Podcasts about tech companies

Why trust a custodian with your sats? Edgar Borja (@k1elsalvador) breaks down K1's self-custody Bitcoin ATM, on-chain and Lightning buying, and Bitcoin in Bolivia. Edgar walks through K1's new update, buying Bitcoin on the Lightning Network and on-chain, what's happening with Bitcoin adoption in Bolivia, and what it's like pitching investors 5,000 times. K1's machines now support on-chain and Lightning purchases with full self-custody, cutting out Strike, Blink, and OpenNode entirely. Edgar demos a live on-chain purchase and explains why every K1 transaction is RBF disabled, so once it's broadcast, it's final.K1 machines now run in 28 countries. That growth reflects real tech innovation in Bitcoin hardware, built for the global market from day one. The team solved for different bill sizes, currencies, and KYC rules in every country they shipped to. Edgar covers how KYC gets configured machine by machine, why K1 stays buy-only for now, and a cash-dispensing module already demoed at the Adopting Bitcoin conference.Small business owners get a direct pitch here. Edgar walks through the three K1 tiers, Mini, Mini Plus, and Mini Global, plus pricing and why the bill acceptor is the hardest part to build. If you're exploring digital asset management or a new source of business growth, a Bitcoin ATM in a coffee shop or corner store is a low-friction way to start.Then there's Bolivia. Edgar saw a fiat currency failing in real time, banks offering 6 Bolivianos to the dollar while the street rate hit 10. The government built its own workaround digital payments network just to keep money moving. This is what pulls people toward hard money. Watching their savings shrink every week, people in Cochabamba are turning to Bitcoin.Edgar also shares K1's education tools: a crayon-and-paper workshop that teaches private keys, and a board game called MerkColor that teaches Merkle trees in under a minute. He talks about pitching investors an estimated 5,000 times on a Bitcoin startup accelerator and reality show, plus his advice for founders: fall in love with the problem you're solving. Hit subscribe, share this with someone who thinks Bitcoin ATMs are just coin machines, and tell us in the comments if you'd trust a machine with your private key.—Bitcoin Beach TeamLearn more about Edgar Borja:X: https://x.com/K1ElSalvadorFacebook: https://www.facebook.com/k1elsalvador Instagram: https://www.instagram.com/k1elsalvador LinkedIn: https://www.linkedin.com/company/k1elsalvador/Web: k1.sv Email Address: borja@k1.svSupport and follow Bitcoin Beach:X: https://www.twitter.com/BitcoinBeach IG: https://www.instagram.com/bitcoinbeach_sv TikTok: https://www.tiktok.com/@livefrombitcoinbeach Web: https://www.bitcoinbeach.com STAY AT BITCOIN BEACH: https://www.stayatbitcoinbeach.com/punta-mango-villasBrowse through this quick guide to learn more about the episode:00:00  Intro01:40  How many countries run K1 Bitcoin ATMs now?02:07  How does a self-custody Bitcoin ATM let you buy Bitcoin on-chain?08:49  Can you buy Bitcoin from an ATM without KYC?13:08  Where are Bitcoin ATMs actually installed in the real world?16:23  Why are Bolivians turning to Bitcoin as their currency collapses?25:18  What is it like pitching a Bitcoin company on a startup accelerator reality show?37:42  How do you teach kids about private keys and self-custody using crayons?47:31  Which self-custody Bitcoin ATM should you buy and how does shipping work?57:29  Is Bitcoin still winning in El Salvador after the IMF deal? Live From Bitcoin Beach

How Do Studios & Tech Companies Think Differently?On this new episode of The Artists Podcast, we're joined by Vicki Dobbs Beck—one of the pioneers shaping the future of immersive storytelling.Formerly Vice President of Immersive Content Innovation at Industrial Light & Magic (ILM), Vicki has helped lead groundbreaking storytelling initiatives across Disney, Lucasfilm, Marvel, and collaborated with Meta to explore the future of immersive media. Variety recognized her as a "Digital Innovator to Watch" for her contributions at the intersection of entertainment and technology.Together, we explore one of the defining questions for the future of storytelling. YouTube Spotify Apple PodcastsFollow: @the.artistspodcast#TheArtistsPodcast #VickiDobbsBeck #IndustrialLightMagic #ILM #Disney #Marvel #Meta #Storytelling #FutureOfStorytelling #ImmersiveStorytelling #ExtendedReality #XR #VirtualProduction #ArtificialIntelligence #CreativeTechnology #Filmmaking #Cinema #Innovation #Hollywood #TechDropping soon

Paul is joined by Tim Crowe, CEO and co-founder of WrxFlo, as they discuss the company's €3 million investment and what it means for Limerick's economy. Hosted on Acast. See acast.com/privacy for more information.

Welcome to the CanadianSME Small Business Podcast hosted by Kripa Anand. Today, we explore AI-driven marketing and digital brand execution for B2B businesses. In 2026, building a dominant market presence requires translating complex technical capabilities into clear, trust-building stories. Joining us is Mikaela Nicholson, Senior Manager of Marketing at Agile Bookkeeping. Key Highlights AI in Marketing: AI is transforming how B2B brands strategize and execute campaigns. Organic Trust: Brands can foster genuine trust even in automated digital environments. Human-Tech Balance: Automation should complement, not replace, human connection. Lead-Gen Web Engine: Effective websites combine SEO, AI visibility, and conversion design. Cross-Sector Insights: Diverse experience drives scalable, adaptable growth strategies. 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.

A.M. Edition for July 14. New York becomes the first state to ban data center construction as builders and operators across the U.S. move to cash out and bring private equity in, to pay for even more data centers. Plus, Middle East oil producers and markets align behind the reality that the region's supply chain may never return to the way it was. And Science of Success columnist Ben Cohen details the company that's harder to land a job at than getting into Harvard or becoming a NASA astronaut. Daniel Bach hosts. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

Amardeep Parmar from Bae HQ welcomes Somayeh Taheri, CEO & Founder at UrbanChainAmardeep Parmar:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Somayeh Taheri: ⁠Urbanchain.co.uk

Andrew Harding from The Heritage Foundation stops by the show to talk about why we should be concerned about China owned tech companies gathering our information.

The U.S. government runs a program that will fund your company before you have a single customer — sometimes before you have a finished product at all.It's called SBIR, and in this episode John Martin breaks down exactly how he used it to go from an early-stage idea to a $1.9M U.S. Air Force funding increase, with a shot at a $30M+ award next. If you've got innovative technology and you've ever wondered whether the government would pay you to develop it, this is the conversation to watch.John is the founder of Bundle AR, an augmented reality and XR training platform now used across the Air Force, Space Force, Army, and the commercial world. We get into how SBIR Phase I and Phase II actually work, what TACFI and STRATFI are, why relationships beat writing proposals, and the single mistake most founders make when they try to sell to the government.––––––––––––––––––––––––––––––––––––––CHAPTERS––––––––––––––––––––––––––––––––––––––00:00 The $1.9M the Government Paid Bundle AR00:42 Who Is John Martin & What Bundle AR Does03:33 How He Got His First Government Check05:57 SBIR Explained: Getting Paid for an Idea07:48 Why SBIR Funding Froze (and Just Reopened)09:14 TACFI: The $1.9M Air Force Funding Increase11:54 STRATFI & the New $30M Award for 202616:01 Why Relationships Beat Writing Proposals17:55 How to Win Before the Contract Is Posted22:09 Live Demo: AR Training for the Military28:54 The Smartest Move: Two Revenue Streams29:32 The Truth About SBIR Mills32:24 The GovClose Gauntlet: Rapid Fire37:12 John's Radio Career: Cronkite, Kiss & Taylor Swift38:57 Advice for First-Time Government Contractors41:00 New Markets: Education, Healthcare & School Security44:04 What's Next for Bundle AR––––––––––––––––––––––––––––––––––––––RESOURCES MENTIONED––––––––––––––––––––––––––––––––––––––GovClose Program Overview (free 20-minute training):https://www.govclose.comSBIR / STTR Program (official):https://www.sbir.gov––––––––––––––––––––––––––––––––––––––ABOUT THE GUEST — JOHN MARTIN, BUNDLE AR––––––––––––––––––––––––––––––––––––––John Martin is the founder of Bundle AR (BUNDLAR), a Chicago-basedoperational intelligence platform company. Over 8+ years, Bundle AR hasbuilt a no-code augmented reality and XR-wearable platform that deliverstraining and knowledge "where work happens" for both enterprise and theDepartment of Defense. The company has won SBIR Phase I and Phase II awards,a $1.9M U.S. Air Force Tactical Funding Increase (TACFI), and is nowqualified for a Strategic Funding Increase (STRATFI). Past clients andengagements include Microsoft (Xbox), Thermo Fisher, Cleveland Clinic, theU.S. Air Force, Space Force, Michigan Air National Guard, and the U.S. Army.Learn more / connect with John:Website: https://www.bundlar.comEmail: john@bundlar.comLinkedIn: [paste John's LinkedIn URL here]––––––––––––––––––––––––––––––––––––––ABOUT RICK HOWARD––––––––––––––––––––––––––––––––––––––Rick Howard is a retired USAF Lieutenant Colonel and former DoD acquisitionsofficer who managed over $82 billion in federal contracts. He is the founderof GovClose and the DoD Contract Academy, with 400+ graduates working asgovernment contract consultants, federal account executives, and businessowners winning federal contracts.Get the GovClose Certification: https://www.govclose.com/sales-certification Our students learn the government contracting skills to :1. Start their own consulting business that can earn up to $400k as a "solopreneur" advising businesses that sell to the government.2. Land high paying sales executive jobs with companies in the public sector.3. Increase government contracting revenue for companies selling to the US government.

John Imah is the founder of SpreeAI, a fashion technology company valued at $1.5 billion, and the youngest millionaire in School of Hard Knocks history after selling two tech companies before age 16.In this episode, John shares his journey from growing up poor in an immigrant family to working at Samsung, Twitch, Meta, and Snapchat before returning to entrepreneurship.He breaks down building AI for fashion, attracting elite talent, selling into major brands, overcoming bullying, losing his mother, and using faith, discipline, and execution to build a lasting company.Do you want more options, shorter, more intense, or more viral versions?Hosted on Ausha. See ausha.co/privacy-policy for more information.

Healthcare innovation is often associated with software developers, startup founders, and technology companies. But what if some of the most important solutions to healthcare's biggest challenges are sitting inside operating rooms, classrooms, and clinical settings every day? CRNAs and nurses see problems firsthand, yet they are rarely invited into the rooms where solutions are created. In this episode, Sharon and Jeremy welcome nurse entrepreneur Rebecca Love, RN, MSN, FIEL and AANA Senior Innovation Specialist Cherissa Jackson to discuss the upcoming AANA Hackathon at the AANA Annual Congress. This event creates a space for clinicians, educators, students, and innovators to collaborate and develop solutions for the daily challenges CRNAs see firsthand. Here's some of what you'll hear in this episode:

Big Tech is looking for land to build its AI data centers. HBCUs are looking for new funding after federal cuts.And partnerships between them, like one announced by Fisk University, could be a mutually beneficial — or could end up being a form of "digital sharecropping," according to strategist Ashley Northington, who wrote about this for Tech Policy Press.

Big Tech is looking for land to build its AI data centers. HBCUs are looking for new funding after federal cuts.And partnerships between them, like one announced by Fisk University, could be a mutually beneficial — or could end up being a form of "digital sharecropping," according to strategist Ashley Northington, who wrote about this for Tech Policy Press.

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

Lavera Alexander is Chief Growth Officer at the Monterey Bay Economic Partnership (MPED).  MBEP is a cross-sector nonprofit that convenes business, government, education, and community leaders to drive economic growth across the tri-county Monterey Bay region, and it sits at the heart of what may be the highest concentration of advanced air mobility companies anywhere in the United States. For years, much of the conversation around drones and advanced air mobility has focused on the aircraft themselves—better batteries, new sensors, autonomy, and the promise of electric air taxis and drone delivery. But as the technology matures, it's becoming clear that building the aircraft may be the easy part. Building the infrastructure and the operational ecosystem needed to support them may be the bigger challenge. Lavera is leading a $7.45 million state-funded initiative that aims to connect four Central Coast airports into one of the nation's first multi-airport advanced air mobility networks. The project is designed to help answer some of the key questions facing the industry today: How do we integrate drones and future air mobility systems into existing airport operations? What infrastructure will be needed? How do communities benefit? And what role can regions play in helping shape the future of aviation? Lavera is an experienced executive with more than two decades of leadership in cross-sector collaboration, and she's now steering one of the most ambitious AAM infrastructure projects in the country. In this episode, we'll talk about how a regional economic partnership found itself at the center of the drone industry, why infrastructure has become one of the biggest challenges facing advanced air mobility, and what this ambitious project could mean for the future movement of goods, services, and people.

Drops in Big Tech companies are pulling stocks lower on Wall Street.

Our listeners can buy one pair of glasses and get 20% off any additional pairs at WarbyParker.com/404 — and using our link helps support the show. This week Joseph speaks to Jake Hanrahan, creator of the independent conflict-focused media company Popular Front. They talk all about conflict journalism and how to get your journalism out there when platforms like YouTube make it all that much harder, sometimes. Popular Front Away Days Plastic Defence: Secret 3D Printed Guns in Europe Youtube Version: https://youtu.be/Yf51H5IuRcM Learn more about your ad choices. Visit megaphone.fm/adchoices

Jed Alpert joins The Great Battlefield podcast to talk about founding Mobile Commons (one of the earliest political texting platforms), selling it and recently buying it back and how he plans to lead Mobile Commons in a new technical age.

“Cheap rides. Driver bonuses. Millions of users. So how is Yango actually making money?” In this episode, Yango Nepal Country Manager Santosh Pandey breaks down the real business behind Nepal's ride-hailing industry from scaling to millions of users, to building a tech ecosystem powered by 34 local Nepali companies. We talk about: Ride-hailing wars in Nepal Yango's competitive edge Gig economy & earning opportunities Why Kathmandu became a priority market And the future of tech, EVs, and logistics in Nepal   Chapters:  00:00 Intro 01:20 First Camera Appearance After Joining Yango 01:50 Launching Yango in Nepal 02:49 Millions of People Already Use Yango 05:36 Yango's Partner Model Explained 06:41 Why Cheap Pricing Worked in Nepal 10:21 Yango Is More Than a Ride-Hailing App 11:00 The 34 Nepali Companies Behind Yango 12:55 Is Yango a Tech Company or a Map Company? 15:28 How Fast Yango Expanded Across Kathmandu 25:28 Ride-Hailing Wars: Who Can Burn More Cash? 34:15 Personal Branding & Success 34:46 The Random Coffee Message That Changed Everything 36:08 “Can I Actually Do This?” - Imposter Syndrome 39:01 One Year Fully Focused on Yango 47:22 “You Don't Need To Go Abroad To Earn 1 Lakh” 01:09:48 Why Gig Economy Jobs Aren't Long-Term 01:10:23 The Real Truth About Gig Economy 01:11:49 What Does “New Nepal” Look Like? 01:12:25 Nepal's Technology Shift Is Finally Happening 01:28:58 Young Teams, Global Exposure & The Future If you love reading, don't miss our newsletter on Substack Link: https://substack.com/@doersglobal?     Want to join us live in the studio as an audience member? Fill out this form: https://forms.gle/xZi8yptyoxkkc6aa8     ✉ Reach out to us at partners@doersnepal.com   

Have you ever thought about raising capital for your business? Many entrepreneurs assume that raising capital means giving up equity, taking on restrictive debt, or waiting until they're much larger to access meaningful funding. But today's financing landscape offers more options than ever for growth-focused companies looking to scale without sacrificing ownership.  Today we're going to talk about thinking strategically about financing, when debt makes more sense than equity, common mistakes often made when raising capital, and what it takes to build a business that's truly ready for growth. Joining us is Michael Wallace, CEO of TIMIA Capital, who specializes in helping technology companies access non-dilutive growth capital designed to support expansion while preserving flexibility. Michael has deep expertise across lending and tech and works closely with entrepreneurs and investment partners to design financing strategies that prioritize scalability and flexibility. Prior to joining TIMIA in 2024, Michael served as President of Torinit and CEO of FindWRK, a recruiting marketplace startup. He also played a pivotal role at Langhaus Financial, joining as its first executive hire and helping scale it into Canada's largest alternative life insurance lender before its successful sale in 2022. Michael began his career in management consulting at Bain & Company. He holds an MBA from the Kellogg School of Management and a Bachelor of Commerce from Queen's University, bringing a strong foundation of strategic and operational leadership to every venture he leads. During the show we discuss: Why giving up equity isn't always necessary to raise meaningful capital When debt is actually the smarter option for growth-focused businesses How non-dilutive financing works and why it's gaining popularity What makes a business "fundable" in today's lending environment Common capital-raising mistakes that cost founders time, money, and ownership How to think strategically about financing instead of reacting when you need cash Why lenders vs. investors look for different things—and how to position for both How to scale while maintaining control and flexibility Resources: https://timiacapital.com/

https://www.youtube.com/watch?v=MXTCE3rXfH4 Podcast audio: In this episode of the Ayn Rand Institute podcast, Ben Bayer and Connor O'Leary discuss Pope Leo XIV's encyclical about AI. Topics include: Valid questions, backwards approach Concerning near-universal acclaim Dependence on religious morality Faith-based moral concepts Appealing to fear and resentment Hostility to human intelligence We need a rational morality Resources:  Ayn Rand, “Requiem for Man,” Capitalism the Unknown Ideal Anthropic vs. Trump: The Moral Responsibility of Tech Companies, Ayn Rand Institute Podcast, March 12, 2026 Marketing AI Without Empowering Resentment, Ayn Rand Institute Podcast, April 23, 2026 Ben Bayer, How to Build a Secular Morality, New Ideal, June 10, 2026 (just came out yesterday) This episode was recorded on June 5, 2026. Image credits: Pope: Carlos Alvarez / Contributor / via Getty Images; Circuit: haydenbird / E+ / via Getty Images

Ottieni 4% annuo lordo x 12 mesi fino a €1 Milione se apri Conto Corrente Arancio Business di ING entro il 25/07. Come si costruisce una delle aziende tech italiane più globali di sempre senza trasferirsi in Silicon Valley? In questa puntata di Made IT incontriamo Max Ciociola, founder di Musixmatch. Oggi Musixmatch non è solo la piattaforma che ha creato il più grande catalogo di testi musicali al mondo dietro ai testi sincronizzati che milioni di persone leggono ogni giorno su Spotify, Apple Music, Instagram e tantissime altre app: è una vera e propria data company specializzata in royalty intelligence e rights management. Partendo da Bologna, Max ha costruito un'azienda con oltre 100 milioni di euro di fatturato, 50 milioni di EBITDA e milioni di utenti in tutto il mondo. Parliamo di startup, imprenditoria, raccolta fondi, acquisizioni, proprietà intellettuale, AI e dell'evoluzione di Musixmatch da piattaforma di lyrics a leader globale nel royalty intelligence e rights management. In questa intervista scoprirai: Come nasce Musixmatch Come convincere aziende come Spotify, Apple e Google a diventare clienti Cosa succede quando 26 fondi vogliono investire nella tua azienda Perché ha scelto di vendere ad un private equity americano Come l'AI sta cambiando l'industria musicale Le lezioni apprese costruendo un'azienda globale dall'Italia Learn more about your ad choices. Visit megaphone.fm/adchoices

The Smart 7 is an award winning daily podcast, in association with METRO, that gives you everything you need to know in 7 minutes, at 7am, 7 days a week…With over 20 million downloads and consistently charting, including as No. 1 News Podcast on Spotify, we're a trusted source for people every day and we've won Gold at the Signal International Podcast awardsIf you're enjoying it, please follow, share, or even post a review, it all helps... Today's episode includes the following:https://x.com/clashreport/status/2064005808325541904/video/1 https://x.com/AJEnglish/status/2063999161653973167/video/1 https://x.com/Reuters/status/2063946552507613265/video/1 https://x.com/BBCWorldatOne/status/2063973139843821632/video/1 https://x.com/nexta_tv/status/2063909683321008497/video/1 https://x.com/SkyNews/status/2063992891492520014/video/1 https://x.com/i/status/2064029993374531885 https://x.com/i/status/2063795677071626511 https://x.com/i/status/2064007779442982957 Contact us over @TheSmart7pod or visit www.thesmart7.com or find out more at www.metro.co.uk Voiced by Jamie East, using AI, written by Liam Thompson, researched by Lucie Lewis and produced by Daft Doris. Hosted on Acast. See acast.com/privacy for more information.

Major AI Companies Eyeing Public Markets Anthropic has confidentially submitted a draft registration statement to the U.S. Securities and Exchange […]

Why do companies with the best intentions end up betraying their customers, employees, and mission? Eric Ries calls it “financial gravity” — an invisible force that pulls even the most principled companies toward corruption, and understanding it is the first step to resisting it.In this episode, Eric Ries, entrepreneur and author of The Lean Startup and Incorruptible, shares why building a great company isn't just about having a strong vision — it's about building structures that protect that vision from external pressure. Eric revisits the core ideas behind the Lean Startup and MVP, explaining how the purpose of a minimum viable product is not to ship fast but to learn fast. He then introduces the central thesis of his new book: that the corruption we see in companies isn't caused by bad people, but by a financial system that pulls organizations away from their values. Drawing on stories of Sol Price, FedMart, Costco, HEB, Novo Nordisk, and Anthropic, he shows that incorruptible companies are built through a combination of ethos — a deep operational commitment to doing right — and structural governance that resists outside pressure. He also unpacks how false metrics like OKRs can hollow out a company's integrity over time, and how Mary Parker Follett's concept of the “invisible leader” helps culture survive beyond any single founder or CEO.Key topics discussed:What “financial gravity” is and why even good companies fall to itThe true purpose of an MVP (hint: it's not about shipping fast)Why OKRs become dangerous false proxies over timeBlueprint for building a truly incorruptible companyWhy Costco and Novo Nordisk resisted forces that killed FedMartMary Parker Follett's invisible leader explainedWhy Anthropic's structure gives it a lasting competitive edgeHow everyday decisions become acts of systemic changeTimestamps:(00:00) Trailer & Intro(02:31) What Two Mega-Trends Make Lean Startup More Relevant Than Ever?(04:03) What Is the True Purpose of a Minimum Viable Product?(11:04) Has AI Actually Made Building Software Cheaper and Better?(13:41) What Two Stories Inspired the Book Incorruptible?(20:38) What Is Financial Gravity and Why Does It Corrupt Even Good Companies?(26:29) What Is Surrogation and Why Do OKRs Become Dangerous False Proxies?(29:55) What Is the Blueprint for Building an Incorruptible Company?(33:53) What Is the Invisible Leader and How Does It Keep Company Culture Alive?(39:56) What Governance Structures Can Shield a Company's Mission from Financial Gravity?(48:27) Why Does Anthropic's Unique Structure Give It a Competitive Advantage in AI?(51:43) 3 Tech Lead Wisdom_____Eric Ries's BioOver the last two decades, Eric Ries's ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader's Guide; and The Startup Way.As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; Virgil, a legal services startup; and IMVU. On The Eric Ries Show, he talks with world-class technologists, thought leaders, and executives building for the long-term. He lives in the San Francisco Bay Area with his wife and three children.Follow Eric:LinkedIn – linkedin.com/in/eriesX – x.com/ericriesPodcast – www.ericriesshow.comWebsite – incorruptible.coNewsletter – news.theleanstartup.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/259.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Meghann Butcher built RepSpark, a B2B wholesale e-commerce platform now moving over a billion dollars a year, without a single line of tech on her resume.She grew up in her dad's apparel and footwear business, hanging around the warehouse at five years old. At 27, when her father's order-entry tool started catching on with independent sales reps, he asked if she wanted to run with it. She said yes, and bootstrapped it from there.In this conversation, Meghann and I get into how a psychology and communications major became the product visionary for a software company, why she still leans on empathy over technical skill to lead, and how staying close to customer pain points built a platform now used by nearly 100,000 retailers.We also talk about being a mom of three while running a growing company, building a drama-free culture, and what it actually takes to scale a bootstrapped business on your own terms.Tune in for a real look at building something durable without the usual playbook.

Geschätzte Lesedauer: 12 Minuten Deutschland ist ein Hightech-Land. Aber ist das auch im Vertrieb so? Wenn ich mir die meisten Vertriebsorganisationen anschaue, dann sieht das Organigramm aus wie vor 20 oder 30 Jahren. Im Jahr 2026, wo alle von KI im Vertrieb, Social Media und Digitalisierung sprechen, kann das eigentlich gar nicht sein. Genau darüber spreche ich in dieser Folge mit Markus Milz, einem der profiliertesten Vertriebsexperten Deutschlands. Wir zeigen dir fünf konkrete Hebel, mit denen du deinen Vertrieb fit für die Zukunft machst – ohne dabei dein Unternehmen auf den Kopf zu stellen. Es geht um echte Praxisbeispiele, neue Tools und eine ehrliche Bestandsaufnahme, warum gerade der deutsche Mittelstand beim Thema digitale Transformation oft hinterherhinkt. Du erfährst, was Jeff Bezos mit seinem Projekt Prometheus vorhat, warum Social Listening dein Cold Calling ersetzt und wie ein digitaler Assistent dir den Vertriebsalltag dramatisch erleichtert. Warum Deutschland im Vertrieb (noch) kein Hightech-Land ist Wir reden so gerne über unsere Ingenieurskunst, unsere Maschinen, unseren Hidden Champions. Und ja, in der Produktion und teilweise in der Logistik sind wir wirklich vorne dabei. Aber wenn ich mir den Vertrieb in den meisten Unternehmen anschaue – Software ausgenommen, und auch da gibt es Licht und Schatten – dann müssen wir ehrlich sein: Im Vertrieb sind wir kein Hightech-Land. Und das ist verrückt, denn Vertrieb ist die wichtigste Funktion im Unternehmen. Sales solves everything. Wenn der Umsatz nicht da ist, sind alle anderen Themen meistens auch nicht mehr viel wert. Markus Milz bringt es auf den Punkt: Er fragt in seinen Keynotes regelmäßig sein Publikum, wer der Meinung sei, dass sich die Welt in den letzten sechs Jahren drastischer geändert habe als in den 25 Jahren davor. 95 Prozent heben die Hand. Dann fragt er, wer das super findet. Da heben nur noch zögerlich 10 Prozent die Hand. Die meisten finden das eher doof – aber kannst du nicht ändern. Die entscheidende Frage ist die nächste: Hast du in den letzten sechs Jahren deinen Vertrieb, deine Strategie, dein Geschäftsmodell drastischer geändert als in den 30 Jahren davor? Da gucken die Leute meistens betreten auf den Boden. Nicht so richtig. Und genau das ist das Problem. Die Geschwindigkeit der Veränderung wird massiv unterschätzt Schau dir an, wie lange Technologien historisch gebraucht haben, sich durchzusetzen. Die Elektrizität: Edison erfand 1880 die Glühbirne. Erst 40 Jahre später war die Welt halbwegs elektrisch. Innovationen brauchten in der Regel fünf bis zehn Jahre, um sich durchzusetzen. Und dann kam ChatGPT. Zwei Monate bis zu 100 Millionen Usern. Heute, keine drei Jahre später, sind wir bei 1,2 Milliarden Usern. Das ist eine Geschwindigkeit, die alles, was wir bisher kannten, in den Schatten stellt. Wenn ich dann ins Publikum frage, wer KI auf dem Handy hat, melden sich 90 bis 95 Prozent. Frage ich, wer es richtig beruflich nutzt, sind es nur noch 20 Prozent. Die meisten nutzen es für Kochrezepte oder ihr Fitnessprogramm. Beruflich – oder gar im Sales – herrscht große Zurückhaltung. Vielleicht mal eine E-Mail schreiben lassen, mal etwas zusammenfassen. Aber dann ist meistens Schluss. Und das ist schade. Denn da fängt es ja erst an. Warum der deutsche Mittelstand zögert: Das Klopapier-Phänomen Markus erzählt eine wunderbare Anekdote von seinem Kollegen Professor Clemens Gewittke: Warum haben die Menschen während Corona eigentlich Klopapier gekauft? Weil Menschen aktionistisch getrieben sind. Wenn etwas Neues kommt und ich nicht weiß, was zu tun ist, mache ich irgendwas. In Frankreich kauften die Leute Rotwein und Kondome. In Amerika wahrscheinlich Waffen. In Deutschland eben Klopapier. Genau das beobachten wir aktuell beim Thema KI im Vertrieb: Es wird Klopapier gekauft. Irgendwas wird ohne Sinn und Verstand probiert. Das hat strukturelle Gründe. Deutschland hat in den letzten 80 Jahren enormen Wohlstand aufgebaut. Drei Millionen Unternehmen, viele Hidden Champions. Und wer viel hat, hat auch viel zu verlieren. Hinzu kommen die etablierten Sätze: „Es hat noch immer gut gegangen." Oder: „Das dürfen wir nicht wegen DSGVO." „Wo werden die Daten gespeichert?" „Das halluziniert doch." „Da gibt es Risiken und Nebenwirkungen." Und vor allem: „Ich will keine Fehler machen." Die deutsche Fehlerkultur als Bremse Eine durchschnittliche Buying-Center-Größe hat sich in den letzten 40 Jahren von drei auf 13 Personen erhöht. 10 Menschen mehr, die in eine Entscheidung eingebunden sind. Warum? Weil keiner mehr Risiken übernehmen will. Aus Angst, Fehler zu machen und damit die Karriere zu ruinieren, wird lieber gar nichts entschieden als das Falsche. Ich habe einen Kunden, der hat die Handynummern seiner Kunden aus dem CRM gelöscht, weil er sie ja nicht besitzen darf. Juristisch vielleicht korrekt – aber bringt das wirklich nach vorne? Eine Statistik bringt es auf den Punkt: 65 Prozent der Unternehmen in Deutschland haben schon einmal eine Investitionsentscheidung wegen DSGVO nicht getroffen. Das läuft möglicherweise nicht ganz in die richtige Richtung. Während wir hier diskutieren, ob Daten auf deutschen oder amerikanischen Servern liegen, baut Jeff Bezos gerade einen 102-Milliarden-Dollar-Fonds auf, um genau diese zögerlichen Unternehmen zu kaufen. Projekt Prometheus: Wenn Bezos vor der Tür steht Jeff Bezos hat einen Fonds aufgelegt, den er Projekt Prometheus genannt hat. 102 Milliarden Dollar. Nicht nur er, ein paar andere sind auch dabei. Der Plan: Gute deutsche und europäische Unternehmen kaufen, bei denen echtes Know-how vorhanden ist – Ingenieurskultur, gute Hardware, tolle Maschinen –, die aber digital und vertrieblich schwach aufgestellt sind. Diese Unternehmen werden gekauft, in die Digitalisierung gebracht und ihr Wert wird auf das 10-, 20-, 50- oder 100-fache skaliert. Deutschland mit dem größten Mittelstand und den meisten Hidden Champions ist für Bezos ein Traumland. Und jetzt hast du als mittelständischer Unternehmer zwei Möglichkeiten: Du wartest, bis Bezos anruft. Oder du nimmst das Thema selbst in die Hand. Stell dir vor, Bezos ruft dich an und sagt: „Ich habe gerade zehn Unternehmen gekauft. Mach die mal fit. Digital, vertrieblich." Wenn du wartest, kauft er deinen Wettbewerber – und dann hast du ein echtes Problem. Das Gute: Du kannst heute mit relativ geringen finanziellen Mitteln sehr viel erreichen. KI ist ein Meister darin, Massendaten zu verarbeiten, zu aggregieren und zu intelligenten Strukturen zusammenzufassen. Was früher Konzernen vorbehalten war, kann heute auch ein 50-Mann-Mittelständler nutzen. Du musst es nur tun. Hebel 1: Inspiration tanken – die Reise nach Aarhaus Wie alles im Leben beginnt auch die Veränderung mit einer Emotion. Mit dem Gefühl: Worüber rede ich eigentlich? Wo will ich hin, wenn ich von Digitalisierung spreche? Wenn du heute zehn Unternehmen fragst, ob sie eine Digitalstrategie haben, sagen alle ja. Bittest du sie zu definieren, was sie meinen, bekommst du zehn komplett unterschiedliche Antworten. Markus empfiehlt einen Besuch in Aarhaus im Münsterland. Eine 40.000-Einwohner-Stadt direkt an der holländischen Grenze, die als digitalste Stadt Deutschlands gilt. Die Idee dort: Alles ist mit allem vernetzt. Du brauchst eine einzige App auf deinem Handy. Damit gehst du in den Supermarkt – ohne Geld, ohne Personal. Du gehst ins Hotel, ins Restaurant, ins Fitnessstudio. Du leihst dir Fahrräder oder Autos aus. Eine App, eine Verbindung. Lohn- und Gehaltsabrechnung, Personaldisposition – alles funktioniert ohne menschlichen Einsatz. KI macht uns wieder menschlicher Jetzt denkst du vielleicht: Total entmenschlicht. Ich sehe das anders. KI ist die Chance, dass wir Menschen wieder menschlicher werden. Wir werden von all dem Mist entlastet, auf den niemand Lust hat – Besuchsberichte schreiben, CRM pflegen, Buchhaltungsbelege sortieren. Stattdessen können wir uns auf das konzentrieren, was nur Menschen können: miteinander reden, Mittagessen gehen, ein Bier trinken, echte Beziehungen aufbauen. Gerade im Vertrieb ist das der eigentliche Wertbeitrag. Hinter Aarhaus steht Tobias Groten, der Chef von Tobit. Das Unternehmen hat in den 80ern und 90ern mit Fax-Software begonnen und sich kontinuierlich weiterentwickelt. Heute haben sie eine eigene KI namens Sidekick. Immer wenn in Aarhaus ein Supermarkt, ein Kiosk, ein Hotel oder ein Restaurant pleite ging, hat Tobias gesagt: „Dann nehme ich das." Und weil er kein Hotelier oder Gastronom ist, sondern Techie, hat er das Konzept Hotel komplett neu gedacht. Das ist Disruption: nicht kontinuierliche Verbesserung, sondern radikales Neudenken. Hebel 2: Social Listening – Leads auf dem Silbertablett Wenn ich in einen mittelständischen Maschinenbauer komme und frage, was seine fünf Hauptvertriebskanäle für neue Projekte sind, höre ich in 95 Prozent der Fälle: Messen, Anfragen, Ausschreibungen, internationale Handelsvertreter und ein bisschen Cold Calling. Das war vor 20 oder 30 Jahren genauso. Wir sind aber im Jahr 2026. Schau dir das Organigramm an: Hier ist Marketing, das macht ein bisschen Homepage und Social Media. Hier ist Vertrieb, der geht raus oder macht das, was er immer gemacht hat. Das kann doch im Zeitalter von KI im Vertrieb nicht mehr sein. Ein konkretes Beispiel von Markus: Er hat einen Catering-Anbieter betreut. Was macht so ein Unternehmen normalerweise? Cold Calling. 100 Anrufe: „Brauchst du eine Kantine?" – „Nein." – „Brauchst du eine Kantine?" – „Nein." Mit etwas Glück sagen zwei oder drei „Lass uns mal sprechen" und am Ende gewinnst du vielleicht einen Kunden. Streuverlust: 98 Prozent. Demotivierend für jeden Vertriebler. So funktioniert modernes Social Listening Jetzt der neue Weg: Massenhaft Daten sind in Social Media verfügbar. Menschen gehen jeden Tag in Kantinen und schreiben auf Facebook oder Instagram, ob es geschmeckt hat oder nicht. KI aggregiert diese Daten. Du stellst fest: Bei Unternehmen XY haben sich in den letzten 12 Monaten 47 Mitarbeiter negativ über das Essen geäußert. Das ist ein klares Signal. Gleichzeitig schaut die KI in Pressemitteilungen: 2022 wurde ein Vierjahresvertrag mit dem aktuellen Caterer abgeschlossen. Der läuft 2026 aus. Die KI identifiziert das Buying Center und liefert dir den Hauptentscheider Peter Mayer inklusive Persönlichkeitsprofil: faktenbasiert, braucht erst Vertrauen, am besten Testimonials einsetzen. Das ist, als würde ein Freund anrufen und dir den perfekten Lead servieren – nur dass du diesen Freund nicht mehr brauchst. Du bekommst es systematisch jeden Tag, jede Woche geliefert. Statt 100 unqualifizierten Calls hast du fünf bis sieben hochwertige Leads. Du bist deutlich effizienter, weil du dich mit mehr interessierten Kunden beschäftigst. Und dein Team muss mental nur noch fünf statt 97 Absagen verarbeiten. Das Thema Resilienz spielt plötzlich eine ganz andere Rolle. Die Konsequenz: Sales und Marketing wachsen zusammen. Marketing liefert dem Vertrieb vorqualifizierte Leads. Du brauchst neue Strukturen – eine aggregierte Abteilung, die Datenmanagement, Sales, Marketing, KI und Digitalisierung unter einem Hut vereint. Mit alten Strukturen geht das nicht. Hebel 3: Das externe Lab – raus aus der Lähmung Warum wird das alles in deutschen Unternehmen so selten systematisch angegangen? Weil zehn Leute mitzureden haben. Weil der Betriebsrat viele Sachen nicht will. Wegen DSGVO, Compliance, Governance. Wegen der Fehlerkultur: Hier sind 100.000 Euro, berichten Sie in drei Monaten. Wenn dann noch keine richtigen Erfolge da sind – zack, ist die Karriere ruiniert. Aus diesen Gründen passiert intern relativ wenig. Oder es wird Klopapier gekauft. Markus' Lösung: ein externes Lab, analog zum Fraunhofer-Prinzip. Du lagerst die Entwicklung aus. Dort gelten komplett andere Spielregeln als im Mutterunternehmen: So baust du ein externes Innovationslab für deinen Vertrieb auf: 30-Tage-Entscheidungsregel: Innerhalb von 30 Tagen muss eine Entscheidung über jede Idee getroffen sein. Kein endloses Hin und Her. 90-Tage-Pilot: Innerhalb von 90 Tagen ist der Use Case pilotiert. Geschwindigkeit ist alles. Datenschutz extern lösen: Das Lab kümmert sich um DSGVO, Betriebsrat und Compliance – nicht deine interne IT. Use Cases systematisch bewerten: Wie groß ist der Impact? Wie hoch der Aufwand? Was ist das beste Verhältnis? Zurück ins Unternehmen: Wenn die Lösung läuft, holst du sie zurück und skalierst sie. Mit diesem Ansatz externalisierst du das, was du intern nicht hinbekommst. Im Lab sitzen Dienstleister, Kollegen vom Kunden und Experten. Sie definieren Use Cases, erstellen eine Roadmap und bringen die Themen schnell auf die Straße. Nach 90 Tagen hast du mega qualifizierte Leads, mega qualifizierte Tools und mega qualifizierte Prozessoptimierungen. Nicht nur im Vertrieb, sondern auch im Einkauf, in HR, in der Unternehmenskommunikation. Hebel 4: Schnittstellenprobleme mit KI lösen Jeder, dem ich das erzähle, sagt zunächst: „Bei uns ist das aber anders. Unsere Branche ist speziell. Unsere Kunden sind anders." Die grundlegenden Dinge bleiben aber gleich. Was sich in fast allen Branchen findet: eine Branchensoftware als zentrales System, dazu DATEV, Excel-Listen, diverse Spezialtools – und die reden kaum miteinander. Ein Beispiel aus der Sicherheitsbranche: Bei einem Großeinsatz wird zuerst ein Angebot an den Kunden erstellt. Dann folgt die Planung für das konkrete Event. Anschließend kommt die Zeiterfassung mit den Logins der eingesetzten Mitarbeiter. Glaubst du, es gibt einen vernünftigen Abgleich zwischen diesen Systemen? Fehlanzeige. Genau hier kommt KI ins Spiel: Sie führt verschiedene Systeme über Schnittstellen zusammen, die vorher nicht miteinander gesprochen haben. Vom analogen Mist zum optimierten Prozess Wichtig: Wenn du einen schlechten analogen Prozess einfach nur digitalisierst, hast du einen schlechten digitalen Prozess. Das bringt nichts. Die Zeitenwende ist der optimale Zeitpunkt, dein Unternehmen neu zu denken. Erst optimierst du die Prozesse und Strukturen. Dann digitalisierst du sie. Dann bringst du KI ins Spiel. Und wenn du das gemacht hast, hast du im Zweifel ein Tool, das du 1.000 anderen Unternehmen deiner Branche auch verkaufen kannst. Riesige Vertriebschancen. Ein konkretes Beispiel aus meinem Alltag: Früher war meine Kreditkartenabrechnung ein Riesenthema. Belege sammeln, am Ende des Quartals kam der Buchhalter, fragte nach fehlenden Belegen – mit wem warst du wann essen? Riesenaufwand. Heute habe ich eine App. Beim Bezahlen geht sofort ein Fenster auf: Beleg fotografieren, Gesprächspartner eintragen. Das CRM greift zu, ordnet einen Buchungssatz zu und schiebt alles automatisch in DATEV. Digitalisierter Prozess. Schneller, besser und am Ende auch billiger – weil die Buchhaltung hinten raus weniger Arbeit hat. Hebel 5: Dein digitaler Vertriebsassistent – treffe Alfred Die fünfte und letzte Stufe ist die Königsdisziplin: ein agentic AI-System, das wirklich für dich arbeitet. Markus und sein Sohn sind beide Batman-Fans. Bekanntlich heißt Batmans Butler Alfred. Genau so haben sie ihren neuen Kollegen genannt. Alfred basiert auf Open-Source-Architektur und hat alle großen Large Language Models angebunden: Gemini, Claude, Perplexity, ChatGPT, Grok. Alfred entscheidet selbst, welches Modell für welche Aufgabe am besten geeignet ist – oder am kostengünstigsten arbeitet. So sieht ein typischer Arbeitstag aus: Markus ist beim Kunden, auf dem Rückweg spricht er über WhatsApp in sein Handy: „Alfred, ich bin in 20 Minuten im Büro. Bestell beim Inder über Lieferando ein Chicken Tikka Masala. Und ich habe mit dem Kunden gerade ein größeres Projekt besprochen – Bedarfsanalyse, Workshop, Mitarbeiterinterviews, dann Training. Erstell schon mal das Angebot, du hast alle Daten." Wenn Markus im Büro ankommt, ist das Angebot zu 90 Prozent fertig. Die menschliche Verbesserungskompetenz bleibt entscheidend Wir Menschen haben eine sehr überschaubare Erstellungskompetenz. Wenn ich vor einem leeren Blatt Papier sitze und ein Marketingkonzept entwickeln soll, brauche ich Stunden. Eine KI liefert mir mit dem richtigen Befehl in Minuten eine 80-Prozent-Lösung. Was Menschen aber wirklich gut können, ist die Verbesserungskompetenz. Aus der 80-Prozent-Lösung machst du mit deiner Expertise eine 100-Prozent-Lösung. Genau deshalb glaube ich übrigens fest, dass das Thema KI im Vertrieb nicht den Tech-Companies gehört, sondern den Experten, die das Unternehmen, den Mittelstand, den Kunden verstehen. Programmieren musst du heute nicht mehr können. Das macht die KI für dich. Aber du musst das Geschäftsmodell verstehen, Erfahrungswissen mitbringen und die Kunden kennen. Auf dieser Basis bauen wir saubere Strukturen und saubere Prozesse. Mein Tipp aus dem Alltag: Wann immer mir jemand eine Aufgabe stellt, über deren Beantwortung ich länger als fünf Sekunden nachdenken müsste, mache ich das sofort mit meinem KI-Agenten. Die 5-Sekunden-Regel ist Gold wert. Quick Takeaways: Die wichtigsten Erkenntnisse auf einen Blick Geschwindigkeit als entscheidender Faktor: ChatGPT erreichte in 3 Jahren 1,2 Milliarden Nutzer – Veränderungen geschehen heute exponentiell schneller als früher. Klopapier-Falle vermeiden: Aktionismus ohne Strategie schadet mehr, als er nützt. Erst Vision, dann Struktur, dann Tools. Social Listening schlägt Cold Calling: Hochqualifizierte Leads auf dem Silbertablett statt 98 Prozent Streuverlust. Externes Lab nutzen: Was intern nicht geht, kannst du auslagern – mit 30-Tage-Entscheidungen und 90-Tage-Piloten. Strukturen neu denken: Marketing, Sales, Datenmanagement und KI gehören in eine integrierte Einheit – nicht in Silos. Digitaler Assistent als Game Changer: Ein agentic AI-System wie „Alfred" erledigt 80 Prozent der Vertriebsadministration für dich. Experten schlagen Techies: Wer Unternehmen, Mittelstand und Kunden versteht, schafft mit KI nachhaltigen Mehrwert. Fazit: Jetzt ist die Goldgräberzeit Wir reden viel von Krise, Unsicherheit und schwierigen Zeiten. Ein Historiker hat es kürzlich treffend formuliert: Die letzten 50 bis 60 Jahre nach dem Zweiten Weltkrieg waren eine absolute Ausnahmesituation. Das, was wir jetzt erleben, ist eigentlich die Normalzeit der Menschheitsgeschichte. Und schau dir an, wann die wirklich großen Unternehmen gegründet worden sind: meistens nicht in den guten Zeiten, sondern in Krisenzeiten. Weil ihre Gründer Trends erkannt haben, die andere übersehen haben. Genau deshalb ist jetzt eine Goldgräberzeit. Es gibt überall Chancen, wenn du sie sehen willst. Den Kopf in den Sand zu stecken hilft nicht – die anderen laufen dann an dir vorbei. Stell dir die Bezos-Frage: Wenn Bezos morgen dein Unternehmen kaufen würde, was würde er anders machen? Welche Stärken hat dein Unternehmen, die mit Digitalisierung und KI im Vertrieb auf das Zehnfache skaliert werden könnten? Mein Call to Action: Buche dir ein Strategiegespräch mit Markus und mir. Wir nehmen uns eine Stunde Zeit, schauen uns deine aktuellen Herausforderungen an und zeigen dir aus unserem Erfahrungshintergrund, wie du schnell zum Hightech-Vertrieb wirst. Die ersten drei, die sich anmelden, bekommen außerdem zwei Bestsellerbücher von Markus obendrauf. FAQ: Die wichtigsten Fragen rund um KI im Vertrieb Was bedeutet Hightech-Vertrieb im Mittelstand konkret? Hightech-Vertrieb bedeutet, dass deine Vertriebsorganisation modern aufgestellt ist – mit aktueller Technologie, intelligenten Prozessen und einer Struktur, die zur heutigen Zeit passt. Es geht darum, KI im Vertrieb, Social Listening, datenbasierte Lead-Qualifizierung und digitale Assistenten so einzusetzen, dass dein Team mehr Umsatz und Marge generiert – und sich gleichzeitig auf das Menschliche konzentrieren kann. Wie kann ich meinen Vertrieb digitalisieren, ohne riesige Budgets zu haben? Das Schöne an aktueller KI-Technologie ist, dass du mit überschaubaren finanziellen Mitteln viel erreichen kannst. Starte mit einem Erkenntnis-Workshop, identifiziere die größten Hebel und beginne mit konkreten Use Cases statt mit Großprojekten. Ein externes Lab kann helfen, schnell Ergebnisse zu liefern, ohne deine interne IT zu blockieren. Was ist Social Listening und wie hilft es im B2B-Vertrieb? Social Listening bedeutet, dass KI öffentlich verfügbare Daten aus Social Media, Pressemitteilungen und Bewertungen analysiert und daraus Verkaufschancen identifiziert. Im B2B kannst du so gezielt Unternehmen finden, die gerade mit ihrem aktuellen Anbieter unzufrieden sind oder deren Verträge auslaufen – inklusive der relevanten Entscheider. Wie überwinde ich interne Widerstände wie DSGVO oder Compliance? Diese Themen sind real, aber lösbar. Ein externes Innovationslab kümmert sich um diese Hürden, weil dort andere Spielregeln gelten als im Mutterunternehmen. So kannst du innerhalb von 90 Tagen pilotieren, was intern jahrelang dauern würde – und holst die fertige Lösung dann zurück ins Unternehmen. Ersetzt KI den Vertriebsmitarbeiter? Nein, im Gegenteil. KI nimmt dir die Routinearbeit ab – CRM-Pflege, Besuchsberichte, Angebotserstellung. Damit kannst du dich auf das konzentrieren, was nur Menschen können: echte Beziehungen aufbauen, Vertrauen schaffen, komplexe Verhandlungen führen. KI macht Vertrieb wieder menschlicher. Sag mir deine Meinung Ich bin echt gespannt: Wo stehst du gerade beim Thema KI im Vertrieb? Bist du schon mitten in der Umsetzung oder noch im Klopapier-Modus? Schreib mir deine Erfahrungen, deine Herausforderungen oder deine Erfolgsgeschichten in die Kommentare. Und wenn dir diese Folge weitergeholfen hat, dann teile sie gerne mit deinem Netzwerk. Welcher der fünf Hebel ist für dich der spannendste?

The expression that if something is offered for free, then you are the product is often used in relation to technology companies, but new research has calculated how much companies are profiting off our data. The Web3 Foundation suggests that over €200,000 worth of personal data is collected from each European by big tech and AI companies over a lifetime, raising concerns over how our data is harvested and used.Joining Ciara Doherty to explain the study is Ciara O'Brien, a Technology Journalist with The Irish Times.

It's just the boys today, as Chris has a slew of updates, while Matt has joined a new cult. They also try their darnedest to understand the appeal of mouse gestures in browsers. This week's Cozy Zone, the gang roasts your (yes, your!) old home screens. Want more from the gang? Cozy Zone is a bonus podcast every Monday where we let loose on all sorts of fun topics. You can get cozy with the Comfort Zone crew for just $5/month or $50/year, which not only makes the bonus episodes possible, but supports Comfort Zone, too. How would you have done our challenges? How would you answer the question at the end of the show? Let us know! Things discussed Garbage can! Razer BlackShark V3 Pro Wireless 3D Printed an ergo stand for Magic Trackpad Strava Follow the Hosts Chris on YouTube Matt on Birchtree Niléane on Mastodon Comfort Zone on Mastodon Comfort Zone on Bluesky

In this Predictable Revenue Podcast episode, Harvard senior lecturer and Unlikely Entrepreneurs author Lou Shipley puts language to that mistake with a simple idea: "the problem with the problem." His point is straightforward: A company does not become real because it raised money, built a product, or hired a team. It becomes real when it solves a problem people care enough about to act on. Before founders think about scaling sales, they need to answer a harder question first: Is this problem actually worth building a company around? Highlights include: Transitioning from Side Hustle to Business (05:16), Transitioning to a Tech Company (10:52), Navigating Stress as a Founder (15:15), Redefining Ambition and Work-Life Balance (27:56), and more... Stay updated with our podcast and the latest insights on Outbound Sales and Go-to-Market Strategies!

-Lobbying by tech industry groups, the Science Based Targets initiative decided to not recommend a protocol that would have made it more difficult for tech companies to use clean energy investments to offset fossil fuel pollution. -To date, Apple has not permitted vibe coding tools on the App Store because they would violate its policies. -Reuters reported that Meta's workers have begun circulating flyers at multiple US offices to protest the company's installation of tracking software on their work computers. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What makes a company worth buying? Most people think it's revenue. Or technology. Or market share. But after spending more than 30 years building Pittsburgh-based engineering firm IQ Inc., founder Barbara VanKirk believes the answer starts somewhere else entirely: People. In this episode of 10 Minute Tech Talks, you'll hear how VanKirk built a company culture around growth, curiosity, and leadership—and why those values ultimately attracted a global acquisition from Critical Software. You'll also hear her candid reflections on being one of the only women in engineering rooms early in her career—and why confidence and belonging still matter in tech today. If you're building a company or building yourself as a leader, this conversation delivers a few lessons worth stealing. Listen VanKirk's entire interview right here. Produced by the Pittsburgh Technology Council, this is a podcast for tech and manufacturing  entrepreneurs exploring the tech ecosystem, from cyber security and AI to SaaS, robotics, and life sciences, featuring insights to satisfy the tech curious.  

On this episode of the Predictable Revenue Podcast, Collin Stewart sat down with Sam Eitzen, co-founder of Snapbar, to unpack a startup story that did not begin with a polished strategy or a deliberate plan to build a company. It began with a signal from the market. That is what makes Snapbar's story so useful for startup founders, bootstrapped operators, and B2B growth leaders. Highlights include: Transitioning from Side Hustle to Business (05:16), Transitioning to a Tech Company (10:52), Navigating Stress as a Founder (15:15), Redefining Ambition and Work-Life Balance (27:56), And more... Stay updated with our podcast and the latest insights on Outbound Sales and Go-to-Market Strategies!

Roman Golovach arrived in America broke, lost, and working as a mover. At 24, he stumbled upon an obscure Soviet book — The Nature of Talent: About the Boy Who Could Fly — and it changed everything he believed about genius, potential, and what human beings are capable of. Today, he runs his own tech company. In this episode, we explore the philosophy behind his transformation, how talent can be developed through simple and fun projects, and the process of unlocking a life you WANT to live.

President Trump is trying to get Big Tech to pledge to supply their own electricity for data centers - rather than passing the cost to consumers. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Progress Software urges customers to patch a critical MOVEit authentication bypass. Washington worries about limited access to advanced AI tools. Paid influencers promote pro-American AI. CISA warns Copy Fail is under active exploitation. The Canvas educational platform suffers a data breach. The Lazarus Group uses ClickFix to target high-value enterprise users. U.S. and Chinese authorities raid scam centers in Dubai. Monday Business Brief. On Afternoon Cyber Tea with Ann Johnson: Tony Sager, Senior VP & Chief Evangelist, Center for Internet Security, joins Ann to discuss the accelerating pace of technology, AI, and global software dependencies. May the Fourth be with your firewall.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. Afternoon Cyber Tea On this segment of Afternoon Cyber Tea with Ann Johnson: Tony Sager, Senior VP & Chief Evangelist, Center for Internet Security, joins Ann to discuss how the accelerating pace of technology, AI, and global software dependencies are reshaping the cybersecurity landscape. To hear the full conversation, check out the episode and subscribe where you get your favorite podcasts to listen to past episodes. The show is going on hiatus. Stay tuned for the next chapter soon. Selected Reading ⁠Progress warns of critical MOVEit Automation auth bypass flaw⁠ (Bleeping Computer) ⁠What Was Discussed at Google's White House Meeting About A.I. ⁠(The New York Times) ⁠US Military Reaches Deals With 7 Tech Companies to Use Their AI on Classified Systems ⁠(SecurityWeek) ⁠A Dark-Money Campaign Is Paying Influencers to Frame Chinese AI as a Threat⁠ (WIRED) ⁠CISA says ‘Copy Fail' flaw now exploited to root Linux systems⁠ (Bleeping Computer) ⁠Edtech Firm Instructure Discloses Data Breach Amid Hacker Leak Threats⁠ (SecurityWeek) ⁠Lazarus Targets macOS Users With New “Mach-O Man” Malware Kit⁠ (GB Hackers) ⁠US, China partner on scam center takedown in Dubai⁠ (The Record) ⁠Cloudsmith raises $72 million in Series C funding.⁠ (N2K Pro Business Briefing) Microsoft for Startups (N2K Networks) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

DW's Lars Bevanger reports on a new a controversial contract between Britain's National Health Service and the US data and spy tech company Palantir. The post Increasing frustration over UK deal with spy tech company appeared first on The World from PRX.

The head of a clean tech start up says it's worrying that many in the sector are looking overseas to solidify their growth, but he says their contribution to reducing emissions shouldn't be ignored.

https://www.youtube.com/watch?v=aRML1GMxiUg Podcast audio: In this episode of the Ayn Rand Institute podcast, Ben Bayer, Mike Mazza, and Tristan de Liège discuss major publicity campaigns launched by AI companies to combat uncertainty and fear about the disruptive power of AI. The AI PR problem Valid marketing problems Valid marketing solutions Special problems re: misuse The invalid inequality concern Invalid regulatory concerns Altruism as appeasement Resources:  Ayn Rand, “What is Capitalism?” in Capitalism: The Unknown Ideal,  “The Age of Envy” in Return of the Primitive, and “Altruism as Appeasement” in The Voice of Reason.  Threats to Regulate Artificial Intelligence, ARI Podcast, May 12, 2023 Anthropic vs. Trump: The Moral Responsibility of Tech Companies, ARI Podcast, 3/12/26 Don Watkins and Yaron Brook, Equal is Unfair This episode was recorded on April 17, 2026 Image credits: Altman: Anna Moneymaker / via Getty Images; Amodei: Chance Yeh / Stringer / via Getty Images

The social media company Snap recently announced it's laying off about 1,000 workers — 16% of its employees. The company said these changes will reduce costs by more than half a billion dollars and help establish a path to net income profitability.This move comes after one of Snap's investors, Irenic Capital Management, wrote a public letter to the company outlining what it needs to do to “save” the company and cut costs.Snap has been a public company for nine years. It's had just a few non-consecutive profitable quarters. Sarah Kunst, a general partner at the venture capital firm Cleo Capital, explains more about when a company has to be consistently profitable.

The social media company Snap recently announced it's laying off about 1,000 workers — 16% of its employees. The company said these changes will reduce costs by more than half a billion dollars and help establish a path to net income profitability.This move comes after one of Snap's investors, Irenic Capital Management, wrote a public letter to the company outlining what it needs to do to “save” the company and cut costs.Snap has been a public company for nine years. It's had just a few non-consecutive profitable quarters. Sarah Kunst, a general partner at the venture capital firm Cleo Capital, explains more about when a company has to be consistently profitable.

Join us as Kira Intrator (MIT-trained urban planner, systems thinker, and social impact technologist based in Geneva) makes the case that AI for Good isn't failing because of models - it's failing because of systems. Kira walks through why so many AI pilots never reach deployment, drawing on her experience building tools scaled across 9,000 users, three ministries, and six countries in Central Asia. You'll learn the five factors that kill AI projects in the development sector, why 80% of clinical AI models are trained on data that can't be deployed outside Western contexts, and what the $2.6 trillion opportunity in developing markets actually requires to unlock. This episode is equal parts systems thinking masterclass and call to action - a rare perspective from someone who has moved AI from prototype to production in places most tech professionals never consider. Timestamps 0:00 Welcome & Introduction 2:47 Kira's Background: MIT, Geneva, Central Asia 3:54 The Core Thesis: It's About Systems, Not Models 5:20 AI is Our Generation's Revolution 6:35 The $2.6 Trillion Opportunity 7:17 The 80% Western Data Problem 8:20 Why AI Projects Fail in Development: 5 Factors 9:28 Systems Mismatch & Low-Bandwidth Environments 9:52 Built for Pilot vs. Built for Deployment 10:29 Ownership, Economics & Sustainability 18:22 Real-World Case Studies 24:16 What Actually Works: Levers for Scale 30:41 The Role of Tech Companies & Foundations 33:39 Crystal Ball: Merging the Two Universes 35:01 A Call to Action 38:48 Wrap-up How to find Kira: https://www.linkedin.com/in/kiraintrator/ Links from the show: Infrastructure & Platforms Anthropic Beneficial Deployments: https://www.anthropic.com/ Google Research Global South Labs: https://research.google/ Lelapa AI: https://lelapa.ai/ Microsoft AI for Good: https://www.microsoft.com/en-us/ai/ai-for-good OpenAI Foundation: https://openai.com/ Research & Innovation Hubs Data Science Africa: https://www.datascienceafrica.org/ Masakhane: https://www.masakhane.io/ Stanford HAI: https://hai.stanford.edu/ Wadhwani AI: https://www.wadhwaniai.org/ Global Governance & Policy OECD AI Observatory: https://oecd.ai/ UNICEF Office of Innovation: https://www.unicef.org/innovation/ World Health Organization AI: https://www.who.int/ Funders & Philanthropies Gates Foundation: https://www.gatesfoundation.org/ Patrick J. McGovern Foundation: https://www.mcgovern.org/ Conferences AI for Good Global Summit (July 7-10, 2026 - Geneva): https://aiforgood.itu.int/ Data Science Africa 2026 (July 20-24 - Kampala, Uganda): https://www.datascienceafrica.org/ Deep Learning Indaba 2026 (August 2-7 - Lagos, Nigeria): https://deeplearningindaba.com/

Preview for Later TodayJack Burnham discusses the security risks of Chinese tech companies, specifically Hikvision. He highlights its role as a top-tier PLA supplier and its use in surveillance for the mass detention of Uyghurs.1954

When you think of data centers in Washington state, you probably think of those in rural areas - outside small towns like Quincy and Prosser. But as demand grows for data centers, companies are looking to the city… some have started to request space in Seattle. We’ll hear more from Seattle Times Climate Reporter Greg Kim. Fill out the Seattle Now focus group survey, here. We can only make Seattle Now because listeners support us. Tap here to make a gift and keep Seattle Now in your feed. Got questions about local news or story ideas to share? We want to hear from you! Email us at seattlenow@kuow.org, leave us a voicemail at (206) 616-6746 or leave us feedback online.See omnystudio.com/listener for privacy information.

This episode comes from Sophia's recent appearance on Scott Ritzheimer's Start, Scale and Succeed podcast — and it's one of the clearest walkthroughs of the Tech for Non-Techies methodology she has ever given on another show. If you have a great idea but no technical background, this is where to start. You'll learn: Why coding skills matter less than you think — especially in the age of AI How to build a five to seven screen test version of your product without a developer or a designer Why you only need five users to uncover 85% of the problems in your product — and how to find the right five What to do when your idea doesn't validate — and why that outcome is still a win Sophia also shares the story of a student who discovered her venture wouldn't work in six weeks for $2,000 — saving herself hundreds of thousands of dollars and months of wasted effort. And she also shares why entrepreneurship never really gets easier — even after an IPO. Timestamps: 00:00 - Introduction: Even IPO founders struggle 02:26 - Is coding really the first thing to worry about? 05:17 - Where to start: Creating a test product with AI 08:51 - Defining your target customer through the problem 11:21 - Building in the AI age: Five to seven screens 14:19 - What happens when users don't like it? 17:25 - The biggest secret: Entrepreneurship is always hard 20:41 - Closing and resources Free AI Mini-Workshop for Non-Technical Founders Learn how to go from idea to a tested product using AI — in under 30 minutes. Get free access here: techfornontechies.co/aiclass Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select "Ratings and Reviews" and "Write a Review" then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Listen to Tech for Non-Techies on: Apple Spotify YouTube Audible Pandora Transcript: https://www.techfornontechies.co/blog/299-you-don-t-have-to-know-how-to-code-to-start-a-tech-company-with-sophia-matveeva

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.Iran's Islamic Revolutionary Guard Core, or the IRGC, announced that it plans to begin attacks on more than a dozen American technology companies operating across the middle east, starting after 8pm Tiran time on April 1st.A pro-Iranian hacking group, known as Hendala, has claimed responsibility for breaching a personal account belonging to FBI Director, Kash Patel.A newly discovered malware-as-a-service platform called Venom Stealer is automating the creation and deployment of quick-fix social engineering attacks, significantly lowering the barrier for cyber criminals.Toy and entertainment company, Hasbro, disclosed that it experienced a cyber attack that disrupted some of its internal operations, in a filing with the U.S. Securities and Exchange Commission.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

Major tech platforms failing to protect children, Culture Friday on competing visions of speech and responsibility, a review of The Super Mario Galaxy Movie, and Word Play on modern use of ancient Greek poetry. Plus, the Friday morning newsSupport The World and Everything in It today at wng.org/donateAdditional support comes from the Joshua Program at St. Dunstan's Academy in Virginia ... a gap year shaping young men ... through trades, farming, prayer ... stdunstansacademy.orgAnd from Dordt University, equipping students to serve others with faith, skill, and conviction while they complete their Master of Social Work degree in just four years.Share the message of Christ with friends and family this Easter using the film, Heaven, How I Got Here. This compelling one-man performance starring Stephen Baldwin tells the story of the thief on the cross next to Jesus. It helps a viewer understand that getting into heaven has nothing to do with living a good life, but relies completely on the grace of God. Available in 30 languages, Heaven, How I Got Here could change the life of someone you know today. Learn more at openthebible.org/heaven

Tesla is not a car company. That’s the biggest lie on Wall Street. Today on Stinchfield, we rip the mask off one of the most misunderstood companies in America. While the media obsesses over delivery numbers and price cuts, they’re missing the real story entirely. Tesla is building the backbone of the future. Artificial intelligence. Robotics. Energy dominance. Autonomous driving. This is a technology empire hiding in plain sight. And if that’s true, then the way investors are valuing Tesla is completely wrong. Joining me is Tyler Herriage of VRA Insider, who lays out exactly why Tesla may be the most important tech company in America today. From the race for full self driving to the rise of humanoid robots, and a power grid revolution that could reshape global energy, this is a conversation that will challenge everything you think you know about Elon Musk’s crown jewel. Is Tesla the next trillion dollar tech superpower hiding behind a car badge? Or is Wall Street already too late to the party? This is the story the mainstream won’t tell. VRAInsider.comSee omnystudio.com/listener for privacy information.

We all love the convenience of our digital devices and connected services.  But what about our ever expanding pile of digital breadcrumbs we leave behind as we go about our day? These breadcrumbs can be swept up by private companies to learn quite about us and target us with specific goods and services.  They can also be collected by government agencies who might use this information for legitimate police work or in some instances, political repression.  So, many people are asking themselves, should I be doing more to protect my personal privacy and how should I go about this.    Tech evangelist and prolific author Guy Kawasaki had asked that same question about a year ago. It started him on a journey to learn more about how to use some of the latest communications tools built from the ground up with personal privacy as its primary goal.  This led Guy to install and use Signal, one of the most popular tools today for personal privacy protection.  But as he started to put Signal into his communications work flow, he realized it was not obvious how to use Signal to its full potential. So, Guy collaborated with Madisun Nuismer to publish a “how to” book for using Signal, “Everybody Has Something to Hide” in January of 2026.   In this episode of the Radical Candor Podcast, Kim and Guy have a wide ranging conversation about Guy's concerns about privacy that inspired him to start using Signal and then to write the book.  They discuss the centrality of privacy in a free and democratic society and how tools like Signal can enhance privacy. Kim also shares her experiences with privacy and censorship in her years working in the Soviet Union (and later Russia) in the early 1990s.  They also debate how much we should all trust so much of our personal data with these large tech companies.  As Guy mentions the old saying, “If you aren't paying for the product, you ARE the product!”.   In the media rollercoaster, tech's reputation is at a low point right now. It's worth remembering that there are a lot of idealistic people in tech who are working hard to solve problems with the goal of making the world a better place. That is part of why we want to highlight Guy's messages and what Meredith Whitaker, Brian Acton, Moxie Marlinspike, and the whole team at Signal are doing. Background on Guy Kawasaki: Guy Kawasaki is the chief evangelist of Canva and the creator of Guy Kawasaki's Remarkable People podcast. He is an executive fellow of the Haas School of Business (UC Berkeley), and adjunct professor of the University of New South Wales. He was the chief evangelist of Apple and a trustee of the Wikimedia Foundation. He has written Wise Guy, The Art of the Start 2.0, The Art of Social Media, Enchantment, and eleven other books. Kawasaki has a BA from Stanford University, an MBA from UCLA, and an honorary doctorate from Babson College. Resources:  Electronic Freedom Foundation (EFF) information on how to use Signal.   Interviews with Meredith Whittaker is the President of The Signal Foundation.   Guy's interview with Meredith Whittaker on his Remarkable People Podcast. Also an informative interview with Meredith on Scott Galloway's Podcast.   CHAPTERS: (00:00) Exploring the New Book: Everybody Has Something to Hide (00:51) The Importance of Signal and Privacy (06:46) Personal Experiences with Privacy and Censorship (11:57) Trust in Tech Companies and Data Privacy (14:27) The Idealistic Problem Solvers in Tech (15:01) Philanthropy vs. Government Aid (15:38) Universal Basic Income as an Experiment (17:02) The Importance of Privacy in Democracy (19:09) The Role of Technology in Privacy (21:04) Evangelizing Signal for Privacy Protection Connect with the Radical Candor team: ⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

In the book “The Anxious Generation,” Jonathan Haidt, a social psychologist at New York University, argues that social-media platforms are detrimental to youths' well-being, and that society needs to treat them as literally addictive. It has spent nearly a hundred weeks on the New York Times best-seller list, and has helped galvanize a movement seeking limits to social media in legislatures, in school districts, and in the courts. David Remnick speaks with Haidt about an Australian law to verify the age of social-media users, the first of its kind in the world, and about lawsuits in California that are aiming to pin liability for harms on social-media companies themselves.  Further reading:  “World Happiness Report 2026,” featuring a contribution from Jonathan Haidt and other researchers  “Mountains of Evidence,” by Jonathan Haidt New episodes of The New Yorker Radio Hour drop every Tuesday and Friday. Join host David Remnick as he discusses the latest in politics, news, and current events in conversation with political leaders, newsmakers, innovators, New Yorker staff writers, authors, actors, and musicians.

Data centers are booming and taking the blame for spiking power costs because of how energy intensive they are. Rosemary Misdary, WNYC and Gothamist science reporter, talks about what Gov. Hochul says she plans to do to reign in the costs to consumers.   Image: Data center infrastructure in the United States, November 2025 (DOE — NREL, Public domain, via Wikimedia Commons)