [00:15:31]: To our, to our devices. And now I'm having it configure DNS for me.Ari Weinstein [00:15:35]: Wow.Swyx [00:15:36]: I'm having it pay my bills, and, like, really, like, tens of thousands of dollars of, like, stuff I'm just sending it over and yoloing with Computer Use and, like, you know, what's the, what's the worst thing that can happen?Swyx [00:15:48]: So that- that's all, that's all really good.Building Safely With Computer Use in the Agents APIAri Weinstein [00:15:50]: Yeah.Swyx [00:15:50]: I think now that you've. you know, obviously, you also have to dogfood your own products and all these things. Now that you've sort of released this in API, what are some pitfalls or tips that you wanna tell developers, because they're about to, I guess, encounter all this, firsthand?Ari Weinstein [00:16:03]: First of all, I'm just really excited that we brought Computer Use into the Agents API. I think this is, really great because obviously a lot of developers are building applications that wanna be able to work with third-party websites and services. And so Computer Use has this universality to it. It can work with anything. So now all of a sudden, developers can build using the same Computer Use implementation that we're building on. I think there's great work to be done if you wanna build your own Computer Use harness, but it's hard. And also, we train our models on our Computer Use harness, so there is, an advantage to using the one that's in distribution for the model. There actually might be a speed and cost and accuracy advantage. So I think it's really great for people to get to build on top of that. And, yeah, you know, I think kind of to the point that you were making, like, I think we're all sort of still in the process and maybe, like, some of us are ahead of many people in the world of, like, getting comfortable with this technology and trusting it. And so I think it's incumbent on us to, sort of build that trust over time by making sure we're building things that are reliable, by building, the right kinds of safety checks, by asking for the user's consent before doing something consequential like making a payment, by, asking, you know, maybe depending on the application, making sure you're only letting it access the websites or applications that it actually needs for the task. So that's, I think, something important to think about. but yeah, I'd really encourage people to try the new Agents API, build all kinds of cool stuff on it. We'd love to hear your feed- feedback if, you know, depending on how it goes.Vibhu [00:17:31]: Have you seen any changes in the way it affects dev workflows? So one of the things with dots is, you know, you're seeing it in Slack.Ari Weinstein [00:17:38]: Yeah.Vibhu [00:17:38]: You're seeing people use voice and build. the example Roman showed of change this app and send me screenshots along the way and all this.Computer Use for Testing and Closing the Software LoopAri Weinstein [00:17:46]: Yeah.Vibhu [00:17:46]: Is anything that you're seeing there in adoption about how people are using Computer Use for coding workflows? Any tips people should take from that?Ari Weinstein [00:17:55]: One of my favorite use cases for Computer Use actually, and one that we see a lot in the wild, is Computer Use letting the agent- actually test the software that the agent has built, which is far more consequential than it sounds. Because traditionally, you know, you'd build something in Codex and then the-- and the Codex builds it for you, and then you have to test it, and you are now like QA for the agent, right? So with Computer Use, you can complete the develop-- the software development life cycle, where, the agent can build software, it can test it. So I have a lot of fun, you know, building stuff, having the agent test it. By the time it comes to me, it's already working. I have, extra fun because sometimes I'm, like, developing Computer Use itself, and so now I have a Computer Use agent that's using my Computer Use agent that's using something else. so yeah, I really, I really think this is a super powerful class of use case.Swyx [00:18:44]: I have a visual play test skill that I've developed that, really catches a lot of design issues,Ari Weinstein [00:18:49]: NiceSwyx [00:18:50]: That, you know, normally when you just look at code, you wouldn't really pick it up. it's also really good for cloning apps, though. If you're using a shitty SaaS and you wanna kill the SaaS You just clone it screen by screen by screen. and Obviously, Computer Use can completely drive everything, take screenshots, note it down, and then clone everything with Codex.Ari Weinstein [00:19:06]: That's really cool.Swyx [00:19:06]: But yeah, thanks for all your progress. I think, that isAri Weinstein [00:19:08]: AbsolutelySwyx [00:19:09]: Our time.Nikunj Handa: What's New in the OpenAI APIAri Weinstein [00:19:10]: Yeah.Swyx [00:19:10]: This is not the last that we're gonna talk.Ari Weinstein [00:19:12]: Yeah, cool. This has been really fun. Thank you guys for having me.Swyx [00:19:14]: All right.Vibhu [00:19:14]: All right. Okay, we're a strict cutoff. We're just gonna dive right in.Nikunj Handa [00:19:17]: Let's do it, yeah.Vibhu [00:19:19]: Okay, so, Nikunj, we're very excited to have you. You shipped a lot on the API side, like we justNikunj Handa [00:19:25]: YeahVibhu [00:19:25]: Talked about with Ari. You can now build with Computer Use agents. Anything you wanna highlight, the API side of changes, and introduce yourself a little and what you do?Nikunj Handa [00:19:34]: Yeah, for sure. My name is Nikunj. I lead product for the API team. Been here for roughly three years. been working on launching models. I feel like that's just been, like, a thing, constant thing throughout my time, here at OpenAI. And, with every new model, we try to, like, basically work super closely with the post-training team, the research team, to figure out what's new in it. and then we, like, expose those capabilities in the API. so that's, like, the basic way of putting it. and if you just look at, everything that's new with GPT-6, the cool new capabilities that we launched were, firstly, async function calling. so what you see with, like a lot of the things that you're seeing in, like, Codex and Dots and everything is that tool calls take so long that you don't have to, like, pause the model's execution while, the tool is running. So you could just, like, kick off a tool call, keep running, keep reasoning, and then check back in. so we launched async tool calling. We launched, like, mid-turn steering, so now you can, like, inject messages while the model is reasoning, in the middle. so as your tool call finishes, you can put in that instructions.Async Tool Calls, Mid-Turn Steering, and WebSocketsSwyx [00:20:43]: And that's also partially a model alignment capability, right?Nikunj Handa [00:20:46]: Yeah.Swyx [00:20:46]: Like, they have to train in the ability to train.Nikunj Handa [00:20:48]: Exactly, yeah. AndVibhu [00:20:49]: I feel like we've had it in the app. You could always, as it's reasoning, you could steer.Nikunj Handa [00:20:54]: Yes.Vibhu [00:20:54]: It wasn't the best. It's gotten much better.Nikunj Handa [00:20:57]: Yeah.Vibhu [00:20:57]: Excited to see how it does this in versionNikunj Handa [00:20:58]: Yeah, and I like our mainVibhu [00:20:59]: And nowNikunj Handa [00:21:00]: Goal in, our main goal in the API is to, like, put things in the API once it's trained into the harness. And so we kinda wait for that moment until it's good enough. And a lot of that is, like, actually being powered by WebSockets, which we launched, a few, I wanna say months ago. And so WebSockets just opens this, like, whole bidirectional, like, communication thing with the model. This is not, the GPT Life thing. I'm just talking about GPT-6. and you can do all these, like, async tool calling, async reasoning, injecting messages. It's a really fun API to work on. I think, like, really enjoying.Swyx [00:21:33]: Yeah. This is why we are the engineering podcast, because we get to talk about WebSockets.UltraFast and the Inference StackNikunj Handa [00:21:36]: Yeah.Swyx [00:21:37]: This also pairs very well with UltraFast, right?Nikunj Handa [00:21:39]: Oh, yeah.Swyx [00:21:39]: Like, that is now, like, I think for the first time ever available in the API.Nikunj Handa [00:21:43]: Yes.Swyx [00:21:43]: Which is, which is basically the theoretical fastest speed you can ever get, Frontier of Intelligence.Nikunj Handa [00:21:49]: Yeah. It's been so exciting to work on that project. I think, before I go into the API, the most fun part of, UltraFast has been just watching the inference team cook with Astra. Like, they're just, like, constantly having these, like, Codex agents running, trying to, like, squeeze out more performance. And, I would say, like, at least for a couple of months, a lot of it was focused on efficiency and driving the cost down, which is how we, like, were able to cut the Luna price by, like, 80%. It was, like, a lot of that was driven by, like, all the inference improvements they landed. And then now they've, like, shifted gears towards, like, how can we make this run as fast as possible? And so UltraFast has just been, like, amazing to see on a mo- on a model like Astra. Like, to go that fast has been really cool. And yeah, WebSockets is like. actually it was like the first time we launched WebSockets, it was for GPT, 5.3 Codex Spark, which was. Can't believe we named a model that, but, you know, that's what we launched it for. And obviously, it helps so much because, like, you gotta have the tool calls. you had, like, really reduced the overhead, of going back and forth with tools. And so, WebSockets is awesome for that.Swyx [00:22:57]: Yeah. it's always cute to see, like, I have my reset usage limit, and then I have my Spark usage limit that I never use.Nikunj Handa [00:23:03]: Yeah.Swyx [00:23:04]: Like, it's there if I want it.Nikunj Handa [00:23:05]: I think it's gone finally.Swyx [00:23:06]: It's gone. It's gone, yeah.Nikunj Handa [00:23:07]: I know it's gone, so.Swyx [00:23:08]: Yeah. you're slowly killing off all the, you know, theNikunj Handa [00:23:11]: The old ones, yeah.Swyx [00:23:11]: Oldies.Vibhu [00:23:11]: This is a great week. I mean, it was the first time we had Frontier Intelligence at extreme speeds.Nikunj Handa [00:23:17]: Yeah.Vibhu [00:23:18]: People really liked it.Nikunj Handa [00:23:19]: Yeah.Vibhu [00:23:19]: SoSwyx [00:23:20]: YeahVibhu [00:23:20]: First time it comes back.Swyx [00:23:21]: Yeah. for, 5.3 Spark is explicitly attributed to Cerebras. You guys are not confirming or denying that, UltraFast is related to Ce- Cerebras, but people are. I'll just say that people do care and, are wondering about it. And you have your own silicon as well. elephant in the room, decision models.Decisions API: OpenAI's Fast Decision ModelNikunj Handa [00:23:38]: Oh, yeah.Swyx [00:23:38]: Decisions API. We were the first podcast to do a big Jev, deep dive with, Diogo, and I also, you know, featured him at AI Engineer. How quickly did you see Jev and go likeNikunj Handa [00:23:49]: Oh my gosh. Yeah.Nikunj Handa [00:23:50]: Yeah. Firstly, like, huge props to Diogo and, like, the Jev team for, like, really inspiring theSwyx [00:23:55]: YesNikunj Handa [00:23:55]: Like, whole segment in the market. Like, obviously Jev comes out, everyone's, like, losing their minds over it. Our users are, like, hitting us up. But also, like, our internal teams are like, “We need, like, a much faster classification system.” We can. I don't wanna, like, get ahead of some of the dots features that are gonna comeSwyx [00:24:16]: WhooNikunj Handa [00:24:16]: But you're gonna see, like, some cool, like, really snappy, fast things built on top of the decisions API. but, you know, like, yeah. Props to Jev for, like, inspiring this whole thing. obviously a bunch of people at OpenAI get nerd sniped by that, and they're like, “How can we, like, make this work? We're not gonna, like-”Swyx [00:24:33]: Okay.Nikunj Handa [00:24:33]: “. train a new model.” ButSwyx [00:24:34]: Like, four weeks ago, this was not on the dev radar, right?Nikunj Handa [00:24:37]: No, not at all. No.Swyx [00:24:37]: Okay.Nikunj Handa [00:24:37]: This is likeSwyx [00:24:38]: WowNikunj Handa [00:24:38]: Jev-inspired and, likeSwyx [00:24:40]: I think you are officially the first one to your lab to, like, clone and, adopt this.Nikunj Handa [00:24:44]: Yeah. Yeah. I feel like, OpenAI has such a strong, like, hacker culture and, like, people are just, like, they get excited about things. And so, guy from inference, this one awesome guy from, the infra team are like, “ this is amazing. We're gonna, like, hack on it.” They build a prototype, it, like, works, and now we- we are just, like, hill climbing on latency and trying to make this as fast as possible, and we wanna, like, launch it in the coming days. so as soon as we hit our, like, latency target, we'll try to get this out.Vibhu [00:25:13]: It's interesting. At the same time of hacker culture, you also, as Sam said, like 99%, one of the most reliable APIs withNikunj Handa [00:25:20]: Mm-hmmVibhu [00:25:20]: I think probably the most usage, which is your team directly. how should people see decisions API? I feel like a lot of people saw Jev, heard the buzz, haven't built with it. You're making it very mainstream.What Decision Models Are Good ForNikunj Handa [00:25:32]: Mm-hmm.Vibhu [00:25:33]: What should people see it as? How should they use it?Nikunj Handa [00:25:36]: Yeah. I think the main use cases we've seen is, like, really fast classification. all the Computer Use demos have been amazing and really cool. I think there will be limitations, of course, in terms of, you know, having Astra, like, write, like, a JavaScript-like script to control your computer, versus having Luna pick, like, one action at a time. I think, it's not gonna be at the same intelligence level, but, like, maybe there's some Computer Use tasks that this is good enough for. So excited to see that come through. the other cool prototype I've seen internally is people hooking it up with GPT Live. So GPT Live is like, you know, our bidirectional, like, real-time,Swyx [00:26:14]: VoicingNikunj Handa [00:26:14]: A- API. And, it's built on this, like, model of front-end models and back-end models. So GPT Live is this, likeSwyx [00:26:20]: Think or talkerNikunj Handa [00:26:21]: Super fast. Yeah, think or, talker thing. So GPT Live is the talker, super fast, really good at delegation, and you have something like Astra sitting at the ba- at the back. But tool calling has always felt, like, really slow in GPT Live. and so people have been, like, putting together these, like, tool calling demos of GPT Live controlling a computer, and it just feels like so much more snappy and natural. So I'm, like, kinda excited to see, like, what people do with Live and with Luna on decisions API. so that'll be pretty exciting. Yeah.Swyx [00:26:55]: So I wanna iron this out for people, especially from the product side, because a lot of people have been putting out Jev clones. There's been about 100 in the last two weeks.What Makes a Decision Model DifferentNikunj Handa [00:27:01]: Oh, really? That's amazing.Vibhu [00:27:03]: The first couple days.Swyx [00:27:04]: But like, it. Like, they can clone a Jev API, which is honestly structured outputsNikunj Handa [00:27:09]: YeahSwyx [00:27:09]: Which OpenAI was first to.Nikunj Handa [00:27:10]: Yeah.Swyx [00:27:11]: Right? So, like, I think let's iron out for people what is a decision model, as far asNikunj Handa [00:27:16]: YeahSwyx [00:27:17]: As far as, like, what is important? It is not just latency. It's not just structured output, right? Because I could just have Luna as it'- The decision model is priced the same as Luna, right?Nikunj Handa [00:27:26]: Mm-hmm.Swyx [00:27:27]: Have turned off reasoning and then have structured output. Do I have a Jev? you know, no, right? And that's theNikunj Handa [00:27:33]: YeahSwyx [00:27:33]: That's the realVibhu [00:27:34]: There's a confidence there.Swyx [00:27:35]: Yeah.Nikunj Handa [00:27:36]: Yeah, totally. I think, the way that. So we haven't trained, like, a new model for this.Swyx [00:27:40]: Yeah.Nikunj Handa [00:27:40]: We're, like, building this purely on top of the same Luna weights that we have.Swyx [00:27:44]: Oh.Nikunj Handa [00:27:44]: So yeah. This is, like, really just Luna. And, on top of that, what you're doing is you're constraining. So, like, structured output's a big part of it. you're really optimizing the inference stack to, like, get very fast on TTFD. And because you can have multiple questions, what you do is, like, you basically run those in parallel,Swyx [00:28:05]: As a batch.Nikunj Handa [00:28:06]: Yeah. You run those in the-- as a batch. you-- All sorts of, like, inference techniques people are working on to try to make it as fast as possible. But I'd say, like, at least our implementation of it at the start and this first version is, like, zero-shotting this on top of Luna, to see how it goes. And obviously, you wanna, like, put it out there. Like, this is OpenAI's, like, classic iterative deployment thing. Put it out there, see what people think, and then, like, we'll make more model improvements, as needed. so yeah. That's, the decisions API.Swyx [00:28:38]: Yeah. And, obviously as a benefit, you have vision. They don't have vision, right?Nikunj Handa [00:28:42]: That's true.Swyx [00:28:42]: Obviously, Jev's comes withNikunj Handa [00:28:43]: Yeah. Like, we get it for free with Luna. Yeah.Swyx [00:28:45]: Yeah. I do think that, like, you know, some of the innovations, it sounds like, it's still to come if it's still the same Luna weights, which is, like, the confidence stuff, like, the in calibration is something that we've talked about on the podcast with, benchmarking calibration. ‘Cause basically, the whole point is that RLHF kind of collapses you towards what you want to hear.Calibration, Architecture, and the Open Research QuestionsNikunj Handa [00:29:03]: Yeah.Swyx [00:29:03]: But, like, not actually, like, what the amount of confidence is.Nikunj Handa [00:29:06]: Yeah. Yeah, totally. I'm eager to see how it pans out. Maybe there's, like, gonna be. These are gonna be, like, the key areas where we may have to, like, hill climbSwyx [00:29:15]: YeahNikunj Handa [00:29:15]: With the, with the future model release. But, yeah.Swyx [00:29:18]: And then architecture-wise, the other thing that's in the debate, obviously, you-- Nobody knows because Jev doesn't talk about it, but the two speculations are, one, maybe diffusion model instead of autoregressive.Nikunj Handa [00:29:28]: Mm-hmm.Swyx [00:29:29]: But you are able to achieve the parallel, generation in your way. And then the other one is some mech interp type thingNikunj Handa [00:29:37]: Mm-hmmSwyx [00:29:37]: That you're, like, analyzing the activations and then just outputtingNikunj Handa [00:29:40]: That would be coolSwyx [00:29:41]: The weights.Nikunj Handa [00:29:42]: Yeah.Swyx [00:29:42]: Which, like, you guys have all done the research on this. People have speculated.Vibhu [00:29:45]: There have been demos onSwyx [00:29:46]: YeahVibhu [00:29:46]: Both of these as well. I think Gemini shared a Gemini diffusion, Gemma diffusion on a Jev-style output.Nikunj Handa [00:29:53]: Oh, sick.Vibhu [00:29:53]: And, interp people have also, you know, pulled out interp from a middle layer, but this is all speculation.Swyx [00:29:59]: It's just like, what are you trying to aim for, right? Because you can achieve the API. Everyone can achieve the API. It's actually pretty trivial. But, like, then there's the speed, then there's the accuracy, then there's the other calibration features.Nikunj Handa [00:30:11]: Mm-hmm.Swyx [00:30:11]: I don't know what else.Nikunj Handa [00:30:13]: Yeah. Yeah. No, totally. It's so cool that this, like, whole space has been kicked off now and people are gonna do so much cool stuff and everyone's gonna learn from each other. And, yeah, I'm excited about it.What Developers Should Build NextVibhu [00:30:24]: I feel like being on the platform team, a lot of your job is to empower builders.Nikunj Handa [00:30:27]: Mm-hmm.Vibhu [00:30:28]: What do you think people should build with decisions API and also Computer Use agents? Any stuff that you've- been building with internally that you think really opens up after the new change?Nikunj Handa [00:30:39]: Yeah. okay, let's think. decisions API, use cases internally have been pretty obvious. Like, the user ops team was, like, jumping on it. We were like, “We gotta classify all of our support tickets.” what else came up? obviously, there were, like, the really cool GPT Live demos. I'm sure, like, the Codex app team might, like, pick this up and try to do something cool with it. So, you know, like, this whole thing started, like, a week ago, so it's, like, very early andSwyx [00:31:06]: Oh, one week.Nikunj Handa [00:31:07]: We're excited. Yeah. Yeah, pretty much.Vibhu [00:31:08]: There was a big push in, evals, LLM as a judge having really low latency there.Nikunj Handa [00:31:13]: Right. Yeah. That'll be interesting to see. and then, with the Agents API, we have-- we're basically, like, having a bunch of first-party products, like, at OpenAI built fully on top of it. we've had the Codex security stuff that just went out that's fully built on top of, the Agents API. We have, sort of the-- we- we are having, like, a meetings type of thing launching today.Agents API and OpenAI's First-Party ProductsSwyx [00:31:40]: Mm-hmm.Nikunj Handa [00:31:40]: I think there was, like, a demo. do you remember, like, the plugin extensions when Sam was showing it? There was, like, a demo for, like, you're in a calendar, you can sort of, like, have your meeting notesSwyx [00:31:51]: Like, drop into a singleNikunj Handa [00:31:52]: Flow into like your spaceSwyx [00:31:52]: Like, Google Docs type thing.Nikunj Handa [00:31:53]: Yeah.Swyx [00:31:54]: Right?Nikunj Handa [00:31:54]: And so the-- all of that stuff is, like, fully built on top of, the Agents API. and yeah, I'm, like, just excited to see. Like, we're just getting this out, and let's see what people build on top of it.Vibhu [00:32:04]: I think you showed it off very well. The whole edit spaces, pages, collaborate, add in your dot. Like, that's a lot, soNikunj Handa [00:32:12]: YeahVibhu [00:32:12]: There's a lot of inspiration people can go to.Nikunj Handa [00:32:14]: Yeah. All possible with Astra, you know. Like, thing- things just move so fast now. LikeSwyx [00:32:19]: YeahNikunj Handa [00:32:19]: People go from idea to execution so quickly, it's amazing.Swyx [00:32:23]: Is there something that you want, people to focus on to give you feedback? Like, what-- like, you know, maybe you're just putting this out there and you want-- and there's, like, a fork in the road and you want developers to help you decide.Responses API Performance and Long-Lived CachingNikunj Handa [00:32:35]: So I think Agents API and decisions API, they are like, these are our newest products. Would love, like, any and all feedback on that to figure out where to take them. I think, over here, we're, like, very open on Responses API, which is sort of like our workhorse over here. like, really focused on performance right now, and the performance comes in, like, two main ways. first is just, like, latency. We've been, like, rewriting the whole Responses API stack to, like, make it as fast as possible from a TTFT perspective, DVD perspective. So there's like-- that, like, continues to be, like, a main area of focus for us. The second thing we've been trying to do is, like, really go deep on caching, particularly with these, like, personal agents that are, you know, like, basically, like, a single thread that just goes on and on forever. We've been, trying to, like, really up our game on caching. We provide now guarantees of, like, cache hits within, like, 30 minutes. We're actually, like, we-- for one of our users, we just launched, like, a much longer cache window. So we have, like, a 12-hour caching guarantee, that we offer so that you have, like, guaranteed cache hits forSwyx [00:33:40]: Is that a public API?Nikunj Handa [00:33:42]: Not yet. That's in preview.Nikunj Handa [00:33:43]: We're gonna, like, try to get that out to everyone as soon as possible. But, like, just pay a little bit more for the cache write, and we, like, guarantee, like, cache reads for, like, a much longer period. So even if, like, your instinct thread, for example, like, you just, like, do something on it and then come back to it, like, three to four hours later, you- you're still getting the caching performance out of it. And launchedVibhu [00:34:04]: And you cut the cost there quite a bit too, right, with the new model?Nikunj Handa [00:34:07]: Oh, yeah. That's right.Vibhu [00:34:08]: Like, 25% cheaper, soNikunj Handa [00:34:08]: Yeah, with, like, driving down cache reads, yeah.Cache Pre-Warming and Cost-Efficient Agent ThreadsVibhu [00:34:10]: For builders, they should implementNikunj Handa [00:34:13]: YeahVibhu [00:34:13]: Because it's significantly cheaper.Nikunj Handa [00:34:14]: Yeah. Yeah. Just, like, building your apps with, like, to be very cache aware and sort of, like, use our prompt diagnostics or cache diagnostics tool to figure out, like, where things are dropping off. And, so the caching part is, like, really important. yeah, I also wanted to talk about pre-warming. We have that in the API now. So, like, if you know that, “Hey, I'm gonna get a cache,” like-- sorry, “I'm gonna get this prompt. I just wanna, like, pre-warm the cache, pay, like, the cache write fee right now, and then, like, have it sort of ready to go for the next 30 minutes for whenever.”Swyx [00:34:49]: And it can spawn many instances of that thread.Nikunj Handa [00:34:51]: Exactly, yeah.Swyx [00:34:52]: Yeah.Nikunj Handa [00:34:52]: You can just keep going and haveSwyx [00:34:54]: Yeah, just keep messing with the prompt thereNikunj Handa [00:34:55]: Tons and tons of that. and so, yeah, like, I'm very excited about getting feedback on, like, the low-level performance things that we can keep making Responses API the most performant and reliable way to, like, build on top of an LLM. And then you basically have our, like, new products where I'm just looking for, like, any and all feedback.Swyx [00:35:15]: Yeah, just use it, right?Nikunj Handa [00:35:16]: So yeah, just useSwyx [00:35:16]: Tell us what toNikunj Handa [00:35:17]: Yeah. Define our roadmap for us, please. So yeah.Swyx [00:35:20]: I think for me, the caching thing, great, right? Like, obviously very needed. But at the end of the day, you're still bumping up against a million-token contextCompaction and Managing Million-Token ContextsNikunj Handa [00:35:28]: Mm-hmmSwyx [00:35:28]: And that's probably not gonna change for the foreseeable future.Nikunj Handa [00:35:31]: Mm-hmm.Swyx [00:35:31]: Like, you still need good compression.Nikunj Handa [00:35:33]: Yeah.Swyx [00:35:33]: What is the best practice there?Nikunj Handa [00:35:34]: Yeah. Yeah, totally. so firstly, OpenAI has its own, like, proprietary compression, compSwyx [00:35:40]: Which is inNikunj Handa [00:35:41]: Compaction.Vibhu [00:35:42]: Compaction.Swyx [00:35:42]: It's in the agents.Vibhu [00:35:43]: It's in the API.Nikunj Handa [00:35:43]: Yes.Vibhu [00:35:43]: Agents API.Nikunj Handa [00:35:44]: Yeah.Swyx [00:35:44]: You decide for us, right?Nikunj Handa [00:35:45]: Yeah, exactly. So in the Agents API, it comes built into the harness. and if you're in Responses API, there's, like, two ways of doing it. One is what we call server-side compaction, which is you basically tell Responses API that if you ever hit this threshold of tokens, just auto-compact it and, like, go back, or sorry, like, reduce the context, being used. And the second way is, like, /compact, which is, like, if you want full control. So you can, like, /compact at any timeSwyx [00:36:15]: I hear youNikunj Handa [00:36:15]: Have your own logic on when to, likeSwyx [00:36:17]: It's not AGI.Nikunj Handa [00:36:18]: It.Swyx [00:36:18]: It's not AGI.Nikunj Handa [00:36:19]: Yeah. Yeah.Swyx [00:36:20]: Yeah. But it, I meanNikunj Handa [00:36:20]: YeahSwyx [00:36:20]: It is the manual override.Nikunj Handa [00:36:21]: Yeah, it is the manual way. And like, I don't know, but a lot of the big coding agents like to do it manually. I mean, like, if you look at the Codex implementation of it in the Code- open source Codex harness, you can see that they use /compact and do it. and, there's also, like, new, by the way, new compaction techniques that we are working on. Some of them you will be able to see in the Codex harness. Like, it's already implemented in the Codex harness. And so, they're like some file-based, systems that we are, like, experimenting with. So yeah, lots of cool stuff going on around in compaction as well.Swyx [00:36:57]: Cool. we are running out of time.Nikunj Handa [00:36:59]: Okay.Swyx [00:36:59]: I think you've talked about, a lot about performance and talked a lot about, the new APIs that you're launching. Can you give us any other hints as to things that you're interested in as far as the future of the platform is concerned?Higher-Level Platform Primitives and the AI CloudNikunj Handa [00:37:13]: We're obviously like very low level. Like, I used to work at Stripe before this, and, at Stripe a lot of the game was like building these higher level primitives and products on top of like the core payments primitives. and, I'm always like curious about what the best way of doing that is in AI. And I think we've had a couple of attempts at that. We like had launched assistance API like way back in the day, and like wasn't really the right fit. We were sort of like going off with this like Agents API, and, it gives you the codex harness, but like where's like the, what's the right amount of flexibility to give in that? That's like an open question. Like how should we like have memory walls and like all of these like higher level like API objects to take away, also like to abstract away more, like storage concepts. Like this is like a whole, like, there's a whole space that I'm like very curious about figuring out how we design. I think a lot of things in AI are just have a low-level API primitive and see an example harness and go and have your coding agent implement that. But how much of that do we build into the API is like a constant question that I'm thinking about.Swyx [00:38:24]: Yeah.Nikunj Handa [00:38:24]: So I don't know if folks have thoughts on that. If anyone has ideas, it would be super interesting to hear.Swyx [00:38:30]: Yeah. The analogy I always bring back to, and we'll end there, is, you're building an AI cloud, right?Nikunj Handa [00:38:35]: Mm-hmm.Swyx [00:38:35]: Like, which is, something that, Sam said a year agoNikunj Handa [00:38:38]: Mm-hmmSwyx [00:38:38]: Where, and you're, it's almost like you're kind of doing the AWS invention and you have to do, okay, this is EC2Nikunj Handa [00:38:45]: YeahSwyx [00:38:45]: And this is S3, and this is like. But you're doing the AI-native versions of each of these.Vibhu [00:38:48]: There are a lot of analogies, so you're pre-warming caches for stuff that you know will beNikunj Handa [00:38:53]: Yeah.Vibhu [00:38:53]: And it's nice that it's all exposed to buildersClosingNikunj Handa [00:38:56]: Mm-hmmVibhu [00:38:56]: ‘cause it just opens up ways that you can build new things.Nikunj Handa [00:38:59]: Yeah, absolutely.Swyx [00:39:00]: Okay.Vibhu [00:39:00]: Awesome. WellSwyx [00:39:01]: That's everything.Nikunj Handa [00:39:01]: Thank you, guys.Vibhu [00:39:02]: Thank you.Nikunj Handa [00:39:02]: Yeah. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

This show has been flagged as Clean by the host. -------------------- 01 Introduction This is the fourth episode in an 8 part series. 02 In the previous episode we looked at the Allen Bradley PLC2, one of the very early successful Programmable Logic Controllers. In this episode we will look at another early PLC, the Siemens S5 series, and use it as an example of both how PLCs became more sophisticated, as well as an example of a different approach to the overall software architecture. Again, I will only touch on this topic lightly, this is not a course on how to program PLCs. However, I will give you enough technical detail to give you are rough idea of what they were like. -------------------- 03 S5 Historical Background Siemens are the largest PLC vendor on a global basis, and have been since the early days. Their first PLC was the S3, released in 1975, two years before Allen Bradley released the PLC2. There seems to be almost no information available on the S3, so I can't say much about it. In 1978 or 1979, sources are unclear about the exact date, Siemens released the S5 series. This was a huge step forward in capability and was the foundation of their PLC product line until replaced by the S7 series in the mid 1990s. In this episode I will focus on the U series, which were an upgrade to the original S5. -------------------- 04 A Complete Family of PLCs Fully developed, the Siemens S5 series was a complete family of products covering the entire size spectrum from smallest to largest. Siemens covered the entire industrial control market, and there was almost nothing they didn't have somewhere in their catalogue. You could spend an entire career using nothing but Siemens products for everything. 05 At the lowest end were the S5-100 series, consisting of the S5-100, 102, 103, 90, and 95. These covered everything from the small "shoebox" all in one form factor to ones which overlapped the mid range in terms of capability except for their compact I/O cards which were smaller and slower than the full size models. The S5-115 formed the mid range, coming in a "full size" form factor in terms of packaging and rack size. The S5-135 and S5-155 extended the S5-115 in terms of speed and memory. 06 In this episode I will focus on the lower end S5-100 series for the sake of simplicity, and will not make any reference to any additional capabilities of the larger models. It will be much too time consuming and confusing to try to cover everything, and there wouldn't be much point to it. -------------------- 07 Data Memory Types If you recall the previous episode, the Allen Bradley PLC2 had a data table, or memory system, that was a single linear range of memory with all I/O and internal capabilities mapped into fixed numerical addresses. Allen Bradley were to abandon this approach in their later PLC5 series introduced in the mid to late 1980s, but we won't cover that here. The Siemens S5 series however took the approach of having different types of memory addresses for different purposes. I will describe those for the S5-100 series now. 08 Input and Output Addresses The standard I/O modules for these compact PLCs had 8 inputs or 8 outputs per I/O module. These module plugged into a flat bus (as opposed to an enclosed rack) with slots numbered from 0 through 31 in decimal. Each of these slots had an associated byte number, and each I/O point on the individual modules had a bit number. 09 Input and output addresses had the following format. Letter , number, decimal point, number. The letter was either I or Q. I indicated inputs. Q indicated outputs. The first number was the byte number, which corresponded to the physical I/O module slot. The second number was the bit number within the byte, starting from zero. 10 So "I2.3" indicated an input module located in the third slot (numbered from zero) and addressed the fourth input (numbered from zero) on that module. Outputs worked the same way. For example "Q 5.1" 11 Analogue modules could be located slots 0 through 7. Analogue if you recall, refers to voltages which have varying levels rather than just on or off, and can represent things like temperature. Each analogue module was assigned 8 bytes per slot, starting at address 64 and going up to 127. In practice many of these so called "analogue" modules had nothing to do with actual analogue voltages, but rather any advanced module which needed more address space was used here. Even later high density digital I/O used these addresses. 12 Each of these addresses could be addressed as bytes or words. In this case input bytes were indicated by an "IB" prefix, and input words were indicated by a "IW" prefix. Output bytes used a "QB" prefix and output words used a "QW" prefix. 13 Flags Flags are individual bits of internal memory that are used for storing intermediate logic values. These began with an "F" prefix. An S5-103 had a total of 2048, numbered from F0.0 to F255.7. 14 Counters Counters started with a "C" prefix. An S5-103 had 128 of these numbered from C0 to C127. 15 Timers Timers started with a "T" prefix. An S5-103 had 128 of these numbered from T0 to T127. 16 Data Blocks Programs needing to deal with byte and word memory could create what were known as "Data Blocks". These were blocks of PLC memory that could be created as the discretion of the programmer. An S5-103 could have a maximum of 254 data blocks numbered from DB2 to DB255. Two additional data blocks were reserved for the PLC's internal operations. Data blocks could contain up to 255 16 bit words. 17 However, the actual number and size of data blocks would in practice be limited by available memory. Even 20 kilo bytes was considered to be a large memory, and this had to be shared with the program. A data block would be "called" to make it the current data block. Then individual words would be addressed with the "DW" prefix. For example "DW5". -------------------- 18 Program Blocks If you recall with the PLC2, there was a "main" program and a series of optional numbered subroutines. In the S5, programs were split into "blocks". These were Organization Blocks Program Blocks Function Blocks Sequence Blocks An S5-103 could have up to 256 of each of these blocks. 19 Organization Blocks When the PLC started up, it would look for Organization Block 1 and begin execution there. You could consider this to be the equivalent of the "main" function in a C program. Organization blocks had an OB prefix. For example OB1. With a very simple program in a very small S5 PLC, all of the program logic could be simply put in OB1. However, with larger programs , it was convention to put the logic in subroutines and simply have OB1 call each subroutine. 20 Program Blocks These had a PB prefix. For example PB10. Normal ladder logic style programming would go in these. It was convention to split up a program into pieces and put piece in its own program block. Normal practice was to have a program block correspond to a particular part of the machine. 21 Function Blocks These had an FB prefix. For example FB64. Function blocks differed from program blocks in that they could take parameters. This allowed you to write an FB that performed some sort of complex data transforms and re-use it by calling it with different parameters. Some instructions could only be used in FBs, not in other types of blocks. There were also built in FBs in some CPU models which performed certain operations such as converting BCD (or Binary Coded Decimal) to normal integer. 22 Sequence Blocks These has an SB prefix. For example, SB25. These were like PBs, but were intended for controlling sequences of operations where each SB performed as single step of the sequence and multiple blocks were used for a sequence. -------------------- 23 Programming Languages The S5 series offered four different programming languages. These were Ladder (abbreviated as LAD) Statement List (abbreviated as STL) Control System Flowchart (abbreviated as SCF) GRAPH 5 24 You should be familiar with ladder if you listened to previous episodes. Briefly however, it is a graphical programming language which followed the appearance of electrical ladder wiring diagrams which were familiar to engineers, technicians and electricians working with manufacturing equipment. 25 Statement List is a text based programming language which resembles an assembly language for an abstract processor architecture. Boolean logic results were calculated and saved on a stack, while integer values were operated on directly from memory or in two accumulators, ACCU1 and ACCU 2. 26 Control System Flowchart is a graphical language that takes the form of rectangular blocks with one or more inputs and an output. These blocks are laid out on a screen and then wired together rather like logic gates in a electronic schematic. 27 GRAPH 5 is Siemens' implementation of the GRAFCET (Graphe Fonctionnel de Commande Étapes Transitions) IEC 60848 flow chart standard. The IEC are the International Electrotechnical Commission, an international standards organization. 28 I will focus on Ladder and Statement List, as these were by far the most commonly used and we have limited time to discuss the S5. -------------------- 29 Statement List As mentioned above, Statement List can be thought of as the assembly language for an abstract CPU architecture. The firmware in the S5 PLC will run the instructions in an interpreter. 30 The RLO Boolean or bit oriented instructions operate on a logic stack known as the RLO, or Result of Logic Operation. Boolean operations typically take one operand. When a boolean operation is executed, if the RLO is empty it simply loads the operand onto the RLO. If the RLO is not empty, then the instruction is executed using the operand and the RLO, and the result replaces the value on the top of the RLO stack. Some boolean instructions do not take an operand, but rather work on the two topmost values on the RLO. If you are familiar with languages such as FORTH, this should sound very familiar. 31 The Accumulators Word oriented instructions work on two accumulators, ACCU 1 and ACCU 2. A load instruction will load a word or byte value from a memory location or a constant into ACCU 1. The value that was in ACCU 1 then gets shifted into ACCU 2. A transfer instruction will write the value in ACCU 1 to memory. The remaining word oriented instructions will operate on the values in ACCU 1 and ACCU 2, and place their result in ACCU 1, overwriting the existing contents. So for example, an add instruction will add the value in ACCU 2 to the value in ACCU 1, leaving the sum in ACCU 1. 32 Ladder As mentioned above, ladder is a graphical programming language which has the appearance of an electrical control ladder diagram as used with actual relays. The is by far the most common programming language used with PLCs. A ladder program however is just another way of presenting statement list. 33 To put it another way, a statement list program can be displayed on a computer screen as either lines of text, or a graphical symbols laid out in a meaningful pattern. All valid ladder programs can be represented as statement list. However, not all valid statement list sequences can be represented as ladder. Statement list is inherently more flexible than ladder, which means that you simply can't always translate both ways fully. 34 None the less, most typical control logic can be represented as ladder, and ladder is the preferred representation as it is quicker to understand at a glance when debugging a software or hardware problem. On the other hand, some statement list instructions have no ladder equivalent, or the algorithm may not lend itself to being represented in ladder. In these cases normal practice is to put the simple control logic in ladder in Program Blocks, and put the non-translatable statement list logic in Function Blocks. 35 Control System Flowchart and GRAPH 5 Like ladder, CSF and GRAPH 5 are actually implemented in statement list, and many of the considerations mentioned above apply to those as well. However, I won't go into any further detail on these here. -------------------- 36 Programming Software and Hardware There were two ways of entering a program into an S5 PLC. One way was the hand held programming terminal. The other was via the STEP 5 programming software. 37 Hand Held Terminal Like many if not most PLC vendors, Siemens sold a hand held programming terminal. There were a series of these, an example being the PG 605. This was a box like a large calculator which had buttons and a small display. The buttons corresponded to various instructions and a numeric keypad, and the display would show the current instruction being entered. To use it you would plug a cable attached to the hand held terminal into the programming port on the front of the PLC CPU module. You could then enter or view the program, one instruction at a time. 38 STEP 5 Programming Software The other way of programming was to use software running on a PC. Siemens called their software STEP 5. This was an integrated editing and debugging program. Originally it ran on CP/M. 39 Siemens sold their own portable PCs with the software preloaded, and with additional built in hardware features such as an EPROM burner. These computers were also known as "PGs". STEP 5 was later ported to MS-DOS by running it on a CP/M emulator which people would use to run it on an off the shelf PC such as a laptop. Later still a native MS-DOS port was produced. 40 This had modes for Statement List. Ladder, and Control System Flowchart. GRAPH 5 was a separate program. You could toggle between STL, LAD, and CSF modes, and if the software could translate what was there form one mode to another, it would. 41 The PC would be connected to the PLC by a cable which had an interface box in the middle. This could be used to download a program to the PLC, upload a program to take a backup, modify a running program, or debug a running program. -------------------- 42 Instructions I won't list all the instructions, as that would take too long. I will instead give a brief overview of them. I will deliberately skip over obscure instructions that would take too long to explain. 43 Basic Operations Boolean Operations There is a full set of boolean operations including and, and not, or, or not, set, reset, and assign. The assign operation would assign the RLO value to the address of the specified operand. 44 Load and Transfer The Load instruction would load the value of a memory address word or byte or a constant into accumulator 1. There are about 2 dozen variations on the load instruction. The transfer operation would write a word or byte from accumulator 1 into memory. There are about a dozen variations of the transfer operation. 45 Timers and Counters There are 5 different types of timers. Timers take the value in accumulator 1 as their preset when started. There are up counters and down counters, which count up and down respectively. Again, these take the value in accumulator 1 as the preset. 46 Arithmetic Operations The only native arithmetic operations in the S5-100U series are addition and subtraction. These add or subtract the values in accumulator 1 and 2. Another set of add instructions allows adding a constant. However, the 103 and 95 CPUs offered integer multiple and divide as integrated function blocks, FB242 and FB243. You would call these function blocks with the desired parameters and they would return their results in other parameters. 47 Comparison Operations There are a complete set of integer comparison operations which operate on the values in the two accumulators. These include greater than, greater than or equal to, etc. The boolean result is saved on the RLO. 48 Block Call and Return There is a complete set of unconditional and conditional call operations to call all of the various types of blocks. There are also unconditional and conditional return operations. Calling a data block makes it the current data block, for load and transfer operations on data words 49 NOP or No Op As implied these instructions have no result. 50 Stop This stops the program executing 51 Binary Word Operations These operate on words in the accumulators. These include AND, OR, XOR, Shift Left, and Shift Right. 52 Bit Operations These test, set, or reset individual bits in data words or in timers and counters. 53 Conversion Instructions There are instructions to perform one's complement or two's complement of the value in accumulator 1. 54 Jump Operations There are unconditional and conditional operations to jump forward or back to a label. This is equivalent to a GOTO operation. 55 Indirect Addressing or "DO" operations These instructions allow jumping to a block or selecting a data word or flag word according to a number stored in another address. Essentially, these instructions are like pointers. 56 Miscellaneous There are also various other instructions that do things like copy blocks of words, or swap the contents of the accumulators, 57 Other Instructions There are still more instructions which I have skipped over as it would take too much time to explain them. 58 Instruction Summary As you can see the S5 PLC offers a very comprehensive set of operations which go well beyond what most people would think of as what a PLC does. This is a very powerful instruction set. 59 On the one hand this is a good thing, as you can do nearly anything. On the other hand, this is a bad thing, as some people take this as license to do nearly anything. In the hands of the wrong person, this results in a complex mass of spaghetti code that not even the person who wrote it can figure out later. 60 This goes against the philosophy of PLC programming, which is to make things as simple and obvious as possible. With a PLC program, perfection is achieved not when it has as many advanced features as possible, but rather when it is not possible to make it any simpler. 61 While Siemens gave the programmer a range of powerful low level instructions from which he could construct nearly anything, many other vendors took a different approach. They instead studied what was needed in major market segments and provided the programmer with instructions that operated at a higher level of abstraction, allowing programs to be written in less time and with fewer instructions. -------------------- 62 Execution Speed Execution speed of instructions varied depending on the PLC CPU model. Here are some typical values for the low end S5-100 series CPUs. 63 A boolean AND instruction executed in 70 microseconds on a S5-100U, or in 4 microseconds on an S5-102U, or in 1.6 microseconds on an S5-103U. 64 Adding two words in the accumulators executed in 55 microseconds on a S5-100U, or in 23 microseconds on an S5-102U, or in 1.6 microseconds on an S5-103U. 65 The S5-103U had a custom logic coprocessor chip, known as the M5. Instructions that were executed on the M5 coprocessor tended to take a uniform 1.6 microseconds. However, complex instructions that were not executed on the M5 coprocessor were often slower than on CPUs that didn't have a coprocessor, such as the 100 or 102. I suspect this is due to some sort of overhead relating to coordination of the two processors. 66 However, most instructions in a normal program are boolean logic, so very fast boolean execution time is what matters. Since every instruction is executed every scan cycle, large programs require faster instruction execution in order to keep up with demand. -------------------- 67 The S7 Series Replaces the S5 In the mid 1990s, Siemens began introducing the S7 series which would ultimately replace the S5, although that took a number of years to happen. 68 The hardware was all new, but from a programmer's perspective, the S7 was an evolution of the S5. Some statement list instructions were dropped and others were added. Most of the basic concepts however remained the same. Porting an S5 program to an S7 was a complete re-write, but it was a fairly straightforward one. 69 The exception to this was the S7-200 series, which came into Siemens from the outside as part of their take over of of the PLC division of Texas Instruments. The S7-200 was an American knock off of a Japanese PLC and sold as a German product. It was none the less an excellent PLC and sold in very large numbers. The S7-200 was eventually replaced by something that was compatible with the rest of the S7 line 70 The S7 series has gone through a number of hardware revisions and remains Siemens' main PLC line today. It can via the S5 trace its lineage back to the 1970s and the early days of PLCs. -------------------- 71 Other Developments Japanese PLC Vendors I haven't covered any Japanese PLC vendors in the same way that I covered the PLC2 or S5. I had planned on doing another episode on one but I don't think it would really add much to what we have already discussed. However, PLCs from Japan are among the top sellers world wide and are generally very highly regarded. 72 Pace of Innovation In terms of general history across the industry, in the early days of PLCs evolution was fairly rapid. Later on, innovation slowed down as compatibility with the existing install base became a bigger factor. Technology has become fairly static, but that isn't necessarily a bad thing from a business perspective. I will return to this subject later however. -------------------- 73 Conclusion In this episode we covered the Siemens S5 series of PLCs. We covered the history of the S5, it memory and addressing, the different types of subroutines, programming languages, the instructions, and how it was succeeded by the S7 series. 74 We saw how PLCs had rapidly evolved from their very early relay-replacement origins to become powerful programmable devices. In the next episode we will look at I/O modules and how they allow a PLC CPU to interface to hardware and control machines. 75 This has been the fourth episode in an 8 part series. -------------------- Provide feedback on this episode.

Send us Fan MailRecorded on 30 September 2026, this episode of "What's New in Cloud FinOps" brings together Frank Contrepois and Stephen Old for a comprehensive roundup of the latest developments across AWS, Google Cloud, Oracle Cloud Infrastructure (OCI), and Alibaba Cloud. With a significant focus on the rapidly evolving world of Generative AI cost management, sustainability, and cloud governance, this instalment was recorded live from the GreenIO Conference foyer in London. Expect a lively, candid flow with real-time analysis.Generative AI & Machine Learning Cost ControlThe episode kicks off with major updates for Amazon Bedrock, SageMaker, and other cloud AI offerings.Amazon Bedrock Agent Core Payments (GA): A key discussion point is that agents can now autonomously discover, access, and pay for paid APIs and content. This presents a major governance challenge, risking unmanaged spend and fragmented cost visibility. The hosts stress the need for spending caps, approval workflows, per-agent budgets, and unified cost aggregation.Amazon Bedrock Cost Allocation & Anomaly Detection: Bedrock now supports IAM principal cost allocation, allowing organisations to track usage back to specific users and roles in the CUR. Additionally, AWS Cost Anomaly Detection now covers third-party Foundation Models like Anthropic's Claude.Bedrock Pricing (OpenAI GPT-5.6): Promotional pricing is available at $4/million input tokens and $20/million output tokens (a claimed 20-33% reduction), valid through at least 21 November 2026.Amazon SageMaker: A new guided, low-code/no-code path in SageMaker Studio simplifies finding cost-effective inference configurations for custom models.Google Cloud BigQuery: Introduced preview monitoring for data agent performance, latency, and conversation costs through Google Cloud observability.Oracle Cloud Infrastructure (OCI): August's AI update expanded hardware choices and on-demand inference for Cohere and Meta models.Calculating AI ROI: Frank highlights an AWS article by Adam Richter on presenting AI return on investment. Steve recalls a previous podcast with Adam discussing a Cornell "levelised cost of AI" study, emphasising the need for measurable value.From AI Pilots to Production: The hosts discuss how many AI pilots run too long without a clear "exit to value" plan. Frank's workshops focus on defining proof-of-value criteria and graduation paths to production. Research shows 59% of organisations report increased AI spend wastage, while only 31% have accurate visibility.Commitments, Pricing Models & The New "SAM" ChallengeA clear trend is the application of traditional cloud commitment models to AI services, creating new challenges where FinOps and Software Asset Management (SAM) collide.Google Cloud: Introduced flexible, spend-based Savings Plans for Gemini Enterprise (10% for one year, 20% for three years) and a forthcoming "deferred execution" option offering up to a 50% discount for non-urgent jobs.Alibaba Cloud: Launched AI Savings Plans for Model Studio on 28 August 2026, with discounts up to 47%. They also introduced batch inference pricing at a 50% discount.The FinOps/SAM Collision: Models like Gemini Enterprise and Azure Copilot Studio blend per-user subscriptions with pay-as-you-go agent workloads. This creates a challenge in determining who owns which piece of the cost and how credits and commitments apply across teams.Hidden Risks & Governance GapsThird-Party AI EULAs: Stephen highlights a critical risk: when a user accepts the End-User License Agreement (EULA) for a third-party model (e.g., Anthropic on Bedrock), they could be binding their entire organisation to the provider's standard terms without legal or procurement oversight.Intellectual Property Risk: Frank points to a recent HBR article discussing how the "how" of AI usage—the prompts and methods—may not be protected, potentially exposing valuable IP.Cloud Infrastructure & Cost Management UpdatesAWS Compute:R9g/R9gd instances (Graviton 4): AWS claims up to 25% better compute, but Frank's analysis shows an 8-25% price uplift. FinOps takeaway: benchmark workloads before migrating.ECS fractional GPU scheduling: ECS now supports fractional GPU allocations on EC2 g6f, ideal for right-sizing smaller ML/AI workloads and improving utilisation.ElastiCache on Graviton 4: Offers ~40% higher throughput and ~31% better price-performance, but with higher memory costs.AWS Database & Analytics:Aurora Serverless: Can now scale to 12 ACU in ~1 second, reducing the need for high steady baselines, but requiring guardrails to prevent cost spikes.RDS for SQL Server BYOM: "Bring Your Own Media" is extended to more regions, requiring careful audit documentation.Oracle AI Database on AWS Exascale: Oracle Exadata DB Services now generally available via AWS, signalling evolving multi-cloud coexistence.AWS Glue 6.0 GA: A ~30% price reduction and runtime upgrades (Spark 4.1, Python 3.13) offer immediate savings opportunities.AWS Glue Data Quality: Anomaly detection is now free with an improved "observation mode".Amazon Kinesis: Can now deliver data directly to S3 in Iceberg format, potentially lowering delivery and query costs.AWS Billing & Lambda:AWS Data Exports: Now supports SQL-based row filtering at the source to create pre-filtered reports.AWS Billing Managed Dashboards: An official, pre-configured version of the popular open-source Cost Intelligence Dashboards.AWS Lambda: Recursive loop detection is now available in all commercial regions, reducing the risk of runaway bills.Google Cloud:BigQuery: Offers cost-effective cross-cloud connections (preview) but restricts Graph processing to Enterprise/Enterprise Plus editions.Billing Reports: A new "originating products" filter helps trace costs back to their source.Semantic Tags: A preview feature to automatically propagate application context to resources for better cost allocation.Storage Rapid Cache: Now allows selective ingest filtering to reduce expenditure.Oracle Cloud Infrastructure (OCI):Autoscaling for Red Hat OpenShift: New capability driven by licensing alignment with Red Hat/IBM.Service Limits: OCI has streamlined the service limit increase experience in the console.Sustainability UpdatesNew Availability Zones: New zones in London (EU-West-2D) and Las Vegas seem focused on AI training, raising questions about sustainability trade-offs.Google Cloud Carbon Methodology: A refresh offers more granular certificates and hourly electricity matching for better precision.Green Software Foundation: The "Beyond Watts" paper encourages evaluating environmental impact beyond just electricity consumption.Productivity AIAmazon Q for Microsoft 365: Now generally available for Excel, Word, etc., bringing AI into daily workflows but raising data governance questions.Referenced Resources & LinksFrank's instance price/performance comparison tool: https://aws.frankcontrepois.com/comparisons/

Fem un recorregut per diversos punts d'Arenys on hi ha murals ceràmics destacats i antics. Ens referim al seu valor patrimonial i als artistes que els han fet. Un d'ells, l'ha fet el mateix creador que en MIC del S3. podcast recorded with enacast.com

S3.5 MechanicalThe original Mechanical Turk appears to be a fraud, but just how dangerous is it to its young female chess-playing operator?Written by Liam Hogan (http://happyendingnotguaranteed.blogspot.co.uk)Narrated by Erika Ventura (https://instagram.com/efventu)Produced by Karl Hughes (https://bsky.app/profile/karlhughes.bsky.social)With music by Tim Kulig (https://timkulig.com/)And Thom Robson (https://www.thomrobsonmusic.com/)The episode illustration was provided by Matt Seff Barnes (https://www.mattseffbarnes.com/)Joshua Boucher is our story programmer.Jasmine Arch manages our community.Mary Pastrano helps orchestrate the chaos.And the show would collapse into static and screams without the ongoing work of Karl Hughes, Georgia Triantafyllopoulou, and James Barnett — AKA Jimmy Horrors.Liam Hogan is an award winning short story writer, with stories in Best of British Science Fiction and in Best of British Fantasy (NewCon Press). He's been published by Analog, Daily Science Fiction, and Flame Tree Press, among others. He helps host Liars' League London, volunteers at the creative writing charity Ministry of Stories, and lives and avoids work in London. More details at http://happyendingnotguaranteed.blogspot.co.ukErika Ventura is an artist, mother, bilingual narrator, and a painting instructor. How does she manage it all? No idea, but her artwork can be seen on Instagram (@efventu) or you can visit her artist page www.facebook.com/BioArtsy Hosted on Acast. See acast.com/privacy for more information.

S3.4 Eagles and ShamblersCheerleaders in love at the end of the world.Written by Donna J. W. Munro (https://www.donnajwmunro.com)Narrated by Erika Ventura (https://instagram.com/efventu)Produced by Georgia Triantafyllopoulou (https://www.instagram.com/audiogeekgr )With music by Epic Stock Media (https://epicstockmedia.com/)And Thom Robson (https://www.thomrobsonmusic.com/)And sound effects provided by https://getsoundly.com/The episode illustration was provided by Matt Seff Barnes (https://www.mattseffbarnes.com/)Joshua Boucher is our story programmer.Jasmine Arch manages our community.Mary Pastrano helps orchestrate the chaos.And the show would collapse into static and screams without the ongoing work of Karl Hughes, Georgia Triantafyllopoulou, and James Barnett — AKA Jimmy Horrors.Donna J. W. Munro's pieces are published in Nothing's Sacred Magazine IV and V, Corvid Queen, Hazard Yet Forward (2012), Enter the Apocalypse (2017), Beautiful Lies, Painful Truths II (2018), Terror Politico (2019), It Calls from the Forest (2020), Gray Sisters Vol 1 (2020), Pseudopod 752 (2021), Shakespeare Unleashed (2023) and others. Check out her novel, Revelation: Poppet Cycle Book 1. Contact her at https://www.donnajwmunro.com or @DonnaJWMunro on Twitter.Erika Ventura is an artist, mother, bilingual narrator, and a painting instructor. How does she manage it all? No idea, but her artwork can be seen on Instagram (@efventu) or you can visit her artist page www.facebook.com/BioArtsy Hosted on Acast. See acast.com/privacy for more information.

We're turning our spare, scattered storage into one giant network drive. Good idea? Maybe.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD

Witamy w 387 odcinku Shufflecast! Chwilę nas nie było, a w tym czasie wydarzyło się dużo w technologii i nie tylko. Dlatego ten odcinek napakowaliśmy szybkimi newsami, ale też większymi blokami. Szczególnie dużo czasu poświęciliśmy najnowszym premierom Apple. Tutaj na pierwszy plan wychodzi wyczekiwany iPhone DUO. Piękna nazwa, Microsoft pozdrawia ;). Zapraszamy na ponad 1,5h naszego gadania. 01:07 - Wstepniak: Unshatter od Linkin Park w kinach 07:29 - (nie)polecajki: Ród Smoka S3, Czarny Ptak, 1670 S3, Silos S3 21:34 - Giereczkowo: znamy datę premiery TF3, nadchodzą Heroes 3 Remake, "Redzi" w formie na Gamescom, The Blood of Dawnwalker, Bus Simulator 27, GTA 6 - zwiastun na Netflix i chore ceny Playstation. 54:11 - Premierowo: Pixele 11/11 Pro, Watch 5, Tag; Garmin Fenix 9, S27 to będzie żart 01:03:20 - Konferencja Apple: iPhone DUO oficjalnie, iPhone 18 Pro i 18 Pro Max, Apple Watch Series 12, Airpods 5 Zachęcamy do zostawienia subskrypcji na YouTube, obserwowania podcastu na Twitterze i Facebooku, a także naszych prywatnych profili: Sławek & Damian.

Guest Bio: PNL (Paranormal NL) Podcast -S4/E37-UPRN Segment #97“Cosmic Metaphysical Energy”. Pre-recorded event with a YouTube Live-Chat Watch-Party on UPRN. Host Jen Noseworthy from Newfoundland & Labrador (NL), Canada talks with PNL Podcast Alumni-Network Member & Guest: Michael Kopf from Dallas, Texas, USA. Michael Kopf was previously on PNL S2, S3, and S4. Michael is a Metaphysical author (Awakening series of m multiple books), Michael is a Reki Master, experiencer, truth seeker, reverend, and multiple Podcast Host with his The Energy That Surrounds Us (TETSU), Riding Through the Unknown and Paranormal Vs & many more. Michael has been a metaphysical paranormal investigator with multiple teams Michael grew up seeing shadow beings, experiencing UAPs and international Paranormal experiences, channeling Spirit, opening & closing portals, and astral & time travelling. He's had several near-death experiences. Michael studied Huna, Timeline Therapy, Tarot Cards, and Gypsy Palm Reading. Michael delves into the fringe paranormal topics, esotericism and disclosure topics in his shows. Follow Michael Kopf on IMDb and https://linktr.ee/michael_koph Shout out to UPRN Producer , Manager Michelle Desrochers. Michelle is also host of The Outer Realm Radio & Beyond the Outer Realm on UPRN www.linktr.ee/michelledesrochers_ If you like my PNL Podcast, and find yourself coming back to the show, Please smash that SUBSCRIBE button for free. Gratitude. It all helps. Follow PNL Podcast & the BOG team on my Linktree. ⁠⁠⁠⁠ https://linktr.ee/paranormalnlpodcast United Public Radio & UFO Paranormal Radio www.uprntalkradio.com

S3.3 My Writing PartnerReading someone's mind can have horrific consequences, even when you don't do it.Written by Sol Roberts-Lieb (http://sol.roberts-lieb.com/)Narrated and Produced by James Barnett (https://www.JamesBarnettCreative.com)With music by Myuu (https://www.thedarkpiano.com/)And Thom Robson (https://www.thomrobsonmusic.com/)And sound effects provided by Freesound.orgThe episode illustration was provided by Matt Seff Barnes (https://www.mattseffbarnes.com/)Joshua Boucher is our story programmer.Jasmine Arch manages our community.Mary Pastrano helps orchestrate the chaos.And the show would collapse into static and screams without the ongoing work of Karl Hughes, Georgia Triantafyllopoulou, and James Barnett — AKA Jimmy Horrors.Sol Roberts-Lieb is a science fiction and speculative fiction author who enjoys exploring big ideas through grounded, deeply human stories. Outside of writing, he works in faculty development in higher education, helping educators improve teaching and learning. He lives in the United States with his wife, their two daughters, and an ever-curious corgi named Spock. Sol is currently writing his first science fiction novel while developing several other speculative fiction projects. Learn more at sol.roberts-lieb.com.James Barnett is the producer of the Night's End podcast and After The Gloaming. Search for them wherever you get your podcasts. You can also catch other works of his at www.JamesBarnettCreative.com Hosted on Acast. See acast.com/privacy for more information.

The Fork In Your Ear Ep#221 "Author, Podcaster, Game Designer" - Podcast Show Notes & Summary 9-12-26   Quick Summary  Tim walks in barely held together and introduces himself as author, podcaster, and game designer — because he just dropped a playable HTML prototype of his Color Clash-inspired dual-grid RTS into the Fork Discord. The ring actually lands on Life first: Nate's kids demand VR mid-show, California is an air fryer, lane-splitters want six inches of courtesy, and both households are eating unexpected bills. Then the game firehose opens — Sony tells a courtroom that reasonable consumers know they don't own digital games, Xbox Cloud gets a 15-hour Ultimate cap, Nintendo dumps a Zelda 40th and a second Direct in two days, Ocarina of Time remake looks like a stop-motion maquette, Metroid Ravenous is metal as hell, and Kojima's canceled Sony IP is now an Xbox game. Entertainment is Coyote vs. Acme beating Warner Bros. at its own gag, Chad Powers season two sneaking back from the dead, Spider-Noir winning Emmys then getting canceled, and Nick Offerman as Big the Cat. Tech is Mac Studio M5 Max sticker shock, Apple's September event, the iPhone Duo foldable, and John Ternus's first week as CEO. Classic four-and-a-half-hour Fork: family chaos, Sony fumbles, Nintendo cooking, and Tim finally putting a game on the table.   Detailed Show Notes  

Grab your emotional support dragon and buckle up fans because we're ready to dish and spill the tea on S3 of "House of the Dragon". Thanks for listening and don't forget to hit subscribe, leave a 5-star rating and write a review. You can find us on Apple Podcasts, Spotify, Amazon Music, YouTube, or wherever you get your podcasts. *Looking for your next fandom adventure? Whether it's a concert, comic con, Broadway show, sporting event, or the fan experience you've been counting down to for months, we've got you covered. Check out our affiliate partner, Vivid Seats, and grab your tickets through the link in our show notes. Every purchase helps support the podcast at no extra cost to you, and helps us keep bringing you more fun, fandom, friendships, and fangirling. https://mave.ly/fandom-finds/post/vivid-seats *Love books, collectibles, and binge-worthy adaptations? If you're between 18 and 24 years old, Amazon's Prime for Young Adults might be one of the best deals out there. Enjoy a 6-month free trial, discounted membership pricing afterward, fast shipping for your latest book haul, access to Prime Video adaptations, and exclusive deals on merch and collectibles. Whether you're building your TBR pile, hunting for your next fandom obsession, or preparing for your next book release, it's worth checking out. Claim your free trial here: https://amzn.to/3SXMH7s Affiliate Disclosure: As an Amazon Associate, The Fan Girls Podcast may earn a commission from qualifying purchases or eligible sign-ups made through this link at no additional cost to you. Every trial signup helps support the podcast and allows us to continue creating content centered around fun, fandom, friendships, and fangirling. Thank you for supporting the show! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

S3.2 Like SilverGrappling with the loss of his significant other, a haunted man struggles to allow her spirit -- and his own -- to rest.Written by Derek Alan JonesNarrated by Justin Fife (https://www.threads.net/@justin.fife)Produced by Georgia Triantafyllopoulou (https://www.instagram.com/audiogeekgr )With music by Epic Stock Media (https://epicstockmedia.com/)And Grzegorz Rusin (https://freemusicarchive.org/)And Thom Robson (https://www.thomrobsonmusic.com/)And sound effects provided by https://getsoundly.com/The episode illustration was provided by Matt Seff Barnes (https://www.mattseffbarnes.com/)Joshua Boucher is our story programmer.Jasmine Arch manages our community.Mary Pastrano helps orchestrate the chaos.And the show would collapse into static and screams without the ongoing work of Karl Hughes, Georgia Triantafyllopoulou, and James Barnett — AKA Jimmy Horrors.Justin Fife is an audiobook narrator and voice actor, and he can be found on Threads @Justin.fifeJoin TOS+ to access over 90 exclusive episodes, get regular stories in higher quality audio, a week early, and ad-free, at https://theotherstories.net/plus/Support the show, get audiobooks, and more at https://www.patreon.com/hawkandcleaverJoin our communities for book clubs, movie clubs, writing exercises, and more at https://theotherstories.net/community/Leave a voicemail or get in touch at https://theotherstories.net/submissionsCheck out our writing courses at https://theotherstories.net/courses/Grab some merch at https://gumroad.com/hawkandcleaverThe Other Stories is a production of the story studio, Hawk & Cleaver, and is brought to you with a Creative Commons – Attribution-NonCommercial-NoDerivatives license. Don't change it. Don't sell it. But by all means… share the hell out of it. Hosted on Acast. See acast.com/privacy for more information.

PHP Podcast – September 2, 2026 Hosts: Eric Van Johnson & Joe Ferguson John’s out, Joe jumps in at the last second, a brand-new intro gets debuted mid-flight, a new PHP Foundation podcast gets announced, Frank’s database issues (not production), and somebody’s selling 37 elePHPants to fund a startup and open-heart surgery. New Intro, New Scenes, and a Last-Second Co-Host Eric kicked off the episode still tinkering with a brand-new intro and scene setup live on air, complete with a fresh montage that (thankfully) still includes Duck Nizzle. With John unavailable, Joe Ferguson jumped on at the last second to keep the show moving, and the two spent the opening minutes wrestling with scene sizing while Joe appeared comically small on screen. The pair caught up on client work: Joe pulled off an Elastic Beanstalk environment flip that nobody noticed (which is exactly how you want those to go), while Frank got thrown right under the bus for deleting the wrong database. Fortunately, it wasn’t production, and the conversation turned into an appreciation session for point-in-time recovery on AWS RDS and DigitalOcean’s managed databases, both of which have saved plenty of bacon during cowboy-hat coding sessions. The PHP Foundation Community Podcast Joe announced a brand-new podcast launching on the same PHP Architect channel, hosted by Elizabeth Barron and focused entirely on contributing to PHP. The first guest is Matt Stauffer, who recently published a fantastic post on the Foundation site about contributing to PHP, with recording set for September 10th. The plan is one Foundation Community Hour per month, plus the Joe, Holly, and Sarah show on weeks when Eric and John aren’t around. Eric and Joe used the moment to clear up a common misconception: people often conflate PHP internals with the Foundation, but they are not the same thing. The Foundation pays people to work on internals rather than steering the language itself. Its whole reason for existing is to get structure and funding around an old open-source project that billion-dollar companies have built empires on while contributing little back downstream. They also touched on recent Foundation board turnover: Roman’s term ended, and he’s rotating off, with Brent Roose joining as JetBrains’ representative — a great fit given Brent’s reach and the volume of PHP content he pushes out. Unlocking the PHP Architect Magazine Back Catalog The hosts made a pitch for the magazine, noting that an active subscription unlocks the entire catalog going all the way back to the very first issue — December 2002, which covered PHP 4.3. Eric got nostalgic about first discovering the print magazine right when PHP 4 came out, when knowledge was scarce, YouTube didn’t exist, and you had to buy O’Reilly books and beg your manager’s education budget for a subscription. Big news for existing accounts: John has opened up everything published before 2022 to anyone with an account on the system, even without an active subscription. Log in, head to magazines, and check what you now have access to. The team also reminded listeners that the magazine pays every contributor, that they’ve built a solid roster of regular columnists, and that PHP Architect is a consulting group available to augment teams, evaluate systems, and more. Where Developers Fit in an AI World Eric put Joe on the spot with a big question: What does a developer’s role look like going forward, given where AI is? Joe leaned positive on the tooling — Claude is excellent at parsing logs, spotting anomalies, and navigating large legacy codebases he isn’t in every day. He described real workflows like running AWS bill reviews for clients and comparing “what does your Claude say” with Frank to catch ridiculous bugs, all while distilling the output down as the human in the loop. But Joe was candid about his concerns too: AI feels force-fed in a way CSS or JS frameworks never did, with a real fear of being replaced by “a bunch of Claude agents in a trench coat.” He raised ethical and environmental concerns, pointing to accusations about his own city’s AI data center and the ripple effects on hardware — RAM prices spiking 400–500%, with Apple reportedly paying far more for iPhone RAM, a cost that always flows back to consumers. Eric compared the AI moment to crypto: a genuinely good technology released to the masses too soon, leading to misuse and eventual regulation. He shared a concrete win, though — using Claude to single-handedly bring a Laravel project shelved since COVID up to date, then using it to generate effort estimates (roughly three years of work) that helped set realistic expectations with a client who didn’t want to hear it. The pair debated whether those estimates accounted for AI-assisted velocity and stressed the importance of guardrails — especially for juniors, since even skilled devs leave S3 buckets open and AI is dangerously confident in its word guessing. TypePHP: Interesting, But Not for Everyone Eric dug into TypePHP, the ahead-of-time compiler from the Swoole team that translates PHP source into C++ and then native machine code. On the surface, he likes the idea — it’s something he wanted decades ago — but he questioned its relevance today: the compiler’s habit of catching bad code before it runs matters less now that PHPStan, linting, and a strong testing culture already fill that gap. Joe pointed out the bigger practical problem: there’s far more talk about TypePHP than actual code being written in it, and it’s clearly geared toward long-running CLI processes rather than web requests. That excludes the kind of PHP most developers actually write, and as Nuno noted in the chat, it isn’t going anywhere until it works with Laravel and the rest of the ecosystem. The hosts agreed the real value would be extracting any gains and folding them back into native PHP, à la HHVM/Hack, and noted transpiling already solves the “I want a different syntax” itch. Joe also pushed back on the “runs everywhere” selling point, arguing that Docker and Podman containers already make PHP CLI tools trivially portable — better than PHARs ever were. Eric landed on a mature take: he wants PHP everywhere and is happy to see it evolve, but he’s come to terms with the fact that he won’t personally use every direction it grows, TypePHP included. ElePHPants, Laracon Archives, and Free Talks A Reddit post caught the team’s attention: someone is selling their 37+ elephpant collection to fund a startup and help offset the cost of upcoming open-heart surgery. Joe noted the PHP tech elephpants are wildly undervalued compared to others in the listing — you can grab an OG PHP Architect orange Archie for under a hundred bucks. Eric showed off his own big Elvis (a gift from Sarah) and eyed the coveted Star Trek elephpant that John once nabbed in a trade at Longhorn. Eric also highlighted the new Laracon Archive project, which collects Laracon conference talks across regions (US, EU, Online, India) with a clean year-by-year interface — great work for anyone who tries to manage conference content. He closed by plugging phptech.tv, which hosts PHP Architect conference talks, including a batch of free talks and even a full free conference this year, with the same back-catalog-unlock model as the magazine. Links from the show: Longhorn PHP 2026 — October 15–16 PHP Foundation — Contributing to PHP blog post PHP Architect Magazine — Subscribe & unlock the back catalog PHPTech.tv — Conference talks, including free ones Host: Eric Van Johnson X: @shocm Mastodon: @eric@phparch.social Bluesky: @ericvanjohnson.bsky.social PHPArch.me: @eric Joe Ferguson Mastodon: @joepferguson@phpc.social PHPArch.me: @svpernova09 Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ OurCVEs Your security posture, on autopilot with OurCVEs CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. PHP Architect Consulting Your PHP codebase deserves a partner, not a contractor PHP Architect provides long-term technical partnerships for organizations that need senior-level PHP expertise that you can depend on https://www.phparch.com/consulting/ Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.09.02 appeared first on PHP Architect.

Wykład dr hab. Macieja Krajcarza w ramach Dnia Paleontologa na Wydziale Geologii Uniwersytetu Warszawskiego [25 kwietnia 2026 r.]Co można wyczytać z izotopów zapisanych w zębach i kościach wymarłych zwierząt? Czy da się na ich podstawie odtworzyć klimat, dietę, środowisko życia, a nawet określić, na jakie ofiary polowały plejstoceńskie drapieżniki?Dr hab. Maciej Krajcarz pokazuje paleontologię od strony laboratoryjnej - jako dziedzinę łączącą geologię, chemię, biologię i analizę ilościową. Bohaterami wykładu są stabilne izotopy tlenu, węgla i azotu, których proporcje zachowane w kopalnych tkankach kręgowców mogą być źródłem niezwykle szczegółowych informacji o środowisku sprzed dziesiątek i setek tysięcy lat.Na początku dowiadujemy się, czym właściwie są izotopy i dlaczego w paleoekologii szczególnie przydatne są izotopy stabilne. Ich skład może zachować się w szkliwie zębów czy kolagenie kostnym i tworzyć swoisty chemiczny zapis życia zwierzęcia.Izotopy tlenu pozwalają rekonstruować między innymi warunki klimatyczne. Przykładem są badania dwóch grup plejstoceńskich nosorożców występujących na terenie Polski: Stephanorhinus, kojarzonych z cieplejszymi i bardziej leśnymi środowiskami, oraz włochatych nosorożców Coelodonta, przystosowanych do znacznie chłodniejszych warunków.Impulsem do badań był niemal kompletny szkielet samicy nosorożca Stephanorhinus, odkryty podczas budowy drogi ekspresowej S3 w okolicach Gorzowa Wielkopolskiego i nazwany „Stefanią”. Analiza szkliwa jej zębów oraz innych okazów wykazała wyraźne różnice klimatyczne między obiema grupami nosorożców. Na podstawie składu izotopowego można oszacować, że średnie temperatury środowiska ich życia różniły się nawet o około 4–5°C.Z kolei izotopy węgla pomagają odpowiedzieć na pytanie, co zwierzęta jadły i w jakim środowisku zdobywały pokarm. Badania pokazują, że nosorożce Stephanorhinus korzystały przede wszystkim ze środowisk leśnych, podczas gdy nosorożce włochate były znacznie silniej związane z otwartymi stepami i tundrostepami.Jeszcze więcej informacji można uzyskać, pobierając kolejne próbki wzdłuż rosnącego zęba. Ponieważ szkliwo tworzy się stopniowo, w jednym zębie może zostać zapisana historia zmian zachodzących w ciągu miesięcy, a nawet kilku lat życia zwierzęcia. W ten sposób możliwe jest badanie sezonowości klimatu i zmian diety pomiędzy poszczególnymi porami roku.Druga część wykładu poświęcona jest izotopom węgla i azotu w kolagenie kostnym. Pozwalają one rekonstruować sieci pokarmowe i określać miejsce danego gatunku w łańcuchu troficznym. Aby dobrze interpretować dane kopalne, badacze analizują również współczesne ekosystemy – na przykład dietę lisów i ich ofiar.Ta wiedza pozwala zajrzeć do jadłospisu plejstoceńskich drapieżników. Badania szczątków z Jaskini Perspektywicznej na Wyżynie Krakowsko-Częstochowskiej pokazują, że hieny jaskiniowe wcale nie były wyłącznie oportunistami żywiącymi się wszystkim, co udało im się znaleźć. Analizy izotopowe wskazują, że specjalizowały się przede wszystkim w dużych roślinożercach - zwłaszcza nosorożcach i mamutach.Inaczej wyglądała dieta lwów jaskiniowych. Mimo że były większymi drapieżnikami, ich podstawową zdobyczą okazują się renifery. Oznacza to, że lwy i hieny mogły funkcjonować w tym samym ekosystemie bez silnej konkurencji o pokarm, ponieważ polowały na inne grupy zwierząt.#Paleontologia #Paleoekologia #Izotopy #Czwartorzęd #Plejstocen #Megafauna #NosorożecWłochaty #Mamut #HienaJaskiniowa #LewJaskiniowy #NiedźwiedźJaskiniowy #Geologia #Biogeochemia #MaciejKrajcarz #UniwersytetWarszawski #DzieńPaleontologa #WszechnicaZnajdź nas: https://www.youtube.com/c/WszechnicaFWW/https://www.facebook.com/WszechnicaFWW1/https://anchor.fm/wszechnicaorgpl---historiahttps://anchor.fm/wszechnica-fww-naukahttps://wszechnica.org.pl/

Parce que… c'est l'épisode 0x33C! Shameless plug 19 septembre 2026 - Montréal Canada - Bsides Montréal 2026 22 septembre 2026 - Belgique - BE-Cyber 24 au 25 septembre 2026 - Belgique - BruCON 1er au 3 octobre 2026 - Krakow, Pologne - AligatorCon 13 et 14 novembre 2026 - Worldwide - DEATHCon 16 au 19 novembre - Rennes, France - European Cyber Week 2026 1 au 3 décembre 2026 - Ottawa, Canada - Forum inCYBER Canada 25 et 26 février 2027 - Québec, Canada - SéQCure 2027 I.A. Café - Enquête au cœur de la recherche sur l'intelligence artificielle Description Cette description a été faite à l'aide de GLM 5.3 Flash, à partir de la transcription qui a été effectuée par ElevenLabs Scribev2. Aucune vérification humaine n'a été faite. Si vous découvrez des erreurs ou des inexactitudes, vous pouvez m'en faire part afin que je procède aux modifications appropriées. Présentation de Caido Cet épisode technique de Polysécure accueille Émile Fugulin et Christopher Guay, cofondateurs de Caido, un projet développé depuis cinq ans. Tous deux proviennent de l'ingénierie, Christopher dirigeant la technique et Émile venant de la sécurité web. Leur produit se définit comme un proxy offensif, une boîte à outils pour les audits web et d'API, destiné aux chasseurs de primes, aux testeurs d'intrusion et aux équipes red team. Le fonctionnement du proxy Le proxy s'insère entre le navigateur et l'API, qui lui envoie toutes ses requêtes. La première étape consiste à faire accepter au navigateur un certificat généré par l'outil afin de terminer la connexion HTTPS localement. Le navigateur envoie ensuite une requête Connect pour établir un tunnel vers le serveur distant. Un proxy ordinaire laisse passer des octets chiffrés sans les lire, mais Caido se substitue au serveur distant et termine lui-même le tunnel pour pouvoir lire et modifier le trafic. TLS et détection de robots L'interception ne pose pas de difficulté puisque le certificat est installé sur la machine de l'utilisateur, peu importe la version de TLS. Le défi se situe du côté sortant, où la détection de robots analyse la structure du paquet TLS, des extensions comme Grease signalant un vrai navigateur, leur absence trahissant un automate. Les CDN comme Cloudflare ou Akamai croisent les empreintes TLS avec les en-têtes HTTP et bloquent facilement selon la réputation des adresses IP. Des plugins répartissent le trafic sur plusieurs IP, mais Amazon a restreint l'usage de son API Gateway après des abus, et les proxys résidentiels demeurent une zone grise sans support officiel. Une stack HTTP maison Le modèle de base demeure la paire requête-réponse, majoritairement en HTTP/1.1, avec un support récent de HTTP/2. La spécification HTTP/1, ancienne et vague, ne sépare en-têtes et corps que par des caractères de contrôle. Cette ambiguïté a donné naissance aux attaques de désynchronisation, où un proxy et un backend interprètent différemment les frontières des requêtes, notamment entre le content-length et le transfer-encoding, permettant d'injecter des requêtes dans une connexion réutilisée pour plusieurs clients. Les stacks standard, rigoureuses surtout en Rust, ne conviennent pas pour injecter des données non conformes, d'où la réécriture complète de la stack maison. Gérer les réponses non conformes exige aussi de la souplesse, un serveur pouvant annoncer dix octets et en envoyer quinze, ces derniers contenant parfois des informations précieuses. Les attaques synchronisées L'attaque last byte sync en HTTP/1 consiste à charger plusieurs requêtes dans des buffers, à ouvrir plusieurs connexions TCP, puis à envoyer le dernier octet de chacune simultanément. Si le verrou applicatif est mal implémenté, un code de rabais peut s'appliquer plusieurs fois. En HTTP/2, le single packet attack exploite le multiplexage des flux sur une même connexion TCP en regroupant les derniers octets de toutes les requêtes dans un unique paquet, ce qui demande de désactiver l'algorithme de Nagle et de composer avec des stacks TCP différentes selon macOS, Windows et Linux. Le streaming et la rupture du modèle requête-réponse HTTP/1 a d'abord introduit le transfer-encoding chunked, puis le web dynamique a amené les WebSockets, qui basculent vers un protocole distinct via un code 101, et les Server-Sent Events, popularisés par l'intelligence artificielle, qui maintiennent une connexion persistante livrant des données de façon irrégulière. Ces protocoles brisent le modèle historique des proxys de sécurité, fondé sur des tables de requêtes et de réponses. Caido gère désormais une deuxième table dédiée aux flux, qu'il espère éventuellement unifier, et le protocole gRPC, fondé sur le streaming, accentue cette rupture. Les connexions persistantes compliquent aussi l'injection, notamment l'automatisation des danses d'authentification. Modification du trafic et workflows Le système de règles match and replace permet de modifier des en-têtes, des parties du corps, ou d'appliquer une règle à chaque message d'un flux. Les usages courants incluent le passage d'un champ admin de false à true, l'activation de fonctionnalités ou l'injection d'un en-tête secret contournant un WAF lors d'un test autorisé. Modifier les réponses sert aussi à vérifier si une injection HTML produirait un XSS sans devoir d'abord contourner le WAF. Le workflow applique des transformations, notamment du JavaScript, sur une portion surlignée d'une requête, par exemple pour recalculer une signature S3, grâce à une machine virtuelle QuickJS intégrée au code Rust et à des modules du runtime LLRT d'Amazon. L'éditeur et les encodages L'outil Replay propose une vue formatée et une vue brute, dont les modifications doivent être réconciliées avec la version originale envoyée au serveur. Un arbre syntaxique abstrait, la gestion du undo-redo et le traitement des caractères échappés ajoutent à la complexité. Les en-têtes HTTP sont encodés en Latin 1 et le corps en UTF-8, alors que l'interface JavaScript fonctionne en UTF-16. La spécification WTF-8 sert à représenter sans perte des octets UTF-8 invalides, notamment dans l'éditeur hexadécimal. Un langage de filtrage nommé HTTPQL cible aussi les requêtes. Des recherches présentées au Defcon par Akamai ont montré comment des caractères Unicode ressemblants, convertis par des systèmes comme IIS, deviennent des vecteurs d'attaque, le best effort demeurant une source de contournements. URL et attaques SNI L'URL n'existe qu'au niveau du navigateur et le fragment après le dièse n'est jamais transmis au proxy. Les manipulations applicatives restent possibles dans les URL, comme l'injection pour un XSS ou du phishing, mais l'attaque protocolaire la plus intéressante concerne l'extension SNI de TLS, qui indique le nom du serveur visé dans le client hello. En envoyant un SNI différent de l'en-tête host, une requête peut être routée par un CDN vers un serveur normalement inaccessible, le contrôle fondé sur le host semblant alors satisfait. Notes Caido Contributeurs .contributors-table { border-collapse: separate; border-spacing: 20px 8px; } .contributors-table td, .contributors-table th { border: none; background: transparent; text-align: center; vertical-align: top; } .contributors-table img.contributor-photo { width: 100px; height: 100px; object-fit: cover; border-radius: 50%; } .contributors-table .contributor-name { display: block; margin-bottom: 4px; font-weight: bold; } .contributors-table .contributor-socials img { margin: 0 2px; } Nicolas-Loïc Fortin Emile Fugulin Christopher Guay Crédits Montage par Intrasecure inc Locaux virtuels par Riverside.fm

S3.1 The Collecting*The young ones would appear strange on most days, but tonight they draw no notice. Tonight is the night of the collecting, and it is the one night of the year when the young ones move about in peace.*Written by Cassandra Daucus (https://linktr.ee/residualdreaming)Narrated by Georgia Cook (https://www.georgiacookwriter.com/)Produced by Georgia Triantafyllopoulou (https://www.instagram.com/audiogeekgr )With music by Guided meditation (https://freemusicarchive.org/)And Kirk Osamayo (http://www.kirkosamayo.com/)And Thom Robson (https://www.thomrobsonmusic.com/)And sound effects provided by Soundly And sound effects provided by Freesound.orgThe episode illustration was provided by Matt Seff Barnes (https://www.mattseffbarnes.com/) Joshua Boucher is our story programmer.Jasmine Arch manages our community. Mary Pastrano helps orchestrate the chaos. And the show would collapse into static and screams without the ongoing work of Karl Hughes, Georgia Triantafyllopoulou, and James Barnett — AKA Jimmy Horrors.**Inspired by H. P. Lovecraft, M. R. James, Shirley Jackson, Robert Aickman, and a ton of fan fiction, Cassandra Daucus (she/her) writes soft horror and dark romance. She is intrigued by how the human mind responds to the unknown, and also enjoys a good gross-out. Her story "Teething" is published in Ooze: Little Bursts of Body Horror, edited by Ruth Anna Evans, and she has also published drabbles in Trembling With Fear and Hungry Shadow Press's Deadly Drabble Tuesday. Cassandra lives outside of Philadelphia with her family and three cats. She tweets at @residualdreamin and her website is https://residualdreaming.com/****Georgia Cook is an illustrator and writer from London. She has written for publications such as Baffling, Vastarien Lit, and Flame Tree press, as well as the Doctor Who range with Big Finish. She can be found on twitter at @georgiacooked and on her website at https://www.georgiacookwriter.com/**Join TOS+ to access over 90 exclusive episodes, get regular stories in higher quality audio, a week early, and ad-free, at https://theotherstories.net/plus/Support the show, get audiobooks, and more at https://www.patreon.com/hawkandcleaverJoin our communities for book clubs, movie clubs, writing exercises, and more at https://theotherstories.net/community/Leave a voicemail or get in touch at https://theotherstories.net/submissionsCheck out our writing courses at https://theotherstories.net/courses/Grab some merch at https://gumroad.com/hawkandcleaver**The Other Stories is a production of the story studio, Hawk & Cleaver, and is brought to you with a Creative Commons – Attribution-NonCommercial-NoDerivatives license. Don't change it. Don't sell it. But by all means… share the hell out of it.** Hosted on Acast. See acast.com/privacy for more information.

PNL UPRN Seg#94 "Mystical Metaphysical" Guest Miya Starfire and CoHost Rob Shelsky Tue Aug 25th, 2026 at 5pm EST Guest Bios: PNL Podcast (Paranormal NL) located in Newfoundland and Labrador (NL), Canada with Host Jen Noseworthy PNLS4/E34-UPRN Segment #94. “Mystical Metaphysical” special Pre-recorded event with a YouTube Live-Chat Watch-Party on UPRN. PNL Host Jen Noseworthy talks with Guest: Miya Starfire (from California). PNL Podcast Alumni Network Member joins as a Co-Host: MUFON investigator & author Rob Shelsky (from North Carolina). Guest Miya Starfire is an intuitive healer, empath, and channel with over 27 years of experience as a licensed massage therapist and energy practitioner. Her work bridges the physical and the unseen, guiding others through awakening, healing, and deeper self-discovery. As a Light Warrior, Starseed, and experiencer, Miya walks a multidimensional path—one shaped by profound spiritual insight, shadow integration, and connection to higher realms. Her journey reflects the truth that awakening is not a destination, but an ever-unfolding expansion of consciousness. Through her Podcast Echoes Beyond the Veil, Miya explores the mysteries of existence, offering guidance, healing, and perspective for those called to remember who they are. Miya believes that once the shadow is embraced, only love remains; and that this moment in time is a powerful opportunity for transformation. https://www.youtube.com/channel/UClWILsrxr2Rni5BiIiR14tQ/streams Co-Host Guest: Rob Shelsky was previously on PNL Podcast Season 3 and 4. Check them all out. Shelsky is an author & MUFON field investigator, has written over 80 Paranormal books, and was invited to speak at the Library of Congress, Washington, D.C. Rob has a degree in science, has written a large number of magazine articles, and has been interviewed on a number of shows such as George Noory's Coast-To-Coast AM Radio show. Shelsky was also the on-camera UFO expert for a Travel Channel TV show. Find his books on Amazon Kindle, Smashwords & more. Follow Rob Shelsky on http://robshelsky.blogspot.com/ Shout out to PNL Podcast Alumni Network Members from S3 & S4 Coley UFO with SNX Radio Big shout out to UPRN Producer, Manager Michelle Desrochers.Michelle is also host of The Outer Realm Radio & Beyond the Outer Realm on UPRN www.linktr.ee/michelledesrochers_ United Public Radio & UFO Paranormal Radio www.uprntalkradio.com

The BUTLER did WHAT now?! Krissy is getting an LGBTQIA+ education this week and we are here for it. Jacob joins Sam on the couch to celebrate The Cringe's 250th episode, to praise our newest conclave addition Rhys we share some tea from a special live screening as well as a Drag Race check in. Is Luke the new Dicko? Also.. Why can't Claudia resist a shield?! Enjoy this silly recap of the seventh and eith episodes of this camp as tits season. Send us a Voice MailSupport the showFollow The Cringe is Real on Instagram, TikTok, YouTube & Bluesky. 

Francis E. Marchlinski, MD, Deputy Editor of JACC: Clinical Electrophysiology, discusses the S3-STaMP study, which evaluated a novel mapping strategy for patients with hemodynamically unstable ventricular tachycardia (VT). The investigators found that STaMP mapping, guided by the S3 functional mapping protocol, rapidly identified critical VT circuit pathways in nearly three-quarters of patients and did so without causing acute hemodynamic deterioration. These findings suggest that S3-guided STaMP mapping may offer a safe and practical approach to improve catheter ablation of otherwise difficult-to-map VT.

Join Dave and Wayne for genre television show news, a glimpse into what the hosts are watching, listener feedback, and analysis of the Apple TV+ series Silo. This week on the SciFi TV Rewatch podcast we discuss the fracturing Sims' marriage and whether there's a chance Camille recognizes that Juliette's not a threat to the Silo. Sheriff Billings continues his search for Orla's killer, but now considers the possibility it's linked to the missing light bulbs.  In our What We're Watching segment, Dave returns to Shrinking and its S3 drop, and Wayne makes a last ditch effort to catch up on his shows before returning to work. Like Dave, he's lukewarm about House of the Dragon. In Listener Feedback, Alan in England and Alan in Missouri provide audio feedback, and Brandon in Illinois and Cincinnati Joe check in via email..  Remember to join the genre television and film discussion on the SciFi TV Rewatch Facebook group for the latest genre television show news and podcast releases. Episode Grade: Dave 8.3  Wayne 8.8

Send us Fan MailJuly Episode — What's New in Cloud FinOpsEpisode SummaryIn this July episode of What's New in Cloud FinOps, Frank and SteveO cover a packed set of cloud and AI cost-management updates across AWS, Azure, Google Cloud, Oracle, Alibaba, and OpenAI. The conversation focuses on how cloud vendors are changing pricing models, improving observability, and adding new ways to optimize compute, storage, and AI workloads.The standout theme is that AI and cloud economics are becoming more dynamic: pricing is shifting by usage pattern, time of day, workload type, and capacity model. The hosts also dig into how organizations can use these changes to improve governance, control costs, and make smarter architecture decisions.Top Topics CoveredAWS EKS and ECS GPU management fee reductionsAmazon CloudWatch intelligent tiering for logsAmazon S3 removing the 30-day minimum for storage class transitionsAWS Billing and Cost Management adding a cost efficiency widgetAWS Data Exports adding standardized Bedrock metadataAWS Lambda publishing logs for managed capacity providersAzure reservation exchange changes and legacy VM RI renewalsAlibaba Cloud time-of-day pricing for frontier AI modelsAlibaba model routing reference architectureOpenAI GPT model price cuts and Bedrock matching those ratesOracle bringing Gemini models into OCIGoogle Data Stream free tier for CDC writesAmazon OpenSearch Service optimized for log analyticsMicrosoft Marketplace single-click SaaS purchasesAWS sustainability dashboard adding water withdrawal dataKey TakeawaysAI pricing is getting more sophisticated.Providers are increasingly using variable pricing, off-peak discounts, and routing strategies to steer usage and improve margins.Cloud optimisation is moving deeper into the platform.Vendors are surfacing more native tools for cost visibility, efficiency scoring, and policy-driven automation.Storage and logging are becoming more cost-aware.New features in S3, CloudWatch, and OpenSearch help teams store more data for less while keeping observability usable.Governance and procurement remain critical.

Just scooting in before the finale, Nat & Ben manage a late recording to discuss the S3 penultimate episode "The Dragon in Winter". Aegon is back (and how!), Aemond is having weird Mummy dreams, and power is making Rhaenyra dumb and horny. Also, there's lots of love for a broken Gwayne and sensible Daeron, hot dragon fighting action and chicken fantasies. Enjoy!

In this episode of Shift AI, Shay Sandler, CEO and co-founder of Vega, joins host Boaz Ashkenazy for a wide-ranging conversation on why the legacy SIEM is breaking down just as security teams need their data the most.Shay grew up in Israel studying math and physics in a famously competitive class of gifted kids, then spent six years in one of the country's most elite military intelligence units before joining Granulate, a cloud cost optimization company, as one of its first ten employees. Granulate was acquired by Intel for $650 million, and after a year inside Intel, Shay left with his former Granulate colleague Ellie to found Vega — a decision he says was pushed forward by a month of reserve duty after October 7th that reminded him how much he missed solving hard problems with his old team.The two dig into why traditional SIEMs, built for a pre-cloud world of centralized data, are collapsing under multi-cloud, multi-region, siloed telemetry — and why teams that once filtered data down to save on SIEM costs now need all of it to keep up with threat actors wielding frontier models. Shay walks through Vega's architecture: querying data in place in commodity storage like S3, federating across regions and clouds without duplication or egress costs, and a deterministic, auditable natural-language-to-KQL layer that lets AI agents run threat hunts a human could fully verify.This episode is for CISOs, security operations leaders, detection engineers, and startup founders building in cybersecurity who want to understand how AI-native architecture, not just AI features, is reshaping enterprise security.Chapters[00:00] Introduction: Shay Sandler's path to founding and leading Vega[02:07] Growing up gifted in math and physics, and finding cybersecurity[04:18] Six years in an elite Israeli intelligence unit, and the "everything is solvable" mindset[06:08] First paid job at 14: distributing meat before dawn in southern Israel[08:37] Granulate, the $650 million Intel acquisition, and missing the startup thrill[10:43] October 7th reserve duty and the spark to build Vega with co-founder Ellie[15:04] How legacy SIEM broke under multi-cloud, siloed security data[17:32] The post-Mythos era: frontier models as tools for threat actors[23:16] Vega's three primitives: commodity storage, federation, and an AI-native interface[27:50] Natural language to KQL as a transparent, auditable layer for AI agents[30:05] AI as a "scalable engineering capability" for lean security teams[36:40] Velocity as the new core metric, and the closing thought: creativity and engineeringConnect with Shay SandlerLinkedIn: https://www.linkedin.com/in/shay-sandler-305508107/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: boaz@shiftai.fm

Send us Fan MailTitle: What's New in Cloud FinOps - June 2026Hosts: Frank Contrepois and SteveOSummaryIn this episode, Frank and SteveO navigate through the latest cloud offerings and AI advancements, revealing how these updates impact performance, costs, and operational strategies across Azure, AWS, and AI applications. Stay tuned for insights into new VM generations, cost management tools, and cutting-edge AI features.Key Topics:Azure's new Cobalt 200V and 100V VMs deliver up to 50% better CPU performanceIntroduction of AWS's Metal 48XL/96XL and enhanced EC2 instances with sixth-generation Intel XeonLatest AWS Graviton 5 processors offering up to 25% better compute performanceEnhanced Amazon EC2 G7 instances powered by Nvidia RTX Pro 4500AWS Cost Management updates, including automatic cost anomaly investigations and new billing toolsAWS's support for region-agnostic throughput reservations in AzureThe rise of AI and automation in cost optimization: new tools, models, and use casesCloud vendors' announcements on energy-efficient storage, reserved pricing, and billing analysis toolsTimestamps:00:00 - Cloud news roundup: performance boosts in Azure VM series00:20 - Azure's new Cobalt 200V VMs: performance and AI workload optimization01:32 - AWS launches Metal 48XL/96XL: CPU advancements and network enhancements02:50 - Introduction of AWS M9G/M9GD instances with AWS Graviton 5 processors04:36 - AWS's latest EC2 G7 instances with Nvidia RTX GPUs for AI and visual workloads05:43 - Cost efficiency improvements with new pricing models and snapshot billing09:28 - Redshift advances with manual snapshot cost reductions10:12 - AI models on Bedrock: GPT 5.5, Codex, and OpenAI integrations12:24 - Innovations in cloud billing: cost explorer, cost anomaly detection, and billing account tools13:00 - Cost & Usage Report 2.0 enhances S3, Athena, and Redshift integration14:23 - Google Cloud billing updates: report export improvements and new filtering options15:19 - AWS's right-sizing and resource optimization enhancements16:12 - Cost explorer AI integrations and automated cost investigations17:10 - New AI-powered tools for cost anomaly root-cause analysis18:16 - Multi-project billing views and resource management in AWS19:04 - Advanced export configurations to streamline billing data handling20:06 - Google Cloud's spot VM real-time availability features21:36 - Enhanced tagging, resource management, and API capabilities in AWS and Google Cloud24:11 - Azure's VM retirements, storage, and reservation updates26:15 - Redshift's new upfront pricing options for reserved instances27:35 - Global provisioned throughput reservations now regional in Azure for flexibility28:44 - Storage charges optimizations and vector query cost reductions on S330:10 - Using finops.frankcontrepois.com for AI-driven FinOps34:08 - The importance of separating AI from automation in cloud efficiency strategies36:17 - Resources like the Phoenix Project and The Goal for understanding process optimization and AI impact37:34 - Support for new resource types and idle recommendation expansion in AWS Compute Optimizer38:50 - Cost and performance insights into specific resource wastage39:16 - AWS's State of Cost Efficiency Report: benchmarking and industry insights40:52 - AWS FinOps agents preview: automated cost and anomaly management workflows42:23 - Programmatic savings plan management and AWS workload optimization43:47 - Cost attribution and telemetry for large language models (LLMs) on Bedrock44:10 - AWS WAF's new AI traffic monetization capabilities for API access control45:45 - Azure Cosmos DB's new cost estimator tool for pre-provisioning modeling46:52 - Top three news picks: upcoming cloud innovations and AI advances47:35 - The growing role of FinOps and AI operational tools in cloud cost managementResources:FinOps toolThe Phoenix ProjectThe Goal by Eliyahu M. GoldrattConnect with the Hosts:Frank - LinkedInSteveO - LinkedIn

Send us Fan MailEps.4 S3   Faith, Free Speech, and the Cost of Speaking TruthThe hosts draw powerful parallels between Kirk's assassination and historical political murders, noting how figures like Martin Luther King Jr. and Robert F. Kennedy met similar fates for speaking truths that challenged the status quo. They examine how mainstream media has selectively quoted Kirk to vilify him while ignoring the nuance of his positions, including his refusal to accept funding offers from political powers.Eps 5 S3 Brothers ReflectThe conversation weaves through personal reflections on loss, with powerful memories of their parents' passings and how those experiences transformed their emotional landscape. "My emotions are different after our parents passed away," one brother admits, highlighting how profound loss reshapes our perspectives on life. Their planned visit to their grandmother takes on special significance as they acknowledge it may be "unfortunately for the last time," while still hoping for more time together.Eps 6 S3 Spiritual Warfare in a Divided WorldThroughout our conversation runs an undercurrent of spiritual warfare—the sense that today's events reflect not just political or cultural struggles but a deeper conflict between good and evil.  Eps.7 S3 A Conservative TalkFrom there, we widen the lens and tackle the mess of the moment: a grim Texas headline, claims of police stand‑downs, and the way leadership and media shape what we fear and what we ignore. We argue about the government shutdown, funding fights, and how outrage becomes a script. Then we crash straight into culture wars with a spicy take on the Super Bowl halftime debate and the Bad Bunny backlash—who gets the biggest stage, who decides, and why the choice feels bigger than music.Eps.8 S3 Everything Is Bad NewsEd Gein's crimes shaped Psycho, The Texas Chainsaw Massacre, and Silence of the Lambs, and talk about what the series gets right: the psychology, the casting, and the uneasy line between truth and myth.Eps.35 S3Saturday Night FeverA bad day of Uber driving, tips, and not wasting gas  • Banking fraud scare, cancelling a card too fast, and getting stuck at Walmart checkout  • Claims of political corruption, investigations that go nowhere, and distrust in accountability Eps 37 S3 A Hot Summer Talk, Work and Media ScandalsJoe buys two lottery tickets and if he wins, he will buy a house for Thee Gooch, Sis, Remo and pay for being in the show and partial for the family, and Thee Gooch will built his own houseEps 38 S3  WIFI Technical Difficulties And Media Thee Gooch's having trouble login in the showJoe describing Thee Gooch audio dropping Thee Gooch is getting annoyed with his Wi-Fi Thee Gooch notifies Joe that he wont be on the podcast next weekJoe explains he was tired from work, he ate and drank 3 beers he fell asleep and threw up while he was sleeping and woke up choking and couldn't breathe and was coughing.Thee Gooch explains why are you eating while you go to sleep, there's people that die from that cause.Support our podcastTip us : http://paypal.me/theetalkerspodcasthttps://www.patreon.com/c/theetalkerspodTip us: https://buymeacoffee.com/theetalkers4uTip us : https://cash.app/$TheeTalkers

Welcome to this week's episode of the PEBCAK Podcast!  We've got three amazing stories this week so sit back, relax, and keep being awesome!  Be sure to stick around for our Dad Joke of the Week. (DJOW) Follow us on Instagram @pebcakpodcast   Please share this podcast with someone you know!  It helps us grow the podcast and we really appreciate it!   Simple 6 signup link https://simple6.co/r/CFUR98   Hugging Face allegedly had to fall back on China's GLM 5.2 to investigate the OpenAI hack after US frontier models refused to help with forensic analysis, unable to distinguish attacker from defender. - https://x.com/coinbureau/status/2079663328021057654?s=46 - https://x.com/t3chfalcon/status/2079998873183949221 Per the claim, safety guardrails on US frontier models blocked forensic assistance during the incident response because the models couldn't tell the investigating security team apart from the attacker, forcing Hugging Face to run GLM 5.2 on its own servers to complete the analysis — a notable reversal given the assumption that domestic models would be the trusted fallback in a crisis.   VW drivers running GrapheneOS say the automaker's app has locked them out entirely, deepening fears carmakers are forcing users back into Google's ecosystem.  VW has now confirmed to German outlet heise that it deliberately blocked GrapheneOS, LineageOS, and /e/OS users via Google's Play Integrity API — the same certification gatekeeper already used by Barclays, HSBC, Monzo, Netflix, and Disney+. - https://cybernews.com/privacy/volkswagen-grapheneos-app-issues/ - https://x.com/intcyberdigest/status/2079992915972018246?s=46 - https://x.com/orwellday/status/2079704437241610443?s=46 Roughly 500,000 GrapheneOS users are affected; VW's app still supports outdated Android versions but rejects the privacy-focused OS, and VW told one user GrapheneOS "is not an official Volkswagen offering." The lockout follows a recent VW API change that also cut off third-party smart-charging and home-automation tools, raising questions about EU Data Act compliance.   A 19-year-old alleged Scattered Spider member's globe-trotting VPN op-sec got shredded by a Windows telemetry ID he probably didn't know existed. - https://cybersecuritynews.com/windows-device-identifier-tracking/ Peter Stokes (dual US-Estonian, 19) allegedly ran an $8M extortion hit on a luxury retailer using voice-phishing, ngrok tunneling, and 77GB of S3 exfil — but the FBI cross-referenced his Microsoft Global Device Identifier (GDID) across Apple, Snapchat, Facebook, and even a Ubisoft login to place the same device on the same IPs in Tallinn, NYC, and Thailand, matching his travel records. Microsoft has since acknowledged using the GDID to track the user across three countries — meaning the VPN masked his network endpoint, but the GDID rendered that protection moot.   Dad Joke of the Week (DJOW)   Find the hosts on LinkedIn: Chris - https://www.linkedin.com/in/chlouie/ Glenn - https://www.linkedin.com/in/glennmedina/ Jason - https://www.linkedin.com/in/jason-seemann-12b7075/

On this episode of Story Archives, Mario and Zach review S3, E6 Titled 'Faceless Men', in which the guys admire Rhaenyra's brilliant decision making and her absolutely effective repeated convos w/ Alicent. We also get a feel for what Alys is really aiming to do with Aemond, see an incredibly heart-warming reunion between Ser Hugh and his wife who was absolutely delighted to see him. We join Ser Ulf for some brews at the pub and Daemon as he takes a stroll with his incredibly dutiful gold cloaks. Over in Tumbleton, Ormund continues his really great job of raising Daeron and welcomes his cousin with open arms, whose just as happy to see him. Oh, and Corlys is definitely not captured by the dude with the bowl cut who definitely didn't magically teleport across Westeros with the perfect intel to grab him. Did I mention Baela is in deep mourning over Jace and taking a solemn oath of fidelity and abstinence? Tune in for all of this and more on this episode of Story Archives.Keep up with all things Story Archives Official:Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠soapbox.house⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Email: ⁠⁠⁠contact@soapbox.house⁠⁠⁠Instagram:⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠@mariobustoInstagram: @ZacharyrnewtonMario's Production Company: 1992films.comJoin this channel to get access to perks:https://www.youtube.com/channel/UCThLu7wlygPt3VuHdSRHArQ/join

Podcast Hosts:Grant Broggi: Marine Veteran, Owner of The Strength Co. and Starting Strength Coach.Jeff Buege: Marine Veteran, Outdoorsman, Football Fan and LifterTres Gottlich: Marine Veteran, Texan, Fisherman, Crazy College Football Fan and LifterJoin the Slack and Use code OKAY:⁠https://buy.stripe.com/dR6dT4aDcfuBdyw5ks⁠Check out BW Tax: ⁠https://www.bwtaxllc.com⁠BUY A FOOTBALL HELMET:⁠⁠⁠⁠https://www.thestrength.co/mrhelmet/?utm_source=The+Okay+Podcast&utm_medium=Podcast&utm_campaign=Okay_Pod⁠Today on the show, the staff brief runs long: S1 explains the pay problem (the internet's been down, mostly), the Strait of Hormuz is functionally closed, the S3 wants you to be the trash, and all record jackets are now purple and yellow by order of the Cajun Sergeant Major. Dwight from Housing has a card for your wife.Then: Jeff hands over November Battery after two years of command, Grant drives a '97 Chevy from Raleigh to Rhode Island (via the Chesapeake Bay Bridge, unintentionally), the charcoal-vs-gas debate is settled forever, SEC Media Days kicks off, and Grant explains why 12 months of building your base of strength is the best investment you'll ever make.Timestamps:00:00 - Intro08:33 - Staff Brief34:24 - New Jack White Album37:18 - Moving Into A Furnished Place42:26 - Gas Grill Debate47:13 - SEC Media Day55:00 - Congrats Jeff! Handing The Stick Off01:04:40 - The Importance of Building A Base of Strength01:12:15 - New Mexico01:14:28 - Cajun Appreciation01:16:28 - Sox Game01:18:37 - Surviving The Chesapeake Bay Bridge01:20:10 - Truck Talk01:30:16 - Shoutout Massenomics For LHLE!01:33:09 - Slides vs Charts01:35:10 - Landing The Plane

PNL UPRN Seg#89 Our Paranormal World Guest Monica Rawlins and Co-Host Guest author Rob Shelsky - July 21st, 2026 at 5pm EST Guest Bio: PNL Podcast (Paranormal NL) located in Newfoundland and Labrador (NL), Canada with Host Jen Noseworthy -S4/E29-UPRN Segment #89. “Our Paranormal World” special Pre-recorded event with a YouTube Live-Chat Watch-Party on UPRN. PNL Host Jen Noseworthy talks with Guest: Monica Rawlins from Texas. PNL Podcast Alumni-Network member & Co-Host Guest author & MUFON investigator Rob Shelsky joins us. Guest Monica Rawlins is a lifelong researcher of the strange and unusual. Monica has been passionately investigating Bigfoot since 2002, blending curiosity, skepticism, and a dash of humor into everything she does. Whether she's exploring Native American legends for her writing, chasing down eerie historical accounts, or swapping spooky tales with guests, Monica brings a storyteller's heart, and a researcher's persistence to the weird and wonderful world of the unexplained. Follow the Island of Misfits and Monica at https://www.youtube.com/@MonicasParanormalWorld Co-Host Guest: Rob Shelsky was previously on PNL Podcast S3/E10 (UPRN#17);S4/E6 (UPRN#66);S4/E13(UPRN#72);S4/E19(UPRN#79);S4/E22(UPRN#82);S4/E23(UPRN#83);S4/E26(UPRN#86). Check them all out. Shelsky is an author & MUFON field investigator, has written over 80 Paranormal books, and was invited to speak at the Library of Congress, Washington, D.C. Rob has a degree in science, has written a large number of magazine articles, and has been interviewed on a number of shows such as George Noory's Coast-To-Coast AM Radio show. Shelsky was also the on-camera UFO expert for a Travel Channel TV show. Find his books on Amazon Kindle, Smashwords & more. Follow Rob Shelsky on http://robshelsky.blogspot.com/ Shout out to PNL Podcast Alumni Network Members from S3 & S4 Bryan Bowden and Mark Eddy from Third Eye Live. Shout out to the Island of Misfits Podcaster Group Paranormal Paul with Paranormal Among Us; Tex's Front Porch; BigFoot Michigan Rob(BMR); Blondes and Boos; Greg Ogles from Relic Films; Brent Thomas with Paranormal Portal, and many more. Check out their work you'll love them all. Big shout out to UPRN Producer and Manager: Michelle Desrochers. Michelle is also host of The Outer Realm Radio & Beyond the Outer Realm on UPRN www.linktr.ee/michelledesrochers_ (JV) Jennifer Vallis-Noseworthy, RN (Jen) PNL Podcast & BOG Team Newfoundland & Labrador (NL), Canada Founder/Host: PNL ("Paranormal NL" Podcast) Founder/Team Lead: BOG ("Boots on Ground" Paranormal Investigation Team) Email: paranormal.nl.podcast@gmail.com Follow PNL & BOG Team at https://linktr.ee/paranormalnlpodcast and https://uprntalkradio.com/

Welcome to the third series of our journey through space and time as a grief-stricken Tenth Doctor finds himself with new companion Martha Jones, and a mysterious looming presence in the background!We've reached the mid-point of Series Three, and the main background arc is really starting to take shape as Martha officially becomes a full companion with all the TARDIS privileges, and a sinister group is feeding anti-Doctor rhetoric into Martha's family as we have a present day playing God experiment and a tense, tragic, trivia time crunch on this week's show before the STELLAR second half of S3 begins, talking S3 Episode Six: THE LAZARUS EXPERIMENT & Episode Seven: 42!We hope you enjoy and join us for more!Our YouTube Channel for all our video content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCvACMX8jX1qQ5ClrGW53vow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The It's A Wonderful Podcast Theme by David B. Music.Donate:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.buymeacoffee.com/ItsAWonderful1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our Patreon: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/ItsAWonderful1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠IT'S A WONDERFUL PODCAST STORE: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.teepublic.com/user/g9design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sub to the feed and download now on all major podcast platforms and be sure to rate, review and SHARE AROUND!!Keep up with us on (X) Twitter:Podcast:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://twitter.com/ItsAWonderful1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Morgan:⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/Th3PurpleDon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jeannine: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/JeannineDaBean⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠_Keep being wonderful!!

An airhacks.fm conversation with Stanislav Bashkyrtsev about: discussion about testing terminology and the difference between unit tests, component tests, System Tests, and integration tests, defining component tests as in-process invocations without HTTP, using RestAssured with MockMvc-style direct endpoint calls, avoiding mocks in favor of real system tests, why code coverage is a misused management metric, the anti-pattern of using reflection to inflate coverage, distinguishing line and branch coverage from actual verification, using coverage from system tests to detect dead code for pruning, mutation testing with PIT to measure assertion quality, testing Quarkus applications, the default Guice and Guava dependencies in Quarkus RESTEasy, starting a new microservice with a separate system-test module, calling endpoints over HTTP with the MicroProfile REST Client or the Java HTTP client, deploying Quarkus on AWS Lambda as a production-like environment, backward compatibility testing with multiple production versions, turning system tests into stress and load tests, testing connection pools and metrics under load, introducing a test-only private API to verify state changes in serverless systems, contract-driven work in large consulting projects, generating JSON and JSONB directly in PostgreSQL and returning it over JDBC, mapping database rows to Java records instead of DTOs, running GraalVM inside the Oracle Database for stored procedures and table triggers, the pendulum between database-centric and application-centric logic, the convergence of SQL and NoSQL databases, CI/CD pipelines with Jenkins and manual production deployment steps, avoiding Jenkins access to production via CGI shell scripts behind nginx, AWS CodePipeline and CodeBuild with CDK-defined infrastructure, event-driven pipelines triggered by S3 put-object events, multi-account roles with short-lived STS credentials, the size of the AWS SDK and reducing it by excluding unused HTTP clients, health checks and Kubernetes liveness and readiness probes, why health checks make little sense for short-lived Lambdas, a version endpoint for deployment smoke tests Stanislav Bashkyrtsev on twitter: @sbashkirtsev

How to protect backups from ransomware starts with a hard truth: attackers aren't just encrypting your data anymore, they're stealing it first — and no backup system on earth undoes an exfiltration. In this encore episode of The Backup Wrap-Up, Curtis and Prasanna dig into what real immutability looks like versus the marketing version, why root access quietly undermines most "immutable" storage claims, the difference between virtual and true physical air gaps, and the exact questions you should be firing at your backup vendor before you trust them with your last line of defense.This one's an encore for a reason — it was one of the most listened-to episodes in the show's history, with listeners sticking around for the full runtime and then some. That's the kind of signal that tells you it's worth a second run.Along the way, they cover S3 object lock and what actually happens if your account gets deleted or your credit card stops working, why compromised admin credentials are involved in the majority of attacks and what your vendor should be able to undo, and why bit rot — rare, but nasty when it's silent — still belongs on your checklist. If you manage backup or DR for a living, or you're just trying to figure out whether your current setup would actually hold up, this episode gives you a concrete list of questions to ask and red flags to watch for.Chapters:00:00 – Cold Open: Ransomware Is After Your Backups01:19 – Welcome & Banter05:50 – Topic Setup: Protecting Backups from Ransomware07:36 – The Extortion & Exfiltration Playbook15:25 – What Is an Air Gap?18:22 – Virtual Air Gaps27:57 – Real (Physical) Air Gaps28:12 – What Is Immutability, Really?29:23 – Bit Rot30:42 – Root Access & the Limits of Immutability32:51 – S3 Object Lock40:20 – Questions to Ask Your Backup Vendor42:31 – What Happens If You Delete Your Account?44:29 – Compromised Credentials & Worst-Case Scenarios46:41 – Final Thoughts

PNL UPRN Seg#87 "UFOs in the Bible" Guest Rev. Michael J.S. Carter. Co-Host Rob Shelsky Tue July 7th, 2026 @5pm EST Guest Bio: PNL Podcast (Paranormal NL) located in Newfoundland and Labrador (NL), Canada with Host Jen Noseworthy -S4/E27-UPRN Segment #87. “UFOs in the Bible” special Pre-recorded event with a YouTube Live-Chat Watch-Party on UPRN. PNL Host Jen Noseworthy talks with Ancient Aliens Guest: Rev. Michael J.S. Carter (from North Carolina). PNL Podcast Alumni Network Member joins as a Co-Host: MUFON investigator & author Rob Shelsky (from North Carolina). Guest Rev. Michael J. S. Carter is originally from Baltimore, Maryland. He moved to New York City in 1980 and lived there for 27 years, working as a professional actor before moving to North Carolina. Michael is an ordained Interfaith Minister, with a BA, and a Masters In Divinity. Michael was chaplain at various hospitals and served various Unitarian Universalist Congregations. Michael is an anti-racism trainer, and was even recognized by a former President. Michael is a UFO Contactee, and authored Alien Scriptures: Extraterrestrials In The Holy Bible. Michael has appeared with George Noory on Coast To Coast radio show & TV show, “Beyond Belief” on Gaia Other TV appearances include being a regular consultant on The History Channel's Ancient Aliens Series. Michael has appeared on America's Greatest Mysteries hosted by Lawrence Fishburne; and was featured in UFO TV Documentaries The Real 4400; UFOs the Hidden Evidence; and Steven Spielberg's Abduction Diaries (which was the precursor to Spielberg's “Taken.” Rev. Carters three other books include: A New World If You Can Take iT: God,Extraterrestrials, and The Evolution of Human Consciousness; God Consciousness: A 30 Day MeditationManual for God Conscious Thinking; The Metaphysics of Spiritual Healing and the Power of AffirmativePrayer; and Enlightenment. Rev. Carter serves as the minister for the Unitarian Universalist Congregation of the Swannanoa Valley. Follow him at https://michaeljscarter.com/ Co-Host Guest: Rob Shelsky was previously on PNL Podcast S3/E10 (UPRN#17);S4/E6 (UPRN#66);S4/E13(UPRN#72);S4/E19(UPRN#79);S4/E22(UPRN#82);S4/E23(UPRN#83);S4/E26(UPRN#86). Check them all out. Shelsky is an author & MUFON field investigator, has written over 80 Paranormal books, and was invited to speak at the Library of Congress, Washington, D.C. Rob has a degree in science, has written a large number of magazine articles, and has been interviewed on a number of shows such as George Noory's Coast-To-Coast AM Radio show. Shelsky was also the on-camera UFO expert for a Travel Channel TV show. Find his books on Amazon Kindle, Smashwords & more. Follow Rob Shelsky on http://robshelsky.blogspot.com/ Shout out to PNL Podcast Alumni Network Members from S3 & S4 Mark Eddy and Sir Bryan Bowden from Third Eye Live. Big shout out to UPRN Manager & Producer: Michelle Desrochers. Michelle is also host of The Outer Realm Radio & Beyond the Outer Realm on UPRN www.linktr.ee/michelledesrochers_ PNL believes in highlighting international Paranormal people, places, and things. PNL believes in promoting global Paranormal-Networking & Paranormal tourism. Thank-you to all the PNL Podcast Alumni-Network guests from season One 2023, Season Two 2024, Season Three 2025, Season Four 2026, and our UPRN & PNL subscribers. PNL airs every Tue at 5pm EST on all digital platforms of UPRN (United Public Radio Network) 107.7 FM New Orleans & 105.3 Gulf Coast https://www.youtube.com/@PNL_Podcast/streams and ⁠⁠⁠https://www.uprntalkradio.com

An airhacks.fm conversation with Thorsten Hoeger (@hoegertn) about: discussion about CloudFormation as underlying infrastructure as code using JSON/YAML to define desired state, CDK introduction as tool to define infrastructure using programming languages like Java that synthesizes to CloudFormation, explanation of CDK construct levels L1 (direct CloudFormation mapping), L2 (type-safe with opinionated defaults), L3 (patterns and abstractions), Custom resources for missing CloudFormation implementations or actions like cleaning S3 buckets, CDK resource provider framework simplifying custom resource creation by handling AWS Lambda lifecycle, Self-mutating pipelines concept using CDK to update own pipeline, CDK for Terraform (CDKtf) enabling Terraform synthesis using CDK constructs, jsii facilitating multi-language support for CDK constructs, Mixins feature allowing immediate modification of constructs without full L2/L3 abstraction, Refactoring constructs to move code between construct tree levels, CloudFormation Naming conventions using PascalCase for constructs and kebab-case for CloudFormation resource names Thorsten Hoeger on twitter: @hoegertn

Nik and Michael are joined by David Steele to talk all things pgBackRest. Here are some links to things they mentioned: David Steele https://postgres.fm/people/david-steelepgBackRest https://pgbackrest.orgpg_basebackup https://www.postgresql.org/docs/current/app-pgbasebackup.htmlBarman https://pgbarman.orgpgmoneta https://github.com/pgmoneta/pgmonetaWAL-G https://github.com/wal-g/wal-gReturn pg_control from pg_backup_stop (patch proposal) https://www.postgresql.org/message-id/flat/86436ff9-eb98-4c8a-825e-3bcae022107b%40pgbackrest.org#e36e2ad2f410d1a9a1152e36aa66bb48Add StorageReadMulti for prefetched multi-file/range reads from object stores (pgBackRest change that needs review) https://github.com/pgbackrest/pgbackrest/pull/2783pg_hardstorage https://github.com/cybertec-postgresql/pg_hardstorageWhy the cycle of open-source sustainability needs to be virtuous (blog post by Gabriele Bartolini) https://www.gabrielebartolini.it/articles/2026/04/why-the-cycle-of-open-source-sustainability-needs-to-be-virtuousARIN https://www.arin.netStefan Fercot https://pgstef.github.io/aboutpgBackRest sponsors at the time of recording: AWS, Supabase, pgEdge, Tiger Data, Percona, Eon, Xata, Dalibo, Data Egret~~~00:00 – Intro & pgBackRest origin story03:07 – Could pgBackRest live in Postgres core? Why it hasn't happened11:37 – Primary vs. standby backups & corruption safety20:36 – Measuring RPO & RTO in practice26:20 – Checksum performance & S3 storage class tips30:02 – Standby replay bottleneck35:12 – Log shipping, streaming replication, and sharding44:56 – Maintenance reality & the sponsorship crisis47:41 – What's next: repo-to-repo backup & RPO-zero streaming56:00 – Incremental backup1:05:01 – Reliability philosophy1:10:05 – Second maintainer, community growth & wrap-up~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Porshea and Tatiana return to talk all things House of the Dragon, S3 episode 2 "Queen's Landing". The drama that is House Targaryen and everyone in Westeros affected by The Blacks vs. The Greens never ends--and we're here to debate it all!Become a supporter of this podcast: https://www.spreaker.com/podcast/for-all-nerds-show--5649266/support.FOLLOW ON SOCIAL: Twitter.Com/ForAllNerds Instagram.Com/ForAllNerds Twitch.TV/ForAllNerds GET YOUR FORALLNERDS MERCH HERE: Forallnerds.com PATREON: Patreon.com/ForAllNerdsFor All Nerds Show is a member of the LoudSpeakers Network (https://soundcloud.com/loudspeakersnetwork) and is published every week on Spotify, Apple Podcasts, Soundcloud, Stitcher, Google Play Music, and iHeartRadio. Visit http://ForAllNerds.Com for more greatness. Email us at: contact at forallnerds dot com

In episode 325 of Absolute AppSec, co-hosts Ken Johnson and Seth Law first break down an informal guide to threat modeling, arguing that overly prescriptive frameworks like STRIDE induce a heavy cognitive load on developers. Instead, they advocate for simplified, creative questions to expose architectural gaps, citing a historical GitHub planning flaw where private repository images were left exposed on S3 by relying solely on URL obfuscation. They warn that while rapid development in 2026 pushes toward automated lifecycles, human oversight, critical logging, and constructive friction remain essential. Next, they dissect a research paper exploring the philosophical definition of a vulnerability, framing it as a system disposition arising from a fault that manifests as a failure only when environmental and attacker conditions are jointly met. This definition sparks a debate on whether a flaw must carry immediate risk to qualify as a vulnerability, particularly when evaluating modern AI challenges like system prompt disclosures or exposed deprecated API paths.

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

Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an bonus 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: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

AI isn't necessarily creating impossible new attacks, but it is drastically lowering the technical barrier to entry for cybercriminals. In this episode, Ashish Rajan speaks with Simon Biggs, Cyber Incident Response Specialist at Varonis, about how AI is accelerating the attack lifecycle. Simon explains how attackers are using AI kits to instantly set up ephemeral phishing portals, query SQL databases in minutes, and bypass AI guardrails to compile Remote Access Trojans (RATs). We also discuss the shift in ransomware tactics from "encryption-first" to "data-theft-first," and how AI empowers attackers to post-process terabytes of stolen data to monetize it in novel ways. For defenders, the message is clear: if your S3 access logs and SQL transaction logs aren't turned on before a breach, your forensics team won't be able to tell lawyers or regulators what data was actually lost. Discover why data classification and proactive logging are the ultimate lifelines for IR teams in the AI age. Guest Socials -⁠⁠ ⁠⁠⁠⁠Simon's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:00) Simon Biggs' Background in Law Enforcement and Varonis(03:10) Is There a Huge Volume of Sophisticated AI Attacks?(04:10) How AI Accelerates SQL Queries and Business Email Compromise (BEC)(05:15) Why AI Kits Are the New Metasploit and BloodHound(08:15) Varonis Threat Labs: Copilot Prompt Injection Vulnerability(09:20) The Forensic Challenge: Auditing Prompts vs. Understanding AI Output(10:30) Tricking AI Guardrails to Compile Malware(12:15) Defensive Strategies: Shadow AI, Permissions, and Logging(15:30) Using Defensive AI and BloodHound for Threat Hunting(17:30) Why Ransomware is Now "Data First, No Encryption"(20:50) The Legal Nightmare of Unclassified Stolen Data(23:20) Why Windows Forensics Can't Tell You What Data Was Stolen(31:20) The Crucial Importance of Enabling S3 and Cloud Audit Logs(35:10) How AI Allows Attackers to Post-Process Terabytes of Stolen DataResources spoken about during the episode:Simon's Research at VaronisArticle about SearchLeak Article about RepromptVaronis Threat LabsThank you to Varonis for sponsoring this episode of Cloud Security Podcast

HI

This is audio only ripped from the video episode. You should be able to find the video on Spotify and Youtube if you want to watch us talk with images on the screen.*HI

In anticipation of S3 lets shake out the Targaryen family tree a bit.

This week on Page 7, Jackie and MJ are joined by Amber "Duncan Sheik on the Brain" Nelson of "SPUN", "The Brighter Side", and "HGX2" to goss' all 'bout Amber's ascent (or descent?) into Millennial CVS Music, and the music at JCP making Jackie feel greeeeeeey, but the grocery store music in NYC has got MJ thinkin' its finally their time! Jackie congratulates the NY Knicks on their win, MJ announces they are fully Knickspilled, and everyone reflects how they somehow didn't die while roof drinkin' in NYC.  Jackie and Amber give an update on their rootin' tootin' wild west DnD sessions, Amber gives a not so glowing review of S3 of "Euphoria", "Doc Meet World" shows some of the abusive underside of the "Boy/Girl Meets World" series.  MJ and Jackie discuss how this was a slow celeb news week, as evidenced by the Jeff Goldblum sometimes eats his dessert first being hawt goss', and Tyra Banks filed a lawsuit against Netflix due to the "Reality Check" documentary, plus even more Tyrasanity! Then we got a list filled with Celebs who had terrifying encounters with stalkers, the Blindz, then a Patriotic Jackie's Snackies @1:11:58 and a cheezzy MJ's Minute Munchies @ 1:17:56 til @1:23:57, plus even more on this weeks Page 7! Want even more Page 7? Support us on Patreon! Patreon.com/Page7Podcast Subscribe to SiriusXM Podcasts+ to listen to new episodes of Page 7 ad-free.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

After a historic market comeback over the last 60 days we evaluate where we are today. Which sectors have benefitted? Will Cannabis ever get some love? Are we bearish/bullish and is this an AI Fueled bubble? Is Cannabis still interesting with an S3 catalyst upcoming?Charting Man Dan joins us to answer all of these questions and reflect on how things have changed since the start the year.Follow Dan / The Chart GuysOn Twitter: ⁠⁠⁠⁠⁠⁠⁠https://twitter.com/ChartGuys⁠⁠⁠⁠⁠⁠⁠The Chart Guys on Youtube: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/channel/UCnqZ2hx679DqRi6khRUNw2g⁠⁠⁠⁠⁠

Greg Drexler from Boardriding Maui joins the show for a deep dive into style, simplicity, and the evolution of wind-powered foiling. We get into Greg's roots in windsurfing and kiting, the design philosophy behind BRM, the early days of parawings, why the new S3 became the benchmark, and how stripping gear down to its essence can unlock more freedom on the water. This one is a great look inside the mind of a designer who has helped shape kites, wings, and now parawings with a focus on feel, function, and keeping the stoke alive.

The culture is obsessed with speed, scale, lean teams, massive output, and automation, but faster output does not automatically mean better direction. In part two of this connected conversation, the focus shifts to the missing human layer underneath AI-powered growth and the belief that more content, more automation, and more velocity always lead to better outcomes. Using S³ Growth Streams™ as the operating lens, you will learn how to strategize before accelerating, synergize before scaling, and systemize before sprinting forever. More importantly, you will see how to separate signal from noise, align tools with real human capacity, and build repeatable rhythms that support growth without requiring constant sacrifice. This episode is not anti-AI, anti-hustle, or anti-ambition. It is anti-default sacrifice, anti-permanent sprint, and anti-output without awareness. It also challenges the obsession with flow state by introducing five operating states that support sustainable growth: capture, clarity, commitment, flow, and reflection. Because flow is not the whole game. Sometimes the most productive thing you can do is capture the signal, get clarity, commit to the right action, recover, reflect, and return to baseline before sprinting again. The goal is not to become more machine-like. The goal is to become more intentionally human while using machines wisely. Beyond The Episode Gems: Buy My Book, Strategize Up: The Blueprint To Scale Your Business: StrategizeUpBook.com Discover All Podcasts On The HubSpot Podcast Network Get Free HubSpot Marketing Tools To Help You Grow Your Business Grow Your Business Faster Using HubSpot's CRM Platform Support The Podcast & Connect With Troy:  Rate & Review iDigress: iDigress.fm/Reviews Follow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTok Subscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass Episodes Need Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com

Chris and Andy talk about the ‘Euphoria' S3 trailer (1:07) and ‘The White Lotus' S4 cast additions of Heather Graham and Rosie Perez (5:42). Next, they talk about the first episode of Netflix's ‘Something Very Bad Is Going to Happen' and why it is an excellent example of longform horror (10:14), before Andy shares his thoughts on ‘Project Hail Mary' (27:40). Later, they are joined by Mina Kimes to unpack the ‘Paradise' Season 2 finale, why she loves the show, what else she's been watching recently, and her take on the Eagles heading into next season (42:00). Drivers wanted. Learn more at http://vw.com Subscribe to the Ringer TV YouTube channel here for full episodes of The Watch and so much more! Hosts: Chris Ryan and Andy Greenwald Guest: Mina Kimes Producers: Kaya McMullen and Kai Grady Additional Video Supervision: Sarah Reddy Learn more about your ad choices. Visit podcastchoices.com/adchoices