Podcasts about SQL

Automation as Core Strategy: Aarin Bailey on RPA, AI, and Scaling MSP OperationsOn the Evolved Radio podcast, Todd interviews Aarin Bailey, COO at Webit Services and former COO at MSP Bots, about treating automation as a core MSP operating strategy. Aarin describes how his automation focus accelerated around COVID by chaining PowerShell scripts, later expanding into Python, GUIs, and modular systems connected via RESTful APIs, with much of the computation running outside the RMM on servers (including SQL and Python) while the RMM remains mainly a monitoring and job-push layer. They discuss whether RMM is a “zombie product,” the ongoing role of PSA/ticketing as a system of record, and managing complexity through separate modules and staff literacy in Python/RPA. Aarin explains build-vs-buy decisions driven by ROI and fit, cites automated triage/dispatch with ~98% accuracy and shifting token costs, argues AI should augment rather than replace humans, and emphasizes documentation, playbooks, and focusing on operational “bad” anomalies. They also cover client tolerance for AI, limiting client-facing AI after hallucinated ticket notes, skepticism about voice AI, and concerns about AI economics and subsidies.This episode is brought to you by Opsleader Pro. A place for MSP owners and managers to get the systems and tools they need to build a stable and growing MSP. Part group coaching, part peer group, everything you need to run a successful MSP. (00:00) - Automation First Mindset (01:10) - Aaron Origin Story (05:04) - From Scripts to Platforms (05:41) - Beyond the RMM Beehive (08:35) - Is RMM a Zombie (12:14) - Managing Complexity Safely (14:33) - Build vs Buy ROI (19:39) - Token Costs and Pair Coding (23:49) - AI Security Reality Check (27:34) - Scaling with Playbooks (30:12) - Hunt the Bad Stuff (30:59) - Blueprints Before Automation (32:46) - Ticket Volume and Vision (33:32) - Saying No as Integrator (35:44) - Healthy Disagreement Dynamics (37:08) - Client Facing vs Backend AI (40:05) - AI Hallucinations and Guardrails (43:05) - Voice AI and Live Answer (46:06) - Costs and Subsidized AI Era (49:26) - Outcome First and RPA Focus (51:36) - Wrap Up and Thanks

“Tęsknię za Ballmerem na scenie.” Łukasz po keynote'cie Build 2026, na którym Satya wymuszał z widowni klaskanie - “nie było wow” - a po osobowościach pokroju Guthriego i Russinovicha został korporacyjny autopilot. Bo to pierwszy od lat Build, gdzie zamiast Azure'owych fajerwerków dostajemy Windows, Windows, Windows.

Going after a new segment sounds sexy… until you actually try to do it.

For the past few years, the conversation around AI has focused on the technology. Which model is best. Which tools to use. How fast everything is changing. But once you start building with it, a different challenge emerges. The technology is often the easy part. The hard part is everything else. The definitions that don't match. The documentation nobody trusts. The tribal knowledge living in someone's head. The processes that work only because a few key people know how to navigate around the mess. Business intelligence exposed some of these problems years ago. AI is exposing even more of them. For years, the people who cared about semantic models were mostly talking to each other. Everyone else had a simpler view: the dashboards worked, the BI nerds were overcomplicating things, and if a slightly different version of yesterday's question showed up, someone could always write more SQL. That worked well enough until AI agents became the ones asking the questions. Agents don't wait two weeks for a developer. They improvise. And the improvisation is different every time. That's the moment the semantic model stopped being a nice-to-have and started looking a lot more like a requirement. Every data quality problem that used to come home to roost the first time you built a dashboard is back, only now the list is longer. AI cares about policies, institutional knowledge, organizational context, and all the things that used to live quietly in people's heads. The one-version-of-the-truth problem just got a much bigger job description. Along the way, Rob and Justin compare notes from the front lines of building with AI, from multi-agent systems and knowledge management to the unexpected ways these tools behave once they leave the lab and meet real organizations. There's a book update in here too. Fair Game is officially available for pre-order, and Rob shares why the independent bookstore route matters more than most people realize. If you've been wondering what happens after the AI works, this episode is a pretty good place to start. Also in this episode: Pre-order Fair Game: Customizing AI to Your Business Is Easier Than You Think Fortune: Big Tech is laying off developers. My company just hired its first. We're both right about AI (By Rob Collie)

Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

https://clearmeasure.com/developers/forums/ Chris Woodruff, or as his friends call him, Woody, is a software architect of over 25 years. Woody loves software engineering, especially allowing applications and services to communicate across networks and through Web APIs. He has received Microsoft MVP awards in SQL, Data and C# in the past, along with multiple years of being awarded the AWS Community Builder Award. He's a current board member of the .NET Foundation Woody lives in Grand Rapids, Michigan, where he explores the many breweries in West Michigan and travels with his family. Woody is also a long-time bourbon fan and loves hunting for whiskey bottles. Website - https://woodruff.dev/ LinkedIn - https://www.linkedin.com/in/chriswoodruff/ Twitter - https://twitter.com/cwoodruff Simplicity-First Website - https://simplicity-first.dev/ Previous Appearances on the Azure & DevOps Podcast: Episode 262 - Chris "Woody" Woodruff: Network Programming https://azuredevopspodcast.clear-measure.com/chris-woody-woodruff-network-programming-episode-262 ---------------------------------------- Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Automatizar processos nas redes sociais pode transformar a produtividade de qualquer social media ou agência. Mas por onde começar?Neste episódio do Papo Social Media, Rafael Kiso e Marcio Silva mostram como as automações evoluíram do agendamento de posts até fluxos avançados com IA e agentes inteligentes, percorrendo todas as etapas da jornada do cliente com exemplos práticos.Descubra como mapear processos antes de automatizar, usar automações de inbox para nutrir leads, aplicar SDR com IA na qualificação em escala e transformar clientes em promotores de marca. Um episódio essencial para quem quer usar automação como motor de crescimento nas mídias sociais.00:00:08 Introdução00:00:27 O que são automações e por que são importantes para escalar resultados00:01:37 Softwares de automação de marketing: contexto e evolução antes da IA generativa00:02:57 Mapeamento de processos: primeiro passo antes de automatizar00:04:01 mLabs e agendamento multicanal: ganho de tempo e escala na publicação00:06:27 Expansão de canais como estratégia de escala com mínimo custo adicional00:06:49 Agendamento de relatórios: automatizando a entrega de dados para clientes e times00:08:15 Coleta de dados automatizada e análise com IA no mLabs Analytics00:10:38 Criar cultura de relatórios internos, não só para clientes00:11:29 As 5 etapas da jornada do cliente como base para aplicar automações00:12:09 Automação na etapa de Descoberta: agendamento de conteúdo em múltiplas plataformas00:12:36 Automação avançada na Descoberta: monitorar tendências com N8N, Google Trends e IA00:13:56 Ferramentas para encontrar assuntos em alta: TikTok Creative Center, YouTube Studio e VidIQ00:15:07 APIs, Make e N8N: como conectar plataformas e criar fluxos automatizados00:17:19 Automação na etapa de Consideração: engajamento, afinidade e frequência de impacto00:18:59 Automações de comentário para inbox no Instagram00:20:01 Estratégia de conteúdo com gatilho de automação para aumentar frequência de contato00:21:28 Frequência de postagem x frequência de impacto: qual realmente importa00:24:00 mLabs Chat: a nova ferramenta brasileira para automações em inbox e comentários - Conheça: https://mla.bs/f0588e49f500:25:17 Automação na etapa de Conversão: funil de stories com gatilho de palavra-chave00:27:41 Captura de leads por inbox: e-mail, WhatsApp e banco de dados na mLabs Chat00:28:04 Testes A/B com landing pages usando respostas randômicas nas automações00:29:37 Marketing conversacional: reduzindo fricção00:30:20 Cuidados ao enviar links de pagamento pelo Instagram00:31:32 Integração com WhatsApp e definição do limite entre automação e atendimento humano00:32:46 MQL, SQL e SDR com IA: qualificação de leads em escala com automação00:35:44 Automação na etapa de Experiência Própria: atendimento ao cliente e social listening00:38:24 Monitoramento de menções fora do perfil e detecção de avaliações negativas00:41:32 Presença digital depende cada vez mais do que os clientes falam, não do que a marca posta00:42:54 Automação na etapa de Experiência Compartilhada: detectar promotores de marca00:44:03 Comunidade de marca: como reconhecer e empoderar brand lovers00:45:15 Capital social e o que motiva as pessoas a compartilharem experiências com uma marca00:48:44 Pesquisa sobre nova jornada de compra: reputação como atributo intangível de decisão - Acesse: https://mla.bs/7e2ea0c96d00:50:43 As duas formas de gerar confiança: indicação e autoridade de conteúdo00:52:40 Automação com níveis avançados: Cloud Bot, N8N self-hosted e agentes de IA – Saiba mais: https://mla.bs/12037841e8  00:54:45 Mapear processos antes de automatizar e manter o ser humano como orquestrador00:56:20 mLabs Edu, smLab, mentoria, formações e kit de aceleração com agentes de IA00:58:53 EncerramentoPotencialize sua gestão de mídias sociais com a plataforma mais usada por agências e profissionais no Brasil! Teste grátis a mLabs agora mesmo: https://mla.bs/8f82d839

גיא ואיתן דנים בשאלת הביקוש לתפקיד שלנו בשוק. האם יש פחות? או שבעצם יש יותר? ומה מקומנו בעולם ה-AI? ואם כבר מדברים על AI אז בואו נדבר על פיצ'רים שקשורים לזה ב-SQL Server! וגם, אנחנו מספרים קצת על סדנא של AWS בנושא PostgreSQL שכדאי לשמוע עליה. קישורים רלוונטים: New T-SQL AI Features are now in Public Preview for Azure SQL and SQL database in Microsoft Fabric - Azure SQL Dev Corner AWS FOR DATA | Transform Your SQL Server Skills to PostgreSQL- Special Workshop with DBArt Statistics are not collected when creating new table and indexes and loading data after. · Issue #990 · olahallengren/sql-server-maintenance-solution

Johan and Gustav reunite for a release notes episode, noting how rare these have become as Microsoft spreads out its update cadence - leaving more time to actually digest features amid the breakneck pace of AI innovation. They open with small wins: BCC support finally arriving in electronic reporting emails, and the new "in" operator simplifying bank reconciliation matching rules. A meaningful discussion emerges around license keys versus feature management. Using product lifecycle state as a springboard, Johan cautions that enabling license keys does far more than surface UI elements - it adds and removes columns and tables underneath. He argues for keeping license keys in their out-of-box configuration since Microsoft doesn't test every permutation. A memorable anecdote: a customer who disabled the CDS integration key only to break dual write entirely. The hosts note that MCP servers with X++ now make analyzing these dependencies far easier. The standout feature is dynamic warehouse work classification through Power Fx. Rather than building location directives in X++ code requiring developer deployment, users can now configure prioritization logic directly from the UI. Johan sees Copilot generating these formulas, though Gustav raises legitimate concerns about configuration drift across environments - prompting talk of treating configuration as version-controlled code through agents and MCP. Commerce gets attention with cross-legal-entity order fulfillment closing the intercompany loop, mid-transaction payment terminal switching for dying batteries, and contextual switching between POS and external apps without full integrations. The episode closes on MCP enhancements: attachment support in the ERP MCP, SQL-based data tools that offload calculations the AI struggles with to SQL, the deprecation of client/server keywords, and Finance & Operations joining the Power Platform API reference.  

“Najgorsze co można zrobić, to zerknąć.” Piotr Karwatka o pętlach kodujących, w których agent pracuje 4 dni non-stop, a Ty masz siedzieć na rękach. Bo jak zerkniesz, trafisz na głupotę, przerwiesz mu tok myślenia i wszystko się posypie. Witamy w świecie, gdzie spec-driven development zastępuje ping-pong z Claude'em.

Run enterprise Postgres workloads on Azure HorizonDB with around 3x the throughput of self-managed deployments — zone-resilient by default, no architectural trade-offs. Call AI models directly from SQL, build durable vector pipelines inside the database, and deliver high-accuracy similarity search at massive scale with DiskANN and AI re-ranking, all without leaving PostgreSQL. Debug and optimize queries faster with the Azure HorizonDB VS Code extension. Visualize execution plans, let Copilot generate fixes, and clone production data to test environments in seconds. Charles Feddersen, PostgreSQL Partner Director PM, shares how to put all of it to work on Azure. ► QUICK LINKS:  00:00 - Azure HorizonDB features 00:57 - Open-source PostgreSQL 02:24 - How it works 03:37 - Performance 04:51 - Enterprise-ready security 05:34 - Memory & storage work together 06:29 - AI Model Management + AI Functions 08:24 - AI Pipelines 09:50 - DiskANN + AI Re-ranking 10:50 - VS Code Extension + Data Cloning 12:31 - Wrap up ► Link References Check out our blog at https://aka.ms/azurepostgresblog ► Unfamiliar with Microsoft Mechanics? As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. • Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries • Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog • Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast ► Keep getting this insider knowledge, join us on social: • Follow us on Twitter: https://twitter.com/MSFTMechanics • Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ • Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ • Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics

From cracked data foundations to multi-agent AI, Starburst Data's co-founder shares hard-won lessons on getting the right data, not just more of it.Topics Include:Matthew Fuller, co-founder and VP of Product at Starburst Data, joins the show.Starburst is built on Trino, a fast SQL engine for federated data queries.Their platform lets users query data across lakes, stores, and databases seamlessly.Governed "data products" give organizations access to their full data estate in context.A strong data foundation is essential before any AI use case can succeed.AI doesn't create data problems — it exposes the cracks already there.Common mistake: assuming everyone in an org defines "customer" or "revenue" the same way.More data isn't always better — getting the right data is what matters.Customers include HSBC, Comcast, Zalando, ZoomInfo, and DBS, many running on AWS.AWS partnership spans technical support, SLA reliability, and proactive product briefings.Advice for product leaders: always anchor new technology back to the customer problem.2026 will be defined by specialized multi-agents working together autonomously.Participants:Matt Fuller – Co-Founder, Vice President of Product, Starburst DataSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show 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 Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

This week the trio covers the Latest Ubuntu, Fedora, and CachyOS news. Btrfs has a big performance win, USB4 brings fast data transfers, the latest kernel RC has prompted a classic Torvalds rant. And then Jonathan flies in to wrap up the show with Open Source AI definition news. For tips, we have quein for turbo-charges who is, Shelly for smarter package management, htmlq for querying a web page, and DuckDB for slick SQL on the command line. You can find the show notes at https://bit.ly/434Hrkg and enjoy! Host: Jonathan Bennett Co-Hosts: Ken McDonald, Rob Campbell, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show 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 Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Nik and Michael discuss autovacuum, including what it does, and the basics of why and how to tune it.  Here are some links to things they mentioned: autovacuum https://www.postgresql.org/docs/current/routine-vacuuming.html#AUTOVACUUMautovacuum configuration parameters https://www.postgresql.org/docs/current/runtime-config-vacuum.html#RUNTIME-CONFIG-AUTOVACUUMWhat's Missing in Postgres? (our episode with Bruce Momjian) https://postgres.fm/episodes/what-s-missing-in-postgrespg_squeeze (our episode with Antonín Houska) https://postgres.fm/episodes/pg_squeezeMy queries to monitor autovacuum (post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/Autovacuum Tuning Basics (post by Tomas Vondra, originally for 2nd Quadrant blog) https://www.enterprisedb.com/blog/autovacuum-tuning-basicsZero autovacuum_vacuum_cost_delay, Write Storms, and You (post by Jeremy Schneider) https://ardentperf.com/2026/04/12/zero-autovacuum_cost_delay-write-storms-and-you/Our episode on long-running transactions / xmin horizon https://postgres.fm/episodes/long-running-transactions~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Ronald, Marco en Jelle zijn terug met een aflevering over criminelen, Cloudflare, consultants en piepers. Dave Maasland verkoopt ESET Nederland aan het Slowaakse moederbedrijf ESET, Ronald duikt in het Follow the Money-interview met TIB-voorzitter Annemieke Zwanenveld over de nieuwe Wiv, toetsing, CTIVD/TIB-samenvoeging, witte jassen en Palantir. Daarna Jelle's human-interest ransomwareverhaal: The Gentlemen RaaS werd zelf gehackt via de hostinglaag achter hun Rocket.Chat, waardoor Check Point kon meekijken in interne chats, payouts, AI-assisted coding en het kantoortje achter ransomware. Marco sluit af met Google Threat Intelligence over Chinese phishing-as-a-service: betere lokalisatie, RCS/iMessage en AI als contextversneller. Daarna het hoofdverhaal: Cloudflare heeft via Anthropic's Project Glasswing Mythos op meer dan 50 repositories losgelaten. Marco legt uit waarom dat niet neerkomt op "druk op knop, vind zero-days", maar op exploit-chain construction, proof generation, signal-to-noise en vooral: een hele vulnerability-research-harness met recon, hunt, validate, gapfill, dedupe, trace en report. Geen magische silver bullet, wel een duidelijke versnelling voor wie de workflow eromheen bouwt. Jelle pakt vervolgens McKinsey Lilli en BCG X erbij. CodeWall liet zien hoe interne AI-platforms zelf attack surface worden: publieke API-documentatie, endpoints zonder authenticatie, SQL-injectie, IDOR, miljoenen chats en files, system prompts, workspaces, modelconfiguraties en complete datawarehouses. Het echte verhaal: organisaties stoppen hun kennislaag, documenten, prompts en besluitvorming steeds meer in platforms. Wie daarin zit, zit bijna in het geheugen van de organisatie. Ronald en Marco sluiten af met het Mossad-pieperverhaal. Naar aanleiding van een nieuw Hebreeuws boek en een interview in The Jerusalem Post lopen ze door hoe de Hezbollah-pagers en walkie-talkies als supply-chain-operatie zouden zijn opgebouwd: techniek, infiltratie, Gold Apollo, BAC Consulting, Iraanse argwaan en de spanning tussen "ongelooflijk knap" en "hier zijn mensen door gestorven". *Bronnen* - Tweakers, "Slowaakse ESET koopt Nederlandse ESET": https://tweakers.net/nieuws/248036/slowaakse-eset-koopt-nederlandse-eset.html - ESET press release: https://www.eset.com/us/about/newsroom/company/eset-market-expansion-europe-asia/ - Follow the Money, "Geheime diensten gebruiken onafhankelijke experts om publiek debat te sturen": https://www.ftm.nl/artikelen/geheime-diensten-zetten-onafhankelijke-experts-in - Check Point Research, "When the Ransomware Gang Gets Hacked": https://blog.checkpoint.com/research/when-the-ransomware-gang-gets-hacked-what-the-gentlemen-leak-reveals-about-modern-ransomware-risk/ - Cloudflare Blog, Grant Bourzikas, "Project Glasswing: what Mythos showed us": https://blog.cloudflare.com/cyber-frontier-models/ - Anthropic, Project Glasswing: https://www.anthropic.com/glasswing - CodeWall, "How We Hacked McKinsey's AI Platform": https://codewall.ai/blog/how-we-hacked-mckinseys-ai-platform - CodeWall, "How We Hacked BCG's Data Warehouse": https://codewall.ai/blog/how-we-hacked-bcgs-data-warehouse-3-17-trillion-rows-zero-authentication - The Jerusalem Post, "Inside Israel's secret operation to turn Hezbollah's beepers into bombs": https://www.jpost.com/israel-news/defense-news/article-896890

Find out why the world's largest banks and enterprises trust CockroachDB for mission-critical infrastructure, and what a decade of AWS partnership means for the future of cloud-native data.Topics Include:Cockroach Labs makes CockroachDB, a distributed SQL database built for resilience.It delivers cloud-native consistency that legacy relational databases simply cannot match.The name "cockroach" reflects survivability — it's designed to never go down.Target customers include major banks, trading platforms, retailers, and gaming companies.AI is forcing enterprises to accelerate database modernization from the board level down.AWS has been a foundational cloud partner for Cockroach Labs for a decade.The CockroachDB-AWS integration spans EC2, S3, Bedrock, and Amazon Q-Transform.AWS partnership shapes both product roadmap decisions and go-to-market execution.New partners should educate themselves first — AWS programs are deep and extensive.CockroachDB now supports native vector search for RAG and generative AI applications.Agentic AI could mean trillions of digital agents demanding real-time data infrastructure.Database modernization and AI adoption will only accelerate dramatically through 2027.Participants:Cassie Zimmerman – Senior Director, Global Strategic Partnerships, Cockroach LabsSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In episode 322, the co-hosts examine critical vulnerabilities, changing security standards, and adaptive defense mechanisms. They deep dive into the recent "Megalodon" breach, identifying it as a direct poisoned pipeline execution attack. Rather than exposing a flaw inside GitHub itself , researchers at Hudson Rock traced the root cause to credentials stolen from developer desktops via infostealer malware, which allowed attackers to push base64-encoded payloads into GitHub Actions workflow YAML files. To counter these types of automated supply chain threats, the hosts praise NPM's newly released "staged publishing" pipeline, which mandates two-factor authentication from human maintainers before releasing packages pushed by automated CI/CD workflows. Shifting to framework flaws, they highlight a catastrophic, vanilla SQL injection flaw discovered in GoCMS during active exploitation. Finally, the duo reviews the emergence of AI-powered honeypots highlighted Talos Intelligence. They conclude that turning the tables on attackers by utilizing LLM-driven "hall of mirrors" environments to impersonate real systems represents an innovative, under-explored AppSec strategy designed to drain attacker resources and trigger high token costs.

This episode is sponsored by XPLUS. In this episode, we explore how teams can move from reactive firefighting to proactive, evidence-based control. We cover why SQL, AOS, and Batch need to be seen as one correlated system, how custom metrics bridge the gap between technical data and real business activity, and what a structured performance optimization cycle actually looks like in practice - from the first alert all the way to a verified fix. Whether you're dealing with regressions after a One Version update, unexplained batch delays, or just the nagging feeling that your system could perform better - this episode gives you a clear framework for taking back control. More from XPLUS: ·         Curious how this works in your environment? Book a 30-minute call with our D365 performance team - https://xplusglobal.com/book-a-demo/  ·         D365 Partner Day: Observability, Testing & Performance: https://xplusglobal.com/event/d365-partner-day-observability-testing-performance/

Microsoft confirms active exploitation of two Defender flaws. Europol dismantles a VPN service tied to ransomware gangs. A nine-year-old Linux kernel bug exposes SSH keys and password hashes. Cisco patches a critical Secure Workload vulnerability, while Drupal fixes a highly critical SQL injection flaw. Android malware quietly signs victims up for premium SMS scams. Webworm upgrades its espionage toolkit with Discord and Microsoft Graph backdoors. Plus, China and Russia deepen cooperation on AI, cybersecurity, and satellite systems. Our guest is Jake Moore, Global Cybersecurity Advisor for ESET, sharing a glimpse into his Infosecurity Europe keynote "The Deepfake Interview." Greg doesn't even work here anymore… Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today, Maria Varmazis speaks with Jake Moore, Keynote speaker for the upcoming Infosecurity Europe conference and Global Cybersecurity Advisor for ESET, getting a glimpse into his session "The Deepfake Interview: Breaking In From the Inside." This interview is part of our partnership with Infosecurity Europe.  Selected Reading Microsoft Defender vulnerabilities exploited in the wild (Help Net Security) Europol Seizes First VPN Used by Ransomware Gangs, Arrests Administrator (Hackread) Nine-Year-Old Linux Kernel Flaw Leaks SSH Keys and Password Hashes (Infosecurity Magazine) Cisco Patches Critical Vulnerability in Secure Workload (SecurityWeek) Android Malware Spotted Subscribing Victims to Paid Services Without Consent (Hackread) Drupal Patches Highly Critical Vulnerability Exposing Websites to Hacking (SecurityWeek) Webworm: New burrowing techniques (We Live Security) Xi and Putin pledge closer cooperation on AI, cyberspace and satellite systems (The Record) Zombie user account let hackers control the city's water (The Register) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week we sit down with Field Solution Architects Anthony Nocentino and Justin Emerson explore an interesting convergence happening in data architecture—the blending of traditionally separate block and file/object storage systems. Likening the experience to a Chocolate Peanut Butter Cup, Anthony (a database expert focused on block storage) and Justin (an expert in unstructured data and file/object storage) discuss how the clear historical distinctions between structured and unstructured data are rapidly blurring. This shift is fueled by modern challenges like high-scale analytics, data governance, and the rise of technologies like Large Language Models (LLMs) and agentic interactions, which no longer care where the data lives. Our conversation dives into the technical tipping point enabled by data virtualization, referencing features like SQL Server 2022's object integration, which allows a database engine to access data stored efficiently on object storage. This capability is far more than an archival play; it helps customers achieve scale-out analytics, improve data governance by maintaining one canonical copy of data across different performance buckets, and simplify tedious operations like SQL backups by bypassing legacy file system complexities. Anthony and Justin highlight how Everpure's platform aligns perfectly with this new reality. Finally, Anthony and Justin discuss the path forward, noting that the technology is underutilized due to organizational silos and an awareness problem. The next big evolution will focus on security and governance for this distributed data via open table formats like Iceberg and catalogs such as Polaris. We close with what currently excites them: Anthony on collaborating with AI (Claude) to create code and speed up outcomes, and Justin on Everpure's core philosophy of simplicity, efficiency, and treating customers like people, particularly in the context of the current economic conditions. To learn more, visit: https://www.everpuredata.com/platform.html Check out the new Everpure digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Career Journeys 04:30 Customer Engagement and SKO 09:55 Vacation Recap 13:45 History of Block and Object Storage 16:04 Why Convergence Now? 20:30 Data Virtualization 25:55 Exploring Access Patterns 29:05 What's Holding Back Adoption 36:02 Simplicity for DBAs

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In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.

В этом выпуске мы погружаемся в мир функционального программирования вместе с Никитой Волковым, архитектором и разработчиком на Haskell и Rust. Обсуждаем, почему «чистота» функций — это не ограничение, а суперсила, как монады помогают нам в повседневной Java-разработке и почему будущее за строгими контрактами и DSL. Во второй части выпуска Никита рассказывает о своем проекте pGenie — инструменте для работы с PostgreSQL, который предлагает альтернативный взгляд на интеграцию с базами данных, делая SQL «источником истины». 00:00:00 Начало 00:02:01 Эволюция мышления: путь от PHP и Scala к Haskell и Rust. 00:07:37 Java 8 vs Haskell. Истинная чистота и детерминизм 00:15:03 Функциональщина, монады, функторы и концепция Optional 00:27:59 Property-based testing 00:36:51 Как FlatMap и монада STM управляют недетерминированным внешним миром 01:01:34 Проект pGenie. Почему Рич Хикки критиковал ORM и в чем боль синхронизации контрактов 01:13:29 Философия OpenAPI для баз данных. Взгляд на БД как на изолированный микросервис со статической структурой запросов 01:38:06 Workflow разработки с pGenie: генерация типизированного Java-кода и защита схемы от даунтайма через файловые сигнатуры 01:50:56 Тренды Open Source в эпоху ИИ: драма вокруг генерации кода, ИИ-слоп и борьба с агентами через «пасхалки» в коммитах 01:59:51 Совет стартаперам и Рубрика «Непопулярное мнение»: почему ИИ убьет динамические языки программирования Гость: Никита Волков https://github.com/nikita-volkov Проект: pGenie на GitHub Книга: «Learn You a Haskell for Great Good! https://learnyouahaskell.github.io/chapters.html “Консалтинг Никиты” https://codemine.io

Nik and Michael are joined by David Ventimiglia to discuss pg_flight_recorder, a new tool he created for monitoring a Postgres database from within. Here are some links to things they mentioned: David Ventimiglia https://postgres.fm/people/david-ventimigliapg_flight_recorder https://github.com/dventimisupabase/pg_flight_recorderSupabase https://supabase.compg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_ash https://github.com/NikolayS/pg_ashpg_cron https://github.com/citusdata/pg_cronpg_tle https://github.com/aws/pg_tle~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Inductive Automation cofounders Colby Clegg and Carl Gould go deep on the origins of Ignition, the road to 8.3, and what AI means for industrial automation.Vlad and Dave host Colby Clegg, CEO, and Carl Gould, CTO, of Inductive Automation together for the first time to trace the full arc of the company. The story begins in 2003, when Sacramento systems integrator Steve Heckman brought Colby and Carl in to build the missing glue layer between OT data and modern IT tooling. What began as logging values into SQL databases became Factory PMI and eventually Ignition.A key thread is why Ignition broke through when larger automation vendors had superior distribution. Colby points to Clayton Christensen's Innovator's Dilemma. Incumbents could not match Inductive's unlimited per gateway pricing or partner with integrators because their own services groups competed with them. Carl adds the culture piece. Inductive refused to gate downloads, kept the module SDK open, made education free, and ran a public forum when competitors called it reckless, a posture they once called innovation without permission.Ignition 8.3 takes center stage, arriving after a deliberate five year gap from 8.1. Carl frames it as the completion of work that began with 8.0 in 2018. Gateway configuration is now stored in open, readable formats on disk, the gateway web interface was rewritten, and the platform supports orchestration, environmental separation, and infrastructure as code workflows Carl expects to become table stakes. The release also adds event streams, a revamped historian, and perspective drawing tools. For integrators still on 8.1, 8.3 is the version built for distributed deployments across many gateways.On AI, Carl is candid that the new MCP server module is intentionally a minimum viable product. It ships as a raw toolkit for integrators to author MCP primitives that expose Ignition data to agentic systems like Claude Code. First party MCP tools are coming, but Inductive wants to define the guardrails before shipping an API surface they will support for years. Carl frames AI as a new axis of software possibility, comparable to the shift from DOS to Windows. Colby ties it back to legacy SCADA conversion, framing the security and reliability gains as a national security issue. The episode closes with notes on the Inductive ecosystem, including a new collaboration with Tiger Data behind TimescaleDB, plus career advice on soft skills, context, and agentic coding tools.About Colby Clegg and Carl GouldColby Clegg is the CEO and cofounder of Inductive Automation, the California based company behind Ignition, the cross platform SCADA, MES, and IIoT software used by manufacturers and integrators worldwide. Carl Gould is the CTO and cofounder, leading product and engineering direction across Ignition. Both joined founder Steve Heckman in 2003 and have shaped the platform's open, integrator first philosophy ever since.Inductive Automation: https://www.inductiveautomation.comTimestamps0:00 Introduction1:00 Meet Colby Clegg and Carl Gould2:00 The origins of Inductive Automation in 20038:00 Going to market and the Innovator's Dilemma10:30 Innovation without permission as company culture18:50 Ignition 8.0 and the leap to Perspective26:00 The five year journey to 8.338:00 The MCP server module and AI in Ignition45:30 AI in the control plane and guardrails52:30 Tiger Data and the technology ecosystem1:02:30 Career advice for the next generation1:06:40 What is ripe for innovationReferencesIgnition Community Conference: https://icc.inductiveautomation.comAbout Your HostsVladimir Romanov is a cohost of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to reduce the risk of modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Colby Clegg on Ignition 8.3 and Industrial Automation: https://www.joltek.com/blog/industrial-automation-colby-clegg-ignition-8-3Connecting Allen Bradley PLCs to Ignition: https://www.joltek.com/blog/connecting-allen-bradley-plc-ignitionDave Griffith is a cohost of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

En el episodio de hoy, el número 796, vengo con muchas ganas de contarte algo que me tiene completamente fascinado.Pero vamos a lo importante: las Skills o habilidades. Si creías que la inteligencia artificial era solo un chat donde escribir preguntas y recibir respuestas, prepárate, porque hoy vamos a ver cómo dotar a nuestros modelos de lenguaje de auténticos "superpoderes" técnicos.¿Qué son realmente las Skills?Imagina que en lugar de darle instrucciones genéricas a tu modelo (lo que conocemos como prompt), le proporcionas una estructura especializada. Una Skill es una herramienta transversal que le enseña al modelo a comportarse como un experto en una materia concreta. Lo maravilloso es que estas habilidades no dependen de un solo modelo; puedes usarlas con Claude, con OpenCode, con Hermes o con cualquier otro agente. Es una forma de democratizar el conocimiento técnico y hacerlo reutilizable.En este episodio te cuento mi experiencia personal utilizando estas habilidades para tareas que, de normal, nos llevarían bastante tiempo de configuración. Desde crear contenedores Docker optimizados hasta gestionar bases de datos complejas sin escribir una sola línea de SQL.Soberanía Digital y Potencia LocalYa sabes que me encanta el lema de "yo me lo guiso, yo me lo como". Aunque existen servicios externos muy económicos para correr estos modelos, nada supera la sensación de tener el control total. Te hablo de mi configuración actual: un Slimbook con una Nvidia GeForce RTX 4060 Ti de 16 GB de VRAM. Con este hardware estoy corriendo modelos como el Qwen de 35 billones de parámetros con una fluidez espectacular. Aquí es donde la soberanía digital cobra sentido: mis datos, mis reglas y mi hardware.Ejemplos prácticos: Docker y SQLiteA lo largo del audio, te guío por dos ejemplos que me han dejado con la boca abierta:Docker Expert.SQLite Expert.La Anatomía de una Skill: Bajo el capóMenciono también el increíble trabajo de Daniel Primo en Web Reactiva, quien ha profundizado muchísimo en este tema de las Skills y cuya guía ha sido una fuente de inspiración fundamental para experimentar con todo esto.Conclusión: El futuro es el lenguaje naturalCapítulos:00:00:00 El troleo a David y la importancia del feedback00:00:41 Introducción a las Skills: Dale "poderes" a tu IA00:01:14 Repaso a OpenCode y el paso a la soberanía digital00:02:11 Mi hardware: Slimbook, Nvidia RTX 4060 Ti y el modelo Qwen00:02:55 ¿Qué son realmente las Skills y por qué usarlas?00:04:18 Ejemplo práctico: Instalando una Skill para Docker00:04:58 Recomendación: La guía de Skills de Daniel Primo00:06:08 Generando un Dockerfile complejo para Rust en dos etapas00:07:34 Anatomía de una Skill: Front Matter, YAML y Markdown00:09:25 Cómo el agente gestiona los tokens y las habilidades00:10:48 Verificación del Dockerfile generado por la IA00:12:11 Trabajando con bases de datos: Skill de SQLite Expert00:13:24 Experiencia real: Revisando código Backend y Frontend00:15:38 Consultas en lenguaje natural sobre la base de datos00:17:40 Tipos de Skills: Percepción, Acción y Pensamiento Complejo00:19:47 Conclusiones: Programar sin programar y modelos locales00:20:29 Despedida y red de sospechosos habitualesMás información, enlaces y notas en https://atareao.es/podcast/796

SummaryIn this episode I'm joined by Chad Holdorf, longtime product and technology leader whose career spans John Deere, Salesforce, Pendo, and now Demandbase, where he leads AI initiatives across the company.We explore how AI is fundamentally reshaping the way modern product teams test, ship, and learn, from debugging customer issues directly against live codebases to product managers and support teams submitting pull requests themselves. Chad shares how tools like Cursor and Claude are collapsing traditional handoffs between product, engineering, and support, creating a much faster feedback loop between customer problems, experimentation, and shipped solutions.We also get into the messy reality behind enterprise AI adoption, including data quality, hallucinations, trust, evals, and why testing AI products inside real customer environments is much harder than most demos make it look. Chad gives us a peek into how his own workflow has changed, how his teams are learning by building in real time, and why this moment reminds him of the early days of Lean Startup, where he and I first met.If you've been wondering what AI-native product development actually looks and feels like inside a real company, this episode is for you.TakeawaysAI is collapsing traditional handoffs between product, engineering, and support teams. Chad described customer support teams going directly into code repositories with AI tools to investigate issues, understand root causes, and eventually submit merge requests themselves.Most enterprise AI demos fall apart when connected to messy real-world customer data. Chad emphasized that “just putting Claude on top of the data” failed quickly without extensive labeling, validation, testing, and human feedback loops. Customers could detect hallucinations within a few prompts.AI systems expose hidden data inconsistencies inside organizations. One example showed AI selecting a custom CRM field that technically produced better targeting results than the field support teams were trained to use, creating confusion about which “truth” was actually correct.Trust has become the critical success factor for enterprise AI adoption. Chad explained that once customers encounter inaccurate outputs, confidence in the system drops immediately, which forces teams to spend enormous time improving prompts, SQL queries, evals, and validation workflows before broader rollout.Product managers are increasingly becoming hands-on builders again. Instead of relying entirely on engineering handoffs, Chad now spends large portions of his week inside Cursor and AI coding agents investigating bugs, generating tickets, reviewing repos, and shaping product direction directly through code conversations.AI-native workflows dramatically compress feedback loops. Problems that previously took days of back-and-forth between support, product, and engineering can now move from customer issue to deployed fix in under an hour through AI-assisted workflows and automated merge requests.The biggest organizational bottleneck is shifting away from engineering speed toward enablement and adaptation. Chad compared this moment to early Agile adoption, where downstream teams like sales, support, and training struggled to keep pace with accelerated shipping cycles. AI is now amplifying that challenge even further.Continuous learning and experimentation matter more than formal process mastery right now. Chad repeatedly compared the current AI moment to the early Agile movement: the people progressing fastest are the ones willing to try tools, build things, stay curious, and learn in public rather than waiting for established best practices or certifications.Guest LinksLinkedIn: https://www.linkedin.com/in/chadholdorf/Demandbase: https://www.demandbase.com/ If your leadership team is about to make a big strategic bet, the real risk usually isn't the idea, it's the assumptions behind it that haven't been surfaced yet. A Decision Sprint is a focused 6–12 week engagement where we extract, map, and test those risks so leaders can make a clear Commit, Correct, or Cut decision before major capital moves. Learn more or apply at precoil.com.

What do colors, soup kitchens, and mountain climbing have in common? They're all part of the mental models that have shaped how we think about analytics, and they're exactly the kind of durable wisdom that matters more than ever in an age of AI slop. This campfire-style conversation among the co-hosts reveals the concepts, books, and aha moments that have stuck with us across decades of analytics work. From the magic of randomization to the critical distinction between outputs and outcomes, we share the frameworks that guide our thinking whether we're writing SQL by hand or asking Claude to do it for us. It turns out the most valuable analytics wisdom isn't about tools or techniques—it's about understanding how humans actually make decisions, build trust, and collaborate effectively. Some things never go out of style. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Mike & Tommy dive into Databricks Genie and the growing hype around data agents, exploring whether the real challenge is natural language chat or the semantic layer underneath—and what Power BI teams must fix before any AI agent can deliver trusted, governed answers at scale.https://www.advancinganalytics.co.uk/blog/genie-is-a-semantic-layer-problem-not-a-chat-problem-1https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/OneLake-catalog-is-now-natively-available-in-Foundry-Generally/ba-p/5178376https://community.fabric.microsoft.com/t5/Fabric-Updates-Blogs/Direct-Lake-on-SQL-with-Fabric-Data-Warehouse/ba-p/5177641https://community.fabric.microsoft.com/t5/Power-BI-Updates-Blog/Modern-Visual-Tooltips-in-Power-BI-Generally-Available/ba-p/5173946Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

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This is episode 326, recorded on May 7th, 2026, where John and Jason break down the Power BI & Fabric April 2026 Feature Summaries — DAX user-defined functions are here in preview, Direct Lake is flexing new modeling muscles, the Dataflows Gen1 community drama has a plot twist, Fabric Data Warehouse finally gets true transactional DDL, and VS Code integration in Fabric notebooks keeps leveling up. It's the April feature summary double-header. For show notes please visit www.bifocal.show

Daniel Wyrzykowski is a Product Manager at Mend.io. In this episode, he joins host Paul John Spaulding to discuss prompt injection, including what it is, whether it's the new SQL injection, and more. Securing The Build is brought to you by Mend.io, the leading application security solution, helping organizations reduce application risk efficiently. To learn more about our sponsor, visit https://mend.io.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.

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Nik and Michael discuss Nik's new project PgQue, a descendent of Skype's PgQ, for running queue-like workloads in Postgres. Here are some links to things they mentioned: Our first episode on Queues in Postgres https://postgres.fm/episodes/queues-in-postgresPgQue https://github.com/NikolayS/pgqueHN discussion https://news.ycombinator.com/item?id=47817349PgQ https://github.com/pgq/pgqpgmq https://github.com/pgmq/pgmqRiver https://riverqueue.comKeeping a Postgres queue healthy (blog post by Simeon Griggs / PlanetScale) https://planetscale.com/blog/keeping-a-postgres-queue-healthyPostgres Job Queues & Failure By MVCC (blog post by Brandur) https://brandur.org/postgres-queuesMy queries to monitor autovacuum (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/monitor-autovacuum-my-queries/SELECT FOR UPDATE considered harmful (blog post by Laurenz Albe) https://www.cybertec-postgresql.com/en/select-for-update-considered-harmful-postgresql/Christophe Pettus blog post https://thebuild.com/blog/2026/05/03/pgque-two-snapshots-and-a-diffOur episode on pg_ash https://postgres.fm/episodes/pg_ashRediscovering PgQ (Alexander Kukushkin slides) https://speakerdeck.com/cyberdemn/rediscovering-pgqTick frequency tuning https://github.com/NikolayS/PgQue/blob/main/docs/tick-frequency.md~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

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

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Every B2B marketer knows the funnel concept is broken. Yet that same model your team is being measured on right now was invented in 1898, and it's surviving because nobody at the top has been given permission to use anything else.Carolyn and Amber react to a recent article in Adweek from Professor Mark Ritson, where he calls the funnel the "cockroach of marketing concepts", a 128-year-old model that has outlived every attempt to replace it. They break down why every critique fails to land, and why the real problem isn't the funnel. It's the classrooms teaching it, the boards demanding it, and the marketers who can't challenge it without risking their careers.Topics covered in this episode:Why a model from 1898 still anchors how B2B companies measure marketing in 2026The gap between what the funnel was designed to do (a market snapshot) and what it's used for (SQL-to-opp conversion, MQL targets)Why every "funnel is dead" critique fails to kill it, and who actually keeps it aliveThe Amazon "full funnel campaigns" moment, and what it says when even the best companies are still using the languageWhy a snapshot in time tells you nothing about where to move budget, why CAC is up, or what to do when pipeline missesIf you've tried to kill the funnel inside your own org and watched it survive every conversation, this is the episode. The cockroach isn't the funnel. It's the system that keeps demanding it.-----------------------------------------------------Want answers now?

This is episode 325, recorded April 17th, 2026, where John and Jason dig into the Real-Time Intelligence section of the Microsoft Fabric March 2026 Feature Summary covering topics such as Business Events, DeltaFlow for CDC, real-time processing with Spark notebooks, and some welcome quality-of-life updates across Event House and workspace monitoring. For show notes please visit www.bifocal.show

Many of us working with databases know the problems of a single point of failure. We build HA/DR technologies into a lot of systems precisely because many of us know if the database goes down, a lot of stuff goes down. Broken software is easier to fix and rollback, but a broken database can be a much bigger problems. We also know an overloaded server doesn't handle a workload well, hence our quest for well-written SQL code, but we often lose that battle with developers. Read the rest of The Dangers of Dependencies

Show Notes: Scarlett Jiang from Vantage AI, an AI product and services firm based in London,  provides a one-minute overview of Vantage AI, highlighting their focus on data foundations and AI transformation. Vantage AI helps companies consolidate data from various systems into a single source of truth. Scarlett mentions the firm's experience with hospitality franchise clients, such as Burger King, KFC, and McDonald's. Mock Demo of Chatbot Scarlett introduces a mock demo of a chatbot designed for hospitality franchise owners. The chatbot can handle real-time queries about sales data, labor costs, and other key metrics. Scarlett explains the process of using the chatbot to query data, including translating natural language questions into SQL, which means users do not need to know SQL.  Custom Dashboard Scarlett introduces the custom dashboard with data intelligence analyst chatbot functionality, allowing users to query via human natural language and retrieve insights from pre-ingested data warehouses.  Sales Performance The chatbot can provide summaries of sales performance, labor data, and other operational metrics. Sales Performance Rank Scarlett shows how the chatbot can handle more complex queries, e.g.: If I had to focus on 3 stores to improve performance this quarter, which would you recommend and why? (chatbot showcase the capability to synthesize sales, reviews, and trend data into  recommended action) Performance Graph The chatbot can provide detailed insights into top and bottom performers, including specific metrics like net sales and transaction counts. Scarlett discusses the benefits of using a chatbot for specific questions, rather than pre-built dashboards. The chatbot can also provide reasoning behind its answers, showing the steps it takes to generate insights. The Process of Building AI Tools Scarlett explains the process of building AI tools, starting with a diagnostic phase to understand the client's data journey and use cases. After the diagnostic, a strategic roadmap is created to prioritize use cases. A quick prototype is then developed, followed by data foundation transformation. The process can range from a few days to several months, depending on the complexity and scope of the project. Accounts Payable Month-end Reconciliation Demo Scarlett demonstrates a workflow automation tool for account payable month-end reconciliation.  Accounts Payable Reconciliation This demo presents a finance use case built around month-end accounts payable reconciliation - a process every finance team navigates. Supplier invoice data sits across two systems: the AP subledger, which holds granular invoice-level detail, and the general ledger control account, which carries a single summary balance that should match. In practice, the two rarely align - late-posted invoices and manual journal entries that bypass the subledger are the most common culprits. This demo showcases an AI agent that pulls data from both sources, identifies and reconciles the gap automatically; and surfaces discrepancies to human reviewers for sign-off or overwrite - eliminating hours of manual investigation at close. Converting PDF Purchase Orders into CSV Files Scarlett demonstrates a tool that converts PDF purchase orders into CSV files. Snowflake Tables The tool extracts key information from the PDF, such as contract terms, payment schedules, and expenditures. The tool can transform the extracted data into a chart format for easier analysis.  Reconciliation Report Payment Breakdown The tool is designed to automate the process of working with large amounts of unstructured data, reducing manual effort.  Cost and Development Time  Scarlett discusses the cost and development time for AI tools, noting that prototypes can be developed quickly. The bulk of the work involves data cleaning, ingestion, and transformation to ensure data accuracy. The development time can range from a few days to several months, depending on the complexity and scope of the project. The cost varies based on the specific requirements and the level of automation needed. Demonstration Videos:  Pre-recorded Demo 1: Data Intelligence demo https://www.vantageglobal.ai/insights/demo-pages/data-intelligence-analyst Pre-recorded Demo 2: Accounts payable month-end reconciliation agent ​https://www.vantageglobal.ai/insights/demo-pages/ap-month-end-reconciliation-agent Pre-recorded Demo 3: Parsing unstructured data to structured data https://www.vantageglobal.ai/insights/demo-pages/purchase-order-explorer-agent Timestamps: 02:24: Demonstration of AI Chatbot for Hospitality Franchise Owners 07:14: Advanced Query Capabilities of the Chatbot 13:06: Process of Building AI Tools at Vantage AI 17:40: Case Study: Account Payable Month-End Reconciliation  28:03: Case Study: PDF to CSV Transformation 34:42: Cost and Development Time for AI Tools  This episode on Umbrex: https://umbrex.com/unleashed/episode-643-scarlett-jiang-coo-at-vantage-global-ai-shares-3-live-client-ai-use-cases/ Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com. *AI generated timestamps and show notes.  

Building Repeatables in Claude: Skills, CLI vs MCP and Token Discipline | Go With The Flow Claude Skills, CLI vs MCP and Token Discipline with Ritu Java | Seller Sessions SEO Description Ritu Java and Danny McMillan on building agentic skills, choosing CLI over MCP, plan mode discipline and the short window to ship before token costs reset. Episode Summary Week 4 of the month, Go With The Flow, and Ritu Java is back from her travels. The world has shipped fast since the last episode: Codex 5.5, Claude 4.7, an Amazon Ads MCP and a fresh round of panic over the rumoured removal of Claude Code from the $20 plan (it was a 2% AB test, not a rollout). Ritu and Danny use the noise to make a sharper point: this is the moment to stop chasing models and start building repeatable systems on the platform you have already chosen. Ritu walks through the three eras of PPC Ninja's automation stack. Apps Script bulk file generators three years ago, Netlify hosted UI apps last year, and now agentic skills that her team chats with in plain English to produce upload ready Amazon bulk files. The same shift applies to data: BigQuery accessed through the Google Cloud CLI rather than through MCP, because CLI is leaner on tokens and works better when the job is heavy on data rather than tool surface. Danny mirrors the move with his event-ops CLI for WordPress, WooCommerce, Stripe and FooEvents reconciliation, and his four tier ExtractFlow cascade (HTTP, headless, stealth, agentic) that bypasses the limits of any single browser tool. The second half is a discipline talk. Plan mode every time. Push back on the first plan because Claude over engineers by default. 30% of your time on workflow scaffolding so the other 70% can be real building. The 21 day Claude rule: when a shiny new tool fires the dopamine, wait 21 days before refactoring around it. Left brain tasks (counting, SQL, deterministic logic) belong in scripts. Right brain tasks (judgment, creativity, hypotheses) belong in the model. Mix them inside a single skill. Skills are micro pieces of your workflow, not magic, and Claude can write them for you from an existing SOP. Key Topics The three eras of PPC Ninja automation: Apps Script, Netlify UI apps, agentic skills CLI vs MCP: when to choose each and why CLI is more token efficient for data heavy work Token economics, the rumoured $20 plan change and why it was a 2% AB test The short window before subsidised tokens get repriced Plan mode discipline and the "push back on plan one" rule Danny's 30 / 70 framework: workflow scaffolding vs building The 21 day Claude rule for resisting tool churn Left brain vs right brain task design inside a single skill The PPC Ninja "5 Whys" skill: deterministic SQL plus non deterministic hypotheses Claude.md, Gemini.md, Skills.yaml and the emerging Agents.md standard Skills for beginners: let Claude write them from your SOP Skill cascading: research, article, LinkedIn post, tweets, slide deck in one chain Timestamps [00:01] Welcome back, Week 4 Go With The Flow, Ritu returns from travels [00:17] Codex 5.5, Claude 4.7 and the "no one is writing code anymore" reality [02:01] Ritu on the three eras of PPC Ninja automation [02:42] Era 1: Apps Script bulk file generators in Google Sheets [03:46] Era 2: Netlify hosted UI apps with input fields [04:48] Era 3: Agentic skills, the bulk file skill trained on Amazon templates [06:22] Cloud talking to BigQuery through the Google Cloud CLI [07:00] Danny: what is a CLI and why it matters for token use [08:00] Amazon Advertising MCP vs CLI based access to the same data [09:33] WordPress horrible to drive via MCP, easy via CLI [10:00] Danny's event-ops CLI: tickets, food tickets, WooCommerce, Stripe reconciliation [12:13] ExtractFlow four tier cascade: soft, medium, stealth, agentic [13:46] Why CLI for the heavy stuff, MCP for the soft touch [14:13] AWS CLI: chat to Claude, push HTML blog posts live in two minutes [15:33] The overwhelm problem and the 5,000costbehindthe5,000costbehindthe100 plan [17:35] The $20 plan rumour: it was a 2% AB test, not a rollout [19:38] Build repeatables, not one offs [20:38] Danny: pick a platform and stop chasing benchmarks [21:16] The 21 day Claude rule for new tools [22:16] Plan mode every time, push back on plan one, get the second plan [23:02] Why am I building it, who is it for, what am I building [23:30] The 30 / 70 split: workflow scaffolding vs real building [25:13] Why long six to fourteen hour Claude runs are usually inefficiency [27:12] Compounding 1% a day across a year [27:47] "I build the things that build things" [28:00] Architecture vs apps: filling the gaps between A and B [29:06] Left brain vs right brain task design [30:01] Why throwing 80/20 at a sales drop diagnosis fails [31:33] The PPC Ninja 5 Whys skill: deterministic plus non deterministic in one flow [34:32] Claude.md, Gemini.md, skills.yaml and the agents.md standard [40:53] Beginners: let Claude write the skill from your SOP, use the interview pattern [42:39] Skill cascading: URL to research to article to LinkedIn post to tweets to slides [44:42] Mixing deterministic and non deterministic inside a single skill [45:39] Wrap up, signal to noise, who is it for Key Takeaways Pick a platform and stop chasing models. A new model ships every week. Time spent benchmarking is time not building. Double down on Claude (or whichever you chose), use the 21 day rule, and let the ecosystem catch up to the shiny thing in your feed. CLI for heavy work, MCP for soft touch. MCP loads tools and skills into context and burns tokens. CLI uses programs already on your machine. For data heavy jobs (BigQuery, AWS, WordPress at scale), CLI wins. For light cross app workflows, MCP is fine. Build repeatables, not one offs. Subsidised tokens will not last. The 100planreportedlycostsAnthropic100planreportedlycostsAnthropic5,000 to serve. Spend the window building scaffolding that compounds, not 14 hour vibe coding runs. Plan mode every time, then push back. Claude over engineers by default. Generate the plan, then say "you have over engineered this, although I want it elegant, go back and review." Plan two is the one you start from. 30% on workflow, 70% on building. Each new dependency, MCP, skill or repo you add to your workflow compounds across every future project. Stop building only the apps. Build the things that build the apps. Left brain in scripts, right brain in the model. Counting, SQL, deterministic logic belongs in Python the moment you can offload it. Save the model for hypotheses, judgment and creativity. The PPC Ninja 5 Whys skill mixes both inside one flow. Skills are micro pieces, not magic. Take an SOP, ask Claude to interview you with decision panels, and let it write the skill. Then cascade skills together: URL to research to long form article to LinkedIn post to tweets to slide deck. Notable Quotes "Instead of doing one offs, it is time to build repeatables. The more people can learn that skill now, the better it will be, because a year from now you may not have access to the same tokens." Ritu Java "If you see something and it looks sexy and it has sex and sizzle and your dopamine is screaming to go after it, wait 21 days. Either Claude will have it, or someone will have a repo, and you can combine it." Danny McMillan "Always use plan mode. Never accept plan number one. Tell Claude: you have over engineered this, although I want it elegant, go back and review. Then start from plan two." Danny McMillan "I build the things that build things. I build the scaffolding the team needs so they can build on top of it." Danny McMillan "Spend 30% of your time on your workflow and 70% building. The 30% compounds across every project." Danny McMillan "If we just hand six months of ad, organic, ranking and SQP data to Claude with no structure, it is going to mess up. It will give you an 80/20 you are not satisfied with, because it is not equipped to handle that volume without scaffolding." Ritu Java "WordPress is horrible to work with through MCP. It falls over all the time. CLI can be amazing for certain things." Danny McMillan Resources Mentioned PPC Ninja : Ritu's Amazon PPC software and agency, base for the BigQuery + CLI stack discussed Claude Code : Anthropic's CLI for Claude, the primary surface used in the episode Anthropic Claude : Claude 4.7 referenced as the current model OpenAI Codex : Codex 5.5 mentioned as the rival shipping fast Google Gemini CLI : Referenced as a sibling agent surface (Gemini.md) Google BigQuery : PPC Ninja's central data warehouse Google Cloud CLI (gcloud) : The CLI Claude uses to talk to BigQuery Amazon Advertising MCP : Amazon's official MCP server for ads data, referenced as the MCP comparison point AWS CLI : Used by Ritu to publish HTML blog posts to ppcninja.com from a Claude chat Netlify : Hosting layer for PPC Ninja's previous era of UI based apps WordPress and WooCommerce : Backbone of Danny's event-ops CLI FooEvents : Ticketing plugin that lives behind WooCommerce in the event-ops flow Stripe : Source of the card fee variation Danny reconciles via CLI ExtractFlow / CloudExtract : Danny's four tier extraction cascade (HTTP, headless, stealth, agentic). Open repo Playwright : The default browser automation tier inside ExtractFlow Agents.md : Emerging AI agnostic instruction file standard alongside Claude.md and Gemini.md Sequential Thinking MCP : The MCP Danny invokes when asking Claude to step through analysis Hosts Danny McMillan : Host of Seller Sessions, founder of DataBrill, building AI native tooling and CLI based workflows for Amazon sellers. Website: https://sellersessions.com LinkedIn: https://www.linkedin.com/in/dannymcmillan Ritu Java : CEO and co founder of PPC Ninja, Amazon PPC software and agency. Specialises in automation, BigQuery pipelines and agentic workflow design. LinkedIn: https://ca.linkedin.com/in/ritujava Website: https://www.ppcninja.com What's Next Next week: Ritu and Danny pick up routines and the new Claude scheduler. In 8 days: Seller Sessions Live 2026 in London on 9 May. Last week to lock in any final discounts. About Seller Sessions Seller Sessions is the leading podcast for serious Amazon sellers, hosted by Danny McMillan since 2017. Go With The Flow is the weekly automation strand where Danny and Ritu Java work through agentic flows, MCPs, CLIs and skills, in real time, on the same stack their teams ship every week. Episode published: 1 May 2026 Series: Go With The Flow (Week 4 of the month) Keywords: claude skills, claude code, cli vs mcp, mcp model context protocol, claude 4.7, codex 5.5, amazon ppc automation, bigquery cli, agentic workflows, plan mode, token optimisation, claude.md, agents.md, ppc ninja, ritu java, seller sessions podcast, go with the flow

In March 2012, the FBI surrounded a hurricane-rated steel door in Galveston, Texas. Behind it sat 30 year old Higinio Ochoa, drinking coffee in his boxers, flushing his one-time pad passwords down the toilet before letting federal agents inside. The operation to capture "w0rmer" had finally terminated.The process had initialized years earlier in childhood IRC rooms and 2600 chat channels. Ochoa taught himself to hack on dial-up connections, installing FreeBSD from thirty floppy disks at eleven years old. By his twenties, he was running cameras and internet infrastructure for Occupy Wall Street camps. When he witnessed police beating a woman having a seizure during a raid, something switched. The technical skills pivoted toward purpose.Cabin Crew launched with surgical precision. Ochoa mass-scanned police systems for SQL injections and admin pages, often not knowing which department he'd compromised until crafting the press release. He signed every hack, tagged every defacement, live-tweeted FBI taunts. His girlfriend posed in a bikini outside the Alabama Department of Public Safety holding signs that read "PwN3D by w0rmer" with GPS coordinates embedded in the photo metadata.Today he consults for governments and holds battlefield accommodations from Ukraine. The smooth hands that once broke into Secret Service-designed systems now defend critical infrastructure at levels where people could die if information leaks.TIMSTAMPS00:00 The Early Days of Hacking04:22 From Hobbyist to Activist08:30 The Shift to Purposeful Hacking13:16 The Rise of Cabin Crew17:58 The Psychology of Hacking and Branding21:11 The Origins of Wormer: A Hacker's Journey25:10 The FBI's Approach: How They Caught Me27:50 The Day of Reckoning: My Arrest Experience32:44 Life in the System: Mental Struggles and Adaptations36:18 Navigating Post-Prison Life: Challenges and Restrictions44:40 Navigating Life Post-Incarceration47:27 The Struggles of Redemption51:19 Finding Opportunities in a Stigmatized Field55:23 The Evolution of a Hacker's Journey58:46 Contributions to Information Security01:01:19 Words of Wisdom for Aspiring Hackers01:05:42 The Dream of a Cybersecurity Bar[Higinio “w0rmer” Ochoa – LinkedIn] - https://www.linkedin.com/in/x0hig Professional profile of Higinio Ochoa, a former Anonymous-affiliated hacktivist turned cybersecurity consultant, where he shares insights on security, research, and his work in the industry.[DEF CON Hacker Conference] - https://defcon.org/ One of the world's largest and most influential cybersecurity and hacker conferences, referenced in the episode as a key part of early hacker culture and later professional engagement.[Cybersecurity and Infrastructure Security Agency (CISA)] - https://www.cisa.gov/ A U.S. government agency focused on cybersecurity and infrastructure protection, mentioned in relation to responsible disclosure and ethical hacking initiatives.[Cloudflare] - https://www.cloudflare.com/ A global web infrastructure and cybersecurity company where the guest briefly worked after prison, playing a role in his transition into legitimate security work.[The Pirate Bay] - https://thepiratebay.org/ A well-known file-sharing platform referenced in the discussion about monitored internet usage and security research environments post-release.

В этом выпуске: гость Дмитрий рассказывает про Desbordante; декаплим некоррелированные предикаты в SQL подзапросах; Ваня продолжает мучить Snapmaker U1; Zed дорос до 1.0; а также делимся по мелочи — LocalSend и Gridfinity, и, конечно, темы слушателей. Важно! Запись выпуска 539 перенесена на 13 мая. [00:00:00] Чему мы научились за неделю LocalSend: Share files to nearby devices… Читать далее →

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This is episode 324, recorded on April 17th, 2026, where John and Jason continue through the Microsoft Fabric March 2026 Feature Summary — the Data Science & AI rebrand with Fabric Data Agents reaching GA, AutoML going GA, multimodal support for AI functions, the Data Warehouse section covering Fabric Data Warehouse recovery, Activator support, T-SQL AI functions, ANY_VALUE aggregate, Custom SQL Pools, SQL audit logs GA, outbound access protection, and the big one — the Database Hub, Fabric's new unified control plane for databases across edge, on-prem, cloud and Fabric. For show notes please visit www.bifocal.show

In this episode of Cloud Wars Live, Bob Evans sits down with Tirthankar Lahiri, Senior Vice President for Mission-Critical Data and AI Engines. Lahiri explains how agentic AI is transforming enterprise applications from simple question-answer systems into action-driven platforms that can reason, remember, and securely execute tasks. He details Oracle's strategy around unified agent memory, private agent factories, deep data security, and open development standards, all designed to help customers build scalable, secure, and flexible AI systems without added cost. AI Built Securely The Big Themes: Agentic AI Becomes Action-Oriented: Tirthankar Lahiri explains that agentic AI represents the next major step beyond generative AI. While generative AI focused largely on answering questions and producing content, agentic AI is designed to take action. It allows businesses to build systems that can reason, decide, and execute tasks autonomously. Oracle sees this as the future of application development, where AI becomes embedded into workflows rather than functioning as a standalone tool. Oracle Builds AI Directly Into the Database: Rather than forcing customers to move data across multiple isolated systems, Oracle's approach is to bring AI directly to the data. Lahiri argues that data is the “ground truth” and moving it creates technical debt, silos, inefficiency, and security vulnerabilities. Oracle's converged database architecture supports multiple data types, including relational, graph, spatial, and vector, inside one unified environment. This eliminates the need for separate repositories and allows AI agents to access all relevant context without fragmentation. Deep Data Security Protects Against AI Risks: Lahiri strongly emphasizes that traditional application-layer security is no longer enough in the age of AI. Since AI can generate SQL and potentially bypass interface restrictions through prompt injection, businesses must secure data directly at the source. Oracle calls this “deep data security.” He uses the analogy of protecting valuables in a safe bolted to the floor rather than simply locking the front gate. Even if someone gets inside the house, the valuables remain protected. Similarly, Oracle enforces security policies at the database level, ensuring agents can only access data users are authorized to see. The Big Quote: "You need to secure data. Need to lock your valuables into the safe deep inside the house." More from Tirthankar Lahiri and Oracle: Connect with Lahiri on LinkedIn or learn more about Oracle AI Database. Visit Cloud Wars for more.

How do you turn complex regulatory data into something customers can actually use, trust, and act on? Recording live from Qlik Connect, I sat down with Robin Astle, Head of Qlik Analytics at Reconomy Group, to explore how data is becoming far more than an internal reporting tool. In Robin's world, it has become a product in its own right, helping some of the world's largest retailers manage compliance, reduce costs, and make smarter sustainability decisions. Robin works across Valpak, a business at the center of environmental compliance and packaging regulation, supporting over 100 enterprise customers across the UK, Europe, and the US. From packaging taxes and recycling targets to government submissions and sustainability reporting, the amount of data involved is enormous, and the stakes are high. In our conversation, Robin shares how the Valpak Insight Platform evolved from manual SQL extracts and spreadsheets into a fully scaled cloud-based analytics platform ingesting millions of rows of data every day. We discuss how that transformation helped reduce onboarding from weeks to days, created up to 90% time savings on CSR and analytics requests, and helped customers reduce compliance costs by up to 15%. We also explore the launch of PackChat, which uses natural language queries to help customers interact with compliance and packaging data without needing deep technical knowledge. Robin explains why context is everything when dealing with environmental regulations, and why building trust in the data model is essential before AI can deliver real value. There is also a bigger conversation here around how businesses can use data to serve customers directly, not just support internal teams. From OEM partnerships and cloud automation to scaling AI-powered services across global markets, Robin shares what it takes to turn data into a revenue-generating service. So as more organizations look to unlock value from the information they already hold, are we still thinking too narrowly about what data can do? And could your greatest untapped product actually be the data sitting inside your business today? Join me for a fascinating conversation from Qlik Connect, and let me know your thoughts. Are you still using data for reporting, or are you starting to think about it as a product?

France pushes digital sovereignty. Adobe rushes an Acrobat Reader patch. Booking.com confirms a targeted breach. SAP fixes a critical SQL injection bug. A sanctions-dodging fraud network resurfaces. ViperTunnel infiltrates U.S. and U.K. firms. GlassWorm spreads across developer tools. Researchers dissect Predator spyware's kernel engine. A lawsuit challenges AI transcription in hospitals. Ted Shorter from Keyfactor unpacks quantum computing at scale. On our Threat Vector segment, David Moulton and ⁠Elad Koren⁠ pull back the curtain on agentic-first security. Preparing for post-quantum perils.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Ted Shorter, CTO and Co-Founder of Keyfactor, discussing the advent of quantum computing at scale, known as "Q-Day". Threat Vector Host David Moulton speaks with returning guest ⁠Elad Koren⁠, Vice President of Product Management for Cortex Cloud at ⁠Palo Alto Networks⁠ on this Threat Vector segment. Together they pull back the curtain on what an agentic-first security experience actually looks like in practice. This isn't a vision deck. The agents are already running. To listen to the full conversation, check it out here. Catch new episodes of Threat Vector every Thursday on your favorite podcast app. Selected Reading France Tees Up Big Public Sector Move Away From US Tech (BankInfo Security) Adobe rolls out emergency fix for Acrobat, Reader zero-day flaw (Bleeping Computer) Booking.com Confirms Data Breach as Hackers Access Customer Details (Hackread) SAP Patches Critical ABAP Vulnerability (SecurityWeek) Triad Nexus Evades Sanctions to Fuel Cybercrime (SecurityWeek) Ransomware-Linked ViperTunnel Malware Hits UK and US Businesses (Hackread) GlassWorm evolves with Zig dropper to infect multiple developer tools (Security Affairs) Predator Spyware's iOS Kernel Exploitation Engine: PAC Bypass, NEON R/W & More (Jamf Threat Labs) Lawsuit: AI Illegally Recorded Doctor-Patient Encounters (BankInfo Security) World Quantum Day (WorldQuantimDay) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices