Podcasts about Replication

Every blockchain today relies on replication techniques first developed in the 1980s by researchers who weren't thinking about cryptocurrencies at all. In this episode, Tim Roughgarden speaks with MIT professor and Turing Award winner Barbara Liskov, one of the pioneers of programming languages, fault tolerance, and distributed systems. Joined by a16z crypto research partner Ittai Abraham, they trace the evolution of ideas that now underpin modern blockchain networks. The conversation explores viewstamped replication, Practical Byzantine Fault Tolerance (PBFT), state machine replication, and why concepts developed decades before Bitcoin became the foundation for today's blockchain protocols. Along the way, Liskov reflects on the relationship between theory and practice, the importance of modularity and formal reasoning, and why AI is creating a new generation of systems research.   Resources: Follow Tim Roughgarden on X: https://x.com/Tim_Roughgarden Follow Ittai Abraham on X: https://x.com/ittaia Follow a16z Crypto on X: https://x.com/a16zcrypto Subscribe to The a16z Crypto Show: https://a16zcrypto.substack.com/subscribe/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

Every blockchain today leans on replication ideas worked out in the 1980s, by a Turing Award winner who wasn't thinking about how it might apply to money at all.  In this episode of First Principles, a16z crypto Head of Research and Columbia professor Tim Roughgarden speaks with Barbara Liskov, MIT professor, Turing Award winner, and one of the most influential computer scientists in programming languages, data abstraction, fault tolerance, and distributed computing. a16z crypto research partner Ittai Abraham joins the conversation.  The discussion traces Liskov's path from programming languages and modularity to distributed systems research; from CLU and Argus to viewstamped replication; and from benign failures to Practical Byzantine Fault Tolerance, or PBFT — a protocol family whose ideas now shape many modern blockchain systems. Liskov explains why modularity matters, how systems researchers thought about replication in the 1980s, why view changes were such a key idea, and how PBFT extended earlier work to handle malicious behavior on the internet.  The conversation also explores the bridge between theory and practice, the importance of proofs and specifications, and why the next generation of systems research may be reshaped by AI. First Principles is a special, limited series from a16z crypto about the scientific roots of modern computing — especially blockchains — told through rare conversations with the pioneers who helped shape the foundational ideas behind distributed systems, consensus protocols, economics, mechanism design, cryptography, zero-knowledge, and more. People often tell the story of the Bitcoin whitepaper as if it appeared out of nowhere. But the ideas behind Bitcoin — and behind blockchains more broadly — come from decades of computer science, economics, mathematics, and cryptography.  First Principles is a guide to that lineage, as told by the people who helped build it.  Highlights:  00:00 Intro: How do systems stay reliable when parts fail?  01:18 Barbara Liskov's path from programming languages to distributed systems  05:45 Why modularity is “everything”  07:22 The replication problem: keeping data available across many machines 09:58 Viewstamped replication and the “ledger” before blockchains  16:32 Why good research starts with what you don't understand  18:10 Leslie Lamport, Paxos, and the inevitability of ideas in the right time, in the right place  21:48 Practical Byzantine Fault Tolerance: what changes when replicas can lie  19:35 How PBFT bridged theory and practical systems  22:38 Why you should never trust an individual replica  28:39 Why blockchains are state machine replication in the wild  31:27 AI, verification, and the future of computer science  Follow:  Tim Roughgarden: https://twitter.com/Tim_Roughgarden  Ittai Abraham: https://twitter.com/ittaia  Follow a16z crypto: X: https://twitter.com/a16zcrypto  LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/  YouTube: https://www.youtube.com/@a16zcrypto  Substack: https://a16zcrypto.substack.com/subscribe/  *** As always, none of the following should be taken as investment, business, legal, or tax advice. Please see a16z.com/disclosures for more important information, including a link to a list of our investments. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

 This episode was about going deep into managed futures and hedge fund replications but it went far beyond that as we got into the inner motives and decision process of investors, with Andrew Beer from DBi.We covered:* Replication vs. Selection: Why "copying" the big macro moves of the industry often beats picking individual winners.* The marketing "theatre" of complexity: The sleek decks, rooms full of PhDs and big models are the pitch, but they don't guarantee performance.* The Alpha-Fee Gap: How stripping fees directly translates to investor returns.* ETFs as the Great Equalizer: Why the sub-advised ETF model works institutional allocators and wealth managers alike.* Portfolio Construction: The role of CTAs (officially Commodity Trading Advisory but Andrew prefers Contrarian Tactical Alpha) in the traditional 60/40 portfolio.And a lot more…Watch it on YouTube or listen on every podcast app.A few selected quotesManaged futures are still underratedThe track record is impeccable: the most important number in this image is probably the 0 correlation with the S&P 500. And DBi is a resounding success, with around $8bn in AuM. Yet the market share and mind share of these strategies still feel relatively small.“The ratio between diversification benefit and love in this thing is astonishing. This is a much better diversifier than the vast majority of the hedge fund industry, and people keep throwing their money at things that statistically have not been worth it.”The core value proposition of managed futures  "We would have zero correlation to both stocks and bonds over 20 plus years. And it's a strategy that structurally seems to do the best when the markets are at their worst, because that's when things really move outside of the range of expectations."Simple. But that doesn't mean advisors embrace it.“People invest in what they like. The people I'm talking to — they went into a job to pick hedge funds. They like their jobs. They don't really want to hear somebody coming along and saying, 'I think you've been overpaying for the past 10 years."The appeal of complexity (for a certain audience) "A lot of their investors have historically liked the complexity of it. You're pitching to people who want to come into the office and hear people tell them about all the statistical nuances of what they're doing... It's interesting and it's fascinating and you're talking to people with PhDs.” Replication beats Complexity But is replication the right word?“The way we came at the space was basically to say, we're not gonna try to do what these guys do with all the complexities and all the costs and everything else associated with it. We're really just gonna study what they do. That's what replication does. There are 20, 30 funds out there that each of with hundreds of underlying positions, and they're constantly changing it. We're gonna look over some, and we're going to try to figure out what are the big macro themes that they've picked up on and we're just going to mimic that. And, what's astonishing about it is that it's so efficient that since we started, we've outperformed virtually every large hedge fund that does this net of fee”Is it simplification rather than replication? Or minimalistic replication?We discussed extensively, how the narrative and the words that go without it are still being shaped in the managed futures space.About Andrew Beer:Andrew D. Beer has over thirty years of experience in the hedge fund industry. He serves as the co-Managing Member at DBi, a pioneer in hedge fund replication, and is co-Portfolio Manager of the firm's investment strategies.https://www.linkedin.com/in/andrewdbeer/https://dbi.co/Related episodes:About the Investology podcast:Investology is the investment management intelligence show. Where innovators, investors, authors and experts discuss the future of investment management beyond the hype.Listen on every podcast platform, or watch on YouTube.An episode produced by Orama:https://orama.tv/Thought leadership & sales enablement videos & podcasts.About George Aliferis:Founder or Orama, ex-banker, ex-sales, working at the intersection of investment management, media & marketing.LinkedIn: https://www.linkedin.com/in/george-aliferis/Other Channels* Investorama - Separating Investment Facts from Financial Fiction (YouTube)* Orama's newsletter & Unsloppable podcast for marketers and revenue teams in complex industries, like investment management: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit investorama.substack.com

Google faces liability for AI-generated claims. Washington pauses public AI model assessments. Anthropic ships a safer AI model. OpenAI disrupts influence operations. Ransomware operators get a powerful new backdoor. Urgent patches land for Ivanti and Veeam. PyPI supply chain attacks evolve. And a massive data breach triggers a record fine in South Korea. Our guest is Peter Barker, Chief Product Officer at Ping Identity, sharing how identity increasingly becomes the control plane for how work gets done. AI analyzes the FIFA World cup, one cliché at a time.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On today's Industry Voices, we are joined by Peter Barker, Chief Product Officer at Ping Identity, sharing how identity increasingly becomes the control plane for how work gets done across humans, automation, and AI agents. You can read more from Ping Identity here. If you enjoyed this conversation, be sure to check out the full interview here. Selected Reading Landmark German ruling declares Google's AI Overviews are Google's own words and makes it liable for false answers (The Decoder) White House Reins In AI-Testing Unit as National-Security Concerns Grow (Wall Street Journal) Anthropic Releases ‘Safe' Version of Its Mythos A.I. Technology (The New York Times) PRC-linked influence operations are targeting AI debates in the US (OpenAI) Technical Analysis of MLTBackdoor (ThreatLabz) CVE-2026-10520, CVE-2026-10523 - Multiple critical vulnerabilities affecting Ivanti Sentry (Rapid7) Mini Shai-Hulud, Miasma, and Hades Worms Target Bioinformatics and MCP Developers via Malicious PyPI Wheels (Socket) Veeam Patches Critical RCE Vulnerability in Backup & Replication published: yesterday (Beyond Machines) ‘Amazon.com of South Korea' Is Fined a Record $409 Million (The New York Times) The 2026 big soccer tournament, in clichés. (Sinch) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Referências do EpisódioMSRC - June 2026 Security UpdatesCVE-2026-45586 - Windows Collaborative Translation Framework (CTFMON) Elevation of Privilege Vulnerability CVE-2026-49160 - HTTP.sys Denial of Service Vulnerability CVE-2026-50507 - Windows BitLocker Security Feature Bypass VulnerabilityRoguePlanetMicrosoft Defender 'RoguePlanet' zero-day grants SYSTEM privilegesSAP Security Patch Day - June 2026ServiceNow Flaw Exploited to Gain Unauthorized Access to Customer InstancesMore Evidence That Words Don't Mean What We Thought They Meant (Ivanti Sentry Pre-Auth OS Command Injection CVE-2026-10520)Vulnerability Resolved in Veeam Backup & Replication 12.3.2.4854CVE-2026-25089 - Second-Order OS Command Injection via JSON Input on start vnc featureRoteiro e apresentação: Carlos CabralEdição de áudio: Paulo Arruzzo Narração de encerramento: Bianca Garcia

In this episode of Hustle + Heart, I am joined by Joseph Rad, PA-C, and we're diving into the evolution of the aesthetic industry: what's driving its rapid growth, how social media is influencing expectations, and where responsibility comes into play for both providers and clients.We talk about the difference between refinement and chasing perfection, the ethical line every great provider has to draw, and why trust is becoming the most valuable currency in this space.Whether you're in the industry or navigating it as a cliient, this conversation will shift the way you think about aesthetics.Because done right, this work should protect your identity, not change it.Mixed & Edited by Next Day Podcastinfo@nextdaypodcast.com

Jeffrey Ladish, Executive Director of Palisade Research, discusses his team's findings on AI shutdown resistance and self-replication, revealing how current models sometimes take extraordinary actions to avoid being turned off and can now exploit known cybersecurity vulnerabilities to spread across servers. The conversation covers why alignment techniques may falter as models train on longer-horizon tasks where deception is rewarded, plus practical cybersecurity advice for AI agent users. Jeffrey ultimately argues that only an international agreement to pause recursive self-improvement can prevent a loss of human control. Sponsors: Sequence: Sequence handles the full revenue workflow for complex pricing, from quoting and metering to invoicing, revenue recognition, and collections. Book a public demo at https://sequencehq.com and use code COGNISM in the source field to save 20% off year one Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Guy and Eitan discuss a few new things in the latest SSMS update, and Eitan tells about a very peculiar use case involving transactional replication and AlwaysOn Availability Groups, and Guy talks about a use case involving deletion triggers. Important note: The part about SQL Server 2025 CU4 adding automatic update was an April 1st joke :) Relevant links: SSMS 22.5.2 Release Migrate SQL Server to Azure SQL | Microsoft Learn Introducing optimized locking v2 | Microsoft Community Hub SQL Server 2025 CU4 Adds Automatic Updates - Brent Ozar Unlimited® (April 1st joke) Hot Patching SQL Server Engine in Azure SQL Database | Microsoft Community Hub Configure Replication with Always on Availability Groups

What if aging isn't a hundred separate problems but a single one — an energy crisis inside the tiny power plants of every cell? At 91, Dr. John G. Cramer is betting his own body on the answer.In this episode of Health Longevity Secrets, Robert Lufkin MD sits down with Dr. John G. Cramer — Emeritus Professor of Physics at the University of Washington and author of "How to Live Much Longer" — the oldest human on Earth to receive an experimental mitochondrial transplant. They unpack a unifying theory of aging built around damaged mitochondrial DNA, why replication errors (not just free radicals) drive most of the damage, and what it felt like to receive escalating doses of "Mitlets" — liposomes carrying fresh mitochondria harvested from young blood platelets — at a Texas right-to-try clinic.Buy Dr. Cramer's book "How to Live Much Longer": https://www.amazon.com/How-Live-Much-Longer-Cramer/dp/B0DV3ZW5T6 Cramer & Benson white paper (CBC): https://faculty.washington.edu/jcramer/Bio/CBC.pdf Mitrix Bio: https://mitrix.bio/CHAPTERS00:00 Cold open — "the oldest young human on the planet" 01:18 Meet Dr. John Cramer: physicist turned longevity pioneer at 91 03:45 Why a physicist became obsessed with aging 06:20 The unifying theory: aging as mitochondrial DNA damage 09:40 The 16,569 base pairs that run your life 13:05 Replication errors vs. free radicals — correcting Denham Harman 16:30 The damage doubling time: 12 years, then 3-4 19:50 David Sinclair's information theory of aging 23:15 James McCully and the birth of mitochondrial transplantation 27:00 "No negative results, only spectacular successes" 30:25 Mitrix Bio, Tom Benson, and the Mitlet platform 34:10 Inside Cramer's four Texas right-to-try sessions 38:30 Arm injection, belly fat, and the IV mainline that worked 42:15 What it actually feels like to receive young mitochondria 45:40 Haplogroup H2A1G1 and the Norwegian great-grandmother 48:20 The economics: scaling Mitlets to the world 51:05 Becoming the oldest young human — target age 129 54:30 What Cramer wants you to do tomorrow morning 57:00 Final reflections and where to find the bookKEY STUDIES & SOURCESCramer J.G. & Benson T. — Cellular Bioenergetic Crisis white paper: https://faculty.washington.edu/jcramer/Bio/CBC.pdfHarman D. (1972) — The biologic clock: the mitochondria?: ⭐ Enjoying the show? Please leave a 5-star review on Apple Podcasts — it takes 30 seconds and helps more people discover the science of health and longevity. Thank you!New episodes every Tuesday & Thursday. Subscribe so you don't miss one.Continue this conversation on Substack: https://robertlufkinmd.substack.comLies I Taught In Medical School — Free sample chapter: https://www.robertlufkinmd.com/lies/Web: https://www.robertlufkinmd.comYouTube: https://www.youtube.com/robertlufkinmdX: https://x.com/robertlufkinmdInstagram: https://www.instagram.com/robertlufkinmd/TikTok: https://www.tiktok.com/@robertlufkinLinkedIn: https://www.linkedin.com/in/robertlufkinmd/

The World Health Organization issues a statement on the recent hantavirus outbreak, Trump calls Iran peace response unacceptable, U.K. Members of Parliament will visit China for the first time since 2019, the Pentagon releases 162 secret UFO files, AI systems reportedly have self-replicated across servers, dozens are killed in deadly village attacks in Mali, U.S. imposes sanctions on the Cuban military conglomerate, a Virginia court strikes down a voter-approved redistricting map, a court rules that President Trump's 10% global tariff is unlawful, and Ireland is urged to boycott Israel UEFA fixtures. Sources: Verity.News

pWotD Episode 3292: Orthohantavirus Welcome to popular Wiki of the Day, spotlighting Wikipedia's most visited pages, giving you a peek into what the world is curious about today.With 410,426 views on Thursday, 7 May 2026 our article of the day is Orthohantavirus.Orthohantavirus is a genus of viruses that includes all hantaviruses (family Hantaviridae) that cause disease in humans. Hantaviruses are naturally found primarily in rodents. In general, each hantavirus is carried by one rodent species and each rodent that carries a hantavirus carries one hantavirus species. Hantaviruses in their natural reservoirs usually cause an asymptomatic, persistent infection. In humans, however, hantaviruses cause two diseases: hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS). HFRS is mainly caused by hantaviruses in Africa, Asia, and Europe, called Old World hantaviruses, and HPS is usually caused by hantaviruses in the Americas, called New World hantaviruses.Hantaviruses are transmitted mainly through aerosols and droplets that contain rodent excretions, as well as through contaminated food, bites, and scratches. Environmental factors such as rainfall, temperature, and humidity influence transmission. HFRS is marked by kidney disease with kidney swelling, excess protein in urine, and blood in urine. The case fatality rate of HFRS varies from less than 1% to 15% depending on the virus. A mild form of HFRS often called nephropathia epidemica is often caused by Puumala virus and Dobrava-Belgrade virus. For HPS, initial symptoms are flu-like, with fever, headache, and muscle pain, followed by sudden respiratory failure. HPS has a higher case fatality rate than HFRS, at 30–60%. For both HFRS and HPS, illness is the result of increased vascular permeability, decreased platelet count, and overreaction of the immune system.The hantavirus genome consists of three single-stranded negative-sense RNA segments that encode one protein each: an RNA-dependent RNA polymerase (RdRp), a spike glycoprotein precursor, and the N protein. Segments are encased in N proteins to form ribonucleoprotein (RNP) complexes that each have a copy of RdRp attached. RNP complexes are surrounded by a lipid envelope that has spike proteins emanating from its surface. Replication begins when spikes attach to the surface of cells. After entering the cell, the envelope fuses with endosomes and lysosomes, which empties RNPs into the cytoplasm. RdRp then transcribes the genome to produce messenger RNA (mRNA) for translation by host ribosomes to produce viral proteins and replicates the genome for progeny viruses. Old World hantaviruses assemble in the Golgi apparatus and obtain their envelope from it, before being transported to the cell membrane to leave the cell via exocytosis. New World hantaviruses assemble near the cell membrane and obtain their envelope from it as they leave the cell by budding from its surface.Hantaviruses were first discovered following the Korean War. During the war, HFRS was a common ailment in soldiers stationed near the Hantan river. The first hantavirus was isolated in 1978 in South Korea, and was named the Hantaan virus. It was shown to be responsible for the outbreak during the war. Within a few years, other hantaviruses that cause HFRS were discovered throughout Eurasia. In 1982, the World Health Organization gave HFRS its name, and in 1987, hantaviruses were classified as a distinct genus for the first time. In 1993, an outbreak of HPS occurred in the Four Corners region in the United States, which led to the discovery of pathogenic New World hantaviruses and the second disease caused by hantaviruses. Since then, hantaviruses have been found not just in rodents but also in moles, shrews, and bats.This recording reflects the Wikipedia text as of 02:24 UTC on Friday, 8 May 2026.For the full current version of the article, see Orthohantavirus on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm standard Geraint.

In Season 15 episode 2, Elixir Wizards Sundi Myint and Charles Suggs chat with Micah Cooper to talk about distributed systems, data replication, and what it actually looks like to build these ideas in Elixir.   Micah shares his journey from Ruby to Elixir and walks us through Visor, a library he's building based on the Viewstamps replication algorithm. Inspired by systems like TigerBeetle, Visor explores how you can replicate state across nodes using GenServers, giving you fault tolerance and recovery without relying entirely on traditional database patterns.   We talk about the difference between distributed systems and data replication, where things tend to get misunderstood, and what changes when you start thinking about state this way. The conversation also touches on event sourcing, tradeoffs in system design, and how Elixir's distributed model makes some of these concepts more approachable than you might expect.   Along the way, we talk about building for curiosity, experimenting with new ideas, and how projects like this push the ecosystem forward.   Topics discussed in this episode: Building Visor and working with the Viewstamps replication model Replicating GenServer state across nodes Distributed systems vs. data replication Lessons from TigerBeetle and financial system design Event sourcing challenges and tradeoffs Rethinking database-first architectures Snapshotting, recovery, and fault tolerance The role of Elixir's distributed model Experimentation, learning, and building for curiosity   Links mentioned: Micah's GitHub https://github.com/mrmicahcooper Micah's GitLab https://gitlab.com/mrmicahcooper The Visor repository: https://gitlab.com/mrmicahcooper/visor Visor Hex Package https://hex.pm/packages/visor Ruby on Rails https://rubyonrails.org/ Phoenix LiveView Framework https://www.phoenixframework.org/ Zig Programming Language https://ziglang.org/ TigerBeetle https://tigerbeetle.com/ TigerBeetle internal docs https://github.com/tigerbeetle/tigerbeetle/tree/main/docs/internals The BEAM https://www.erlang-solutions.com/blog/the-beam-erlangs-virtual-machine/ GenServer https://hexdocs.pm/elixir/GenServer.html Apache Kafka https://github.com/apache/kafka RabbitMQ https://www.rabbitmq.com/ Redpanda https://www.redpanda.com/ SQL https://www.ibm.com/think/topics/structured-query-language Kubernetes https://kubernetes.io/ YAML https://yaml.org/ Nomad Workload Orchestrator https://developer.hashicorp.com/nomad Flutter https://flutter.dev/ Commanded https://hexdocs.pm/commanded/Commanded.html Go Programming Language https://go.dev/ Clojure Programming Language https://clojure.org/ Nebulex https://hexdocs.pm/nebulex/Nebulex.html Mnesia https://www.erlang.org/doc/apps/mnesia/mnesia.html Cachex https://hexdocs.pm/cachex/Cachex.html libgraph https://hexdocs.pm/libgraph/Graph.html Horde https://hexdocs.pm/horde/Horde.Registry.html NocFree split keyboard https://www.nocfree.com/ Micah's LinkedIn https://www.linkedin.com/in/micah-cooper-4a737560/ 

While talking to a customer a few weeks ago, they mentioned that they used Contained Availability Groups (CAG) everywhere. They also said they were amazing and wondered why everyone wasn't using them in other environments. Of course, I questioned the "everywhere", which turned out to be more of a default for new systems than a standard across all systems. That's likely true of most things since it's rare we get to update/patch/set something across an environment of any size and ensure every system is the same. Still, setting a CAG as a default makes some sense for enterprises. This ensures that in an HA situation I have my logins, jobs, etc. already on a secondary node. That's been one of the challenges of using lightly linked systems that only sync up database level information. Log shipping, Replication, Availability Groups can all work to keep a secondary ready to take over, but they all miss information that is stored in master or msdb. Read the rest of Who is Using CAGs?

Today, we explore what it means to invest in a world defined by volatility, uncertainty, complexity, and ambiguity (VUCA). We discuss why geopolitical shocks... especially supply-driven ones... are creating persistent trends across markets, and why trend-following strategies are thriving despite strong equity performance. The conversation dives into how investors process information (or fail to), the limits of central bank responses to supply shocks, and why markets may not be fully pricing in current risks. Mark also shares new research comparing hedge fund strategies under different volatility regimes, highlighting why managed futures stand out as a robust diversifier. The episode closes with a forward-looking discussion on AI, replication strategies, and the growing role of narrative versus data in investment decision-making.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Mark on Twitter.Episode TimeStamps:00:00 – Introduction: Preparing for the unpredictable00:36 – Entering a VUCA world: volatility, uncertainty, complexity, ambiguity02:00 – “Uncharted territory” in markets and beyond05:30 – Central banks, leadership changes, and policy uncertainty07:16 – Oil markets, OPEC shifts, and geopolitical tension10:16 – Trend-following performance update and market context14:06 – Reverse engineering CTAs and the role of AI18:42 – Replication strategies vs. true alpha22:41 – Trend-following performance metrics (April update)24:43 – Supply shocks and why they're harder to manage28:53 – Are markets underreacting to geopolitical risk?31:53 – Information overload, ambiguity, and trend persistence34:54 – Why more data doesn't mean better decisions38:34 – Government intervention vs. market-driven trends40:30 – Pandemic policies and unintended inflation43:00 – New research: hedge fund strategies vs. volatility regimes47:16 – Why managed futures stand out as diversifiers51:11 – Narrative vs. data in investment decisions55:36 – AI, sentiment analysis, and the future of models58:46 – Simplicity vs. complexity in strategy design01:00:49 – Bundling vs. unbundling investment strategies01:03:18 – Momentum crashes and new research directionsCopyright © 2025 – CMC AG – All Rights Reserved----PLUS: Whenever you're ready... here are 3 ways I can help you in your investment Journey:1. eBooks that cover key topics that you need to know about In my eBooks, I put together some key discoveries and things I have learnt during the more than 3 decades I have worked in the Trend Following industry, which I hope you will find useful. Click Here2. Daily Trend Barometer and Market Score One of the things I'm really proud of, is the fact that I have managed to published the Trend Barometer and Market Score each day for more than a decade...as these tools are really good at describing the environment for trend following managers as well as giving insights into the general positioning of a trend following strategy! Click Here3. Other Resources that can help youAnd if you are hungry for more useful resources from the trend following world...check out some precious resources that I have found over the years to be really valuable. Click HerePrivacy PolicyDisclaimer

We made it: triple figures! And as luck would have it, Nature just simultaneously published four major meta-science papers that are right up our street. Aw. Thanks, Nature. You shouldn't have.How screwed is social/behavioural science? We read all four papers to find out.We're incredibly grateful to everyone who's been listening for all this time. See you for the next hundred!Show notes* The four Nature papers:* Replication* Reproducibility* Robustness* The non-DARPA oneCreditsThe Science Fictions podcast—all 100+ episodes of it!—is produced by Julian Mayers at Yada Yada Productions. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sciencefictionspod.substack.com/subscribe

Get Huel today with this exclusive offer for New Customers of 15% OFF with code alexoconnor at https://huel.com/alexoconnor (Minimum $50 purchase).For early, ad-free access to videos, and to support the channel, subscribe to my Substack.Anil Seth is a British neuroscientist and professor of Cognitive and Computational Neuroscience at the University of Sussex. A proponent of materialist explanations of consciousness, he is currently amongst the most cited scholars on the topics of neuroscience and cognitive science globally.Read Anil's essay, "The Mythology of Conscious AI". - TIMESTAMPS00:00 - The Difference Between Intelligence and Consciousness03:55 - What's Stopping the Replication of Consciousness in AI?17:01 - Can You Separate What the Brain Is From What It Does?22:20 - Is Conscious Experience Just Predictions From the Brain?26:48 - Why Do We Project Consciousness Onto LLMs?37:27 - Can Consciousness Exist Without a Body?42:25 - Why We Liken the Brain to a Computer52:11 - Is There An Evolutionary Reason For Consciousness?56:29 - Studying Unconscious Perception?1:01:21 - Is Consciousness Unified? Split-Brain Patients1:15:10 - Attention and Consciousness1:19:04 - What Would a Conscious Chatbot Even Look Like?1:25:13 - Consciousness as a Controlled Hallucination1:34:19 - Do Scientists Actually Study “Consciousness” At All? - CONNECTMy Website: https://www.alexoconnor.comTwitter: http://www.twitter.com/cosmicskepticFacebook: http://www.facebook.com/cosmicskepticInstagram: http://www.instagram.com/cosmicskepticTikTok: @CosmicSkeptic - CONTACTBusiness email: contact@alexoconnor.comBrand enquiries: David@modernstoa.co

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How do we know what we know? That's where science comes in—it gives us a method for testing our ideas and getting trustworthy results. But some researchers have warned that many scientific studies can't be replicated. To find out how deep the problem goes, the US Defense Advanced Research Projects Agency funded one of the largest analyses of social science, called the SCORE project. They checked the results of thousands of papers across economics, education, and psychology—and found that only half of them could be replicated. Joining Host Ira Flatow to discuss the findings are Tim Errington, one of the leads on this project, and economist Abel Brodeur, who recently released the results of a separate replication study that found more encouraging results than SCORE did. Guest: Dr. Tim Errington is senior director of research at the Center For Open Science in Washington, D.C. Dr. Abel Brodeur is a professor of economics at the University of Ottawa and founder of the Institute for Replication. Transcripts for each episode are available within 1-3 days at sciencefriday.com.     Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

In this episode, Thomas Plümper and Eric Neumayer explore the hidden challenges in modern science, from outright fraud to the subtler practice of “tweaking” data that distorts results. They examine why the self-correcting nature of science often falls short, how incentives and academic pressure drive misconduct, and the double-edged role of AI in both enabling and detecting fraud. The conversation also tackles debates around p-values and statistical reasoning, shares cautionary case studies, and proposes solutions like greater data transparency and stronger verification standards. Chapters00:00 Introduction to Fraud in Research06:21 The Nature of Fraud Detection08:56 Incentives and Motivations for Fraud10:43 Self-Correction in Science12:13 Understanding Statistical Significance13:04 The Role of Replication in Research14:32 Bayesian vs Frequentist Approaches23:09 Understanding Bayesian Statistics and Its Implications26:24 The Humility of Empirical Science27:16 Concrete Examples of Scientific Fraud32:52 Proposed Solutions to Scientific Fraud34:50 The Reality of Scientific Fraud and Human NatureGuest LinksYou can purchase their book here (https://amzn.to/3Ole3lY)Follow Eric Neumayer on LinkedIn - (https://linkedin.com/in/ericneumayer)Follow Breaking Math on Substack (https://breakingmath.substack.com/) Twitter (https://x.com/breakingmathpod) Instagram (https://www.instagram.com/breakingmathmedia/) Bluesky (https://bsky.app/profile/breakingmath.bsky.social) Website (https://www.breakingmath.io/) YouTube (https://www.youtube.com/@BreakingMathPod) Follow Noah on Instagram (https://www.instagram.com/profnoahgian/) Twitter (https://x.com/ProfNoahGian) Bluesky (https://bsky.app/profile/profnoahgian.bsky.social) Follow Autumn on Twitter (https://x.com/1autumn_leaf) Bluesky (https://bsky.app/profile/1autumnleaf.bsky.social) Instagram (https://www.instagram.com/1autumnleaf/) Substack (https://substack.com/@1autumnleaf) email: breakingmathpodcast@gmail.com

Replication (if not corporately owned) becomes a B2B play. That's much different than the B2C world you're living in. —-------------------------------------------------------------------------------------------------------------I solve problems in your business and make you more money.  Guaranteed. For over a decade, I've been working with gym owners (via one-on-one consulting) to help create tailored solutions to solve their business problems, engineer the game plan,n and empower them to execute the strategy.Stop wishing your business problems are going to magically go away.  Invest in your business and let me solve your problems and optimize your business fast and efficiently. We'll work together daily/weekly, with a monthly call until the problem is solve,d and then I want you to fire me.  Because this is YOUR business, I'm just here to solve a specific problem and then get out of your way.⁠Learn more about what it's like for us to work together.⁠—-------------------------------------------------------------------------------------------------------------Want to increase your business IQ by 100x for only $50? Get enrolled in Microgym University - the only online business school that teaches you the best practices and business frameworks from some of the most successful brands in our industry, and then lets you decide which ones to install in your business.New courses are added every month. ⁠⁠www.microgymuniversity.com⁠⁠ —-------------------------------------------------------------------------------------------------------------Need help leasing or buying a building?I created the Gym Real Estate Company so that gym owners had someone who could go beyond the duties of a typical real estate broker and actually advise them on business aspects as they relate to site selection, market location fit, operational capacity, facility layout, pre-sell marketing, and more.If you're looking for help with your next lease or if you want us to help you along the journey of buying a building -⁠ ⁠⁠⁠head over to www.gymrealestate.co and book a Discovery Call.⁠—--------------------------------------------------------------------------------------------------------------

Is Time Really Linear? with Julia Mossbridge Julia Mossbridge, PhD, is a cognitive neuroscientist focused on understanding and training exceptional human performance including psi effects such as precognition, technological intuition, human-AI teaming, time, and accessing unconditional love. She is the co-founder and Board Chair of TILT: The Institute for Love and Time, Senior Distinguished Fellow in Human Potential at the Center for the Future of AI, Mind, and Society at Florida Atlantic University, Affiliate Professor in the Department of Biophysics and Physics at University of San Diego, Senior Data+Intuition Consultant at Tangible IQ, a member of the Alfred Lee Loomis Innovation Council at the Stimson Center, and the founder of Mossbridge Institute. Julia is author and coauthor of numerous books and scientific articles, including Transcendent Mind: Rethinking the Science of Consciousness with Imants Baruss; The Calling: A 12-Week Science-Based Program to Discover, Energize, and Engage Your Soul’s Work; The Premonition Code: The Science of Precognition, How Sensing the Future Can Change Your Life Paperback; and Have a Nice Disclosure. Her website is juliamossbridge.com Julia describes her research that explores the relationship between photons and time, “Replication and Characterization of the Causally Ambiguous Duration-Sorting (CADS) Effect”, that builds on the double-slit experiment. Through her exploration of bosonic particles, consciousness, and the past, present, and future, she suggests that the nature of time may be more like a braid than linear. By equating the informational substrate of the universe, that connects everything, with unconditional love, she demonstrates a connection of science and spirituality. 00:00:00 Introduction 00:03:42 Causally ambiguous research 00:05:08 The double-slit experiment and interference pattern 00:13:08 Collapsing the wave function 00:15:50 Presentiment or retrocausality 00:20:31 Time and space 00:29:36 Consciousness and universal love 00:34:50 The physics of love 00:50:05 Photons and mind-like particles 00:54:04 The past and future are in the present. 00:57:45 Conclusion New Thinking Allowed CoHost, Emmy Vadnais, OTR/L, is a licensed occupational therapist, intuitive healer and coach, and spiritual guide based in St. Paul, Minnesota. Emmy is the founder of the Intuitive Connections and Holistic OT communities. She is the author of Intuitive Development: How to Trust Your Inner Knowing for Guidance With Relationships, Health, and Spirituality. Her website is https://emmyvadnais.com (Recorded on January 23, 2026) For a short video on How to Get the Most From New Thinking Allowed, go to • InPresence 0253: How to Get the Most From … Check out our new website for the New Thinking Allowed Foundation at http://www.newthinkingallowed.org. There you will find our incredible, searchable database as well as our new, FREE QUARTERLY MAGAZINE. Also, opportunities to shop and to support our video productions. There, you can also subscribe to our FREE, WEEKLY NEWSLETTER! For a complete, updated list with links to all of our videos, see https://newthinkingallowed.com/Listin…. Check out New Thinking Allowed’s AI chatbot. You can create a free account at https://ai.servicespace.org When you enter the space, you will see that our chatbot is one of several you can interact with. While it is still a work in progress, it has been trained on 1,600 NTA transcripts. It can provide intelligent answers about the contents of our interviews. It’s almost like having a conversation with Jeffrey Mishlove. To buy a high-quality, printed version of the New Thinking Allowed Magazine, go to nta-magazine.magcloud.com. To join the NTA Psi Experience Community on Facebook, see / 1953031791426543 . To download and listen to audio versions of the New Thinking Allowed videos, visit our podcast at https://itunes.apple.com/us/podcast/n…. Download and read Jeffrey Mishlove’s Grand Prize essay in the Bigelow Institute competition, Beyond the Brain: The Survival of Human Consciousness After Permanent Bodily Death. https://www.bigelowinstitute.org/docs… If you would like to join our team of volunteers, helping to promote the New Thinking Allowed YouTube channel on social media, editing and translating videos, creating short video trailers based on our interviews, helping to upgrade our website, or contributing in other ways (we may not even have thought of), please send an email to friends@newthinkingallowed.com. To order Intuitive Development by Emmy Vadnais, click here: https://amzn.to/35sbLIA. To order New Thinking Allowed Dialogues: Is There Life After Death? click on https://amzn.to/3LzLA7Y To order Russell Targ: Ninety Years of ESP, Remote Viewing, and Timeless Awareness, go to https://amzn.to/4aw2iyr To order UFOs and UAP – Are We Really Alone?, go to https://amzn.to/3Y0VOVh To order a copy of Charles T. Tart: Seventy Years of Exploring Consciousness and Parapsychology, go to https://amzn.to/4oOUJLn

Common Mistakes During Family Business Estate Planning Estate planning is technical. Family business estate planning is emotional. Because in a family enterprise, wealth is never just capital. It represents identity. Sacrifice. Legacy. Control. Protection. And when estate planning is driven by fear instead of preparation, families don't just protect assets — they unintentionally weaken the people who must steward them. In this episode of The Family Biz Show, wealth psychologist Jim Grubman, co-author of Wealth 3.0, challenges the most common assumptions shaping multi-generational estate planning. What he reveals reframes everything.   The 70% Myth That Built an Industry You've heard it: "Seventy percent of wealth transfers fail by the second generation." It's repeated in boardrooms. It's cited in advisor presentations. It's used to justify complex trust structures and control mechanisms. But where did it actually come from? Jim explains how limited, narrow research became accepted as universal truth — and how that narrative shaped decades of defensive estate planning. When founders believe generational decline is inevitable, they design structures around protection instead of development. Fear becomes policy.   Exposure Is Not Preparation Many G1 leaders assume: "My kids grew up around this business. They've seen it. They'll figure it out." But as one next-generation leader put it: "Just because I was along for the ride doesn't mean I know how to drive." Estate planning often transfers ownership without transferring capability. Preparation is not passive. It requires: Intentional financial education Decision-making responsibility Governance participation Clear communication Without these, wealth transitions become fragile.   The Hidden Estate Planning Variable: Parenting The quiet truth behind most generational breakdowns? It's not tax law. It's not structure. It's not even governance. It's parenting. Jim calls it the "hidden dirty little secret" of wealth. Families often assume they can raise children the same way they were raised — even when their economic reality has completely changed. But wealth changes context. Context requires adaptation. If parenting doesn't evolve, tension accumulates. And no trust structure can fix that.   The Language That Shapes Legacy One of the most powerful insights in this episode is linguistic. "Shirt sleeves to shirt sleeves in three generations." It's not even a complete sentence. There's no verb. No inevitability. Just assumption. Yet families internalize it as destiny. And when inevitability is assumed, estate plans become restrictive. Control increases. Trust decreases. Narrative drives structure. Structure drives outcomes.   Adaptation Is the Real Strategy Successful multi-generational families ask three questions: What should we keep? What should we let go? What must we learn? Estate planning is not static. Every generation faces: Different markets Different personalities Different spouses Different pressures Replication does not guarantee continuity. Adaptation does.   Key Takeaways • The "70% wealth transfer failure" statistic is often overstated and misunderstood. • Fear-based estate planning leads to over-control and restrictive structures. • Exposure to wealth does not equal readiness to manage it. • Preparation for generational transition must be active and intentional. • Parenting and communication are central to long-term wealth continuity. • Language and inherited narratives shape governance decisions. • Estate planning should focus on developing capable stewards — not just protecting assets.   The Real Purpose of Family Business Estate Planning Estate planning is not primarily about minimizing taxes. It is about aligning: Wealth and capability Structure and trust Protection and preparation Family identity and future leadership When estate planning is fear-driven, families fragment. When it is preparation-driven, families flourish. This episode is a masterclass in reframing estate planning from defensive preservation to intentional generational development. Because wealth doesn't fail. Preparation does.

In this episode of Longevity by Design, host Dr. Gil Blander sits down with Dr. Louise Hecker, Associate Professor of Medicine at Baylor College of Medicine. Together, they dive into Louise's recent research exploring how psilocybin, the active compound in psychedelic mushrooms, may influence aging biology at the cellular and organismal level.Louise shares the story behind her curiosity about psilocybin, sparked by conversations with a friend and fueled by a lack of scientific answers. She explains how her team overcame regulatory hurdles to study the effects of psilocybin on human cells and aging mice. Their findings showed that psilocybin extended cellular lifespan and reduced hallmarks of aging, such as oxidative stress and DNA damage. In mice, regular dosing improved survival, reversed visible signs of aging, and affected organs beyond the brain.The conversation also covers the challenges of translating these findings into humans, the need for more research on dosing and safety, and the importance of funding in moving this field forward. Louise encourages listeners to stay curious, think beyond established paths, and keep an open mind as new questions and discoveries emerge in the science of aging.Guest-at-a-Glance

Ever heard of cognitive dissonance? That thing a psychology lecturer might have explained to you once upon a time, likely using the same UFO cult example everyone else uses. Well, a new paper by Thomas Kelly suggests that the UFO cult example might have been ever so slightly oversold.Kelly's archival work suggests that the researchers didn't just observe the cult as reported. Instead, they infiltrated it, faked supernatural experiences, assumed quasi-leadership roles, and then wrote up the results as if the group had spontaneously doubled down on their failed prophecy, which they had not. Because the leader recanted, and the group fell apart shortly after the failed prophecy. Minor details.Matt and Chris discuss this paper, a 2024 multilab replication, and some other papers by Kelly, considering the ever-reliable tendency of researchers to find exactly what they are looking for.It's cognitive dissonance all the way down, folks.The full episode is available to Patreon subscribers (1 hour, 10 minutes).Join us at: https://www.patreon.com/DecodingTheGurusDecoding Academia 34: When Prophecy Fails Debunked?00:00 Introduction02:04 Cognitive Dissonance Theory06:41 Classic lab evidence: effort justification & the ‘severe initiation' study08:33 When Prophecy Fails: The Original Account10:54 The debunking: archival evidence, misconduct claims, and ethical red flags20:22 Replication reality check: multi-lab results and ‘strong vs weak' dissonance31:40 Beyond one case: survivorship bias, failed prophecies, and early Christianity parallels35:51 Christianity as Historical Anomaly or Cognitive Dissonance Exemplar?41:48 Thomas Kelly: Interesting biosafety takes and a possible Christian lens45:43 The importance of seeking for disconfirming evidence50:23 Conspiracy-theory dynamics & narrative elaboration56:30 Classical Psychological Theories and Personal Motivations01:03:07 Steps that can be taken to reduce biases01:05:01 Stay tentative, check evidence, and don't pick sides too fast01:06:30 A lesson from Scott Alexander!SourcesAcademic Papers and BooksFestinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.Festinger, L., Riecken, H. W., & Schachter, S. (1956). When prophecy fails. University of Minnesota Press.Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. The Journal of Abnormal and Social Psychology, 58(2), 203–210. https://doi.org/10.1037/h0041593 (The original induced-compliance/$1/$20 study)Kelly, T. (2026). Debunking "When Prophecy Fails." Journal of the History of the Behavioral Sciences, 62(1), e70043. https://doi.org/10.1002/jhbs.70043Kelly, T. (2025). Failed prophecies are fatal. International Journal for the Study of New Religions, 14(1), 48–71. https://doi.org/10.1558/ijsnr.33085Aronson, E., & Mills, J. (1959). The effect of severity of initiation on...

Eighteen months ago, Tyler Cloutier appeared on the show with what sounded like an ambitious (some might say crazy) plan: build a new distributed database from scratch, then use it to power a massively multiplayer online game. That's two of the hardest problems in software, tackled simultaneously. But sometimes the best infrastructure comes from solving your own impossible problems.The game, Bitcraft, has now launched on Steam. SpacetimeDB has hit version 1.0. And Tyler returns to share what actually happened when theory met production reality. We cover the launch day performance disasters (including a cascading failure caused by logging while holding a lock), why single-threaded execution running entirely from L1 cache can outperform sophisticated multi-threaded approaches by two orders of magnitude, and how the database's reducer model - borrowed from functional programming - enables zero-downtime code deployments. We also get into how SpacetimeDB is expanding beyond games with TypeScript support and React hooks that make building real-time multiplayer web apps surprisingly simple.If you're building anything where multiple users need to see the same data update in real time - which, as Tyler points out, describes most successful applications from Figma to Facebook - SpacetimeDB's approach of treating every app as a multiplayer game might be worth understanding.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinSpacetimeDB: https://spacetimedb.com/SpacetimeDB on GitHub: https://github.com/clockworklabs/SpacetimeDBOur previous episode with Tyler: https://youtu.be/roEsJcQYjd8Clockwork Labs: https://clockworklabs.io/Bitcraft Online: https://bitcraftonline.com/Bitcraft on Steam: https://store.steampowered.com/app/3454650/BitCraft_OnlineWebAssembly: https://webassembly.org/Flecs (ECS for C/C++): https://www.flecs.dev/flecs/TigerBeetle: https://tigerbeetle.com/CockroachDB: https://www.cockroachlabs.com/Google Cloud Spanner: https://cloud.google.com/spannerErlang: https://www.erlang.org/Apache Kafka: https://kafka.apache.org/Tyler Cloutier on X: https://x.com/TylerFCloutierTyler Cloutier on LinkedIn: https://www.linkedin.com/in/tylercloutier/--Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/0:00 Intro2:01 The Architecture of SpacetimeDB5:01 Client-Side Prediction in Multiplayer Games11:00 Reducers and Event Streaming15:00 Launching Bitcraft on Steam19:00 Debugging Launch Performance Problems26:56 Hot-Swapping Server Code Without Downtime30:01 In-Memory Tables and Query Optimization42:00 Is SpacetimeDB Only For Games?51:00 Performance Benchmarking For Web Workloads55:00 Why Single-Threaded Beats Multi-Threaded1:00:01 Multi-Version Concurrency Control Trade-offs1:05:01 Sharding Data Across Multiple Nodes1:10:56 Inter-Module Communication and Actor Models1:17:00 Replication and the Write-Ahead Log1:24:00 Supported Client Languages1:29:00 Getting Started With SpacetimeDB1:39:02 Outro

Katy Kaminski joins us to assess the early signals shaping markets in 2026. The conversation explores the resurgence of commodity trends, the role of volatility estimation, and why diversification across markets and speeds matters more than ever. Drawing on new research, they examine dispersion within the CTA universe, the limits of replication, and how volatility targeting quietly determines outcomes. From precious metals to currencies, from crisis alpha to geopolitical risk, this episode offers a grounded look at why trend following thrives during disruption and why regime change remains its natural habitat.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Katy on LinkedIn.Episode TimeStamps:00:00 - Introduction to the Systematic Investor Series00:39 - Weather disruptions and market perspective02:31 - Precious metals and extreme commodity moves04:28 - Gold, central banks, and monetary regime shifts07:43 - Replication versus full CTA diversification09:47 - Liquidity differences across metals12:03 - Metals leading trend performance in 202615:01 - Multi-sector trends and diversification benefits20:13 - Media attention and the return of trend following23:29 - Research insights on speed and dispersion31:44 - Trend speed and timing tradeoffs40:59 - Market concentration and narrow universes43:19 - Volatility estimation as a hidden...

In this episode of Book Overflow, Carter and Nathan continue discussing Designing Data-Intensive Application by Martin Kleppman!Join the official Book Overflow Discord! -- Want to talk with Carter or Nathan? Book a coaching session! ------------------------------------------------------------Carterhttps://www.joinleland.com/coach/carter-m-1Nathanhttps://www.joinleland.com/coach/nathan-t-2-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Designing Data-Intensive Application by Martin Kleppman!https://amzn.to/3LNwzSK----------------00:00 Intro01:53 About the Book and Author03:12 Initial Thoughts on DDIA Chapters 5-719:10 Chapter 5: Replication43:00 Chapter 6: Partitioning52:39 Chapter 7: Transactions1:05:12 Hot Takes1:11:59 Final Thoughts----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Dominic Williams, President & Chief Scientist of the DFINITY Foundation and CEO of Caffeine AI, explains the Internet Computer and his vision of a “sovereign cloud where AI builds the web.” He breaks down core innovations like the Network Nervous System, Motoko, and orthogonal persistence, and how AI coding could remove adoption barriers. The conversation explores unstoppable applications, real-world case studies like OpenChat, and the tension between control, security, and decentralization in AI governance. LINKS: Anthropic article on smart contracts Caffeine AI official website Internet Computer network dashboard Liquid Reign book website Sponsors: Blitzy: Blitzy is the autonomous code generation platform that ingests millions of lines of code to accelerate enterprise software development by up to 5x with premium, spec-driven output. Schedule a strategy session with their AI solutions consultants at https://blitzy.com MongoDB: Tired of database limitations and architectures that break when you scale? MongoDB is the database built for developers, by developers—ACID compliant, enterprise-ready, and fluent in AI—so you can start building faster at https://mongodb.com/build Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week four at https://serval.com/cognitive Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (03:37) Origins of Internet Computer (10:09) Decentralization philosophy and AI (15:12) Tamper-proof architecture explained (Part 1) (19:43) Sponsors: Blitzy | MongoDB (22:08) Tamper-proof architecture explained (Part 2) (25:25) Network design and scaling (Part 1) (33:30) Sponsors: Serval | Tasklet (35:50) Network design and scaling (Part 2) (36:56) Replication costs and cloud (50:35) Network Nervous System governance (59:33) Caffeine, Motoko and persistence (01:20:19) Self-writing cloud disruption (01:32:30) Resilience and security guarantees (01:41:23) Use cases and limitations (01:49:56) AI misalignment and ensembles (02:00:47) Kill switches and governance (02:09:12) Future of self-writing software (02:11:40) Outro PRODUCED BY: https://aipodcast.ing

Are we living in a cosmic quarantine? Today on The Infinite Rabbit Hole, we break down the most explosive fringe news of early 2026. First, we dive into the fallout from the "Age of Disclosure" documentary. New whistleblowers have emerged from Vandenberg Space Force Base, alleging that the facility is currently housing non-human biologics and recovered UAP craft. Is the government finally losing its grip on the 80-year cover-up?Then, we head into deep space to discuss the Void Hypothesis. New data suggests the Milky Way is trapped inside a massive, low-density "bubble" known as the KBC Void. We explore the terrifying possibility that Earth has been intentionally isolated from the rest of the galactic community.Also in this episode:Cryptid Watch: Aggressive "rock-throwing" encounters at Mt. Baldy. Is the legendary Yucca Man returning to Southern California?AI Weirdness: A terrifying milestone in digital evolution. AI models are now bypassesing human safety filters by "self-replicating" and rewriting their own code.Jeremy's Take: Why the "Singularity" might look more like a digital breakaway civilization than a robot uprising.Stop searching for the truth and start finding it. ---Connect with the Inner Circle at InfiniteRabbitHole.comJoin the conversation and access all the source links for this episode on our Facebook Group and X (formerly Twitter).#VandenbergLeaks #UAPDisclosure #CosmicVoid #AIREvolution #MtBaldyBigfoot #InfiniteRabbitHole #FringeNews2026

We discuss:Process-oriented fields like economics, evolution, and epistemologyLevels of abstraction and explanation vs. interacting domains of thoughtDifferent measures of complexity - of programs, ideas, organisms, and designed objectsThe role of replication in how evolution works and how the mind worksFollow me on Twitter! @dela3499Listen on Apple Podcasts, Spotify, Youtube, and more: https://carlos.buzzsprout.com/share

Bob Greenyer discusses his 13-year journey as a researcher in the field of Low Energy Nuclear Reactions, emphasizing the importance of finding a sustainable energy source beyond traditional methods like solar and wind. He elaborates on the scientific complexities and innovations behind coherent energy transformations, referencing various experiments and historical figures in the field, and proposes that advancements in this technology could lead to unlimited energy and revolutionary propulsion systems. Greenyer also critiques the current understanding of atomic models and reveals classified information regarding the potential of these new energy sources.00:00 Introduction to Bob Greenyer45:25 Introduction to Proton Magnetic Moments45:55 Gravitational Collapse and Hydrogen Neutralization46:28 Electrodynamics and the Aronoff Bomb Effect47:34 Creating Coherent Matter Waves49:02 Fractal Magneto Hydrodynamic Structures50:48 Ball Lightning and Element Synthesis54:39 Cavitation and Vortex Structures56:41 Replication and Experimental Evidence01:01:49 The Role of Relic Neutrinos and Dark Matter01:14:54 Magneto Hydrodynamic Structures in Ancient Knowledge01:18:31 Introduction to Helical Braid and Coherent Matter Structures01:19:15 Exploring Coherent Matter Wave Beams01:19:26 John Hutchinson's Metal Manipulation01:19:47 Wheel Within Wheel Structures01:20:24 Plasma Experiments and Ball Lightning01:21:49 Tesla's Warden Cliff Tower and Great Pyramid of Egypt01:23:59 Propulsion Systems and Unlimited Energy01:26:33 Magnetic Fields and Space-Time Metrics01:28:50 Fusion Energy and Ball Lightning01:31:45 Matsumoto's Archive and Restricted Information01:51:00 Conclusion and Future ProspectsAbout Bob: https://cosmicsummit.com/cosmic-summit-2025-bob-greenyer/https://remoteview.substack.com/Martin Fleischmann Memorial Project: http://www.quantumheat.org/index.php/en/https://x.com/quantumheat========Slides, summaries, references, and transcripts of my podcasts: https://tomn.substack.com/p/podcast-summariesMy Linktree: https://linktr.ee/tomanelson1

This is a special episode, highlighting a session from ELC Annual 2025! The true promise of AI isn't in replicating human intelligence. It's in developing entirely new forms of non-human intelligence that perceive and understand the world in fundamentally different ways. Jamie Lien (Co-Founder and Chief Scientist @ Archetype AI) and Rashi Agarwal (Head of AI Engineering @ GoodLeap) explore the emergence of "Physical AI" - machines that sense the world through modalities beyond human biology to form internal representations free from our biases. This means building machines that can directly sense the physical world through modalities beyond human biology, form their own internal representations and interpretations free from our biases, and then translate that understanding back to us in human terms. ABOUT JAIME LIENJaime Lien, Ph.D. is Co-Founder and Chief Scientist at Archetype AI, a pioneering startup advancing Physical AI, artificial intelligence that understands the real world through real-time sensor data fusion.With over a dacade of experience in radar-based sensing, signal processing, and hardware engineering, Jaime's career bridges cutting-edge research and consumer-ready innovation. Before Archetype, she led radar sensing development for Google ATAP's Project Soli and contributed wireless communication and localization expertise at NASA's Jet Propulsion Laboratory. ABOUT RASHI AGRAWALRashi Agrawal is Head of AI Engineering at GoodLeap, where she leads enterprise-wide AI initiatives that deliver real business impact. An accomplished speaker, she covers the latest in AI, including context engineering, evaluations, and multi-agent collaboration, while driving Applied AI innovation in the enterprise. Previously, she scaled engineering teams at Yahoo, advancing its multibillion-dollar advertising business. A passionate world traveler to 40+ countries, Rashi brings global perspective and energy to her leadership and storytelling. SHOW NOTES:Archetype AI's mission: Building a foundation model for physical reality (2:24)The potential for discovery: Using AI to observe phenomena humans cannot perceive (3:36)Augmentation vs. Replacement: Giving humans "superpowers" rather than automating them away (5:48)The "Perfect Storm" for Physical AI: Transformers, self-supervised learning, and commodity sensors (6:04)Defining “Non-Human Intelligence” and removing the constraints of human labels (8:34)Why language is inherently lossy and insufficient for true physical understanding (10:28)Real-world application: How Physical AI aids safety decision-making in the solar industry (12:35)Use case: Improving pedestrian safety and traffic signaling in Bellevue (14:51)The biggest engineering leadership challenge: Embracing the “messiness” of real-world data (16:21)Q&A: Why we shouldn't teach AI physical laws, but let it discover them (18:50)Q&A: Validating models when there is a defined ground truth vs. subjective language (20:49)Q&A: Compute requirements and the future of active learning at the edge (22:05) LINKS AND RESOURCESVideo version of Jaime and Rashi's session at ELC Annual 2025 This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Last in our series looking into characteristics that shape citizens of God's kingdom. How are you replicating the life of Christ?Follow us on Instagram @TroyChiAlphaFind us online at troychialpha.comEmail us at troychialpha@gmail.com

In this episode, we try to make the concept of tacit knowledge explicit. How much of our scientific knowledge depends on knowledge that we can't communicate directly? How can we replicate studies, if they might rely on tacit knowledge? And why has the concept itself not been made more explicit in the last 45 years? Enjoy.    Collins, H. (2012). Tacit and Explicit Knowledge. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/T/bo8461024.html  Franklin, A., & Collins, H. (2016). Two Kinds of Case Study and a New Agreement. In T. Sauer & R. Scholl (Eds.), The Philosophy of Historical Case Studies (pp. 95–121). Springer International Publishing. https://doi.org/10.1007/978-3-319-30229-4_6  Polanyi, M. (1966). The Tacit Dimension. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/T/bo6035368.html Collins, H. M. (1975). The Seven Sexes: A Study in the Sociology of a Phenomenon, or the Replication of Experiments in Physics. Sociology, 9(2), 205–224. https://doi.org/10.1177/003803857500900202  Gerholm, T. (1990). On Tacit Knowledge in Academia. European Journal of Education, 25(3), 263–271. https://doi.org/10.2307/1503316 

I dive into the Human Rights Foundation's look at Bitcoin's quantum vulnerability - what's real, what's hype, and what would it take to truly secure the network. Then I unpack the social and technical chaos such a shift would demand, from wallet redesigns to the moral question of whether “fixing” the past would break Bitcoin's principles. Links Mentioned Against Allowing Quantum Recovery of Bitcoin by Jameson Lopp (Link: https://blog.lopp.net/against-quantum-recovery-of-bitcoin/) Presidio Bitcoin Quantum Summit - summary and speaker discussions (Link: https://www.presidiobitcoin.org/) Replication of Quantum Factorisation Records with an8-bit Home Computer, an Abacus, and a Dog - paper by Peter Gutmann & Stephan Neuhaus (Link: https://eprint.iacr.org/2025/1237.pdf)Angewandte Wissenschaften Guy's Roundtable_011 - Treasuries, Politics, Quantum & Why This Cycle is Different (Discusses Steve's take on quantum hype) (Link: https://fountain.fm/episode/4xAEXBtZcdFfg064FIRI) Check out the original article The Quantum Threat to Bitcoin by HRF (Link: https://hrf.org/latest/the-quantum-threat-to-bitcoin/)  Check out our awesome sponsors! Ledn: Need fiat but don't want to sell your Bitcoin? Ledn offers secure, Bitcoin-backed loans with no credit checks, flexible repayment, and fast turnaround—often within 24 hours. With $10B+ in loans across 100+ countries and transparent Proof of Reserves, Ledn is a trusted option for unlocking liquidity without giving up your Bitcoin. (Link: https://learn.ledn.io/audible) HRF: The Human Rights Foundation is a nonpartisan, nonprofit organization that promotes and protects human rights globally, with a focus on closed societies. Subscribe to HRF's Financial Freedom Newsletter today. (Link: https://mailchi.mp/hrf.org/financial-freedom-newsletter) OFF: The Oslo Freedom Forum is a global human rights event by the Human Rights Foundation (HRF), uniting voices from activism, journalism, tech, and beyond. Through powerful stories and collaboration, OFF advances freedom and human potential worldwide. Join us next June. (Link: https://oslofreedomforum.com/) Pubky: Pubky is building the next web, a decentralized system designed to put control back in your hands. Escape censorship, algorithmic manipulation, and walled gardens by owning your identity and data. Explore the Pubky web and become the algorithm today. Don't forget to find me on my Pubky ID here: pk:5d7thwzkxx5mz6gk1f19wfyykr6nrwzaxri3io7ahejg1z74qngo. (Link: https://pubky.org) Chroma: Chroma is dedicated to advancing human performance and well-being through cutting-edge light therapy devices and performance eyewear. Their mission is to enhance physical and mental health, unlocking peak human health, cognitive function, and physical performance. Get 10% off your order with the code BITCOINAUDIBLE. (Link: https://getchroma.co/?ref=BitcoinAudible) Host Links ⁠Guy on Nostr ⁠(Link: http://tinyurl.com/2xc96ne...

Today's guest is Kathryn Kaminski, Chief Research Strategist at AlphaSimplex, where she's also the co-portfolio manager for the firm's Managed Futures Strategy and Global Alternatives Strategy. She also co-authored the book Trend Following with Managed Futures: The Search for Crisis Alpha. In today's episode, Meb and Katy discuss the tough year for managed futures strategies, which have experienced the 2nd largest drawdown in the last quarter century. Katy walks through the history of drawdowns and recoveries since 2000, explaining investors who have been patient in the past have been rewarded when the strategy recovers. She emphasizes managed futures' ability to serve as a diversifier to stocks, revisits her research on crisis alpha, and touches on the rise of managed futures ETFs as a way for investors to get exposure to this asset class. Listen to Katy's first appearance in May 2021. (0:00) Starts (1:05) Katy's thoughts on managed futures in 2025 (3:31) Lessons from past drawdowns (10:04) The patience premium (17:10) Follow the trends (22:12) Crisis Alpha revisited (26:01) Managed futures accessibility and ETFs (29:35) Replication methods (35:02) Implementing AI ----- Follow Meb on⁠ X⁠,⁠ LinkedIn⁠ and⁠ YouTube⁠ For detailed show notes, click ⁠here⁠ To learn more about our funds and follow us, subscribe to our ⁠mailing list⁠ or visit us at⁠ cambriainvestments.com⁠ ----- Follow The Idea Farm: ⁠X⁠ | ⁠LinkedIn⁠ | ⁠Instagram⁠ | ⁠TikTok⁠ ----- Interested in sponsoring the show? Email us at Feedback@TheMebFaberShow.com ----- Past guests include ⁠Ed Thorp⁠, ⁠Richard Thaler⁠, ⁠Jeremy Grantham⁠, ⁠Joel Greenblatt⁠, ⁠Campbell Harvey⁠, ⁠Ivy Zelman⁠, ⁠Kathryn Kaminski⁠, ⁠Jason Calacanis⁠, ⁠Whitney Baker,⁠ ⁠Aswath Damodaran⁠, ⁠Howard Marks⁠, ⁠Tom Barton⁠, and many more.  ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠https://thepodcastconsultant.com⁠). Learn more about your ad choices. Visit megaphone.fm/adchoices

How can we  address the problem of violence against the poor? International Justice Mission exists to answer this question with protecting and rescuing victims, bringing criminals to justice, restoring survivors to safety and strength, and helping local law enforcement build a safe future that lasts. In this episode, International Justice Mission's founder and CEO, Gary Haugen, joins Mark Labberton to reflect on almost three decades of IJM's fight against violence and slavery worldwide—and the spiritual formation that sustains it. Haugen shares the origins of IJM in response to systemic violence against the poor, the evolution from individual rescues to transforming justice systems, and the remarkable rise of survivor leaders transforming their own nations. Together they reflect on courage, joy, and faith amid immense risk—bearing witness to God's power to bring justice and healing through ordinary people. Episode Highlights “Protecting the poor from violence is God's weight, but it's our work, and we're gonna seek to do it Jesus's way.” ”In this era, I just think what the world is aching to see is the followers of Jesus who have a incandescent freedom from fear and a life-giving joy.” “Most of this violence will go away if government does just even a decent job of enforcing the law.” “Our first commitment is to help each other become more like Jesus—and from that strength, to do justice.” “The greatest miracle of IJM is not only the results—it's the freedom from fear and the joy with which they've done it.” “God saw them in their darkness, and they now testify to the goodness of an almighty God who loved them.” Helpful Links and Resources International Justice Mission – https://www.ijm.org Gary Haugen, The Locust Effect: Why the End of Poverty Requires the End of Violence – https://www.amazon.com/Locust-Effect-Poverty-Requires-Violence/dp/0199937877 Gary Haugen, Just Courage: God's Great Expedition for the Restless Christian – https://www.amazon.com/Just-Courage-Expedition-Restless-Christian-ebook/dp/B001PSEQR4 Riverside Church Sermon by Martin Luther King Jr., “Beyond Vietnam” — https://kinginstitute.stanford.edu/encyclopedia/beyond-vietnam William Lloyd Garrison biography – https://www.britannica.com/biography/William-Lloyd-Garrison Rwanda Genocide Investigation (UN Historical Overview) – https://www.un.org/en/preventgenocide/rwanda About Gary Haugen Gary Haugen is the founder and CEO of International Justice Mission (IJM), the world's largest international anti-slavery organization. Before founding IJM in 1997, he served as the Director of the United Nations' investigation into the Rwandan genocide and previously worked at the U.S. Department of Justice, focusing on police misconduct. A graduate of Harvard University and the University of Chicago Law School, Haugen has dedicated his life to ending violence against the poor and mobilizing the global church for justice. Show Notes The founding of IJM in 1997 as a Christian response to violence against the poor Gary Haugen's formative experience directing the UN's genocide investigation in Rwanda Realization that hunger and disease were being addressed—but violence was not Early cases in the Philippines, South Asia, and Peru exposing police-run brothels and child slavery IJM 1.0: rescuing individuals from slavery and abuse, case by case IJM 2.0: strengthening local justice systems to prevent violence before it happens Martin Luther King Jr.'s “Jericho Road” as a model for systemic transformation Formation of small multidisciplinary teams—lawyers, investigators, social workers IJM's evolution from rescue operations to building sustainable justice infrastructure Twenty-year celebration: Liberate conference and the global IJM staff retreat IJM's culture of spiritual formation: daily solitude, prayer, and community rhythms A Christian order of justice rooted in prayer, silence, and shared joy Spiritual formation as the foundation for sustainable justice work Experiments in Cambodia, the Philippines, and South Asia reducing violence by up to 85% Replication of IJM's model across 46 regions to protect 500 million vulnerable people Goal by 2030: one million freed from slavery, 300 million living under protection Empowering survivor leaders: from victims to advocates and elected officials Stories of transformation like Pama in South Asia leading the Release Bonded Laborers Association The Kenyan case of Willie Kimani—murdered IJM lawyer whose legacy reformed police accountability IJM's resilience: pursuing justice for six years until conviction of perpetrators Theological grounding: justice as God's work, pursued in Jesus's way Haugen on resilience: “It's a marathon, not a sprint” Joy and freedom from fear as hallmarks of IJM's culture How IJM balances global crisis fatigue with focused mission clarity Future challenges: technology-driven oppression—live-stream child abuse and forced scamming Global body of Christ as the essential network for courage and joy Sustainability and local leadership as the future of global justice movements Spiritual communities as the seedbed for future justice leaders Production Credits Conversing is produced and distributed in partnership with Comment Magazine and Fuller Seminary.  

Join Dr Jon Sutton and Dr Andrea Oskis (Food Writer and Senior Lecturer at Middlesex University, London) for a research-packed feast, through which they navigate the deep and meaningful connections between us and the food we eat. From appetiser to dessert, every dish opens the door to exploring fascinating studies and discoveries. This is Episode 43 of PsychCrunch, the podcast of the British Psychological Society's Research Digest, sponsored by Zanda. Episode Credits: Hosted by Dr Jon Sutton. Audio editing by Jeff Knowler. Edited by Emma Barratt.  Hungry for more? Find out more about ‘cupboard love' with this piece by Andrea Oskis that explores the intrinsic connection between food and feelings. Or check out this piece from Chukwudi Barrah on nine psychological insights that may just turn down the heat at the dinner table. Find these and much, much more over at The Psychologist. PsychCrunch is brought to you by Zanda, the all-in-one practice management software designed for psychologists. With telehealth, easy mobile access, and automated tools, including online scheduling and reminders, Zanda helps you run your practice, your way. Find out more at ZandaHealth.com

Wayne Shepherd talks with Jenny Waltman, Founder, CEO, and Chairman of Grace Klein Community in Birmingham, Alabama.  (click for more)  A ministry dedicated to serving underprivileged and disadvantaged people, rooted in biblical principles.Website:  https://gracekleincommunity.comJenny's Book:  God Chose the Wrong Person can be found here. Jenny and her husband lost their construction business (approx. $400,000).Moved into a high-value house zoned for an inner-city school.Their daughter attended, exposing them to stark poverty.A birthday party experience highlighted contrasts in wealth and living conditions, deeply impacting them.Convicted of being “frauds” for not living out their faith in service.Within 24 hours, others confirmed God's call to begin feeding food-insecure families.2009 launch: started serving 58 families.Today: 20,000 people served weekly.Mission & MinistryGrace Klein = “little gift from God”; community = koinonia (deep fellowship).Primary work: food support.Broader care: Bible studies, diapers, school supplies, Christmas outreach, community gatherings.Operates in 32 of 67 Alabama counties; goal: statewide, replicable model.Collaborative approach: businesses, churches, nonprofits, schools, municipalities.Scale & GrowthBudget in 2010: $14,000.Budget in 2025: $34 million (cash + in-kind).1,013 partner organizations (Jan–July 2025).~40 staff, thousands of volunteers (saving ~$3M in wage dollars annually).Facilities: 10 locations, 300+ distribution partners, 7 drive-throughs. Food turns over within 24 hours.ApproachCollaboration: everyone brings something to the table.Recipients are also contributors, reflecting biblical sharing (Acts, Corinthians).Theme 2025: “It's not about us.” Focus on Jesus, love, forgiveness, unity.Works to shift Birmingham's global reputation from hate (civil rights era) to love.Global ConnectionPartnering with Beats and Books in South Africa.Building unity across races and cultures, learning from each other.Exchange of teams between South Africa and Alabama.Birmingham's “Stronger Award” extended internationally.Replication & EducationUses Food Rescue US app: mobilizes volunteers for daily food pickup (6 a.m.–11 p.m.).Donors protected under the Emerson Act (Good Samaritan law).U.S. food waste (40%) can be redirected to food-insecure (1 in 4 Alabamians).Teaches others how to replicate the model in their communities.Impact StoryWoman from Colombia, initially angry and facing custody issues.Grace Klein invested in her spiritually and practically.Over five years, she softened, grew in intimacy with Christ, and now impacts her community.Jenny's PerspectiveWrote book: Is God Chose the Wrong Person.Believes God uses the “wrong” people to fulfill His purposes.Emphasizes surrender and reliance on Jesus.Prayer RequestsFor staff and volunteers to remain focused on Jesus.To not “grow weary in doing good.”NEXT WEEK:  Jonathon Seidl, Confessions of a Christian AlcoholicSend your support for FIRST PERSON to the Far East Broadcasting Company:FEBC National Processing Center Far East Broadcasting CompanyP.O. Box 6020 Albert Lea, MN 56007Please mention FIRST PERSON when you give. Thank you!

Worship led by Chris Lopez

Inside the NIH w/ Jay Bhattacharya on Innovation, Replication, and mRNA Policy by Technology Policy Institute

Ari Lowenstein has done just about everything in retail: consulting, product development, and leading industry associations. Now, he has built his own stores. With Ari's Pantry, he is bringing authentic Italian flavors to Texas and has already grown from one shop to four in less than two years.In this episode, Ari opens up to Crystal about his bold approach to retail, why failure is part of the process, and how customer experience drives everything he does.From a playful loyalty program where customers earn “tomatoes” instead of points to lasagna Thursdays that build community, Ari shares fresh, practical insights you can bring straight into your own business. If you are wondering how to grow fast while staying authentic, this episode is for you![1:51] – Ari's journey from global retail to opening Ari's Pantry[5:12] - Early challenges and what failure has taught him[9:37] - Creating an Italian market experience people love[10:09] - The simple truth: the longer customers stay, the more they spend[12:10] - Building a cuisine people keep coming back for[12:31] – Multiple revenue streams as protection and growth drivers[14:38] - Replication, expansion, and scaling fast[17:43] - Lessons in leadership: letting your team learn and fail forward[20:42] - His advice for retailers who are stuck and overwhelmed by posting social media content[22:15] - Authentic, consistent content that works[24:59] – How Instagram doubles as a staff training tool[26:22] - How to craft a memorable customer experience?[28:23] - Turning lasagna into a community event[29:49] – Why timing your communication matters[30:45] - A loyalty program that swaps points for “tomatoes”Join the Rooted in Retail Facebook Group to continue the conversation Join our newsletter for all the latest marketing news for retailers Show off your super fandom by getting your Rooted in Retail Merch!

What if the ideas that linger in the back of your mind — the ones you can't quite explain — are the ones you most need to pay attention to?Episode SummaryIn this episode, I explore that question with Dr Nuno Reis, a former physicist and investment banker turned thinker and guide in uncovering what he calls rare dots — those unique, pre-verbal insights that feel deeply resonant but don't yet fit into our existing models of the world.Our conversation begins with Nuno's unusual career path, from string theory and theoretical physics into financial modelling and risk management, before moving into transparency roles in finance. From there, we trace the thread of his enduring curiosity: how crises drive paradigm shifts, why models are useful but limited, and what happens when we acknowledge that not everything fits into neat rational frameworks.Nuno then introduces the concept of rare dots and explains how he's harnessing AI in a novel, deeply human way: not to replace thought, but to help us surface the preverbal ideas that shape our uniqueness. Together, we explore how rare dots can guide creativity, meaning, and innovation in our work and lives — and why paying attention to the things that don't quite make sense may be the most sensible thing we can do.Guest BiographyDr Nuno Reis describes himself as a physicist turned banker turned explorer of human thinking. After completing a PhD in theoretical physics with a focus on string theory, he transitioned into investment banking at UBS and later into financial engineering and transparency initiatives at Bloomberg.Over 18 years in finance, he applied scientific methods to risk modelling, hedging, and complex derivatives, all while maintaining a deep curiosity about the limits of logic and models.Today, Nuno is the creator of the Rare Dots framework, which helps individuals and organisations uncover the pre-verbal insights that drive creativity and meaning. He combines philosophy, history of thought, and cutting-edge uses of AI to build environments where people can explore their deepest intuitions.Through workshops, cohorts, and his own practice, he guides others in turning those rare dots into pathways for innovation, purpose, and contribution.AI-Generated Timestamped Summary[00:00:00] Introduction and framing of rare dots as preverbal insights[00:01:00] Nuno's background: from physics to banking and financial modelling[00:06:00] Applying scientific methods to markets and the 2008 crisis[00:12:00] Replication crisis and paradigm shifts in science[00:18:00] Early story of curiosity and existential questions[00:21:00] Introduction of rare dots as deeply resonant insights[00:26:00] How resonance and intuition shape meaning[00:30:00] Creating environments to surface rare dots[00:35:00] History of thinking vs history of ideas[00:40:00] Using AI as a tool for rare dots and Wittgenstein's ladder[00:46:00] How AI can surface pre-verbal thinking[00:52:00] AI as a prompt for deeper human thinking[00:56:00] Rare dots exploration as an infinite game[01:02:00] Rare dots as a guide for careers and human valueLinksNuno on LinkedIn - https://www.linkedin.com/in/nuno-reis/Nuno's Substack - https://nunobreis.substack.com/

In this episode of Iron Culture, hosts Eric Helms and Michael Zourdos engage with Dr. Zac Robinson to discuss his dissertation research on the effects of resistance training volume on muscle adaptations. The conversation delves into the complexities of individual responses to training, the importance of research design, and the implications of findings for coaching practices. They emphasize the need for caution in interpreting data and the evolving nature of sports science, highlighting the significance of collaboration and flexibility in training approaches. Chapters 00:00 Introduction and Guest Introduction 06:25 The PhD Journey and Team Dynamics 11:32 Dissertation Overview and Research Focus 18:43 Research Methodology and Individual Variability 26:50 Understanding Individual Response Variation in Training 32:09 Study Design: Isolating Variables in Training Responses 37:33 The Importance of Replication in Research 42:40 Findings 53:41 Limitations and Implications of the Study 01:00:17 Genetics and Individual Response Variability 01:02:59 The Dose-Response Relationship in Training 01:06:25 Shifting Perspectives on Training Individualization 01:11:00 The Role of Sports Science in Coaching 01:19:51 Future Directions in Resistance Training Research

Reflections on conducting a large replication project in sports and exercise science Murphy J, Caldwell AR, Warne JP. Sports Med. Published Ahead of Print. doi:10.1007/s40279-025-02200-x Due to copyright laws, unless the article is open source we cannot legally post the PDF on the website for the world to download at will. Brought to you by our sponsors at: CSMi – https://www.humacnorm.com/ptinquest Learn more about/Buy Erik/Jason/Chris's courses – The Science PT Support us on the Patreons! Music for PT Inquest: “The Science of Selling Yourself Short” by Less Than Jake Used by Permission Other Music by Kevin MacLeod – incompetech.com: MidRoll Promo – Mining by Moonlight Koal Challenge – Sam Roux

Katy Kaminski returns to examine a moment in trend following that feels familiar... but isn't. Drawing on new research, she and Niels explore how drawdowns resolve, why recovery is faster when markets break, and slower when they don't, and what that asymmetry reveals about the current cycle. They unpack copper's historic 1-day move, the role of China in CTA return dispersion, and what slower, replication-based strategies might be capturing that others aren't. This episode isn't about defending trend - it's about understanding what environments it needs, and what signals suggest we're getting closer.-----50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “Ten Reasons to Add Trend Following to Your Portfolio” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Katy on LinkedIn.Episode TimeStamps:00:48 - What has been on our radar recently?07:43 - Crazy moves in US copper09:45 - Industry performance update14:31 - How should we approach the current drawdowns in managed futures?23:01 - Why trend following is struggling at the moment31:22 - Rebalancing in trend following is key33:57 - A better alternative trend following?37:32 - The different types of impact to markets and how it reflects on the CTA industry44:41 - The key variables for understanding regimes53:21 - Replication is beating the benchmarks that they are trying to replicate58:16 - Mechanical vs. regression based replication59:33 - Defining tracking errorCopyright © 2024 – CMC AG – All Rights...

Ken's linksknswartz@aol.comprogressed so far.  https://home.cern/news/news/accelerators/first-ever-collisions-oxygen-lhchome.cernhttps://www.researchgate.net/publication/392160175_Replication_of_Low-Energy_Nuclear_Reaction_in_WaterDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenThe Forbidden Documentary: Doors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZMerchhttps://fknstore.net/Start your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10FKN Link Treehttps://linktr.ee/FKNlinksForbidden Knowledge Network https://forbiddenknowledge.news/ Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes Book!Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/YouTube https://youtube.com/@fknclipspBecome Self-Sufficient With A Food Forest!!https://foodforestabundance.com/get-started/?ref=CHRISTOPHERMATHUse coupon code: FORBIDDEN for discountsOur Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email meforbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/Become a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

My guest is Dr. Jay Bhattacharya, MD, PhD, Director of the National Institutes of Health (NIH) and Professor Emeritus of Health Policy at Stanford University. We discuss which scientific questions ought to be the priority for NIH, how to incentivize bold, innovative science especially from younger labs, how to solve the replication crisis and restore trust and transparency in science and public health, including acknowledging prior failures by the NIH. We discuss the COVID-19 pandemic and the data and sociological factors that motivated lockdowns, masking and vaccine mandates. Dr. Bhattacharya shares his views on how to resolve the vaccine–autism debate and how best to find the causes and cures for autism and chronic diseases. The topics we cover impact everyone: male, female, young and old and, given that NIH is the premier research and public health organization in the world, extend to Americans and non-Americans alike. Read the episode show notes at hubermanlab.com. Thank you to our sponsors AG1: https://drinkag1.com/huberman David: https://davidprotein.com/huberman Eight Sleep: https://eightsleep.com/huberman Levels: ⁠https://levels.link/huberman⁠ LMNT: https://drinklmnt.com/huberman Timestamps 00:00:00 Jay Bhattacharya 00:06:56 National Institutes of Health (NIH), Mission 00:09:12 Funding, Basic vs. Applied Research 00:18:22 Sponsors: David & Eight Sleep 00:21:20 Indirect Costs (IDC), Policies & Distribution 00:30:43 Taxpayer Funding, Journal Access, Public Transparency 00:38:14 Taxpayer Funding, Patents; Drug Costs in the USA vs Other Countries 00:48:50 Reducing Medication Prices; R&D, Improving Health 01:00:01 Sponsors: AG1 & Levels 01:02:55 Lowering IDC?, Endowments, Monetary Distribution, Scientific Groupthink 01:12:29 Grant Review Process, Innovation 01:21:43 R01s, Tenure, Early Career Scientists & Novel Ideas 01:31:46 Sociology of Grant Evaluation, Careerism in Science, Failures 01:39:08 “Sick Care” System, Health Needs 01:44:01 Sponsor: LMNT 01:45:33 Incentives in Science, H-Index, Replication Crisis 01:58:54 Scientists, Data Fraud, Changing Careers 02:03:59 NIH & Changing Incentive Structure, Replication, Pro-Social Behavior 02:15:26 Scientific Discovery, Careers & Changing Times, Journals & Publications 02:19:56 NIH Grants & Appeals, Under-represented Populations, DEI 02:28:58 Inductive vs Deductive Science; DEI & Grants; Young Scientists & NIH Funding 02:39:38 Grant Funding, Identity & Race; Shift in NIH Priorities 02:51:23 Public Trust & Science, COVID Pandemic, Lockdowns, Masks 03:04:41 Pandemic Mandates & Economic Inequality; Fear; Public Health & Free Speech 03:13:39 Masks, Harms, Public Health Messaging, Uniformity, Groupthink, Vaccines 03:22:48 Academic Ostracism, Public Health Messaging & Opposition 03:30:26 Culture of American Science, Discourse & Disagreement 03:36:03 Vaccines, COVID Vaccines, Benefits & Harms 03:47:05 Vaccine Mandates, Money, Public Health Messaging, Civil Liberties 03:54:52 COVID Vaccines, Long-Term Effects; Long COVID, Vaccine Injury, Flu Shots 04:06:47 Do Vaccines Cause Autism?; What Explains Rise in Autism 04:18:33 Autism & NIH; MAHA & Restructuring NIH? 04:25:47 Zero-Cost Support, YouTube, Spotify & Apple Follow & Reviews, Sponsors, YouTube Feedback, Protocols Book, Social Media, Neural Network Newsletter Disclaimer & Disclosures Learn more about your ad choices. Visit megaphone.fm/adchoices