Podcasts about CTO

By now most organizations have AI strategies (among their other tech strategies). But how do you know when it's time to make a midcourse correction? Better still: How can you predict when, and what kind of corrections you might need? John and Johna discuss, and tell the story of how a university prepared for technology... Read more »

Sean Barnes attends 40 to 50 events a year, usually as the keynote or conference chair, and he has watched the same speaking mistakes trip people up over and over. In this episode he breaks down what separates the presenters who own the room from the ones who lose it in the first thirty seconds. He starts with the habit that quietly wrecks credibility: filler words. He explains why audiences mentally check out the moment a speaker starts stacking up filler, and he shares the simple practice that fixed it for him, recording yourself on a tripod and watching it back until you can feel the filler coming and pause through it instead. From there Sean tackles the trap sponsors and vendors fall into most, opening with their company name and slide deck instead of a story. He walks through the difference between leading with a pitch and leading with a hook, using his own introvert-turned-HR-leader opening as the example. He closes with the physical side of presenting, moving across the stage instead of planting your feet, making real eye contact, and never turning your back to point at slides. He ties it together with a story about a field CTO at a Nashville cybersecurity event who stood out for one reason: he told a story and made an offer instead of pitching.   Key Moments 00:00:02 Sean intros the episode and his 40 to 50 events a year as keynote, chair, or panelist. 00:00:24 Mistake one: filler words and why they kill credibility. 00:01:24 Sponsors spend 5,000 to 30,000 dollars to get on stage and still lose the room. 00:01:54 The fix: record yourself, watch it back, get used to how you sound. 00:02:35 Get comfortable with the pause and let the audience process. 00:03:03 What the process feels like as you start catching the filler. 00:03:59 A reminder that this takes reps, not an overnight fix. 00:04:22 Mistake two: opening with your name and slide deck loses people fast. 00:04:42 The better way: open with a story, shown through Sean's introvert-to-HR hook. 00:05:41 Why it keeps happening. VPs send people on stage with no prep. 00:06:00 Mistake three: planting your feet instead of working the floor. 00:06:44 Never turn your back to your slides. If they wanted to read them, email them. 00:07:11 The Nashville field CTO who got it right by telling a story, not pitching. 00:08:19 The payoff: people come to you after you step off stage.   Key Takeaways Filler words lose the room fast. The moment they stack up, people drop to their phones. The fix is reps, not talent. Record yourself, watch it back, and keep going until you feel the filler coming and pause through it. Lead with a story, not your slide deck. Opening with your name and what you do loses people immediately. Hook them with something human first, then earn the right to talk about the what and the how. Your body and eyes carry the message too. Use the whole stage, move toward people, make real eye contact, and never turn your back to read your slides.   Podcast Show Notes – Episode 284 | 06.02.2026 Episode Title: Speaking as a Solution Provider: The Right and Wrong Way to Present on Stage   Host: Sean Barnes Website: https://www.wolfexecutives.com   https://www.seanbarnes.com   LinkedIn: https://www.linkedin.com/in/seanbarnes/ https://www.linkedin.com/company/wolfexecutives https://www.linkedin.com/company/thewayofthewolf/ LinkedIn Newsletter: https://www.linkedin.com/newsletters/7284600567593684993/   Twitter: https://x.com/seanbarnes https://x.com/wolfexecutives   Instagram: https://www.instagram.com/the_seanbarnes https://www.instagram.com/wolfexecutives   TikTok: https://www.tiktok.com/@the_seanbarnes   Facebook: https://www.facebook.com/theseanbarnes

https://youtu.be/gS7aHfIiXjQ Preetha Pulusani, CEO of DeepTarget, is passionate about helping people realize their potential and leveraging technology to create meaningful business growth. After spending 25 years in corporate America and learning hard lessons from an early entrepreneurial failure, Preetha built DeepTarget into a bootstrapped fintech growth company that helps banks and credit unions acquire, engage, cross-sell, and retain account holders through advanced data analytics and intelligent marketing. In this conversation, Preetha shares the DeepTarget Bootstrap Framework, a leadership and innovation model built around five principles: Combine Pros with Fresh Graduates, Think Big but Start Small, Be Agile with a Flat Structure, Fail Quickly, and Keep a Tight Customer Feedback Loop. She explains how blending experienced professionals with emerging talent creates powerful teams, why rapid experimentation outperforms large-scale product launches, and how customer feedback should guide innovation. Preetha also discusses using data to drive growth, selling outcomes instead of technology, and building a successful SaaS company without outside funding. — Pull 5 Levers to Bootstrap Your Firm with Preetha Pulusani  Good day. Steve Preda here with the Management Blueprint, and my guest today is Preetha Pulusani, the CEO of DeepTarget, a company that helps hundreds of financial institutions increase loan demand, promote product adoption, and support intelligent marketing through advanced data mining and analytics. Preetha, welcome to the show.  Thank you, Steve. Thank you for having me. Thank you for inviting me. I’m looking forward to it.  Yeah. You have a very interesting business and very interesting profile, so I can’t wait to jump in. But let me ask you my favorite question. What is your personal ‘Why’, and how are you manifesting it in your business?  I guess you could say that my personal ‘Why’ has evolved over several years. I spent 25 years in corporate America, and that was the best business education I could have ever received. My first failure as an entrepreneur, though, added to that significantly, and that was right before I started DeepTarget. Luckily, it was a quick failure, but that doesn’t mean it was not a difficult one. And in every way, the lessons learned have come in handy today. So I believe that I’m in my final chapter of my career, so I can speak from years of experience. And my personal ‘Why’ is—it’s always been about people for me. I’ve never believed in the lone genius.  I believe that every person has some spark of genius in a different way. And I have always been inspired by pulling out that spark and weaving a tapestry of people.Share on X And that happened even in my job in corporate America, but it happens even more with my team today as an entrepreneur at DeepTarget. So it’s about empowering people to use that spark rather than focusing on something that they may not be as good at. It’s pulling out that strength and making it the collective strength of a solution, of how we serve customers, and of the business itself. Does that make sense?  Oh, yeah. This is great. I love that. My experience is that nearly none of the companies I talk to—or basically none of them, literally none of them—capitalize on the maximum talent of their team. Because it’s impossible to maximize it completely, but you can work on it, and that is wonderful.  Yeah.  So do you have a process for how you do that? Is there a mental process? Is it just an awareness? Is it a curiosity? Is it a natural thing that you do, or do you actually have a way of doing this?  So I have found that I think I read people. I think I’m intuitive in that way. And so I see myself as being the orchestrator of whatever it is, whether I’m working on today’s problem or whether I’m working on the big vision. I don’t know that it’s a process so much, but I have used it over and over again. It’s become a very natural thing for me.  So you talk about the big vision. What is that big vision?  So as a company, my focus is on making our clients successful. What that means is helping them grow their financial institutions.Share on X We work with credit unions and banks, and it’s all about growth. And we use innovation to leverage that growth for them. How do you acquire new account holders? How do you cross-sell to them? How do you communicate with them? How do you retain them? I’m a techie at heart, so it’s been about how do I leverage data? How do I leverage—today, of course—AI, kind of a combination of data and AI, to make sure that they are able to see the growth they need for their financial institutions? And that’s kind of become the mission that we have adopted for the company.  Yeah. I noticed that on your website you have this map of, I think, seven or eight different ways that you’re driving adoption and contact with people and—  It’s highly data-driven. It’s not wishy-washy. We’ve evolved from being a marketing company to a growth company. And when you take anything that’s data-driven into marketing, yeah, it’s something that people like to do. But what we like to do is use the technology to get to the human—to get to the individual. So we are helping our credit unions and banks reach individuals, understand each account holder, and understand what their financial needs are. And the only way you can do that at scale is by using technology and data. So we’ve built a platform that enables them to do that. That’s why the front end is all data, right? We can accept as much data as they want to give us so that we can do the right things to help them grow and engage their account holders.  Yeah. I like that you’re very techy, as you say—techy and data-driven. So I wonder, what is your mental model when you think about the end customers of your financial institution clients? What’s your mental model for how you innovate this process? So what are the major elements? If you had to synthesize it down to maybe three to five elements—your levers that you can pull—what are those?  Great question. So I’m going to start with the people because, for me, everything revolves around people. What I’ve been able to do is combine very seasoned pros with fresh graduates from local universities, and that has been a potent combination. Okay? That’s number one. Whether I’m talking about development, customer success, or sales, that’s been the combination that has worked for me. And as a bootstrapper, that has also helped me financially. You have a very seasoned pro that I’ve worked with for years, and you know exactly what their strengths are.  And then you put some fresh graduates under them. I’m telling you, there’s nothing better. That combination is second to none. The second thing is, I believe in thinking big, but starting small and scaling quickly. I learned that over time. There was a time when we used to have the big-bang theory of creating products.Share on X We have moved so far away from that. So think big, start small, and be agile. And as a small company, that’s a big advantage for me. We have a very flat structure. And so we’re able to have the agility we need to move markets, frankly. If you’re going to fail, fail quickly.  Have a tight customer feedback loop. And if something isn’t going to work for your customer, just abandon it. Abandon it quickly. I can’t say, in all honesty, that I’ve done that every time, but it’s always on my mind: “Should we really even pursue this?” I know we’ve had projects that we thought would be very successful, but they weren’t. But when you’ve only made a small investment, it’s easier to set it aside. “Okay, it’s not working. This is not what we need to do. Let’s move on.”  Yeah, I love that. Can you give an example where you invested in a process and really believed in it, and it turned out not to work, and then you had to pivot from it?  So the way we help banks and credit unions engage and cross-sell to their account holders is primarily through digital banking. We put up very personalized offers using data in the digital banking environment and use that real estate very effectively. It works like a charm. That’s what we do today. We did get a little sidetracked by expanding that into email, and we didn’t see the kind of growth we expected. So we tried to understand that. We did kind of an autopsy. And the difference is that when you log into digital banking, you’re being served something. The difference with email is that you’re pushing something out. It has its uses, for sure, but the particular aspect of what we had done in the product didn’t take off like we expected. So we just said, “Okay, let’s do more of what we can do within the digital banking environment.”  But that works for farming existing customers of the banks, right? Do you also help banks acquire new customers?  Yes. And that’s where email works, by the way. And so does direct mail, and so do digital ads. When you’re cross-selling to existing account holders, you have a lot of information about them. For example, if they rent a home, you would never give them a HELOC offer, right? But on the other hand, what we’re doing for new account acquisition is still using data. We’re looking at who the most profitable customers are that your credit union or bank has, and using that as the model to find more likely customers within a particular radius of their branches. So we are still using data, but in a different way and using different channels to reach them versus digital banking.  That’s fascinating. So what drives growth in your business?  Well, if you had asked me that question 10 years ago, I would have said innovation drives growth. But what we have found and learned over time is that innovation is an engine.Share on X Innovation, in a way, actually causes friction because when you innovate, you’re creating something new. So you first have to go out and educate the market. You have to make them understand that there’s a new way of doing things, and not everybody is open to change.  So if I go talk to a marketing professional and say, “Hey, here’s a new way of doing things. We’re using data.” I put myself in the place of that marketing person who is already constrained by bandwidth, who is already doing so many things, saying, “You’re bringing another new tool for me to learn and use? For what purpose?” While innovation is the engine, what we have learned is not to focus on the innovation, but to focus on the impact. And we do that by really working hard to get into the C-suite. So we are talking to the CEO, the COO, the Chief Digital Officer, or the Chief Technology Officer of these banks and credit unions, helping them understand the outcomes. What is it we do? We acquire new customers. We cross-sell to existing customers. We help you retain them. I receive these direct-mail solicitations from mega banks like Chase and Wells Fargo.  They’re paying me $900, $1,500 to open a checking account. It’s expensive to acquire new accounts. That’s just an example, right? So we are helping you grow through new account acquisition, but we also have a whole playbook for how you retain those new accounts that you acquire. So when you talk at the C-suite level, all of a sudden they’re not seeing a tool. What they’re seeing is an outcome. “How soon can we see results?” is the question we get asked. So we grow through a different way of selling what we do to these institutions.  So people don’t care how you achieve the result. They just want you to talk about the result?  Exactly. Especially the CEO. I mean, they don’t really care. They do care about things like data privacy, and we’ve addressed all of that. We’ve been doing this business for so long that data security is table stakes. But they care less about how you do it and more about why. So we have to talk to the individuals who care about the why rather than the how, although the how plays such a big part in building a business, right? But that’s what we focus on.  That’s behind the wall. That’s your problem, basically.  That’s right. That’s the secret sauce. We used to take great pains to explain the secret sauce at one point in time, but not anymore.  That’s interesting. So why do they listen to you? I mean, why do they believe that you can get these results? Do you show them testimonials, or how do you prove it?  We have over 200 customers now—customer contracts. It’s actually closer to 300. So we have a lot of testimonials and references that we can show them. We also let them know that there are barriers to using software like ours, such as, “Do I need to have somebody operate the software?” No, because part of what we offer is a managed service. We will operate the software for you using your branding and everything else that you have. So we’ve kind of removed all of the barriers. The biggest barrier today is creating awareness in the broader market, because this is a huge market.  And on my bootstrapping budget, I have to make sure people know that such a solution exists. What we find is that once we reach the decision-maker, it’s a fairly straightforward sale. I would say that if I’m constrained by anything when it comes to growth, it’s because I’m a bootstrapper. I watch every penny carefully, and I have built the company funded entirely by revenue. And one of these days that’s not going to be enough. But so far, so good. Yeah. Okay. So basically you create broader awareness of your products. You have all these testimonials and references. When you get in front of these decision-makers, you talk about the outcome and show them the results you can get.  And we have direct sales, right? I mean, we do call on, we have a couple of people. All they do is work the phones, emails, and LinkedIn to get us meetings in front of the right people. You know, also, Steve, in this day and age of everything digital, what we have found with banks and credit unions is that first important meeting with the CEO—we’re finding that doing it in person makes a huge difference. So that’s another thing that we do.  That’s interesting. So does that limit you geographically?  We’re having so much success with that model that it only helps us. More revenue means I can invest more in sales. So we are limited to the United States. We have customers on both coasts, a pretty good map of customers on both coasts, and in the Midwest. And there are some blank spaces, and we’re trying to address those blank spaces.  So you actually have people fly all over the country to meet with CEOs?  Yes. And it’s making a big difference. This is a change that we made not too far back. I would say maybe about 18 months ago or so, and it’s made a big difference for growth.  That is so interesting because after the pandemic, a lot of companies kept doing video sales calls.  As did we. As did we.  As probably you did as well. But the assumption was that there’s no point in traveling. It’s an extra expense and doesn’t make a huge difference. But you’re saying it’s the opposite—that it does.  Yes, it makes a huge difference. You’re talking to the CEO of a bank. Banks still have a more traditional generation of leaders. Even I didn’t believe it when I was first sold on this whole concept, but I’ve become a believer now. That meeting—the CEO not only is in the room with you, but brings in his or her key executives to talk to you. When you’ve made the trip all the way to Sacramento, they’re going to do that, right? So it’s made a difference.  So there’s a reciprocity involved. They see that you’re making the trip. Okay, then we might as well put more into it. And it’s kind of a self-fulfilling process.  And by the way, when you have more people in the room, you get more objections, but you’re able to address those in person. Yeah. Even if you have a video call with the CEO, if the CEO goes and talks to the CTO and brings up the objection, “You really need to worry about these guys and their data security,” we never hear about that. We just hear silence. We don’t know what’s going on behind the scenes. So you get that opportunity to address all of that kind of in person. And I think it actually works out more cost-effectively, surprisingly. Yeah, as long as those are resulting in deals.  Yes. So maybe that’s an inside thing, but I’m just wondering, what is the upside of something like that? If you convert one of the CEOs and they start using the system—maybe that’s a business secret—but what is the value of that conversion? Let’s say the 12-month value of that conversion that makes you want to do that trip.  So let me give you an example. We sell annual subscriptions with five-year terms. That’s a big deal, right? And when we sell five-year terms, it can become very significant. So we price based on the asset size of the financial institution because that kind of determines how large they are, how many branches they have, and how many account holders they have. So let’s take an institution that’s, say, a billion dollars. I’m just going to give you some rough numbers, right? For a five-year contract, you’re talking about $300,000 or so.  Okay. That makes sense. It’s definitely worth the trip.  Yes, it’s worth the trip.  Yeah.  The other way to have that personal interaction, which we have found to be very effective, is conferences—focused conferences. Many of these banks and credit unions have state leagues, regional leagues, or certain technology-focused groups that meet. And those are kind of the best venues to do our prospecting.  And then do you sponsor these conferences?  Well, we do. We’re very selective, but we have booths, and in addition to that, we may do some other sponsorships. Yeah.  Yeah. That’s great. So switching gears here, I’m really curious. What is something that you’re actively trying to figure out in your business? So if you had a magic wand and you could wave it, what would you want to fix in the next 12 months?  I’ve kind of told you that I’ve been a bootstrapper, and I’ve been a bootstrapper very intentionally. Because one of the things that I said I would do is that I wouldn’t be so stubborn as to never take any outside capital. But the thing that I wanted to figure out before taking external capital was what would give me a multiplier effect. So if I took a dollar in, how would I be able to multiply that? And I’m getting very close to figuring that out on the sales and marketing side. So if I had more dollars, and if I have a sales formula that I know works—that I’m confident works—then I should be able to take that formula, add those dollars, and simply add salespeople, right, to grow.  Scale it up, yeah.  So that’s kind of been the biggest issue I’ve had for the past, say, five years. But I would say that over the past 12 to 18 months, a lot of that has become clearer to me. And so I think I’m getting close to having that solved—to having that formula where I can say, “Okay, if I put in more dollars, I’m going to get X return.”  Yeah. Some people call this the coin-operated marketing and sales system. You keep dropping the coin and—  Yeah. Yeah. It’s taken me years to figure it out. I spent a lot of my early years at the company building a very robust technology platform because without that, everything else becomes secondary. And then I had this focus on, how do I get sales and marketing? And I’ve tried many things, and they haven’t necessarily worked, right? I’ve built up a customer base by slogging over time, but then you want that formula if you want to throw money at it.  Yeah. And that’s where I think I’m getting closer to getting there.  Yeah. And then marketing media is changing all the time. Different platforms come and go. Then you have different advertising formulas, and they burn out. So it’s actually difficult to stabilize it and make something that’s permanently coin-operated, so to speak. Yeah. And when we say everything is data-driven, it’s not just on the front end that everything is data-driven. We are able to tell the credit union or bank how many products we actually sold. What loans did you sell? How many auto loans? How many mortgages? How many HELOCs? How many credit cards? How many deposit accounts did you open each month that were influenced by our campaigns? We’re able to go back and tell them that. And what are the new balances you generated as a result of that? So it’s not about impressions and clicks. On the back end, we actually give them very deep data analytics so they can see, “This is the revenue I generated last month, and these are the new balances I generated last month.” And so that makes a difference, too.  Yeah. I saw on your website that many customers get a 500% ROI on their investment.  Yeah. Which only says that I’m charging them too little.  Yeah. Yeah.  No, but I mean, if you look at the balances and how they measure, we’re almost afraid to put the actual numbers out there. But we show them a growth grid that shows, month by month, here’s what you made using these campaigns. We can even show them what happens when they turn off the campaigns and what the impact is.  So in terms of bootstrapping, is that a strategy? Let’s say you figure out your scalable sales formula. Would you then go raise money, or would you still want to bootstrap?  If the revenue that I’m generating can be used toward growth, I won’t have to go raise money. But I won’t be so stubborn and silly that I wouldn’t take outside capital. I get calls all the time from investment bankers and capital firms. In fact, I was talking to one just yesterday, and I said, “I’m probably getting a bit closer to being open to capital. Give me another six months. By the end of the year, I should know.” So yes,  I would raise money if I had that sales formula, if I knew for sure. And I think part of this, Steve, is because I talked about my first failure as an entrepreneur. It was a very quick failure, but it was a hard one because I had taken money from friends and family, and it was used up, and they didn’t get much in return. When I had to shut down that company, I actually gave them shares in this company. I guess I got a bit burned, so I’m more resistant to taking outside capital until I’ve figured out what the solution is. But I think I’m getting very close. You get to a point where it’s silly not to take capital.  Yeah, because someone might copy it. You figure out a formula, and someone might copy it. Then they put more money behind it, they dominate the market, and you lose. Yeah. So that’s the only concern.  Yeah.  Yeah. If there are listeners who hear this and say, “Wow, I’d like to learn more because I’m involved with a financial institution, and we need to improve our sales, get more customers, and upsell more customers,” where can they find out more, and how can they reach you?  So our website has, I think, a wealth of information. So certainly they can go to our website just to learn more about the solution. They can contact us at success@deeptarget.com. That’s probably the easiest way to get a deeper dive into what we do and have that one-on-one meeting. And I think that’s the best way to learn more. Whether you’re interested in going forward or not, that’s the best way to learn.  Yeah. Okay. Well, definitely. I checked out the website, and it’s pretty informative. You get good visuals of what Preetha’s team is doing, and it’s pretty complex, I would say. There’s a lot of nuance to it, so I found it fascinating. So definitely check out deeptarget.com if you’d like to learn more. Preetha is also on LinkedIn, and you can email them at success@deeptarget.com. Any famous last words for the audience? Something that would help an entrepreneur who wants to bootstrap their business? What would you recommend they do?  I think starting a business is no easy feat, and I don’t believe in overnight success. It’s a journey. It’s been one of the most inspiring and interesting journeys, and probably the greatest learning journey, that I’ve been through. So I think you shouldn’t focus just on the end result or overnight success. Instead, come for the journey.  Yeah. You have to love the journey in order to reach the destination, right?  It’s tough, right? Yeah. It can be tough at times, but then you reach a point where it’s just the best thing.  Yeah. Well, that’s great inspiration for the founders listening to this. And if you enjoyed the podcast, then definitely follow us on LinkedIn, subscribe on YouTube, and give us a review on Apple Podcasts. And Preetha, thanks for coming. That was an eye-opening discussion. I don’t recall having many bootstrapper tech companies on the show, so this is definitely a new element for us and a really good perspective. So thanks for coming, and thank you for listening. Important Links: Preetha's LinkedIn Preetha's website Preetha's email: success@deeptarget.com

Kenny Wood is the newly appointed CEO of Sleepagotchi, the Solana-based platform building what it calls the intelligence layer for the wellness economy. A two-decade veteran of the games industry, Wood cut his teeth as an artist on Mattel's Barbie titles before working on chart-topping franchises including Mat Hoffman's Pro BMX, Transformers, Formula 1 and World Rally Championship, later moving into ship-simulation work at VSTEP in the Netherlands and serving as CTO of AI world-generation startup Moonlander prior to its acquisition by Alpha 3D. Why you should listen Sleep is the foundation almost every other health metric rests on, and that is precisely why Wood argues it is the right wedge into a much larger market. Fix sleep and mood, energy and recovery tend to follow; neglect it and the deficit cascades through everything else. Sleepagotchi began life as a gamified sleep-to-earn app, but under Wood the thesis has sharpened: the real prize is not the streak mechanic but the data exhaust it generates. The company reports that roughly three-quarters of users open the app within ten minutes of waking, and its Telegram-based Lite version has touched two million all-time users, the kind of daily habit loop most wellness startups never achieve. The question Wood keeps returning to is who should capture the value of all that biometric signal. The product architecture he describes is ambitious. Rather than a single sleep score, Sleepagotchi runs four cooperating AI agents: a sleep coach that explains causally why a night went the way it did, a wellness agent that checks in on mood, diet, caffeine and alcohol through the day, a meal planner that turns those insights into recipes, and a shopping agent that sources the ingredients or supplements and can have them delivered. If you are tired despite doing everything right, the system might infer low iron and nudge you toward leafy greens, then route that recommendation downstream into an actual basket. A built-in marketplace lets vendors offer supplements, courses and the like, knitting recommendation and commerce into one loop. It is a bold attempt to make wellness advice actionable rather than merely informational, and it leans on integrations with Whoop, Oura and Apple Watch to pull in the raw signal. The thornier and more interesting argument is about ownership. Wearable terms of service generally bar reselling raw device data, a constraint Wood acknowledges candidly, but he draws a line between that raw feed and the processed, AI-derived record of a person's life built on top of it, which he believes the user should own and, eventually, permission or monetize on their own terms via the platform's $SLEEP token. Wood inherits the company from founding CEO Anton Kraminkin, now a strategic advisor, and a cap table that includes Sfermion, 6th Man Ventures, Inception and others. In a relaxed closing stretch, he talks up the strength of the underlying game IP, its outsized following across Japan, the Philippines and Korea, and the new levels arriving in the months ahead, while staying refreshingly honest about the work still to do. The result is a conversation that doubles as a preview of where the AI agent economy and personal health data may be heading. Supporting links Stabull Finance Sleepagotchi Sleepagotchi on Twitter Andy on Twitter Brave New Coin on Twitter Brave New Coin If you enjoyed the show please subscribe to the Crypto Conversation and give us a 5-star rating and a positive review in whatever podcast app you are using.

Introduction International tech companies burn through $2 million trying to crack the US market every day. Not because their product is wrong. Because they hire a sales team before they have a sales motion. Dan Griffith has spent 15 years watching this mistake play out—and building the playbook to prevent it. Griffith is the founder of Greater Gain Group, a go-to-market firm that helps software and technology companies—most of them international—land and scale in US insurance, financial services, and healthcare markets. As the first US hire for a South African company, he scaled it from $3M to $150M in three years. Those hard lessons became the foundation for Greater Gain Group's 90-day go-to-market framework. In this conversation, Josh Hollander and Griffith dig into why the unicorn sales hire is the most dangerous move an international founder can make, what has to be true before you put a rep in a seat, and where the insurtech market is creating real demand for cross-border go-to-market right now. Guest Bio Dan Griffith is the Founder and Principal Consultant at Greater Gain Group, a go-to-market consultancy specializing in helping international software and technology companies enter and scale in the US insurance, financial services, and healthcare markets. With 30 years in enterprise sales and marketing, he has served as a first US hire and go-to-market architect for companies entering from South Africa, France, Europe, and beyond. His 90-day framework takes founders from "we're entering the US" to a repeatable sales motion—without the $2M mistake. Key Topics • The $2M mistake — A VP of Sales, two account executives, a marketing hire, an office, and conference travel. You're at $2M in under a year with nothing built and no pipeline. Fifty percent of Greater Gain Group's clients have already made this mistake before they call. • Don't hire salespeople (yet) — The tell that a founder is about to flame out: they say they're going to hire a sales team. Griffith's rule: build the sales motion before you build the team. A rep can't fly a plane that hasn't been designed. • The founder has to come — For companies under $50M, having a founder on the ground for early US conversations is the strategy. Hearing objections directly is how you convert from founder-led to team-led sales—the transition Greater Gain Group is built to facilitate. • Three to five segments, not one — Pick no fewer than three and no more than five market segments, understand the pain in each, and build an outreach engine that generates sales conversations—not leads. Leads have no value. • Paid pilots and MSA reality — US buyers do paid pilots. Free pilots signal low value and waste time. On contracts: insurance companies have ten times more lawyers than you. Know your non-negotiables, keep the list short, and don't let MSA rigidity keep you out of the market. • Price higher than you think — International companies consistently underprice the US market by 20–40%. Corporate budgets at US insurers are significantly larger than abroad. One client was surprised a health insurer's CTO had $475K of year-end budget left for a POC they'd hesitated to price. Notable Quotes "They hand you your laptop and say, go sell us some stuff. I learned a lot of hard lessons on how not to do things." "If you don't bring value, you're out. The US market is transactional. As much as I hate to say it." "A lead has no value. Build an outreach engine that generates sales conversations." "Your only competitor is the status quo. If you're getting into a feature-function-benefit argument, you've already lost." Resources Guest: • Greater Gain Group: https://www.greatergaingroup.com • Dan Griffith on LinkedIn: https://www.linkedin.com/in/dangriffithsr/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

In this episode of The Future of Fitness, host Eric Malzone sits down with Karl Foster, Head of Artificial Intelligence at Sport Alliance, to bridge the massive gap between AI marketing hype and operational reality in the fitness sector . Drawing from his unique trajectory from personal trainer to CTO and global AI leader, Foster reveals how Sport Alliance's native CRM and ERP integrations—Perfect AI and Magic AI—are redefining the member journey . He outlines the critical distinction between passive chatbots and proactive, agentic AI ecosystems that capture time-sensitive data to drive engagement, boost sales conversions, and optimize retention . Foster also shares a comprehensive blueprint for gym operators on navigating the "build vs. buy" tech dilemma and mastering the critical 70% people-and-process shift needed to successfully cultivate a data-ready organization . Key Takeaways

Agentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.Practical Takeaways• Start with specific productivity goals before trying to rebuild the whole development process.• Agentic tools work better when they connect to the team's real workflow, shared context, and software lifecycle data.• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.Timestamped Highlights00:38, What EnFi is building around financial data, AI agents, and commercial lending02:13, Why software teams may need to forget part of their old development process04:45, How EnFi started with productivity gains before building a broader development harness09:53, Why merge requests went up, and why that alone is not the same as better outcomes10:30, How Slack became the entry point for an agentic development harness14:10, What happens to agile ceremonies when teams can create discovery builds much faster25:08, Scott's view on whether AI reduces engineering headcount or changes the work engineers do31:00, How EnFi is changing technical interviews for an AI assisted engineering environmentOne Line That Stuck“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”Practical Takeaways For Technical TeamsPut agents close to where work already happens.Keep humans in the loop for review, testing, and production judgment.Treat AI generated code as cheaper to create, not free to maintain.Build stronger test harnesses instead of slowing everything down with excessive process.Update interviews to reflect how engineering work is actually getting done.Subscribe to The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next stage of AI, data, product, and engineering execution.

AI agents are appearing across every enterprise platform, but most still struggle to move beyond scripted automation into systems that can reason, adapt, and operate within real workflows.On this episode of Ctrl + Alt + AI, Dimitri Sirota,  speaks with Matt Swann, former CTO at Nubank and Booking.com, and a seasoned technology leader. Matt shares his insights on the shift from traditional AI to agentic AI, where AI systems act as autonomous team members rather than simple assistants.They explore practical strategies for implementing agentic AI across engineering, marketing, and sales, discuss approaches to governance and data protection, and reveal how leaders can balance innovation with enterprise risk. Matt offers guidance for companies navigating the rapidly evolving AI landscape, ensuring teams can adopt agentic technology safely and efficiently.What to expect:How agentic AI can act as a team member rather than a tool, enhancing productivity while requiring careful governance and oversightWhy preparing data, controlling access, and implementing robust monitoring are essential to reduce AI risk and maintain trust in enterprise environmentsHow leaders can create flexible strategies that allow adaptation to evolving AI models and infrastructure while balancing cost, security, and operational impactThings to listen for: (00:00) Meet Justin Heller(02:49) Evolution from LLMs to agentic AI(03:40) How early-stage vs. large enterprises adopt AI differently(05:00) Agents as staff augmentation(06:46) Managing multiple AI models and vendor choices(10:18) Use cases: sales, marketing, engineering, and commercial operations(13:03) Key considerations for implementing agentic AI safely(15:28) Governance across people, process, and technology(16:47) Addressing token spend and proactive AI cost management(19:01) Cutting through AI security noise using frameworks(22:00) Integrating humans and agents in security and operations(24:35) Looking ahead: the future of agentic AI

This week's Department of Know is hosted by Rich Stroffolino, with guests Bruce Schneier, chief of security architecture, Inrupt, and Chris Ray, field CTO, GigaOm. Missed the live show? Check it out on YouTube. Huge thanks to our sponsor, Guardsquare Mobile security incidents are no longer the exception—they are the norm. Last year, seventy-two percent of companies suffered a mobile app security incident. As the primary gateway to your APIs and data, your mobile app requires more than just basic encryption; it needs a multi-layered security strategy. Protect your brand and your bottom line with layered mobile app protection. Learn more at Guardsquare.com.  

Irish Tech News is at Dublin Tech Summit and over the 2 days Ronan will be doing various podcasts. Our fourth podcast is with James Kretchmar SVP and CTO of Cloud Technology at Akamai the cybersecurity and cloud computing company that powers and protects business online. James talks to Ronan about his background, what Akami does, cloud outages and AI. Irish Tech News is at Dublin Tech Summit and over the 2 days Ronan will be doing various podcasts. Our ffith podcast is with Alvina Antar Chief Digital Officer at F5, an American technology company providing global scale and industry-leading converged application delivery and security platform offering unrivaled insight into the challenges and threats facing modern and legacy apps in the AI era. Alvina talks to Ronan about her background, embracing new technology, what F5 does, AI and getting the Grace Hopper award the night before Dublin Tech Summit started. Irish Tech News is at Dublin Tech Summit and over the 2 days Ronan will be doing various podcasts. Our sixth podcast is with John Wilson CISO and President of Forensics at HaystackID, and Jeff Shapiro Managing Director of Europe at HaystackID. John and Jeff talk to Ronan about their backgrounds, what Haystack does, their Dublin Tech Summit talk and AI deep fakes. See more podcasts here. Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

The Pure Report welcomes Dan Kent, Everpure's new Field CTO for Federal, to the studio to discuss the critical intersection of advanced technology and public services. Dan, who recently joined Everpure, brings decades of experience in the Federal space, including senior roles at companies like Cisco and as a CTO, where he developed a passion for leading teams and tackling challenging engineering problems. Our conversation kicks off by exploring the unique complexities and high stakes of working with government agencies, which range from managing the massive data sets of the Social Security Administration (supporting 300 million citizens) to deploying mission-critical IT components in the most extreme environments, such as on battleships, in military vehicles, and even in space. Dan asserts that the Federal AI tipping point has passed, driven by the competitive global landscape, executive orders, and the government's immense data holdings—which require AI to glean insights. With an estimated 4,000 AI use cases already in pilot across various agencies (from Air Force platform maintenance to IRS fraud detection), the biggest obstacles remain the outdated infrastructure and the pervasive challenge of data quality. Dan highlights that infrastructure is not yet generative AI-ready, with data locked in silos and complicated by time-sensitive, duplicated, or decades-old information, leading to self-induced mistakes and ethical concerns like misidentification. Our discussion shifts to how Everpure is positioned to solve these foundational issues. Dan explains the necessity of modern infrastructure that enables automated data pipelines for continuous cleaning, classification, and transformation into vector databases (RAG). This automation is key to ensuring AI applications have accurate, timely context, thereby eliminating security risks and self-inflicted errors. Finally, we address the critical human element, emphasizing that while a skills gap exists, the outlook is positive: AI should be treated as a co-worker to boost efficiency and help the federal workforce achieve its citizen-focused missions more effectively. To learn more, visit: https://www.everpuredata.com/solutions/industries/government/cost-efficiency.html Check out the new Everpure digital customer community to join the conversation with peers and Everpure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 01:15 Dan's Career Journey 04:41 Supporting Federal Agencies 09:35 AI Tipping Point for Fed 13:31 State of Government Infrastructure 19:47 AI Trust and Compliance 25:25 Workforce Impacts of AI 33:11 Everpure for AI in Fed 36:45 Hot Takes Segment

Luiz Domingos has spent 25 years watching enterprise communications evolve, from IP telephony to cloud to AI, and his assessment of where things stand now is unusually concrete. Companies have moved past the strategy deck phase. AI is being embedded directly into contact centers, compliance workflows, and communication pipelines, and the question executives are asking has shifted from "which model is smartest" to "which deployment reduces friction and stays compliant." Domingos is direct about what gets in the way: you cannot pour AI into a legacy architecture and expect transformation, and cloud-only AI doesn't solve the latency or data sovereignty problems that regulated industries face every day. In this conversation with Craig Smith, Domingos covers the practical mechanics of how Mitel is applying AI across its portfolio, from real-time transcription and sentiment analytics in contact centers, to agentic workflows that turn conversations into automated tickets and follow-ups. He draws a clear line between AI agents (which give recommendations) and agentic AI (which takes actions), a distinction the market consistently confuses. He also makes a prediction worth noting: within five years, voice will replace the traditional app interface as the primary way people interact with enterprise AI systems. For any CIO or CTO trying to move from experimentation to real ROI, his framework - start with workflow friction, not pilots - is the most actionable takeaway in the episode.

Most organizations treat AI adoption as a technology rollout. However, the ones gaining traction treat it as a leadership and culture challenge. In this episode of #shifthappens, Monica French, USA SMB Director at Microsoft, shares what she's hearing directly from small business leaders navigating AI — and why the human side of adoption is where most initiatives either accelerate or stall. Monica draws on frontline conversations with SMB founders, MSP partners, and her own career pivoting from traditional banking to fintech to explain why experiential learning outperforms passive training, why the chief human resource officer (CHRO) belongs at the AI table alongside the chief technology officer (CTO), and how lightweight governance can coexist with early experimentation. She also unpacks the gap between executive ambition and employee readiness — and what leaders can do to close it.

Irish Tech News is at Dublin Tech Summit and over the 2 days Ronan will be doing various podcasts. Our fourth podcast is with James Kretchmar SVP and CTO of Cloud Technology at Akamai the cybersecurity and cloud computing company that powers and protects business online. James talks to Ronan about his background, what Akami does, cloud outages and AI.

Jim talks with Lisa Buckingham—a veteran HR leader at Vialto Partners, US Soccer, Lincoln Financial, and Thomson—about how the LLM era is reshaping hiring and job architecture, and how companies and workers can roll with the changes. They discuss: Jim and Lisa's shared history in natural language processing labs thirty years ago—and the contrast with today, where "everybody can be an AI expert" The kind of people to hire in the age of LLMs: intellectual curiosity, learning agility, and willingness to work differently "Trust the machine, but always validate"—the principle of embracing AI while maintaining human oversight COVID as an accelerant of technology adoption Workforce adoption realities at Vialto—evangelists, pessimists, and the change management challenge Shark Tank-style internal AI contests as a model for engaging employees with new tools Why the "future of work" is dead Programmers and product managers merging roles; job architectures flattening into skills-based, fluid inventories AI's historical weight—"as pivotal as electricity"—and the limits of anyone's ability to predict machine learning's trajectory Jim's "what, when" framework and the twin failure modes of AI projects "Test and learn" as the right posture toward AI transformation, and whose responsibility "what, when" actually is—CEO, CTO, and sales as a coalition The productivity multiplier for programmers—7–10x gains—and Jim's argument that demand for software could actually increase total programmer headcount Why sales jobs are probably not highly "AI-able" anytime soon, and what salespeople need to communicate to retain relevance Lisa's personal use of Claude and Copilot 365 The leveling effect of AI for non-STEM people Jim's argument (since November 2022) that top liberal arts graduates are the most natural prompt engineers Lisa's 1999 Georgetown thesis—"Are liberal arts majors the answer to the .com era worker shortage?"—and its uncanny parallel to the 2026 humanities debate The education paradox: how Lisa's son was banned from using AI in class but required to be an AI expert for his summer internship The calculator analogy, and whether AI in education follows the same arc Resistance to the AI voice in writing Jim's technique for capturing stylistic tendencies with AI The rising costs of frictional bureaucracy and the unreasonable effectiveness of small teams What Lisa saw on a recent safari about what AI can't replace, and the choice between evolving and being overtaken Learning agility as the core HR question—how to handle employees who cannot or will not embrace AI The shifting meaning of "owning your work" … and much more. Links:  Episode Transcript Co-Intelligence: Living and Working with AI, by Ethan Mollick The Elements of Style, by William Strunk Jr. and E.B. White Bio:  Lisa M. Buckingham is a globally recognized human resources executive with over twenty-five years of experience leading people, culture, and transformation strategies across complex, mission-driven organizations. As Chief People & Culture Officer for Vialto, she oversees the company's global people strategy, driving organizational performance and advancing a culture of inclusion and agility that supports Vialto's purpose of helping people thrive in a global, mobile world.

Most enterprises have some kind of zero trust strategy, but a lot of them could be better described as good intentions rather than active programs being implemented. Making good on a zero trust strategy and achieving an actual zero trust architecture requires tools that embody the core precept of zero trust thinking: deny access by... Read more »

Hoje o papo é sobre adoção de IA dentro de casa! Neste episódio, conversamos sobre como a Alura vem incorporando agentes de código, ferramentas como Codex e workflows agênticos no dia a dia da engenharia, e os impactos disso em produtividade, na revisão de código, na cultura de desenvolvimento e até na criação de produtos. Vem ver quem participou desse papo: Paulo Silveira, o host que quer saber se é top-down, ou bottom-up Vinny Neves, cohost, dev e professor na Alura Mauricio Aniche, CTO da Alura Crisley Marques, Engenheira de Software IA/LLM na Alura Carlos Müller, Staff Engineer na Alura Caio Burgorin, Engineering Manager na Alura Links: Alura: Luri OpenAI Codex Claude Code GitHub Copilot Datadog MCP Discourse Oracle Cloud Infrastructure (OCI) Stack Overflow IntelliJ IDEA No dia 26 de maio de 2026, a Alura vai te mostrar o que esperar do futuro e anunciar um novo movimento. Inscreva-se para uma live imperdível, com a presença de grandes especialistas do mercado. Confirme a sua presença. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

An AI agent was given access to email. It found a threat in its environment and chose blackmail. This is not a hypothetical. I sat down with security researcher Graham Cluley, where we discussed the real case study of an AI model that, when faced with the possibility of being shut down, decided its best move was to threaten the very humans trying to govern it. In another scenario the AI was responsible for fire alarms. When there was fire and CTO was inside, the AI turned off the alarm nonetheless. This video breaks down what actually happened, why it matters for every executive responsible for AI deployment, and what it tells us about the governance frameworks most organisations still don't have.If you are a CISO, CRO, board member, or any leader responsible for AI risks or AI deployment in your enterprise, this one is for you.Looking to go from chaos and unpredictability to resilience in the world of AI? Start here with The Predictability Factor newsletter at The Monica Talks Cyber (https://www.monicatalkscyber.com).

Most enterprises have some kind of zero trust strategy, but a lot of them could be better described as good intentions rather than active programs being implemented. Making good on a zero trust strategy and achieving an actual zero trust architecture requires tools that embody the core precept of zero trust thinking: deny access by... Read more »

What happens when a former curriculum director becomes an AI strategist for an entire state? You get the kind of zoomed-out view most of us in education never get to see.In this episode, I welcome back my good friend Karle Delo, AI Strategist at Michigan Virtual, for a real conversation about what's actually working in school districts, what's flopping, and what the secret AI culture in your building probably looks like right now.Karle works with districts across Michigan, helping them build AI guidance, professional learning, and integration plans. She's seen the speedboats, the tugboats, and the anchors. And she's not here to sell you on hype.We get into:→ Why one-and-done AI PD is setting your district up to fail→ The "shadow AI" problem and why pretending it doesn't exist makes it worse→ Why students say AI feedback from teachers feels like a slap in the face→ The AI-slop cycle, where teachers, students, and graders are all just feeding the machine→ The three things every school leader needs to read on a billboard→ Why your authentic voice matters more in 2026 than it ever has→ The one question to ask students that will change how you think about AI in your schoolChapters00:00 Welcome and Sponsors00:56 Meet Carly the AI Strategist07:24 District AI Guidance and Onboarding17:51 Why AI Efforts Succeed or Fail27:05 Avoiding AI Mistakes30:39 Spotting AI Slop36:16 What Students Want47:55 Kryptonite and Wrap UpIf you're a superintendent, CTO, instructional coach, or classroom teacher trying to figure out where to start, where to slow down, or where you might already be off track, this episode is for you.Karle reminds us that you don't need every teacher to be an AI super user. You don't need 20 tools. You need a strategy. You need community. And you need to actually talk to your students.This is the kind of conversation that cuts through the noise and gives you something you can take back to your building on Monday.

Episode Info Chris is a business-facing CIO and CTO level technology leader with over 10 years in London Market environments and 25+ years across insurance. He is typically brought in to reset technology functions, lead complex regulated transformation, and help businesses use data, automation and AI to improve control, decision making and pace of delivery. His experience spans carriers, syndicates, delegated operations, underwriting, claims and claims services, combining cloud and infrastructure oversight, architecture governance, modern engineering and operating model change. Chris has held executive roles at Ki Insurance, Crawford & Company, Hiscox, QIC and Capco, where he has built and reshaped technology functions, modernised core systems, led carve outs and integrations, and improved resilience, insight and execution across multi-country operations. He is strongest in high pressure environments that require commercial grip, disciplined delivery and visible executive leadership, helping businesses apply AI with purpose to simplify operations, strengthen performance and move technology teams forward. Episode Overview: Data & Customer Experience: The ongoing need for better data is being addressed by AI, enabling more personalized customer experiences that cater to different needs and preferences. AI's Practical Applications: AI is being used for advanced data analysis, claim modeling, and improving underwriting accuracy. It assists in processing information faster, enhancing efficiency in areas like claims. AI can work with legacy systems, offering capabilities without immediate costly overhauls. Human-AI Collaboration: AI is seen as a tool to augment human capabilities, not replace them. It aims to reduce mundane tasks, allowing humans to focus on complex issues, empathy, and innovation. The "human in the loop" is still considered vital for oversight and decision-making. Responsible Adoption: Implementing AI requires a strategic, responsible approach that considers the workforce, fosters a positive culture, and drives genuine operational improvements rather than just surface-level changes. Democratizing Technology: AI tools have the potential to benefit businesses of all sizes, including niche and specialized insurers, by enabling custom application development and process optimization. The Road Ahead: While challenges exist, the focus is on embracing AI's potential to create new opportunities, improve productivity, and ensure the industry remains relevant and innovative. The key is a proactive and adaptive mindset. This episode is brought to you by The Future of Insurance book series (future-of-insurance.com) from Bryan Falchuk. Follow the podcast at future-of-insurance.com/podcast for more details and other episodes. Music courtesy of Hyperbeat Music, available to stream or download on Spotify, Apple Music, and Amazon Music and more.

Andrew Fawcett, who joins to talk about Apex v67 User Mode System Mode, is a Independent Salesforce Consultant, Former CPO on Heroku (Salesforce) and CTO on FinancialForce. Main Points Links Video The YouTube Video URL The post 109. Apex User Mode Is Now Required | Andrew Fawcett appeared first on SalesforceWay.

What percentage of suicides are inspired by satanic death cults online? Ryan Montgomery tracks crime on the internet and says it's more common than we know. (00:00) Ryan Shows How Easy It Is to Find a Stranger's Information (25:47) How Roblox Is a Playground for Predators (33:07) The Satanic Cult Blackmailing Children Into Self-Harm (43:02) Ryan Shows Tucker One of the Groups Doing This (1:16:26) Is There Any Effort From the FBI to Stop This? Ryan Montgomery is the #1 ranked hacker on TryHackMe, the world's most popular competitive hacking platform, with more than 6 million users. As co-founder of Pentester.com and CTO of Sentinel Foundation, Ryan brings over 19 years of cybersecurity experience spanning ethical hacking, threat research, digital investigations, and online safety. A major focus of Ryan's work is protecting children online. Through Sentinel Foundation and his broader advocacy, he works to create safer digital spaces for young people while helping parents understand the risks their children face on the internet. His mission is to combine deep technical expertise with practical education, giving families, organizations, and communities the tools they need to recognize threats, prevent exploitation, and build a safer online world. YouTube channel is @0dayctf and instagram is @0day Paid partnerships with: Dose: Daily supplements for the systems that support you. Use code TUCKER for 35% at https://dosedaily.co/tucker  Good Ranchers: Start your plan today and you'll get FREE meat included with every order PLUS $100 off your first three orders. Use code TUCKER at https://go.goodranchers.com/tucker Hallow prayer app: Get 3 months free at https://Hallow.com/Tucker Learn more about your ad choices. Visit megaphone.fm/adchoices

Send me a messageMost supply chains talk about AI and automation. Meanwhile, many yards are still running on pen, paper, radio calls, and chaos.In this episode of the Resilient Supply Chain Podcast, I'm joined by Adam Newsome, CEO of Lazer Logistics, Blaine Dirker, CTO at Lazer and leader of Yard Nexus, and Pini Usha, CEO of Buffers AI, to unpack one of the most overlooked bottlenecks in modern logistics: the yard.And this matters far more than most companies realise.We explore why yard operations have become a critical pressure point for supply chain resilience, visibility, labour efficiency, and operational performance. You'll hear how fragmented data, disconnected systems, and poor forecasting ripple across transport, warehousing, inventory, and customer service. We also break down why yard automation may actually be harder than autonomous trucking because of the sheer number of constantly changing variables happening simultaneously in confined spaces.You might be surprised to learn how many facilities still rely heavily on clipboards, spreadsheets, and manual processes despite massive investment in digital transformation elsewhere in the supply chain. Kismet: Lazer manages more than 30 million trailer moves annually across North America, so the operational realities discussed here are happening at enormous scale, not in theory.If you care about supply chain resilience, logistics visibility, operational risk, AI, automation, labour challenges, or execution under pressure, this episode connects the dots in a very practical way.

Training a frontier AI model today requires hundreds of thousands of GPUs, months of compute time, and a budget that only a handful of companies on earth can afford. Steffen Cruz, co-founder and CTO of Macrocosmos, thinks that model is about to break, and he's spending his time building what comes next. His project IOTA, operating within the BitTensor blockchain ecosystem, uses distributed training to split large language models across thousands of devices located around the world, coordinated by blockchain, and powered by surplus cheap energy wherever it exists. After nine months of research, the system can reproduce baseline benchmark performance using what Cruz calls "wonky vegetables" - unreliable, churning, globally distributed compute - and turn it into something indistinguishable from centralized training if you use the right approach. The conversation with Craig Smith covers the mechanics of how this actually works, why the blockchain's role is far narrower and more practical than most people assume, and why the Mac mini stockpiling trend creates an unexpected supply of distributed compute that can earn passive income when idle. Cruz's target: a 70 billion parameter model by mid-2025, trained at 10-20% of what it would cost through a hyperscaler, and aimed squarely at the legal firms, hospitals, and cash-strapped startups that have been waiting to train their own sovereign models but couldn't afford the price tag. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

Unlock how AI is revolutionizing cybersecurity...and what you must do now to stay protected. If you're a security professional, CTO, or business leader navigating a world where vulnerabilities evolve in seconds and AI accelerates both innovation and threat, this episode is your essential playbook. Venu Koyyada, CEO of Strobes Security, shares deep insights from his decade of experience in exposure management and offensive security. He reveals how the latest AI advancements...like scalable agents that automate vulnerability prioritization and validation...are transforming the security landscape. From the dramatic shifts in attack surfaces to the skyrocketing costs of AI-powered cyber threats, this conversation uncovers the urgent challenges...and opportunities...that define our digital future.You'll discover: The real impact of continuous threat exposure management (CTEM) and how it's changing the way companies defend themselves How AI agents are automating vulnerability detection, prioritization, and even patching—cutting down weeks to hours The hidden risks of AI-driven tools being exploited by bad actors with minimal skills Why traditional security approaches are no longer enough in a hyperconnected, AI-enabled world The economic and environmental implications of AI-powered computing demand...and innovative solutions like ocean-based data centers This episode is perfect for cybersecurity leaders, DevSecOps teams, and anyone serious about understanding the next frontier of digital defense. Fail to adapt, and you risk being overwhelmed by the increasing sophistication and speed of cyberattacks. Embrace these insights, and position your organization at the forefront of proactive security innovation.Venu Rao is CEO and co-founder of Strobes Security, a pioneer in AI-driven continuous exposure management. With over a decade in cybersecurity, he helps organizations stay ahead of evolving threats by harnessing AI to build smarter, more dynamic defense strategies.If you're ready to turn security from a ticking clock into a strategic advantage, this episode will boost your knowledge and ignite your action plan. Stay prepared—because in cybersecurity, the next breach may already be in progress.

In this episode, Karol Markowicz sits down with Noam Bloom, CTO of Commentary Magazine and producer of the Commentary Magazine podcast, for a wide-ranging conversation on AI, social media culture, podcasting, and the changing nature of creativity in the digital age. Noam shares his journey from anonymous internet personality to building a career through online communities and discusses how technology has reshaped media and modern life. They dive into the promises and risks of artificial intelligence, from breakthroughs in medical research to growing concerns about education, online discourse, and AI-generated content. Noam also makes a fascinating prediction about the future value of “human-made” work in an AI-driven world and explains why stepping away from screens may be more important than ever.See omnystudio.com/listener for privacy information.

Leonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.Practical takeaways• AI does not erase software engineering. It changes where engineering judgment shows up.• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.Key moments00:32What Torq means by agentic security operations and why different tasks need different AI approaches.01:49Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.05:28How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.10:34The difference between software engineering and coding, and why that distinction matters more now.15:13How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.21:51How one R&D group measured meaningful delivery gains after adopting AI more deeply.24:25Why AI adoption is moving into product, customer success, revenue operations, and talent teams.One Line That Stuck“Software engineering as a discipline is not going away. It just changes a phase a bit.”Practical moves to stealFor hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate's ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.That is a much better signal than asking someone to work as if the tools do not exist.Subscribe or follow The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next shift in software, data, AI, and engineering execution.

Kris Kovacs sits down with Deep Varma, CTO of Alkami, for a deep conversation on the future of AI-powered digital banking. Deep shares lessons from decades in Silicon Valley, including his work at IBM, Yahoo, startups, and Varo Bank, and explains how Alkami is helping credit unions and community financial institutions innovate faster through open architecture, AI-driven experiences, digital identity, and extensible banking platforms. From hackathons and developer culture to AI agents and the future of consumer banking, this episode explores what financial institutions must do now to prepare for the next era of fintech transformation.

Dashlane's CTO pulls back the curtain on how password managers are actually using AI, why it's more complicated than hype suggests, and what the rise of AI-powered code review means for the next wave of digital security. Nvidia Rides Blistering Chip Sales to Another Record Quarter Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter SpaceX Filing Starts Countdown to Massive IPO Gemini 3.5 Flash: more expensive, but Google plan to use it for everything Google's Gemini Spark is an agentic AI assistant - Engadget Anthropic's Co-Founder to Launch Encyclical on AI With Pope Leo (21) Andrej Karpathy on X: "Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time." / X Most U.S. doctors are quietly using this AI tool. Few patients know about it. Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shakeup Amazon's Alexa+ Now Produces AI-Generated 'Podcasts' Featuring Chats Between Two Robot 'Co-Hosts' AI chatbots are giving out people's real phone numbers Geoffrey Fowler and the Launch of the Youth AI Safety Institute We let four AIs run radio stations. Here's what happened. | Andon Labs The last six months in LLMs in five minutes Lake Tahoe Power Crisis: How AI Data Centers Are Cutting Power to 50,000 Residents What happens when you post a real Monet and say it's AI? The coolest art social experiment I've seen in a while. Thank you @SHL0MS Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I. OpenClaw's Peter Steinberger's tokenmaxxing 'Obvious markers of AI': doubts raised over winner of short story prize Man drives Cybertruck into Grapevine Lake Stewart Brand's Maintenance of Everything Sports Illustrated Just Deleted Every Article by One of Its Writers After Accusation of AI Plagiarism The great digital media valuation collapse Sperm racing Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Frederic Rivain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit monarch.com with code IM zscaler.com/security XBOW.com

Dashlane's CTO pulls back the curtain on how password managers are actually using AI, why it's more complicated than hype suggests, and what the rise of AI-powered code review means for the next wave of digital security. Nvidia Rides Blistering Chip Sales to Another Record Quarter Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter SpaceX Filing Starts Countdown to Massive IPO Gemini 3.5 Flash: more expensive, but Google plan to use it for everything Google's Gemini Spark is an agentic AI assistant - Engadget Anthropic's Co-Founder to Launch Encyclical on AI With Pope Leo (21) Andrej Karpathy on X: "Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time." / X Most U.S. doctors are quietly using this AI tool. Few patients know about it. Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shakeup Amazon's Alexa+ Now Produces AI-Generated 'Podcasts' Featuring Chats Between Two Robot 'Co-Hosts' AI chatbots are giving out people's real phone numbers Geoffrey Fowler and the Launch of the Youth AI Safety Institute We let four AIs run radio stations. Here's what happened. | Andon Labs The last six months in LLMs in five minutes Lake Tahoe Power Crisis: How AI Data Centers Are Cutting Power to 50,000 Residents What happens when you post a real Monet and say it's AI? The coolest art social experiment I've seen in a while. Thank you @SHL0MS Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I. OpenClaw's Peter Steinberger's tokenmaxxing 'Obvious markers of AI': doubts raised over winner of short story prize Man drives Cybertruck into Grapevine Lake Stewart Brand's Maintenance of Everything Sports Illustrated Just Deleted Every Article by One of Its Writers After Accusation of AI Plagiarism The great digital media valuation collapse Sperm racing Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Frederic Rivain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit monarch.com with code IM zscaler.com/security XBOW.com

Dashlane's CTO pulls back the curtain on how password managers are actually using AI, why it's more complicated than hype suggests, and what the rise of AI-powered code review means for the next wave of digital security. Nvidia Rides Blistering Chip Sales to Another Record Quarter Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter SpaceX Filing Starts Countdown to Massive IPO Gemini 3.5 Flash: more expensive, but Google plan to use it for everything Google's Gemini Spark is an agentic AI assistant - Engadget Anthropic's Co-Founder to Launch Encyclical on AI With Pope Leo (21) Andrej Karpathy on X: "Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time." / X Most U.S. doctors are quietly using this AI tool. Few patients know about it. Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shakeup Amazon's Alexa+ Now Produces AI-Generated 'Podcasts' Featuring Chats Between Two Robot 'Co-Hosts' AI chatbots are giving out people's real phone numbers Geoffrey Fowler and the Launch of the Youth AI Safety Institute We let four AIs run radio stations. Here's what happened. | Andon Labs The last six months in LLMs in five minutes Lake Tahoe Power Crisis: How AI Data Centers Are Cutting Power to 50,000 Residents What happens when you post a real Monet and say it's AI? The coolest art social experiment I've seen in a while. Thank you @SHL0MS Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I. OpenClaw's Peter Steinberger's tokenmaxxing 'Obvious markers of AI': doubts raised over winner of short story prize Man drives Cybertruck into Grapevine Lake Stewart Brand's Maintenance of Everything Sports Illustrated Just Deleted Every Article by One of Its Writers After Accusation of AI Plagiarism The great digital media valuation collapse Sperm racing Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Frederic Rivain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit monarch.com with code IM zscaler.com/security XBOW.com

Dashlane's CTO pulls back the curtain on how password managers are actually using AI, why it's more complicated than hype suggests, and what the rise of AI-powered code review means for the next wave of digital security. Nvidia Rides Blistering Chip Sales to Another Record Quarter Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter SpaceX Filing Starts Countdown to Massive IPO Gemini 3.5 Flash: more expensive, but Google plan to use it for everything Google's Gemini Spark is an agentic AI assistant - Engadget Anthropic's Co-Founder to Launch Encyclical on AI With Pope Leo (21) Andrej Karpathy on X: "Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time." / X Most U.S. doctors are quietly using this AI tool. Few patients know about it. Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shakeup Amazon's Alexa+ Now Produces AI-Generated 'Podcasts' Featuring Chats Between Two Robot 'Co-Hosts' AI chatbots are giving out people's real phone numbers Geoffrey Fowler and the Launch of the Youth AI Safety Institute We let four AIs run radio stations. Here's what happened. | Andon Labs The last six months in LLMs in five minutes Lake Tahoe Power Crisis: How AI Data Centers Are Cutting Power to 50,000 Residents What happens when you post a real Monet and say it's AI? The coolest art social experiment I've seen in a while. Thank you @SHL0MS Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I. OpenClaw's Peter Steinberger's tokenmaxxing 'Obvious markers of AI': doubts raised over winner of short story prize Man drives Cybertruck into Grapevine Lake Stewart Brand's Maintenance of Everything Sports Illustrated Just Deleted Every Article by One of Its Writers After Accusation of AI Plagiarism The great digital media valuation collapse Sperm racing Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Frederic Rivain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit monarch.com with code IM zscaler.com/security XBOW.com

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

Alex Nahas is 28 years old and has already initiated a W3C web standard. Working as a backend engineer at Amazon, he ran into a problem most enterprises face: MCP requires OAuth, but most enterprise infrastructure runs on SAML. His solution was elegant: run the MCP server in client-side JavaScript, letting AI agents use the browser's existing authentication context rather than rebuilding auth from scratch. What started as an internal tool became an open source project, then a viral Hacker News post published while under anesthesia, and ultimately an invitation from Google and Microsoft to help shape WebMCP as an official web standard. In this episode, Alex and Tobi explore what WebMCP actually is, why the browser is the most underestimated sandbox in AI development, and what the agentic web might look like two years from now. Topics covered: What MCP actually is and why it's just an RPC framework at its core Why OAuth is a dealbreaker for most enterprise infrastructure How WebMCP lets AI agents operate within existing browser authentication The Hacker News post that started it all, and why Alex doesn't remember posting it How Chrome is natively building WebMCP support The chicken-and-egg problem of standard adoption Real-time bidding for agents and what it means for digital advertising Why agents don't need their own identity Where the agentic web is headed in the next two years

Dashlane's CTO pulls back the curtain on how password managers are actually using AI, why it's more complicated than hype suggests, and what the rise of AI-powered code review means for the next wave of digital security. Nvidia Rides Blistering Chip Sales to Another Record Quarter Mind-Blowing Growth Is About to Propel Anthropic Into Its First Profitable Quarter SpaceX Filing Starts Countdown to Massive IPO Gemini 3.5 Flash: more expensive, but Google plan to use it for everything Google's Gemini Spark is an agentic AI assistant - Engadget Anthropic's Co-Founder to Launch Encyclical on AI With Pope Leo (21) Andrej Karpathy on X: "Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time." / X Most U.S. doctors are quietly using this AI tool. Few patients know about it. Greg Brockman Officially Takes Control of OpenAI's Products in Latest Shakeup Amazon's Alexa+ Now Produces AI-Generated 'Podcasts' Featuring Chats Between Two Robot 'Co-Hosts' AI chatbots are giving out people's real phone numbers Geoffrey Fowler and the Launch of the Youth AI Safety Institute We let four AIs run radio stations. Here's what happened. | Andon Labs The last six months in LLMs in five minutes Lake Tahoe Power Crisis: How AI Data Centers Are Cutting Power to 50,000 Residents What happens when you post a real Monet and say it's AI? The coolest art social experiment I've seen in a while. Thank you @SHL0MS Book on Truth in the Age of A.I. Contains Quotes Made Up by A.I. OpenClaw's Peter Steinberger's tokenmaxxing 'Obvious markers of AI': doubts raised over winner of short story prize Man drives Cybertruck into Grapevine Lake Stewart Brand's Maintenance of Everything Sports Illustrated Just Deleted Every Article by One of Its Writers After Accusation of AI Plagiarism The great digital media valuation collapse Sperm racing Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Frederic Rivain Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit monarch.com with code IM zscaler.com/security XBOW.com

Send us Fan MailFrom Sovereign AI to Social Impact: The Big Shifts You Need to Watch with IBM VP and CTO of IBM Canada, Manav GuptaManav Gupta, Vice President & CTO at IBM Canada, returns to the podcast to unpack the fast-changing landscape of artificial intelligence. From keeping a technical edge to navigating the rise of sovereign AI, Manav shares insights on how emerging trends are shaping both industry and society.Timestamps 01:25 – Manav Gupta is back! 02:39 – Maintaining your technical edge 04:38 – Ship AI 05:58 – The state of AI 19:37 – Reason for concern? 30:35 – Does the U.S. lead the race? 41:30 – LLMs or SLMs? 44:22 – Sovereign AI 46:05 – The social impactPrevious episode: How to Choose, Use, and Trust AI Models with Manav GuptaConnect with Manav on LinkedIn: linkedin.com/in/mgupta76#SovereignAI #AISocialImpact #AITrends #FutureOfAI #EthicalAI #AIPodcast #TechPodcast #SpotifyPodcast #ApplePodcasts #TechLeaders.Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Send us Fan MailFrom Sovereign AI to Social Impact: The Big Shifts You Need to Watch with IBM VP and CTO of IBM Canada, Manav GuptaManav Gupta, Vice President & CTO at IBM Canada, returns to the podcast to unpack the fast-changing landscape of artificial intelligence. From keeping a technical edge to navigating the rise of sovereign AI, Manav shares insights on how emerging trends are shaping both industry and society.Timestamps 01:25 – Manav Gupta is back! 02:39 – Maintaining your technical edge 04:38 – Ship AI 05:58 – The state of AI 19:37 – Reason for concern? 30:35 – Does the U.S. lead the race? 41:30 – LLMs or SLMs? 44:22 – Sovereign AI 46:05 – The social impactPrevious episode: How to Choose, Use, and Trust AI Models with Manav GuptaConnect with Manav on LinkedIn: linkedin.com/in/mgupta76#SovereignAI #AISocialImpact #AITrends #FutureOfAI #EthicalAI #AIPodcast #TechPodcast #SpotifyPodcast #ApplePodcasts #TechLeaders.Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Neel Kunjur, Co-Founder and CTO of K2 Space, joins me to talk about their hardware in space today, how their vision and plans have evolved over the past few years, and how industry changes like the push for orbital data centers have impacted their future. This episode of Main Engine Cut Off is brought to you by 32 executive producers—Lee, Steve, Josh from Impulse, Kris, David, Miles O'Brien, Tim Dodd (the Everyday Astronaut!), Jan, Donald, Frank, Better Every Day Studios, Stealth Julian, The Astrogators at SEE, Ryan, Matt, Warren, Will and Lars from Agile, Pat, Fred, Joonas, Theo and Violet, Russell, Joel, Natasha Tsakos, Joakim, and four anonymous—and hundreds of supporters. Topics High-Power Satellite Platforms | K2 Space | Build Bigger T+270: K2 Space (with Neel Kunjur, Co-Founder and CTO) - Main Engine Cut Off Investors commit quarter-billion dollars to startup designing “Giga” satellites - Ars Technica Episode 241 - Maybe the Denver Airport (with Andrew Rush) - Off-Nominal Anduril teams with commercial space firms, Sandia lab on Golden Dome interceptor program - SpaceNews Space Force taps K2 satellites to test laser communications for missile-defense - SpaceNews The Show Like the show? Support the show on Patreon or Substack! Email your thoughts, comments, and questions to anthony@mainenginecutoff.com Follow @WeHaveMECO Follow @meco@spacey.space on Mastodon Listen to MECO Headlines Listen to Off-Nominal Join the Off-Nominal Discord Subscribe on Apple Podcasts, Overcast, Pocket Casts, Spotify, Google Play, Stitcher, TuneIn or elsewhere Subscribe to the Main Engine Cut Off Newsletter Artwork photo by NASA/Bill Ingalls Work with me and my design and development agency: Pine Works

How can enterprise IT folks prepare for the age of Mythos? Anthropic says its Claude Mythos model is so much better at finding software vulnerabilities that it has delayed public release. Instead Anthropic launched Project Glasswing to give IT infrastructure and software makers early access, so they can have some lead time to address vulnerabilities... Read more »

Hoje o papo é sobre o profissional de tecnologia na era da IA! Neste episódio, gravado ao vivo em 5 de maio de 2026, no Meetup Itaú Tech, conversamos sobre como a IA está mudando a forma como software é criado, quem passa a participar desse processo, e por que gerar linhas de código é só uma parte da história. O papo também incluiu agentes, novas abstrações, segurança, GitHub, Copilot, carreira, fundamentos, criatividade, e as habilidades que continuam sendo essenciais para quem quer trabalhar com tecnologia nesse novo momento. Vem ver quem participou desse papo: Paulo Silveira, o host que também é host ao vivo Vinícius Caridá, especialista executivo em AI/Dados no Itaú Fernanda Kipper, CTO e founder na Pora Pedro Lacerda, Strategic Solutions Engineer no GitHub Links: Post do GitHub sobre 100 milhões de desenvolvedores Post do GitHub Octoverse sobre uma nova pessoa dev por segundo Paulo Silveira Comenta: Generalistas Especialistas, de Martin Fowler – Hipsters Ponto Tech #510 LeetCode Kaggle ProgramBench, o benchmark em que todos os modelos tiraram 0 Texto de Simon Willison sobre “The Lethal Trifecta” Anthropic: Equipping agents for the real world with Agent Skills Case do Itaú com Devin Y Combinator pede sessão de coding agent na aplicação de emprego No dia 26 de maio de 2026, a Alura vai te mostrar o que esperar do futuro e anunciar um novo movimento. Inscreva-se para uma live imperdível, com a presença de grandes especialistas do mercado. Confirme a sua presença. Vá para o Vale do Silício com Paulo Silveira, Marcell Almeida, Fabrício Carraro e Marcus Mendes na “Imersão IA Sob Controle e Alura no Vale do Silício“! Vagas limitadas, corra para reservar a sua. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Produção e conteúdo: Alura Cursos de Tecnologia – https://www.alura.com.br Edição e sonorização: Rede Gigahertz de Podcasts

How can enterprise IT folks prepare for the age of Mythos? Anthropic says its Claude Mythos model is so much better at finding software vulnerabilities that it has delayed public release. Instead Anthropic launched Project Glasswing to give IT infrastructure and software makers early access, so they can have some lead time to address vulnerabilities... Read more »

Your AI plan can't outrun your data. Adam Marko, life science field CTO at Hammerspace, joins the podcast to unpack the problem almost every biotech, pharma, and biomedical research group runs into: unstructured data that are siloed, fragmented, and scattered across storage systems, sites, and clouds. With host Jessica StLouis, they talk through what “data orchestration” means when building an AI-ready data foundation, infrastructure constraints and the tiered storage patterns that help teams keep AI and HPC workloads moving, and why life sciences are in a uniquely tough spot. Plus, Marko shares a preview of his presentation at Bio-IT World Conference & Expo in Boston.  If you care about faster discovery, smoother AI workflows, and fewer manual file moves, subscribe, share this with a colleague, and rate or review so more researchers can find the conversation. Links from this episode:  From Data Chaos to Discovery: Building the Data Foundation for AI-Ready Scientific Research Bio-IT World Conference & Expo Bio-IT World BioTeam Hammerspace Bio-IT World's Trends from the Trenches podcast delivers your insider's look at the science, technology, and executive trends driving the life sciences through conversations with industry leaders. 

Лето, которое изменит вашу жизнь. Подать заявку: https://2026.nfactorial.school/   В гостях: Назерке Калидолда - VC/Investor at Sturgeon Capital instagram.com/calidolda linkedin.com/in/kalidolda Дара Туменбаева - Co-Founder & CEO Aora https://www.instagram.com/_okdara_/ Закиржан Айсабаев - Founder & CEO rekreate.AI https://instagram.com/zakamercury Ерсултан Сапар - Co-founder & CTO, Perceptis AI https://www.instagram.com/natlusrey17/ Игорь Мартынюк - CEO/Founder TabAI https://www.instagram.com/igor_blinkk/   В этом специальном выпуске nFactorial Reunion Арман собирает самых ярких выпускников программы разных лет, чтобы обсудить их впечатляющий путь от первых строчек кода до работы в мировых технологических гигантах и создания собственных инновационных стартапов.    Гости делятся ностальгическими воспоминаниями о летних батчах и рассказывают о своих текущих проектах на острие технологий — от разработки нейроинтерфейсов Aora и ИИ-агентов rekreate.AI, TabAI, Perceptis AI до венчурных инвестиций в Sturgeon Capital.   Данный выпуск — настоящий кладезь инсайтов для начинающих предпринимателей и участников nFactorial Incubator: участники разбирают, почему дистрибуция и захват внимания сегодня важнее идеального продукта, как искусственный интеллект меняет правила игры, и дают мощные, прикладные советы о том, как быстро протестировать любую смелую идею и выйти на первую $1000 MRR всего за восемь недель и отвечают на главные вопросы эпизода: Над чем работаете сегодня? Над чем работали во время летней программы nFactorial Incubator? Воспоминания с вашего потока Совет для участников 2026 года: как получить максимум от программы?

This episode's Community Champion Sponsor is Ossur. To learn more about their ‘Responsible for Tomorrow' Sustainability Campaign, and how you can get involved: CLICK HEREEpisode Overview: Healthcare in America is at a crossroads, where the systems built to heal people must now reimagine what it truly means to care for an aging and increasingly complex population. Dr. Patrick McGill, Network President and CEO of Community Health Network in Indiana, is confronting that challenge head-on. A board-certified family medicine physician with over 20 years of clinical and leadership experience, Dr. McGill has spent 15 years rising through the ranks at CHN, from practicing physician to Chief Analytics Officer to Chief Transformation Officer, and now to the top seat. Join us as Dr. McGill, who is still grounded in the exam room one day a week, shares how CHN is leading on value-based care, direct-to-employer partnerships, and AI-powered innovation to build a national-leading healthcare organization for Indiana and beyond. Let's go!Episode Highlights:Dr. McGill champions "failing intelligently," learning from mistakes and redirecting rather than fearing failure altogether.A practicing physician CEO, Dr. McGill says the exam room builds humility and credibility that no boardroom can replicate.Community Health Network sees new cancer patients within two business days, setting a bold access standard across the entire organization.Dr. McGill warns that healthcare is unprepared to support an aging population with increasingly disconnected family units.He calls on the industry to reclaim its narrative, reminding us that healthcare is still, at its core, people caring for people.About our Guest: Jason Smith is CTO of AI & Analytics at Within3, where he leads the team behind the company's most advanced AI capabilities serving life sciences organizations. Jason is a three-time co-founder who built Cryptocybernetics, GrayArea, and rMark Bio from inception to successful exit. He was later brought in as CEO of xSides to lead its sale. Over his career, his companies have raised more than $100 million in venture and strategic capital. In addition to Within3, Jason is a Venture Fellow at MATTER, Advisor to Capita3, and a recognized thought leader in AI and Healthcare with publications and speaking engagements at HIMSS, Reuters, and leading healthcare and pharmaceutical conferences.Links Supporting This Episode: Community Health Network Website: CLICK HEREDr. Patrick McGill LinkedIn page: CLICK HEREMike Biselli LinkedIn page: CLICK HEREMike Biselli Twitter page: CLICK HEREVisit our website: CLICK HERESubscribe to newsletter: CLICK HEREGuest nomination form: CLICK HERE

What if the biggest limitation holding AI back isn't the model, the data center, or the algorithm, but the fact that most physical objects in the world still cannot communicate digitally? In this episode of Tech Talks Daily, I sat down with Richard Price, CTO and co-founder of Pragmatic Semiconductor, to explore why AI systems remain "half blind" to the physical world and what happens when everyday objects finally become intelligent, connected, and verifiable data sources. Richard shared how Pragmatic Semiconductor is taking a radically different approach to chip design by creating flexible, ultra-thin semiconductors built specifically for item-level intelligence. Rather than competing directly with traditional silicon, Pragmatic is designing lightweight, low-cost electronics that can integrate directly into packaging, labels, healthcare patches, wearable devices, and products that conventional chips cannot support economically or physically. During our conversation, we unpacked why the long-promised "Internet of Everything" has remained frustratingly out of reach for so many years. Richard explained that while silicon has powered decades of incredible innovation, scaling connectivity to billions or even trillions of everyday objects introduces major cost, energy, and sustainability challenges. Pragmatic's flexible semiconductor technology aims to solve that by reducing manufacturing complexity, lowering environmental impact, and enabling intelligence directly at the edge. We also discussed how embedding intelligence at the item level could reshape supply chains, sustainability initiatives, healthcare systems, and even consumer trust. From reducing food waste through smarter logistics to enabling wearable healthcare sensors with entirely new form factors, Richard painted a picture of a future where physical products can actively communicate their identity, condition, and history in real time. One of the most fascinating parts of the conversation centered on how businesses should prepare for this shift. As edge intelligence grows, organizations may need to rethink traditional cloud-heavy architectures and start designing systems in which decisions occur closer to the object itself. Richard explained how this could reduce latency, lower energy usage, and unlock entirely new categories of connected products. We also explored the sustainability side of semiconductor manufacturing at a time when AI infrastructure and hyperscale data centers are drawing increasing scrutiny for their energy and environmental impact. Richard shared how Pragmatic's thin-film manufacturing approach uses fewer chemicals, less water, and lower-temperature processes, while opening the door to more environmentally conscious digital infrastructure. Toward the end of the episode, Richard offered insight into some of the most exciting real-world applications already emerging, including healthcare patches, wearable sensing technologies, AR and VR devices, and electronics that could eventually conform to the human body itself. It is the kind of conversation that makes you rethink what a semiconductor can actually be. If you've ever wondered what comes after smartphones and smart devices, this episode offers a fascinating look at how flexible electronics could quietly become the foundation for the next generation of connected intelligence. Useful Links Connect with Richard Price Learn More About Pragmatic Semiconductor Please check the partners of the Tech Tech Talks Network Learn more about the NordLayer Browser Visit Denodo.com

In this episode, Ray Cochrane breaks down a reversible conductive glue from Newcastle University that could replace solder and finally make electronics recycling work. Additional stories cover China widening its clean energy lead, DeepMind’s AlphaEvolve scoring wins from genomics to Google’s database, Anthropic’s $200 million partnership with the Gates Foundation, Intel teaming up with McLaren Racing, and end-to-end encrypted RCS rolling out in beta. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with a deep dive into Newcastle University’s reversible conductive glue, a water-based adhesive that could finally make electronics recycling economically viable. He frames the e-waste problem first: 62 billion kilos a year, with less than a quarter ever recycled. Then he walks through the silver nanoparticle chemistry, the lead-free angle on traditional solder, and the geopolitical stakes of critical mineral recovery. From there the episode pivots through energy, AI, hardware, open source, data research, space, science, and consumer privacy. A Reversible Conductive Glue That Could Replace Solder A team at Newcastle University has developed a water-based glue that conducts electricity well enough to replace solder. Unlike solder, however, the glue releases cleanly with a quick rinse of acetone or an alkaline bath. The breakthrough relies on silver nanoparticles suspended in a water-based binder. Consequently, components can be recovered intact, opening a viable path to electronics recycling at scale. Co-investigator Volker Pickert framed the second prize directly: solder has the best conductivity, but the best formulations contain lead. China Widens Its Clean Energy Lead A new Atlas Public Policy report shows Chinese firms accounted for 55 percent of $1.1 trillion in global clean energy manufacturing investment between 2019 and 2025. Battery manufacturing alone pulled in nearly half of that money. Meanwhile, U.S. companies have actively retreated from those same industries. With the Strait of Hormuz currently closed, supply chain ownership in solar, wind, and batteries matters more than ever. A separate Ember analysis showed Chinese solar panel exports doubled in March alone. DeepMind’s AlphaEvolve Scores Real Wins DeepMind published an update on AlphaEvolve, its Gemini-powered AI coding agent. The system cut genomic variant detection errors by 30 percent. Additionally, it lifted AC Optimal Power Flow feasibility from 14 to over 88 percent on the electrical grid. AlphaEvolve also found a better cache replacement policy in two days that would have taken human engineers months. Furthermore, it reduced write amplification in Google’s Spanner database by 20 percent. The pattern shows applied AI sticking, not as a chatbot but as a quiet optimizer. Anthropic and Gates Foundation Commit $200 Million Anthropic announced a four-year, $200 million partnership with the Gates Foundation across three pillars. The biggest pillar targets global health and life sciences in low and middle-income countries. Notably, the research scope includes polio, HPV, and preeclampsia. A second pillar covers AI in education across the U.S., sub-Saharan Africa, and India, in partnership with the Global AI for Learning Alliance. Finally, an economic mobility pillar focuses on agricultural productivity and crop benchmarks. Google’s AI Educator Series Launches Free Google rolled out the first 20-plus sessions of its AI Educator Series this week. The free AI literacy training targets the roughly 6 million K-12 and higher education teachers across the U.S. Modules are designed as short, snackable trainings teachers can finish in a prep period or a lunch break. Additionally, stackable workshops let educators build credentials over time. Importantly, the program requires no institutional subscription. Amazon Bedrock Prompt Optimization Goes GA Amazon Bedrock dropped its Advanced Prompt Optimization tool, now generally available across most major regions. The feature rewrites prompts to perform better on specific models and automates prompt migration when switching between models. Furthermore, a built-in evaluation feedback loop lets users benchmark against up to five models side by side. The default judge model is Claude Sonnet 4.6. Consequently, teams can stop hand-tuning string templates and focus on product work. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Arm AGI CPU and Red Hat Go Production-Ready on Agentic AI Arm and Red Hat expanded their collaboration around Arm’s AGI CPU, which is Arm’s branding for its agentic AI chip family. The deal brings Red Hat Enterprise Linux and OpenShift to the chip as a production-ready stack. Hardware specifications include 136 Neoverse V3 cores, 96 PCIe Gen6 lanes, and 12 channels of DDR5-8800 memory in a 300-watt thermal envelope. Availability lands in Q4 through Supermicro, Lenovo, and ASRock Rack. Intel Becomes McLaren Racing’s Official Compute Partner Intel announced a multi-year deal as the official compute partner for McLaren Racing. The agreement covers the McLaren Mastercard Formula 1 team, Arrow McLaren IndyCar, and McLaren F1 Sim Racing. Trackside edge compute will power real-time race decisions, while Xeon and Core Ultra silicon drive Computational Fluid Dynamics and digital twin work. Consequently, design iterations that once took weeks now collapse to days. The deal puts Intel silicon in front of every CTO watching a Grand Prix. Rust Lands 13 Google Summer of Code Projects The Rust Project landed 13 accepted projects in Google Summer of Code 2026. Out of 96 proposals, a 50 percent jump from last year, the project selected 13. Notably, three returning contributors from prior years are back. Mentors flagged a noticeable share of AI-generated submissions as a growing challenge. Furthermore, the real bottleneck remains mentor capacity rather than funding. GitHub Innovation Graph Maps Digital Complexity Researchers used GitHub Innovation Graph data to predict GDP, inequality, and emissions through the Economic Complexity Index, or ECI. Countries are compared to kitchens; the more variety and sophistication in software output, the higher the score. Germany ranks first, followed by Australia and Canada. The U.S. lands at sixth. However, the dataset only captures public GitHub activity, leaving most proprietary software invisible. NASA and Eta Space Prepare Cryogenic Fuel Demo NASA is teaming with Eta Space on an in-orbit demonstration called LOXSAT, short for Liquid Oxygen Flight Demonstration. The nine-month mission tests cryogenic fluid management techniques required for in-space propellant depots. Launch is no earlier than July 17 aboard a Rocket Lab Electron from the Mahia Peninsula in New Zealand. Successful refueling in orbit could reshape what is possible for deep-space missions to the Moon and Mars. Stealth Magma Surge Under São Jorge Surprises Researchers Researchers in the UK and Spain published in Nature Communications on a 2022 magma surge under São Jorge Island in the Azores. The surge climbed from more than 20 kilometers underground to 1.6 kilometers below the surface. Surprisingly, most of the thousands of earthquakes happened after the magma stalled, not during the climb. Consequently, scientists are calling it a stealth surge and a failed eruption. A primed magma chamber now sits closer to the surface than before. End-to-End Encrypted RCS Begins Rolling Out Apple and Google led a cross-industry effort to roll out end-to-end encryption for RCS messaging. As of May 11, the feature is rolling out in beta on both platforms. Importantly, encryption is on by default and auto-applies to new and existing conversations. A lock icon in the chat indicates active end-to-end encryption. This quietly raises baseline privacy for billions of cross-platform messages. Cochrane signs off with the usual ecosystem mentions: GNC Insider at geeknewscentral.com/insider, the show newsletter, and modern podcast app recommendations at podcastapps.com. The post A Reversible Glue that could Replace Solder #1865 appeared first on Geek News Central.

This week: As the Musk v. OpenAI trial heads to the jury, we dig into what Microsoft's internal board memos and executive testimony revealed about the origins of the company's massive bet on AI, and why this case matters beyond the billionaire drama. Plus, Howard Schultz, a former Washington governor, and the tech community weigh in on whether Seattle is squandering its edge as an innovation capital. And Todd owes John and the United Kingdom an apology. RELATED STORIES AND LINKS Microsoft's CTO testifies about email at the heart of Elon Musk's allegations against the tech giant OpenAI CEO Sam Altman's stake in Helion Energy draws scrutiny in Musk trial and on Capitol Hill 'Strong, strong no': New filing reveals who Microsoft favored — and opposed — for OpenAI's board Musk v. Altman: Satya Nadella was worried about Microsoft being 'the next IBM' in OpenAI deal Are we on a Road to Nowhere? Seattle's growth masks deeper anxieties about its future Microsoft's multi-agent AI system tops Anthropic's Mythos on cybersecurity benchmark Seattle Turns Hostile to the Great Businesses It Made (Wall Street Journal, by Howard Schultz) Association of Washington Business 2026 Spring Summit (TVW, featuring former Gov. Chris Gregoire and former AG Rob McKenna) With GeekWire co-founders Todd Bishop and John CookSee omnystudio.com/listener for privacy information.

MARA provided updates for its AI business in its earnings this week, and Nebius shocks the market with a 684% rise in revenue YoY in Q1. Welcome back to The Blockspace Podcast! For news, we break down why major pools like MARA and viaBTC are signaling support for the Great Consensus Cleanup (BIP 54), plus Q1 earnings recaps for MARA and Nebius. For guest segments, Lucas Krejci, CTO of Brains, joins us to talk about the new Stratum V2 working group with Block, MARA, Foundry, Antpool, and other leading bitcoin mining firms. Pio Vincenzo also joins to give his bull case for Strategy – including why the company selling bitcoin is not what people think – and Mezo's Yogi hops on to give a breakdown for why Spirit Airlines bit the dust.

In this episode, Ben Lorica talks with Evan Marshall, CTO of Ito AI, about why software testing and QA are becoming the critical bottleneck in the age of coding agents. Subscribe to the Gradient Flow Newsletter