Podcasts about Parth

College Clinical Adviser Denise Voon MCOptom talks to Dr Parth Shah MCOptom, optometrist and sports vision consultant to a Premier League football club, about his career at the intersection of optometry and sport.They discuss how goalkeepers are trained to track and react to the ball, what optometry can bring to professional football, and how the science of sports vision applies in everyday practice.

Tokens to the Future is a new limited series from Possible about how AI is expanding what people can build—with more tokens, custom agents, and a community behind them. In this first episode, Reid sits down with Parth Patil, co-creator of Reid AI and co-architect of the Token Grantee program, to lay out the thesis behind it. Through the program, a small inaugural group of creators and builders—working across film, gaming, comics, print, digital art and more—each receive a recurring grant of $1,000 a week in tokens, access to a custom fleet of AI agents, and an ongoing collaboration with Parth. There are no restrictions on which tools they use; the freedom to choose is the point. Grantees will join the series over the next few months. Each episode features one of them showcasing how they're using these tools to build products that could reshape their work, role and industries. Along the way, Reid and Parth discuss how AI agents are becoming persistent collaborators, why the biggest opportunities may lie in creative rather than technical domains, and how closing the gap between an idea and a finished product is changing entrepreneurship. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

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

Après neuf ans d'hésitation, les députés et sénateurs français ont fini, le 7 mai 2026, par voter à l'unanimité une loi-cadre sur la restitution des objets pillés pendant la colonisation. C'est une victoire pour les anciennes colonies françaises comme le Bénin. Mais le retour en Afrique de ces biens culturels ne risque-t-il pas d'être stoppé l'année prochaine s'il y a un changement de majorité en France ? C'est l'une des questions que Christophe Boisbouvier a posées à l'historienne franco-béninoise Marie-Cécile Zinsou, qui préside la Fondation Zinsou à Ouidah, près de Cotonou. RFI : Avec tous ces lieux de mémoire, avec tous ces musées, quelles sont les ambitions du Bénin de Romuald Wadagni ? Marie-Cécile Zinsou : Alors écoutez, le Bénin s'est engagé depuis quelques années dans une préservation de son patrimoine et dans une nouvelle politique qui est totalement axée à la fois sur son histoire et sur l'avenir. Donc, je crois que le nouveau président va s'inscrire dans le chemin qu'il avait tracé aux côtés de Patrice Talon, notre précédent président, ces dernières années. On est un peu dans la continuité, c'est ça ? Il me semble qu'on est effectivement dans la continuité je pense, le président Wadagni était notre ministre des Finances pendant les dix dernières années. Donc, il me semble qu'on est dans quelque chose qui ressemble à une continuité. À l'origine de ce renouveau mémoriel, il y a l'action initiale du ministre Aurélien Agbenonci, il y a dix ans déjà, quand il a réclamé à la France de François Hollande la restitution des trésors royaux du Bénin. Est-ce que la loi-cadre votée par la France répond à vos attentes ? Ce qui est absolument fondamental, c'est ce que le courrier du Bénin a déclenché en France. Et la réponse qu'Emmanuel Macron y a apportée à Ouagadougou en 2017. Puisqu'on peut vraiment dater le début du processus de restitution à partir de ce discours. Donc aujourd'hui, la loi-cadre, après neuf ans d'attente, arrive pour donner une forme à ces restitutions et pour éviter les lois d'exception qui avaient eu lieu jusqu'alors pour à la fois le Bénin, le Sénégal et la Côte d'Ivoire. Oui, c'est-à-dire que jusqu'à présent il fallait une loi par restitution ? Oui, il fallait des lois spécifiques. Et la loi-cadre, elle va être efficace très rapidement puisque le gouvernement français est motivé et l'a fait savoir. Les parlementaires ont voté à l'unanimité cette loi, ce qui montre bien que la représentation nationale française est tout aussi concernée par les questions de restitution. Et il y a de nombreuses demandes en cours du côté du continent. Donc, c'est une loi qui, avec un gouvernement et une représentation nationale française motivés, peut être extrêmement efficace. Est-ce qu'il y a des failles malgré tout dans cette loi ? Alors cette loi, elle propose qu'il y ait des commissions bi-nationales, donc entre l'État demandeur et l'État français pour examiner le cas des biens qui ont été pillés illicitement. Evidemment, si la France est motivée, elle met en place une commission qui regarde avec intérêt, objectivité et bienveillance les demandes de restitutions. Si le prochain gouvernement était moins motivé par les restitutions, ces commissions pourraient être nettement plus dures et pourraient bloquer certains processus. Je pense que c'est pour ça que c'est très important qu'il y ait des premières restitutions assez rapidement, parce que, après, ce sera un phénomène inarrêtable. Et le président français l'a dit à Nairobi, a priori, c'est une loi irréversible. On ne reviendra pas sur les restitutions. Il faut créer une dynamique ? Il faut créer une dynamique et il faut créer des précédents pendant cette année où toutes les étoiles semblent alignées. Quels sont les pays développés les plus réticents à restituer les œuvres qui ont été pillées au 19ᵉ et au XXᵉ siècle ? Alors chaque pays est complexe. En Europe, le plus réticent est peut-être celui qui a le plus restitué, et les gens ne s'en rendent pas forcément compte. Mais aujourd'hui, on peut dire que le Royaume-Uni est celui qui se protège le plus. Notamment parce que les Britanniques sont face à des demandes de restitutions européennes, et notamment des Grecs qui réclament les frises du Parthénon, qui sont au British Museum. Et donc, à la fois, le Royaume-Uni refuse de légiférer et bloque une partie des demandes. Et en même temps, c'est le Royaume-Uni qui a restitué plus de 70 biens déjà à travers ses musées non nationaux, donc à travers ses musées universitaires et ses musées régionaux. Donc, le Royaume-Uni est peut-être celui qui, dans la loi, est le moins ambitieux, mais qui restitue le plus. Alors qu'il y a des pays comme la Belgique qui ont restitué officiellement, massivement, puisqu'il y a plus de 1 200 objets qui sont déjà concernés par la loi belge, et en même temps, il y a un seul masque qui a fait le voyage de retour au Congo. Est-ce que les différentes institutions béninoises qui ont été créées ces derniers mois, notamment le Comité scientifique national auquel vous appartenez… Est-ce que tout cela peut encourager les pays qui détiennent des biens culturels et qui ne veulent pas s'en séparer ? Est-ce que cela peut les encourager à les restituer à des pays demandeurs comme le vôtre ? Il me semble que l'exemple du Bénin a été important, notamment avec la première restitution, avec la première loi d'exception de la France, quand les 26 objets du Trésor royal d'Abomey sont revenus, le Bénin a fait une démonstration assez éclatante de ce que pouvait être le retour du patrimoine et le partage à tous de ces collections. Donc, je pense que ça a aussi permis de faire sauter des verrous qui étaient bien souvent des préjugés sur une base raciste, ou sur juste une base de se dire que l'Afrique n'était pas intéressée à son patrimoine, que les Africains n'allaient pas au musée. Ça, c'était des propos qu'on entendait dans la société française. Tout à coup, à partir du moment où on a fait la démonstration que les gens se sont passionnés pour cette exposition, qu'elle a été ouverte à tous en grand, je crois que les gens ont réalisé ce que c'était, en fait, que d'être privé de son patrimoine depuis des dizaines ou des centaines d'années, et d'y avoir enfin un accès. Donc, je crois qu'évidemment certains pays, qui prenaient la question très à la légère, ont vu la preuve devant leurs yeux que c'étaient des questions fondamentales et qu'on ne pouvait pas rester sur des préjugés souvent racistes qui étaient prédominants.

You can get early access to our episodes as well as video version of this episode by signing up and supporting our work through Patreon. This week, Asim, Amrita, Sujoy, and special guest Parth from TooManyTats revisit Kabhi Alvida Naa Kehna — the glossy, messy, deeply divisive relationship drama that pushed Bollywood into uncomfortable territory. Twenty years later, does Karan Johar's infidelity epic still work? The gang unpack Shah Rukh's aggressively miserable “Dev,” Rani Mukerji's quietly heartbreaking performance, Sexy Sam's outrageous energy, and why this film feels both ahead of its time and completely stuck in the 2000s. There's also a detour into today's Bollywood stars, OTT-era morality, Cannes discourse, Pati Patni Aur Woh Do, and whether Maya and Dev would actually survive modern Toronto rent prices. Expect classic Khandaan chaos, emotional damage, soundtrack worship, and a surprising amount of analysis about chewing gum etiquette. You can also check out Parth's Animation Studio Mikudi here Segments 00:00 – Introducing Parth / tattoos & Om Shanti Om dreams 04:15 – Why Kabhi Alvida Naa Kehna now? 07:50 – Pati Patni Aur Woh Do review 12:20 – Kartavya, caste politics & OTT thrillers 22:40 – Alia Bhatt, Cannes & Bollywood discourse 31:35 – Revisiting KANK after 20 years 44:30 – Why Shah Rukh's Dev is so hard to love 57:00 – Marriage, infidelity & Karan Johar psychology 01:14:30 – The hotel room scene & Bollywood taboo-breaking 01:22:50 – Rani Mukerji's performance appreciation 01:28:35 – Soundtrack deep dive: Mitwa, Tumhi Dekho Na & more 01:40:30 – Would Maya and Dev actually last? 01:42:30 – Dream casting a modern KANK remake

Can you still win patient loyalty when you walk away from insurance contracts?Dr. Parth Kansagra did just that, launching a premium-priced, insurance-free dental practice in Orange County against all conventional wisdom. Dr. Kansagra shares why he walked away from burnout and chose the hard road of building a practice on his own terms, leaning on his MBA skills, strong patient communication, and a vision for comprehensive patient care.He unpacks the pivotal moment that inspired him to drop insurances cold turkey, then takes us behind the scenes of his high-touch transition: spending months meeting every patient face-to-face to personally explain the new model. Dr. Kansagra gets real about the financial and emotional stress of entrepreneurship, how losing insurance-driven patients opened doors for true cosmetic-focused care, and the steps he took to make premium pricing feel trustworthy with transparent menus, longer exams, and even a six-year warranty. Listen in for honest lessons on patient relationships, marketing that actually works, and how building real trust became his not-so-secret advantage.What You'll Learn in This Episode:How dropping insurance transformed patient relationships and practice cultureStrategies for communicating change and building trust with existing patientsThe financial realities and risks of going insurance-free from day oneWhy personalized attention became the cornerstone of Dr. Kansagra's approachTips for converting bargain-seekers into loyal, high-value patientsUnexpected growth tactics: Groupon whitening offers, memberships, and “social media scholarships”Building a transparent fee structure including warranties and easy refund policiesThe importance of direct doctor access (and giving out his Google number!)How “banked” membership plans and referral networks can drive real growthPress play to learn how Dr. Kansagra builds real trust in his practice!‍Sponsors:‍Oryx: All-In-One Cloud-Based Dental Software Created by Dentists for Dentists. Patient engagement, clinical, and practice management software that helps your dental practice grow without compromise. Click or copy and paste the link here for a special offer! https://thedentalmarketer.lpages.co/oryx/Click here for a special offer!Guest: Dr. Parth KansagraPractice Name: Bespoke Dental StudiosCheck out Parth's Media:Practice: https://bespokedentalstudios.com/Juma (Heathcare Wallet for Patients): https://getjuma.com/Instagram: https://www.instagram.com/dr.parthk/LinkedIn: https://www.linkedin.com/in/drparthkansagra/‍Host: Michael AriasJoin my newsletter: https://thedentalmarketer.lpages.co/newsletter/‍Join this podcast's Facebook Group: The Dental Marketer Society‍Love the Podcast? Follow on Your Favorite App! https://lnkfi.re/TDMPod

Welcome to the CanadianSME Small Business Podcast, hosted by Maheen Bari. Today, we explore how operational discipline and supply chain clarity are becoming the real drivers of sustainable growth. Joining us is Parth Davé, Founder and CEO of NexaFlux Inc. Parth shares how businesses can move from reactive chaos to structured, predictable operations. Key Highlights Scaling Signals: Parth explains how to identify when systems can no longer support growth. Cost of Poor Coordination: Parth highlights how inefficiencies silently erode margins. From Chaos to Control: Parth shares how to build predictable operating rhythms. Supply Chain Gaps: Parth explains common hidden issues uncovered through diagnostics. Scaling Safely: Parth outlines the top priority for maintaining control while growing. Special Thanks to Our Partners: UPS: https://solutions.ups.com/ca-beunstoppable.html?WT.mc_id=BUSMEWA ADP Canada: https://www.adp.ca/en.aspx For more expert insights, visit www.canadiansme.ca and subscribe to the CanadianSME Small Business Magazine. Stay innovative, stay informed, and thrive in the digital age! To learn more about how we are supporting the ecosystem, please visit the CanadianSME Small Business Foundation at smbfoundation.ca. Disclaimer: The information shared in this podcast is for general informational purposes only and should not be considered as direct financial or business advice. Always consult with a qualified professional for advice specific to your situation.

For many life sciences teams, the first wave of AI has looked like copilots: smart search, quick answers, and help on demand. Useful, but passive. In this episode, Dr Andree Bates is joined by Parth Khanna, CEO and co-founder of ACTO, to explore what comes next: moving beyond copilots into role-based AI agents that proactively close knowledge gaps, improve field readiness, and operate safely inside regulated environments.Parth shares his path into life sciences and tech, including founding an early NLP company in 2012 and then building ACTO after speaking with over 100 life science companies about field force effectiveness. Today, ACTO supports tens of thousands of professionals and hundreds of brand launches, and Parth argues the industry is now entering the “agentic era” where the real differentiator is not just model access, but how organisations build context, control, and change management around AI.A key theme is why generic AI tools often fail inside enterprises. Parth outlines four requirements for agent success: context (role and job-specific personalisation), connection (stitching data sources and agent-to-agent workflows), control (testing, monitoring, observability), and change management (reducing fear and driving adoption). Without these, he says, many copilots and assistants end up underused, with people quietly reverting to old workflows.Parth then introduces ACTO's concept of role-based “super agents”, designed around a real job description (for example an MSL). Rather than a disconnected swarm of task bots, a “queen bee” orchestrator agent delegates to worker agents, checks outputs against compliance guardrails, and can be assessed with exams to quantify risk before deployment. This approach, he argues, makes AI both more powerful and safer for regulated field teams.Finally, the conversation looks ahead. Parth believes the future of work depends on pairing AI capability with distinctly human strengths: strategy, judgement, and human connection. The winners won't be those who automate the most tasks, but those who redesign roles so humans and agents amplify each other.Topics CoveredWhy copilots are useful but fundamentally passiveThe shift from AI that responds to AI that actsWhy generic tools fail: context, connection, control, change managementAdoption reality: why many AI assistants go unusedQuantifying risk and moving from black box to observable AIRole-based super agents and the “queen bee” orchestrator modelTesting agents with exams before field deploymentGuardrails, compliance, and agent-to-agent quality checksHuman skills AI can't replace: strategy, judgement, connectionThe future of MSL and field excellence in an agentic eraEularis helps pharma and biotech leaders turn AI activity into board-defensible strategy and measurable commercial outcomes.If your organisation has plenty of AI in motion but very little that moves the commercial needle in a way the board can see, start with our 10-Day AI Diagnostic Sprint. It's a focused diagnostic that surfaces what's actually broken and what's blocking results, before you invest in a larger strategy effort.The Sprint diagnoses the problem. The AI Strategic Blueprint that follows is where we build the board-defensible strategy and plan.Details at eularis.com.About the PodcastAI For Pharma Growth is the podcast from pioneering Pharma Artificial Intelligence entrepreneur Dr Andree Bates, created to help pharma, biotech and healthcare organisations understand how AI-based technologies can save time, grow brands, and improve company results.This show blends deep sector experience with practical conversations that demystify AI for biopharma leaders, from start-up biotech right through to Big Pharma. Each episode features experts building AI-powered tools that are driving real-world results across discovery, R&D, clinical trials, medical affairs, market access, regulatory, insights, sales, marketing, and more.

Welcome back to You Can't Kill the Boogeyman Podcast with your favorite spooky couple, Robby and Sammi! Today's BONUS EPISODE features an interview with author Parth Patel. You'll get to hear all about Patel's journey as a horror author and his upcoming book, "Sharkfin Cove."Produced by: Limitless Broadcasting Network.For more info, merch, and all the other podcasts, visit: www.limitlessbroadcastingnetwork.comFollow the show on Instagram @boogeymanpod! Follow your horror hosts on Instagram @robert1950studios and @thesam.a.lamYou can also find us on TikTok @1950Studios Email your comments and spooky suggestions to us at boogeymanpod@gmail.com!

(1) Tigers roster additions, keepers, a new GM hire by Penny - Parth Upadhyaya (2) Opening Day, MLB Dodgers dominant, Sweet 16 & Will Wade + LSU wildin'

J&J Show--Hour 2 Friday 3/20/26--Parth Upadhyaya from the Daily Memphian on Penny, the Tournament, and CBB + Tonight is Grizzlies "Wrestling Night" at FedExForum on 92.9 FM ESPN

J&J - RELOAD - THURSDAY HOUR 2 3/12/26 with Parth Upadhyaya with the Tigers in Birmingham on Penny and Ed Scott meetings

Send a textMiguel Armaza interviews George Robson, Partner at Sequoia Capital, one of the most legendary venture firms in the world. Founded in 1972 by Don Valentine, Sequoia has backed some of the most successful tech and fintech companies in history including Nvidia, Apple, Google, Nubank, and Stripe. Sequoia-backed companies make up over 25% of the Nasdaq's market cap, and add up to $13+ trillion in market cap.George is one of 19 investing partners at Sequoia and was previously a product manager at Revolut. We discuss how Revolut built a multi-product machine by treating every team like a startup, how Sequoia uses storytelling to calibrate its investors on what generational founders look like, and why the next wave of fintech winners will be full stack companies that internalize the technology instead of selling it to incumbents.Timestamped Overview00:00 Intro & George's Background04:14 Finding My People at Revolut07:37 Building Self Sufficient Cross Functional Teams10:57 Focus on Growth and Profitability14:45 Crucial Decision and Growth17:49 High-Conviction Investment Strategy19:07 Parth's Inspiring Entrepreneurial Path23:38 Entrepreneur Growth and Resilience28:19 Storytelling Key to Early Success31:05 Effective Board Dynamics34:14 Entrepreneurs Investors and Problem Discovery36:42 Outsiders Tackling Big Markets40:21 Rewriting Business Laws of Gravity44:04 Humbling Reads and Insights46:17 Doug Leone Fierce and KindWant more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary? Check out the Fintech Leaders newsletter and join 80,000+ readers and listeners worldwide!Miguel Armaza is Co-Founder and General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: bit.ly/3jWIp

(1) Tigers news, Tubby vs. Penny tenure, tonight & more with Parth from the DM (2) Memphis Tigers hoops - FAU revisited

(1) Tiger Hoops is in trouble after historic 6-game losing streak (2) Daily Memphian Reporter Parth Upadhyaya talks to Penny after ECU loss

(1) Tigers vs. Wichita St Preview with DM's Parth - what should Ed Scott do? (2) Dejounte Murray and Nola and the NBA Tankathon update

(1) Parth Upadhyaya from the DM with the Tigers - vs USF, Penny next steps? (2) Draft Picks and tank a thon talk - look at Magic

For episode 677 of the BlockHash Podcast, host Brandon Zemp is joined by Parth Kapadia, Co-founder and CEO of OpenVPP.OVPP is Building The Internet of Energy by Providing Regulated Digital Asset Rails for Power & Utility Providers. OpenVPP is led by Co-Founder & CEO, Parth Kapadia. Parth brings a wealth of experience from the electric utility industry, including roles at Exelon Corp and AutoGrid (acquired by Uplight, a Schneider Electric company), where he served as Director of Technical Product Management.

(1) Memphis Tigers vs. North Texas preview from Parth Upadhyaya from the DM (2) Breaking: Jaren Jackson Jr news that he's out for Utah & will have surgery

Hour 2--J&J Show Thursday 1/29/26--Tigers tonight @ FedExForum and they are struggling -Parth from DM with J&J + Bama and the Charles Beiako sago and Memphis connection thought from J&J

This episode is our third and final installment of a special, three-part Reid Riffs miniseries focused on what it actually means to become AI-native. In this episode, Parth shares how founders can rethink work by breaking problems into modular pieces, orchestrating AI agents in parallel, and collapsing timelines that once required entire teams days of iteration. Using real-world examples like coding agents that tackle week-long engineering challenges to reimagining how content can be localized across languages and regional markets, the conversation explores how AI enables small teams to operate with outsized leverage. Along the way, Reid and Parth discuss what separates real AI traction from “AI theater,” how founding teams are evolving, and why the most powerful AI often works best when it fades into the background, quietly amplifying human creativity and ambition. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

It's the Hump Day edition of Sports 56 Mornings with Greg & Eli. We'll go around the horn in the sports world and cover a variety of topics from the NBA, NCAA & NFL. In the first hour, we will be joined by Memphis Tigers Basketball Beat Writer, Parth Upadhyaya, who is still in Wichita, Kansas due to the winter weather. Parth will discuss the current state of the Tigers, ahead of this Thursday's conference rematch with Florida Atlantic

(1) DM's Parth Upadhyaya LIVE from Tulsa with the Tigers- on the roster / game (2) NFL Mike McDaniel + J& J on shoes, shoe sizes and energy lol

This episode is our second installment of a special, three-part Reid Riffs miniseries focused on what it actually means to become AI-native. Instead of a news or headline-driven conversation, Reid sits down one-on-one with AI engineer and strategist, Parth Patil, for a deeper exploration of how AI is changing the way people and organizations work. In this second episode, they discuss why most enterprises are still talking about AI without truly integrating it (“AI theater”), and how the real shift begins inside everyday workflows rather than strategy decks. Together, they explore how language models and agents can reduce friction in communication and coordination, reinvent meetings, and turn unstructured information into actionable insight (with examples). They also examine how AI-powered analysis, automation, and parallelized agents are accelerating decision-making, reshaping roles, and moving work from execution toward orchestration. Parth and Reid both highlight how an open mindset and experimentation are required to collaborate effectively with these systems as AI evolves from a productivity tool into a foundational layer for thinking and leadership. Subscribe below to catch the third episode for startup founders and their early teams building AI-native companies.  For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/ 

(1) Ja Morant and the Grizzlies creating a scene in Germany. J&J latest updates (2) Parth Upadhyaya, Daily Memphian, on the Tigers win over Temple & Penny

This is the first of a special, three-part Reid Riffs miniseries. Instead of a news-and-headline driven conversation, Reid sits down one-on-one with Parth Patil, an AI engineer and strategist, for a deeper exploration of what it actually means to become AI-native. In this first episode of the series, Parth and Reid discuss how individuals can better leverage LLMs, agents, and creative tools daily. They trace the shift from seeing AI as a productivity boost to understanding it as a meta-tool, as well as unpack techniques like role-based prompting, meta-prompting, and voice as a high-bandwidth thinking interface. Along the way, they discuss the humility required to collaborate with these systems, the move from a single copilot to orchestrating fleets of specialized agents, and how these tools are already reshaping workflows. Subscribe below to catch the second episode on how large companies can integrate AI, as well as the third episode for startup founders and their early teams building AI-native companies.  For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  01:07 – When ChatGPT became an “everything tool” 03:11 – Role-based prompting and meta-prompting 07:04 – Ego, humility, and the GPT-4 inflection point 10:41 – Why voice is the highest-bandwidth interface 14:15 – Choosing models and building an AI stack 18:09 – From one copilot to fleets of agents 21:11 – When agents go wrong 25:40 – Using AI as an agent, not a chatbot 28:34 – Building real systems with AI agents 32:49 – Context engineering and advanced prompting 36:03 – Becoming AI-native 40:34 – Closing

(1) J&J take calls on "Should the Grizz trade Morant?" (2) Parth Upadhyaya, Tiger Hoops Beat Writer Daily Memphian, on Ja Morant & TIgers vs. FAU (3) What's the future of Ja?

Join Sanjana and Parth as they discover the New Year secret, the secret of now, gratitude, lessons, small promises,and discoveries. This story reminds us to slow down, notice the moment, and grow, laugh, and shine every day.

(1) the Grinch vs. Bears conti' then Memphis Tigers need a solid performance vs. Miss State. (2) Tigers roster, Memphis vs Miss State who preview, Parth Upadhyaya in-studio (3) Robert Irwin Jewelers, Official Sponsor of the J&J Show on 92.9 FM ESPN

Memphis vs. Miss State Game PREVIEW: Parth Upadhyaya, Daily Memphian, with J&J Show

(1) Louisville vs. Memphis cont' then Zach Edey's future and a comparison to big man injuries/NBA Weight Comps (2) Parth Upadhyaya, Daily Memphian Tigers Reporter, previews Tigers v U of L (3) Charles Huff shaping the Memphis Tigers Roster with Southern Miss athletes

THE LATINUM GIRLS. Bryan and Katie livestreamed this month's animated spotlight as part of Geekscape's fundraiser for Big Brothers Big Sisters of Northern New Jersey. Come take a listen to their thoughts and judgment on the midway point of Lower Decks' fourth season. Some bawdy Betazoids fluster the crew of the Cerritos in "Empathological Fallacies." Later, a trip to Ferenginar proves overwhelming for Boimler, Tendi, Rutherford, and Mariner. Learn more about your ad choices. Visit megaphone.fm/adchoices

(1) Memhis Coaching Search (2) USA Soccer receives a very favorable draw for their group play (3) Parth Upadhyaya- Daily Memphian Tigers Basketball Beat -on Memphis v Baylor

Parth Jindal and Prabodha Acharya of JSW Group join Climate Rising to discuss how one of India's largest industrial conglomerates is reducing the carbon intensity of its steel business while scaling infrastructure for a fast-growing economy. They share how JSW built vertically integrated operations—from power to cement to ports—through industrial symbiosis, and why energy efficiency, renewable power, and circular practices are at the heart of its decarbonization roadmap. The conversation explores India's dual challenge: meeting rising domestic steel demand while managing its climate vulnerability. Parth and Prabodha explain JSW's green investments, hydrogen pilots, carbon capture initiatives, and why cost competitiveness, stakeholder pressure, and industrial policy shape the path forward. This is part of our Global South series, which also features Tata Power and organizations in Brazil at the intersection of business and climate. Explore more episodes at climaterising.org.

Hour 3--J&J Show Friday 11/21/25--Memphis Tiger Hoops "Must Win" + Parth Upadhyaya from the Daily Memphian live from Bahamas w/the Tigers

We just wrapped our second Young Men's Conference, and I'm still taking in the power, the stories, and the lessons this year brought.From 220 registered to nearly 100 young men showing up in person, this year felt bigger, bolder, and more aligned with the work we've been building: helping young men normalize vulnerability, step into their strengths, and see themselves in community with mentors who truly see them.This episode is a reflective solo cast on what went well, what surprised us, and how we're preparing for a global expansion of the movement in 2026.The growth of the Young Men's Conference from 50 to 100 participantsWhat young men actually said about their experienceWhy “normalizing vulnerability” remains a radical actWhat we learned from the 100+ youth who didn't show up, and how to reach them next yearThe global partners activating this movement around the worldA call to action for parents, mentors, schools, and anyone who cares about young menTimestamps(0:00) Welcome & why this episode is a reflection on the conference(2:30) What worked: attendance, workshops, volunteers & community support(4:48) How we expanded to a bigger venue and deeper partnerships(7:14) Parent testimonials and powerful stories from young men(9:40) Why “normalizing vulnerability” is the theme—and why it matters(11:30) What we learned from 220 registrants but only 100 attendees(14:07) Alberto & Parth share behind-the-scenes insights(18:00) Why this work matters even more after recent tragedies(20:30) The “critical gap” and our responsibility to young men(22:15) A call to action as we prepare for 2026Join / Contribute to the Young Men's ConferenceJoin/Contribute to our Young Men's Conference 2026: https://everforwardclub.orgJoin our Skool Community: https://www.skool.com/efc-young-mens-advocates-2345Submit Questions, Reflections, or Episode IdeasEmail us: totmpod100@gmail.comCreate your mask anonymously: https://millionmask.org/Connect with Ashanti BranchInstagram: https://www.instagram.com/branchspeaks/Facebook: https://www.facebook.com/BranchSpeaksTwitter: https://twitter.com/BranchSpeaksLinkedIn: https://www.linkedin.com/in/ashantibranch/Website: https://www.branchspeaks.com/Support the Podcast & Ever Forward ClubHelp us continue creating spaces for young men to be seen, heard, and supported:https://podcasters.spotify.com/pod/show/branch-speaks/supportConnect with Ever Forward ClubInstagram: https://www.instagram.com/everforwardclubFacebook: https://www.facebook.com/everforwardclubTwitter: https://twitter.com/everforwardclubLinkedIn: https://www.linkedin.com/company/the-ever-forward-club/#unmaskingwithmaleeducators #millionmaskmovement #takingoffthemask #totm #doace #UNWME #diaryofaconfusededucator #youngmensconference #normalizevulnerability #everforwardclub

(1) ... J&J Family talk then J&J discuss NBA including Denver being legit & Knicks solid in the East (2) Memphis vs. UNLV and Pastner this Sunday - Parth Upadhyaya previews w/J&J

(1) Ja Morant, Iisalo and the Grizzlies minutes situation & style of play? (2) Parth Upadhyaya, Daily Memphian, talks about Tiger Hoops Home openers vs SF (3) Robert Irwin Jewelers & a massive weekend for Tiger Football tonight!

Hour 3--J&J Show Friday 10/24/25--Vikings and NFL, Tiger Football, & Parth Upadhyaya in-studio on Arkansas vs. Tigers St Jude game

(1) Penny Hardaway Tiger success and look at attendance vs. Grizzlies (2) Parth Upadhyaya joins J&J in-studio to glaze on last night's Tigers event (3) New NBA streaming show "Starting 5"

Finishing up the conversation on Banana Ball in Memphis, Penny Hardaway Narrowing Down his Search for a New Assistant Coach; Parth Upadhyaya on J&J's John Calipari Interview, Advertising for St. Jude Tipoff Classic, Penny Deciding on Starters, The American Poll Results; Jason Ditches His Laptop to close the show

(Saskatoon Insight Meditation Community)

(1) Memphis Chic-Fil-A continued discussion then Impressive weekend slate in the NFL. Mariota and the Commanders. Raiders (2) Parth Upadhyaya in-studio on Penny loving his roster & comp to older teams (3) Bama vs. UGA this weekend & how will Memphis fare against FAU?

(1) Arkansas vs. Memphis FB and the history of Memphis in big games last decade (2) Parth Upadhyaya, Daily Memphian, on Tiger Football game, Curtis Givens too

Send us a textSummaryIn this episode, Parth Shah, CEO of Polimorphic, discusses the transformative role of AI in government communications and public service. He shares insights from his background in civic technology and the challenges faced by public agencies in managing communications, public records, and community engagement. Parth emphasizes the importance of AI in enhancing customer service, addressing misinformation, and improving accessibility for diverse populations. He also highlights the need for governments to adapt to technological advancements to meet rising public expectations and maintain effective service delivery.Support the showOur premiere sponsor, Social News Desk, has an exclusive offer for PIO Podcast listeners. Head over to socialnewsdesk.com/pio to get three months free when a qualifying agency signs up.

CardioNerds (Drs. Amit Goyal, Elizabeth Davis, and Keerthi Gondi) discuss the approach to asymptomatic severe aortic stenosis with expert faculty Drs. Parth Desai and Tony Bavry.   They review the natural history of aortic stenosis, current guidelines for treating severe aortic stenosis, multiparametric risk stratification, trial data on aortic valve replacement for patients with asymptomatic severe aortic stenosis, and a practical approach for our patients today.   This episode was supported by an educational grant from Edwards Lifesciences. All CardioNerds education is planned, produced, and reviewed solely by CardioNerds.  Enjoy this Circulation Paths to Discovery article to learn more about the CardioNerds mission and journey.  US Cardiology Review is now the official journal of CardioNerds! Submit your manuscripts here.  CardioNerds Aortic Stenosis SeriesCardioNerds Episode PageCardioNerds AcademyCardionerds Healy Honor Roll CardioNerds Journal ClubSubscribe to The Heartbeat Newsletter!Check out CardioNerds SWAG!Become a CardioNerds Patron!

(1) College Football Week 2 - Can Miss State upset Arizona State? & More (2) Parth Upadhyaya, Daily Memphian, in-studio on Tiger rankings & Belichick (3) Les Smith returning to Memphis with family tonight

Hour 3--J&J Show Friday 8/29/25--Memphis Women's Soccer & John's childhood on Sycamore View & Parth Upadhyaya discusses Memphis Tigers Bball and landscape of recruiting.

Hour 1--J&J Show Wednesday 8/20/25---Grizzlies lease info & Parth Upadhyaya joins J&J in Seg 2 on Tigers/FedEx deal