Podcasts about QA

Email your pictures to orlofsky59@gmail.comSponsored By: Jeffrey Bolduc:A message from the mixed (up) multitude: I have listened to every podcast you've given, including the QA and Parsha in 5. I am not a Jew. Just a goy who wants Torah to spread. I would like to sponsor 10 episodes. God bless you all.

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「這是長期以來，結構性不正義的重大平反。」長期關心童年性侵倖存者的《報導者》副總編輯張子午說。8月14日，憲法法庭宣布「115年憲判字第6號判決」，2005年以前《刑法》關於兒少性犯罪追訴期20年的規定違憲。這讓聲請釋憲的11位童年遭性侵的倖存者，終獲得案件被發回法院重新審理的機會。 然而，他們仍將面臨案發數十年後蒐證不易等嚴酷考驗；其他未趕上這班釋憲列車的2005年以前兒少倖存者，恐怕也無法因釋憲而脫離「追訴期已過」困境，這是為什麼？ 本集透過五個QA，告訴你憲法法庭解釋20年追訴期違憲的理由及後續影響。 來賓｜《報導者》副總編輯張子午 製作團隊｜詹婉如、林彥伶

Frank Force joins us to talk about how game developers can ship faster, learn better, and avoid getting trapped by perfectionism. He shares lessons from AAA development at studios like Volition, Midway Games, and others including milestone pressure, polish, crunch, and speedrun-style bug hunting on PsiOps. He also breaks down his current push to release Pyroot, a C++/DirectX Metroidvania he started 6–7 years ago, on Steam Early Access. The conversation dives into why Frank believes browser-based development, JavaScript, game jams, and sharing work early can be powerful ways to build skills and a portfolio. He challenges the idea that web development can't produce “real” games and explains why low-friction tools can make it easier to experiment, iterate, and actually finish projects. Frank also gets into sizecoding and creative programming through JS1K, JS13K and Dwitter, the origins and growth of his open-source LittleJS game engine, procedural audio with ZZFX, and the mindset of removing rather than adding. Plus, he shares how AI is already helping game developers find bugs, prototype ideas, and give small teams more leverage, while also discussing his concerns about sunk-cost thinking, exploitative player psychology, and where the industry may be heading. Topics include: • Breaking into game development • Building a portfolio • Shipping games instead of endlessly polishing • JavaScript and browser-based game development • AAA development lessons • Indie game development • Game jams and rapid iteration • Sizecoding, JS1K, JS13K and Dwitter • LittleJS and open-source development • Procedural audio and ZZFX • AI tools for game developers • Going indie full time━━━━━━ TIMESTAMPS ━━━━━━ 00:00 - Career Advice Myth 00:36 - Meet Frank Force 00:56 - Pyroot Early Access 02:53 - AAA Lessons and Crunch 05:12 - Speedrunning for QA 05:43 - JavaScript Game Dev 09:02 - Ship Fast Share Early 12:03 - Advance Inside Studios 14:36 - Breaking In and Portfolios 18:03 - Sunk Cost Trap 19:27 - AI for Game Development 34:33 - Industry Concerns and Hope 38:57 - Size Coding JS1K 41:44 - Recreational Programming 41:57 - Code Golf vs Sizecoding 43:24 - Demo Scene Culture 44:31 - JavaScript Sizecoding Shift 45:26 - Procedural Assets Challenge 46:13 - Remove Not Add Mindset 48:05 - JS13K Sweet Spot 51:27 - Dwitter Raycasting Magic 54:23 - LittleJS Origin Story 57:10 - Open Source Community Growth 58:47 - Driven Wild and Self Promotion 01:00:48 - ZZFX Sound Without Assets 01:03:03 - Optical Illusion Breakthrough 01:06:25 - Make Stuff and Share It 01:09:11 - Going Indie Full Time 01:13:18 - Generative Art NFT Detour 01:17:12 - Monetizing Web Games 01:20:15 - Advice and AI Tools 01:22:47 - Final Thanks ━━━━━━━━━ ABOUT THE GUEST ━━━━━━━━━ Frank Force has been building real-time interactive software for over 25 years, from AAA titles like DOOM and Red Faction: Guerrilla to LittleJS, his open source game engine with over 4,000 stars on GitHub. He runs Frank Force Games in Austin, Texas, where he works on engines, graphics, games, and tools, and writes an unreasonable number of very tiny programs. ━━━━━━━━━ LINKS & RESOURCES ━━━━━━━━━ https://frankforce.com/ - Frank's website with links to projects and longform writeups https://killedbyapixel.itch.io/dr1v3n-wild - Frank's arcade driving game (started as a 13k game) https://killedbyapixel.github.io/LittleJSArcade/ - 50+ open source games made with LittleJS https://cosmodial.3d2k.com/ - Cosmodial is Frank's newly released open source star atlas https://js13kgames.com/ - JS13k games the 13k size JavaScript coding competition ━━━━━━━━━ GAME DEV ADVICE ━━━━━━━━━ Whether you're a student, aspiring game developer, or industry veteran, you'll find practical takeaways and real stories from inside the world of game development.

Why treating the cell, not the protein, could turn chronic disease treatment into something closer to a cure. You've built single-cell pipelines that spit out clusters, p-values and target lists, but nothing that survives contact with the clinic. What if the clustering method itself is quietly leading you astray? Adam Freund is Founder and CEO of Arda Therapeutics, a biotech using single-cell sequencing to find the pathogenic cells driving chronic disease. He spent seven years as a Principal Investigator at Calico Life Sciences, building a research lab on the biology of ageing and helping grow the company from 15 to more than 200 people, and holds a PhD in Molecular and Cell Biology from UC Berkeley. You'll get a working model for how Arda's discovery engine turns single-cell and spatial transcriptomic data into causal cell targets. Adam explains why a common statistical shortcut in single-cell analysis produces disease signals that don't hold up and how cell depletion could replace daily dosing with a handful of treatments that reset the immune system. Ross and Adam cover how Arda finds pathogenic cell populations across hundreds of donors, why chi-squared tests on cell clusters can substitute cell count for donor count without anyone noticing, and how B-cell depletion therapies proved that removing a cell can beat blocking its pathway. This one is for data science leaders and computational biologists building single-cell pipelines, not listeners after a general intro to drug discovery. Key Takeaways - Chi-squared tests on cell clusters draw their statistical power from the number of cells, not the number of donors, so a single oversampled patient can produce the same p-value as a hundred-donor study. - Rituximab clears 100% of B cells from circulation yet does nothing for lupus because the disease-driving cells live in tissue, not blood, a lesson now shaping where Arda tests its own molecules. - Neighborhood analysis scores each cell by the donor identity of its nearest neighbours rather than forcing cells into predefined clusters, producing a continuous disease-enrichment map with no cluster boundaries. - When depleted cells regrow, they often come back without the trait that made them harmful in the first place, which means a handful of doses can hold a chronic disease in remission for months. Chapter Markers 00:00 Why cell depletion beats pathway blocking 01:05 Welcome Adam Freund to the show 01:30 From Calico Life Sciences to founding Arda 03:29 Why blocking one pathway rarely works 05:32 B-cell depletion as the proof of concept 08:14 Building a modular library of depletion tools 10:46 Single-cell sequencing removes the need for a hypothesis 11:43 Why clustering is a dial, not ground truth 15:24 The chi-squared trap in single-cell analysis 20:40 Neighbourhood analysis and donor-weighted scoring 23:44 Moving from enrichment to causality 26:32 Inside Arda's lead fibrosis program 30:33 Why solid tissue testing beats blood samples 34:25 Simulating depletion in spatial transcriptomic data 38:49 The case for intermittent dosing over daily pills 43:58 The data infrastructure behind Arda's platform 48:46 Where spatial and protein data are heading Useful Links & Resources - Adam Freund on LinkedIn: https://www.linkedin.com/in/adam-freund-0657654 - CorrDyn: https://corrdyn.com Connect With the Show - Host Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Host Ross Katz on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ If your team runs single-cell pipelines, how do you currently decide on the number of clusters, and have you ever checked whether your significance scales with donor count rather than cell count? Tell us in the comments; we're building a running list of data QA checks for biotech data science teams. Visit corrdyn.com to learn how CorrDyn can help your organization extract value from data.

Manual testing never died. IoT is the proof. In this episode, Joe Colantonio talks with Oleksii Cherkashyn, a QA team lead, automation engineer, and IoT testing specialist who built a complete test architecture from scratch across hardware, firmware, mobile, web, and API. IoT breaks the usual automation playbook. You are not testing a shopping cart. You are connecting a real microcontroller, flashing a sketch, validating that a physical command produced the right change in a dashboard widget, and then doing the same thing again through the REST API and the mobile app. No marketplace framework covers that, so Oleksii built his own. You'll learn: Why manual QA remains essential in IoT and which cases can never be automated How Oleksii built a custom Node.js library to simulate up to 50,000 device connections for performance testing without buying the hardware Why he chose WebDriverIO over Playwright and the mobile automation reason behind it How he uses WebDriverIO MCP and cloud coding agents every day to create and repair tests The hard truth about dependent test chains and where AI agents still hallucinate How to handle time based scenarios like sleep modes, daily triggers, and delayed notifications Static versus dynamic provisioning, and how to test OTA firmware updates What to do first if your company hands you an IoT device and you have never tested hardware If you work in test automation and you want a look at the layer of testing that AI is not coming for, this one is for you. Listen up, and check out the links below for everything mentioned in this episode.

I show Etsy sellers how I create consistent AI product mockups in ChatGPT using a reference image, a reusable mockup skill, and a project folder. Learn how to generate Etsy listing mockups that stay aligned with your brand aesthetic and use a QA sub-agent to refine the final image.  

The crew discusses their upcoming trip to Gamescom in Cologne, Germany, and dives into rumors and realistic release timelines for Bethesda's highly anticipated The Elder Scrolls VI. Danny, Paris, and Pete get into a heated discussion about Rockstar Games potentially locking an extended look of Grand Theft Auto VI behind a timed Netflix exclusivity window. Was the Xbox One really as bad as people remember? The hosts look back at the 2013 launch, Kinect's downfall, backwards compatibility, and the games that made the era great. They also share their thoughts on Insomniac's Marvel's Wolverine, the open-world gameplay loop fatigue, and the massive wave of new video games dropping this Fall.Gamescom Prep & Logistics The crew prepares for their upcoming trip to Gamescom in Cologne, Germany, marking Danny's first time attending the event. Parris warns about the massive crowds and max-capacity public halls, recommending using Wednesday's media day to scout out business center appointments early. The Elder Scrolls VI Release Window Predictions Following Asha's visit to Bethesda to see an early build, the hosts discuss realistic expectations for The Elder Scrolls VI. While some speculate a 2027 launch, the consensus leans toward 2028 or 2029 to allow thorough QA testing and avoid past launch issues. GTA 6 Extended Look on Netflix Debate A heated debate breaks out over Rockstar Games potentially offering an extended look at Grand Theft Auto VI on Netflix prior to a YouTube release. Danny defends the marketing strategy as smart industry expansion, while Parris expresses concern over setting paywall precedents and drawing attention away from Gamescom announcements. Reflecting on the Xbox One Era The hosts look back at the Xbox One, highlighting its strong games lineup (Titanfall, Sunset Overdrive, Quantum Break), the Xbox Controller, Xbox Play Anywhere, and the birth of Game Pass and Backwards Compatibility. They discuss how poor messaging, Kinect bundling, and the initial $500 price point overshadowed an otherwise solid console generation. Marvel's Wolverine & Fall Gaming Wave The team discusses Insomniac's Marvel's Wolverine, addressing preview feedback regarding potential open-world gameplay loop fatigue. They look ahead to the packed Fall release schedule, noting the overwhelming amount of big games dropping through September and beyond. Connect with us: Danny Peña Parris Lilly Pete Toledo Riana Manuel-Peña Send us questions - fanmail@gamertagradio.com | Speakpipe.com/gamertagradio or 786-273-7GTR. Join our Discord - https://discord.gg/gtr chat with other GTR community member.

What's your gold standard for deciding whether AI-written code is ready for production? In Episode 42 of Talking Postgres, Simon Willison—creator of Datasette, co-creator of Django, and prolific open-source developer—joins Claire to share how AI is changing the way he builds software today. We dig into why his test for shipping AI-generated code is “Could I explain this to somebody else?”, how engineering management skills can be surprisingly useful when managing AI agents, and how the bottleneck is no longer writing code but understanding it. Also: personal credibility, deep research, the Winchester Mystery House, slop proxies, and Simon's observation that “Features are cheap. That doesn't mean you should build them all.”Previously on Talking Postgres:Talking Postgres podcast Ep 30: AI for data engineers with Simon Willison Links mentioned in this episode:Blog: Simon Willison's BlogProject page: Datasette, for finding stories in dataGitHub repo: sqlite-utilsBlog post: Release of alchemy-utils 0.1a0 (featuring Postgres & DuckDB)Blog post: There are no lossless transformations of natural-language text, by Sophie AlpertPodcast episode: The Open Weight Revolution with Simon Willison, on Oxide and FriendsPodcast episode: An AI state of the union, on Lenny's Podcast Wikipedia: Winchester Mystery HouseIdea in Mythical Man-Month: Conceptual integrityBlog post: SQLite compressed text-history prototypes, by Simon WillisonTools: Simon's miscellaneous tools repoIn this episode, we covered:00:00 Intro & music04:00 A week of work before breakfast09:12 Red-green TDD makes agents exercise every line14:42 A million lines mean nothing if you don't understand it18:28 Finding low-hanging fruit among open PRs & issues22:07 Getting work done while walking the dog23:01 Gold standard: “Could I explain this to somebody else?”28:27 Slop proxies add no value at all30:49 Aggressive nitpicking reviews35:01 Everything in software engineering is about trade-offs41:16 Racoon heist game is only fun for 1 minute 15 seconds43:46 New Year's resolution to be more ambitious48:11 You have to learn to throw things away51:57 Features are cheap. That doesn't mean you should build them all1:00:45 Engineering management skills are so useful1:04:44 QA specialists should be having a great time1:07:53 Writing is thinking, don't outsource it1:08:23 Skill atrophy is a choice you make1:10:07 I believe in people who are motivated1:11:55 Systems are not set up to deal with this volume1:18:48 Research agents stopped being absolute garbage1:21:49 The whole point of the “human in the loop”1:26:10 Dream: I want there to be more small businesses

Wasim Osman: The Scrum Master as a Router, Redefining Success Through Efficient Communication Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "A big part of the Scrum Master role is to work as a router—handling a lot of conversations with a lot of different devices, even though the internet is coming in through one cable." - Wasim Osman   For Wasim, success as a Scrum Master looks like a router in a house: many conversations, many stakeholders, all flowing through one point that has to prioritize, schedule, and stay on time. If the router slows down, everything downstream stalls—and the Scrum Master becomes the blocker. Early in his career he was overwhelmed juggling three or four engineering teams, so he built himself a personal Kanban board and ran his own quick standup every morning and again at the end of the day, just to see what moved, what stalled, and why. Getting organized with himself is when the work started to feel satisfying, because he stopped being the bottleneck. But he's honest about the hardest part: people rarely tell you to your face when you're slowing them down. The feedback is already there—you're just not hearing it. So you have to ask for it, react well when you get it (or people stop offering), and sometimes name the awkward truth first: "Our meetings used to be better—what happened?" That opening gives everyone permission to be honest.   Self-reflection Question: If you're the "router" for your teams, where are you quietly becoming the bottleneck—and who would tell you if you were? Featured Retrospective Format for the Week: What Went Well / What Went Wrong / Outliers + Open Discussion Wasim's go-to retrospective is deliberately bare-bones: what went well, what went wrong, and—the part that makes it his own—what were the outliers, followed by open discussion. He added the "outliers" question years ago after noticing that team members kept raising points that weren't clearly good or bad, but were still significant enough to discuss. Outliers give people "a bucket for sharing information that has no polarity"—an early signal about the future, a quiet concern, something that doesn't fit the other two columns but matters. He sometimes pairs it with lightweight metrics: sprint completion rate, or tickets that sat too long in review or QA, surfacing patterns the team wouldn't otherwise notice.   Self-reflection Question: What "outlier" signal has your team been sitting on—neither a win nor a problem yet—that deserves an open conversation before it becomes one?   [The Scrum Master Toolbox Podcast Recommends]

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In this episode 600 of the TestGuild Automation Podcast, Joe Colantonio talks with Jason Arbon, founder of Testers.ai, Jank.AI and IcebergQA and author of the new book Testing AI: Engineering Confidence in Non-Deterministic Systems. Take Our 2027 Survey Now: https://testgld.link/27data Jason makes a case most testers have not heard yet. Coding is being absorbed by AI. Specification work is thinning out. Product, development, and test roles are converging into one. And when the music stops, the only seat left belongs to the person who can look at what the machine produced and make an evidence backed call on whether it ships. He calls that confidence engineering, and he argues it is not a rebrand of QA. It is what QA was always supposed to be. Along the way, Joe and Jason get into the containment problem and why alignment, not lockdown, is now the real safety goal. They dig into why testing cost scales quadratically, meaning ten times more generated code creates roughly a hundred times more testing demand. Jason also pushes back hard on skeptics of agentic testing, pointing out that almost nobody has run the obvious experiment of testing a site themselves for a week and comparing their results against what AI finds. You will also hear Jason's most practical piece of advice in the whole conversation. If you are not running the same suite five times against the same build and looking at the actual results, not just flake, you are not testing seriously in an AI world. Plus a detour into grokking, the Chinese Room, and Geoffrey Hinton, because it would not be a Jason Arbon episode without one. Listen up!

Sheldon Poon, Founder of Drive Marketing, helps established brands go from Wholesale to D2C through performance-driven advertising grounded in data, technology, and human expertise. He is driven to build a company that reflects his values—one where people enjoy their work, have flexibility and autonomy, and continuously learn from and teach one another. In this conversation, Sheldon introduces The A.I.D.A. Framework—Attention, Interest, Desire, and Action. He explains how businesses can eliminate friction across the customer journey, use clean data to improve AI-powered campaigns, and balance automation with human strategy. Sheldon also discusses why genuine relationships are driving B2B growth, how AI is reshaping internships and early-career development, and how Drive Marketing helps successful wholesale brands build direct customer relationships without jeopardizing their valuable retail partnerships — Go from Wholesale to D2C with Sheldon Poon  Good day, listeners. Today, I have Sheldon Poon as our guest. He’s the Founder of Drive Marketing, a Montreal-based paid ad agency specializing in performance-driven marketing with a heavy emphasis on using data to make informed strategic decisions. Founded in 2013, Drive Marketing positions themselves as a technical-first partner that bridges the gap between business goals and technical execution. So they’re not a creative agency. They are a technical agency improving your ads. Sheldon, welcome to the show.  Yeah, thanks for having me, Steve. I’m happy to be here. Yeah. Great to have you here. And my first question is, what is your personal why, and how are you manifesting it in the business? How are you contributing to human flourishing?  That’s a very big question for a small ad agency.  It is.  Yeah. So when I first started the company, I kind of, like a lot of entrepreneurs, right, it came from me kind of looking at my life and where I was in my career and kind of getting frustrated at what I was seeing with my day-to-day job. So I was in a very, very privileged position. I was able to take a bit of a sabbatical from my day job where they held my position, which was great, because if everything went downhill, I could always just jump back to my job.  And I was like, “You know what? Like, I have an opportunity here. I’ve always wanted to start a business.” But because I had been working for somebody else for almost, at that point, I think like a decade and a half, I had a lot of experience, and I saw a lot of things that I was like, “This is what I don’t want.” So when I started my company, my internal mission and vision was to build the company that I would want to work at, to build the company that is kind of true to myself and my values, and something that I would enjoy going into day to day.Share on X And then what does all that mean? So the company that I wanted to work at, as I started working and hiring people, what I found was I wanted to have flexibility in my schedule. We treat everybody on the team as an adult. Everybody’s responsible for their workload. And as long as they meet their deadlines, I don’t care when you’re clocking in, clocking out. I’m a night owl. I like working till like midnight, 1:00 a.m. I don’t like doing mornings, so I’m often coming into the office late.  A lot of my team are doing the same thing. Some of them are early risers, and they like to leave early to have their afternoon free. So as long as everybody’s more or less—everybody’s on top of their tasks and more or less working during the day so that we can have our interactions and meetings, that’s perfectly fine. Another thing I think that's core to what I wanted to build was that I've always been very passionate about learning and teaching. In another life, I was teaching high school.Share on X  And when I started building the company, one of the things that we made very clear when we’re hiring is that everybody on the team learns because they’re passionate about what we’re doing. And everybody on the team teaches. Even if you’re just an intern coming in, we need to know your perspective because you’ll have a different perspective than me, who’s been doing this for like two decades, where I’m kind of blind to maybe some of the newer stuff that’s happening or some of the stuff that’s like on the ground.  And I want to know when I talk to our interns, when I talk to our juniors, like, “Hey, guys, what are you seeing?” Like, you guys are—you guys are like 20 years younger than me in some cases. Like, I want to know what’s going on with the kids. My personal, like, kind of like personal why of this is that it’s not so much about the end goal. I want to make sure that I enjoy my day-to-day, I enjoy what we do, and I’m not going to pull my hair out going into the office. So, like, I think we built something where everybody on the team is excited to work on stuff because they’re doing the work that they would want to be working on.  They’re learning the stuff that they would want to be learning, and then they get to implement it in a way that is impactful for the company, for the client, for what we’re doing. And my challenge as a leader is to figure out, okay, how do we take that exciting stuff and make it so that it generates money for the company so that we can all continue doing this? Yeah, this is very inspirational. I love it. You’re tapping into people’s curiosity, desire. I like the learning organization idea as well. So do you have a framework for this? Because this podcast is all about frameworks.  Yes.  How can someone create something like that? What are the steps to create it?  So it’s not one framework. Like I said, I’m very passionate about a lot of different topics, and one of the things that’s a personal interest of mine is just business. So, like, looking at business case studies, and from that you start seeing what works and what doesn’t. It’s a lot of different frameworks kind of layered on top of each other for various reasons. For the practical ad side, what we do for our clients strategically, there's a framework there that we like to use called AIDA. So attention, interest, desire, action.Share on X  Very standard funnel. The idea is that the reason we like using this is because when you break down the customer journey, this process is just logical. It’s been proven time and time again. If you’re going into Marketing or Business 101, this is one of the first things that you’re going to learn about. Like, what is a funnel? A funnel is, you know, the client’s never heard of you before, and you do something to grab their attention.  Now they’ve heard of you, but that’s not enough. You have to have many touch points, and you have to build interest. So, like, once they’ve heard about you, you got to make sure that it’s a good fit and they’re interested in the pain point or the thing that you’re fixing for them in their lives. And then if you hit them up with that interest message often enough, they’ll start imagining how their life is better with your product or service, and that creates desire. And then once you continue hitting them up at desire, as long as you don’t have any friction between the desire piece and the final call to action, then you’ll get that action at the bottom.  And the thing that people talk about, like, there’s a lot of people that talk about this framework, but I don’t think they’ve studied it properly. Because when you actually look at the history of it, this framework was first introduced in 1898 in a magazine that was talking about ads. This framework arguably predates the term marketing. And when you think about how that framework looks in modern day, it’s changed a lot, right?  When you were running ads in the early 1900s, you could get away with saying whatever the heck you wanted and making whatever wild promises just to get people’s attention. The way that you did it was you maybe had, like, some sort of print or some sort of, like, literally talking to people, getting them to show up to a convention or an expo back in the day, and having those interactions with their audience.  Fast-forward over 100 years, and it looks very different how you grab attention today. Paid ads. People talk about SEO. People talk about viral content. It’s very much changed. What hasn’t changed is the framework. So what that tells us is that human behavior, right? So the framework is based on human psychology: attention, interest, desire. These are all things that are, like, within the human experience. That has not changed. What’s changed is the other end. The technology has changed, so how you implement that.  So the strategy is still the AIDA that we use, right? But our tactics are constantly changing. Now we’re looking at AI. The new thing about ads is that Google just announced that they’ve added an extra option next to PMax, which is the AI option, so that you can actually start showing up in Gemini. So now this has changed our tactics. But our strategy is still based on this human psychology. That's the framework over on kind of the front end.Share on X And then, like I said, we have a bunch of other frameworks. I don’t know how much you want me to get into it, but for hiring, for internal processes, for all sorts of things.  Yeah. No, no, no. I love it. I love the AIDA. It’s been a long time I heard about it, and I love the simplicity of it, and I think it still works. So you mentioned something that really interests me. You said that you grab the attention, interest, building the desire, and as long as there’s no friction in the desire, then you’re going to get—the call to action is going to work. So what do you mean friction, and give me some examples of what it could look like.  So when you’re talking about AIDA and you’re talking about funnels, especially when you’re talking about online, it’s all very, very quick, right? So there’s a couple of terms here. So when you’re at top of funnel, your main thing is—it’s called a funnel because at the top it’s very, very wide in terms of the audience you want to hit up, and you want to try to get out to as many people as possible. So a lot of people, when they’re thinking of marketing, they’re thinking of the top of funnel, whether they realize it or not.  Further down is you’re trying to get a more complex message across. So the practical way to think about it is at the top you’re trying to run ads that’s just very flashy and getting people to stop the scroll. So if they’re on Instagram or something, you literally want them to stop, and you want them to read a quick thing that gets them to click. Once they’ve clicked on it, they’re ending up on a landing page, and that landing page has information that hopefully connects to the audience in a meaningful way, and they’re interested in reading more.  An example of friction would be if you click on an ad that’s purple, and then you end up on a landing page that’s black and white or red. That difference in color, that difference in style creates a friction point that psychologically, it makes the person pause and go, “Wait, did I just end up on the wrong page?” So when you have, like, a very close alignment between each of those levels, it reduces the friction and increases the chances that people will go down.  So the most important friction point that people need to be aware of is, like I just talked about the top of funnel, the opposite end, the bottom of funnel, is where there's the highest intent.Share on X So you can imagine at the top of funnel, because people have never heard of you before, their intention to buy from you is probably very low or very limited because they don’t know you yet. But once you’ve done all that work to get to know them and create a relationship with the brand, you’re at the bottom of funnel, and that point is critical.  You need to make sure that by the time you’ve warmed them up and everything’s ready to go and the target person is very interested in taking that final step, that somebody else doesn’t come and grab them. For example, like, the very real-world example. If you think about some of the biggest marketing companies in the world, so let’s take McDonald’s and Burger King as examples, right? Because everybody knows those brands. If McDonald’s has taken so much effort to be on billboards and be in front of you, and you’ve seen the brand and you know it, and you’re walking down the street and you’re hungry, and then at that moment of intent, as you’re walking around downtown, you’re like, “I could go for a quick burger right now,” and you know that you’re looking for a McDonald’s, but as you’re walking, you come across a Burger King, you’re like, “Yeah, that’s close enough.”  And then you’re going to walk into the Burger King and do the transaction there. Online, what that looks like is when somebody is doing that final search. So let’s say Summit OS is selling T-shirts, and you’ve done a bunch of work and people love the brand and they’re looking for a specific type of T-shirt that you sell, but they can’t remember quite exactly what it is. They’re like, “Oh, I think it was Summit OS something T-shirt.” And then when they type that in, somebody else’s sponsored ad comes up.  Well, now they probably grabbed that traffic that you took all that time to warm up. If you end up on the landing page, when you scroll down to the bottom, okay, I want to buy this T-shirt. I’m happy with everything that I’ve seen, but now you’re asking for an email, you’re asking for, like, a bunch of things. You click on a button, it doesn’t work. Those are extra friction points that prevent a person from taking action. When you’re looking at the entire funnel, you got to make sure that from a technical standpoint, from a user experience standpoint, from a getting-in-front-of-them-in-the-right-moment standpoint, everything hits, especially in this digital economy where everything’s very, very fast.  So anything that goes wrong, anything that creates a question, anything that creates a frustration point or any opportunity for somebody else to come in and grab that traffic at that moment of intent, those are friction points that you want to address and you want to track and you want to find. And you can imagine how there’s a lot of complexity in that. There’s a lot of testing, there’s a lot of work to make sure that that doesn’t happen.  That’s fascinating. Do you have a process for how you do this, how to eliminate frictions?  Yeah. We have a team that does QA and walks through the entire thing and has to do constant testing because what we’ve seen is a lot of other agencies are using AI tools and shortcuts. And lo and behold, a lot of these tools don’t work and they give back false positives, false negatives, and it’s not as good as just taking the time to go through the funnel yourself and make sure everything’s working properly. And it sucks because it’s a lot of work, but that’s how you get the best results, is just by having someone double-check it.  And it’s not to say that we don’t use automation. Like, we use a ton of automation, we use a lot of tools. But the final QA is a lot of the time us and the client going through, like, “Okay, everybody, before we hit go, let’s just all go through this before we spend, like, you know, 10K on this and just make sure that everything’s working properly.”  Yeah. Love it. Love it. So you’re building this ad agency, a technical agency, so you focus on making the flawless experience. What drives growth in your business?  That’s a good question. That’s something that, honestly, we’re struggling with a little bit. So if anybody’s following me on social media, I’ve been doing a short series. I’m probably going to finish it off soon, but it’s called Watch Sheldon Struggle to Grow His Agency. And it’s literally how I’ve been trying to figure out how to crack lead generation for ourselves and our journey through, like, where we were maybe about a year ago to where we are today.  And we’re still trying to answer that question for ourselves in terms of, how do we get more leads and more clients in a very difficult economy, in a very difficult time? And we’re trying to figure that out. And people can look up our case studies. I think we’re quite proud of the work that we’ve been able to do, and having that technical know-how has given us a huge advantage in terms of performance numbers. And that alone, in the past, through my network, was enough to keep us busy because people would just seek me out whenever they had a problem with their technical stack or their ads.  I’d say in the last maybe 18 months or so, like a lot of other agencies, the word-of-mouth stuff has kind of dried up, where people are kind of, like, holding back their spend. We’ve had a bunch of contracts that were put on hold because of the economic situation around the world. Nobody knows what’s going on. Everybody wants to hold back spend. Everybody wants to sit and wait. So even though people are still contacting me, the flow of projects has slowed down because people are more hesitant to pull the trigger and go. It’s also taking longer for us to have these conversations. Because that organic piece kind of slowed down, we’ve been trying to find inorganic pieces.  I’ve been doing more networking. I’ve been trying to figure out building out our own funnel, which we should’ve done years ago. The thing that’s driving growth for us right now is probably the things that are not scalable, right? In the age of AI, when you’re working with a business, you want to be working with a person. Like, nobody wants a partner who’s just a machine. So doing the things that are not scalable means really doing the things that are human, having human interactions, having human conversations, going out for coffee with people, meeting people at conventions and workshops and, you know, like, literally just getting our name out there in a way that is more human and just having real conversations.  And despite all the other testing that we’ve done with the cold emails, with the ads, with this, that, and the other thing, like, that seems to be the thing that’s working the most, is just kind of putting my name out there and saying to people, like, “Hey, if you have a problem, come talk to me. We’re not necessarily going to have a contract together, but hopefully we can have a quick coffee and I can at least get you past a technical hurdle.  I can give you some insight into what you’re struggling with and give you a hand.” And that kind of goodwill has led to more conversations and more contracts and new contracts than anything else that we’ve done. For our clients, because our clients are more direct-to-consumer, so they’re usually bigger brands that are selling a quick product, quick service to the general public. They’re not B2B, they’re B2C. On that front, it’s literally what we just talked about, following the AIDA playbook, making sure the funnel’s good, making sure the ads are firing, making sure the data’s there, and then that’s how we get our crazy numbers, like 12X return on ad spend, you know, 14X return on ad spend.  That’s just following the system and doing the work. So for our clients, we have it under control for their growth. For our own growth, for the B2B side, that’s where I’m realizing that, like, we need to step away from all this AI noise, and people are trying to do these AI BDRs and all these things, and it just doesn’t work because people want to have a real human conversation.  Yeah. No, I totally see the same thing. The easier the automation, the more it just becomes noise.  Exactly. It’s democratized automation, which means that, like, it’s no longer the thing that stands out. You have to do something different.  Yeah. And people are becoming resistant, and they recognize if something is AI-created, and they just tune it out, right?  Yeah, exactly. Exactly.  Yeah. It’s fascinating. How much of your time is actually spent on networking and doing the human things as opposed to running your business?  I’m very fortunate that my team mostly runs the day-to-day at this point, so I’m spending most of my time doing all the other stuff. So, like, trying to do the networking, trying to get our name out there, and then everything else is just admin and paperwork. So in terms of actually running the day-to-day of the company, our head of operations, Brent, does a fantastic job of running the team.  Jack, our head of marketing strategy, does an amazing job of making sure the ads are firing properly. Whenever I get involved in the day-to-day, they get annoyed at me because I’m like, I’m coming in, I don’t know what’s going on, and my hair’s on fire. I’m trying to do X, Y, Z, and then I jump out and I just create chaos. So most of the time they’re telling me, like, “Yeah, listen, go out and talk to people. We don’t want you at the office. We don’t want you doing the work.”  So how fast is your product evolving?  Oh, constantly. I mean, the way that I explain it to people, right? A lot of people right now, especially running ads, right? Because that’s our core product, that’s our core service, is running ads and making sure that we maintain that competitive advantage for our clients. Because of the advent of AI, everything’s constantly changing. Already it was a very technical space because a lot of people seem to think that whether or not your ads do well, they think it’s a creative exercise, and I think it was up until about eight months ago.  But today, I would argue that it’s skewed, and it’s more of a technical exercise. Because these ad platforms are running AI algorithms, the quality of the data that you feed into it now is more important than ever. And like I said, there’s been a lot of changes in the last few months to the point where now it’s no longer about how good your content is. It’s about how good your targeting is, how good the data you’re feeding it is, how good your audience is kind of reacting to it, and then making intelligent tweaks to your content based on the feedback that you’re getting back from the AI.  So these are things that are constantly evolving. Like I said, even though the strategy doesn’t change, the tactics and the technology is a constant change. So the AI thing that I was talking about where Google just released or just opened up the option to do advertising with AI responses, that’s less than, I think, three months old. So that’s the new thing that we’re now testing with our clients to see how is this working, how is this working.  The other thing too is that the way I describe it, a lot of people now—the new thing that we just noticed within the last few months—is that people are saying, like, “Oh, I can just use an AI agent to set up my ads.” But these AI agents are based on large language models, right, the LLMs. And the way that large language models work is that they’re trying to give you a statistically accurate response, which is great for certain things, not so great for other things. So the way I tell people is that if you were to take an LLM, again, which is what most of these agents are based on, you were to graph all the possible responses for accuracy, what you would end up with is a bell curve, right?  And on the bell curve, you have on one end, you have these are all the answers that are definitely wrong. And on the other hand, these are also answers that are, like, they’re novel, but we don’t know how accurate they are. And then in the middle, at the top of the bell curve, right in the middle is the statistical most accurate response that it thinks it can give you. So it’s always going for that statistical middle with these LLMs. So when it’s setting up ads and it’s doing strategy, it’s going for what is most likely the safe result, right?  The least interesting.  It’s the least interesting.  Yeah.  Now, when you’re running ads, right, it’s a race. My background is in software and computer science, so when I’m doing coding and I’m using Claude Code, I want statistical middle. I don’t want it trying new things with my code. I don’t want it to be a bad programmer like a junior, but I don’t want it to be doing stuff that’s so advanced and novel that it’s not tested, it’s going to break. I just want the middle, right?  Engineering middle. But when we’re running ads, it’s a race. And if you’re in the middle of the pack in a race, you’ve lost. So one of our big advantages is that we’re using a lot of AI for automation. We’re using a lot of automation tools to do our day-to-day. But when we’re coming up with a strategy and building out the ads, it’s a human that’s doing that because we want to be at this far end that’s going to be ahead of everybody else. We don’t want to be middle of the pack.  So from that sense, it’s constantly changing to keep on top of these things to figure out what’s new, what’s going on. The ad platforms are changing constantly because, you know, the way that we interact with the internet is changing constantly. So, like, yeah, it’s constant, it’s a constant race.  So, but doesn’t that also bring you back to the creative part?  It does.  How to be creative, maybe not design-wise, but some other way?  It does, but in a way that I think a lot of creative agencies don’t understand. Because a creative agency will focus on the story, and they will focus on, you know, telling a good story, and they are thinking, if I can put it this way, they’re thinking very linearly. So one of the big mistakes that we see, like I talk to a lot of other agency owners, and every once in a while I help consult on the side, just to give them some advice in what they’re doing, what’s working and what’s not.  And one of the things that I see a lot is that a lot of these agencies have not understood how the new PMax campaigns and how the new Advantage+ campaigns work, so they’re working against the AI algorithm. So a lot of these other agencies, they’re more content than they are technical. So what they’re doing is they’re trying to tell a good story from the content, and then what they’re trying to do is they’re trying to find the demographic that they think will match with the content. So they’re content first, right?  So what they’re trying to do is, if they’re putting together a story for a product, they then in their heads think, “Okay. Well, I think I need to go for, you know, women living in suburbs that are between this age and this age and this income range and are interested in XYZ thing, and that’s how I’m going to set up the system, and then they’re going to see my ad, and then there should be a connection there.” That’s not how it works anymore. So in that setup, I can see how it’s super important to understand the story and get all that done.  The modern way that we’re doing things that’s different is that when we’re building out our content, we work with the content agency or we work with the marketing team to say, “What is your interest piece that’s going to get people to stop scrolling? What’s the interest piece that’s going to get people to want to read more? How do we build that desire for them? And then give me those in different assets, different pictures, different videos, different texts, different descriptions, different headers.”  What we then do is we take all of that content, and we put it into an asset library on PMax. So what we do is instead of telling PMax to look for XYZ demographic, we don’t tell it to do any of that. We let it go broad, and it does the opposite. What it does is, and of course, this is after the caveat. We’ve already set up the landing pages. We’re collecting all the data. We’ve tested all that. The data modeling is solid, so the entire funnel, all the micro actions, everything, we know that is being fed cleanly into the system, because that’s important. What the system’s then going to do is it’s going to, like, not just A/B test, but it’s going to take every combination that it can think of with all the different assets, with the videos, the text, the this and that and the other thing, and it’s going to start testing all the different combinations quickly.  And mixed in there is all the interest, attention, desire, everything. The algorithm is then going to figure out, based on all the data points that it has on us, it has, like, 57,000 data points, I think, Meta has on us. When a user has this pattern of data, they seem to resonate with this combination of assets. This pattern resonates with this combination. So bit by bit, what it does is it will start creating audiences for you that resonate with different kind of, like, asset packages. And then lo and behold, what we see is that emergent is the attention content all gets grouped together, and now we have an audience that’s ready for attention.  The interest piece, this is all the content that we had flagged for interest. Here’s our interest audience. And then the AI is basically working to figure out for you, not based on anything other than random data points and patterns, what is the audience for you? And then it’ll be able to then say, “Okay, now that we have a pattern to look for, we have, like, another 100,000 people that match this pattern and we can start advertising to them. And that’s the proper way to do it. But the thing that people are uncomfortable with is that it’s a black box. We don’t know what their demographic is. We don’t know where they live on the internet. We don’t know what their interests are. We don’t know any of that. It’s just random data points and math that has created this audience for us. And if you were to extract those people and put two or three of them next to each other, you’d go, like, “I don’t understand why these people have anything in common.”  But somewhere in the data, the data is signaling that they are going to resonate with your content. So this is what I mean. Like, the content is still important, but I think it’s skewed, like, a little bit. I’d say, like, now it’s, like, 60% data and math and 40% content, whereas before, it was much more heavy on the content side.  Very interesting. And this is how your platforms will find the lookalike audience based on your sample audience?  Exactly. Exactly.  Yeah. That’s fascinating. So other than the lead generation, what’s one thing that you’re actively trying to figure out in your business?  Other than that, we’re just having a lot of fun with all these new AI tools. I’m getting a few members of my team working with Claude. A few of us are working with ChatGPT. We’re trying to see, like, what these platforms can do, what are the things it can automate. I think one of the really cool things is now something that used to take, like, weeks to create code around, it can be done in an afternoon.  So it’s like, as soon as we identify a business problem, something that my team has to do, like, you know, three or four times a week that slows us down, now I can tell my devs, like, “Hey, guys, like, this is a business problem that we have. Can you guys just come up with a quick tool to do?” And we can build it, and we can just have it. So we’re trying to experiment more and more with how we can kind of speed up our development process.  And because we’ve been developing for so long, there’s a few things that are kind of like the—a few of the standard ways to run a development team that are now starting to be questioned because these LLMs are getting better and better at taking over some of these mundane tasks, that we’re starting to revisit what is our QA strategy when we’re actually coding. Like, what’s a more important skill set? One of the things I think that we’re trying to navigate right now is what is our hiring process for interns going forward?  So in the past, when we were looking for juniors and interns, the skills that we were looking for was like, what is your ability to figure out the answer to this question, right? Given a complex question, given a complex task, how are you going to be able to figure out how to solve this? Because of the rise of LLMs, finding the answer is no longer as important as asking the smart question. And the challenge that we’re facing right now is that asking a smart question requires critical thinking. A lot of critical thinking requires experience. The best critical thinkers have decades of experience behind them. How do you get that from an 18-year-old who’s coming from school?  They literally have no experience. That’s why they’re coming to us as interns. So we’re trying to figure out, like, okay, how do we square that? And we’re very proud of the internship work that we do. It’s a way for us to give back to the community. We work with a lot of the colleges and universities in town. Like I said, I myself came from having taught high school, and I’m very passionate about education, so I want to do good by that industry, by those students.  But we’re having a bit of a hard time because we don’t want to necessarily disadvantage them, but we’re also trying to figure out how do you find a good critical thinker among kids with no experience. So that’s probably the thing that we’re trying to figure out right now.  Yeah. That is fascinating. And that is a big issue for young people coming up, how to get those internships. Internships are harder to get, and probably this is the main reason for it, because people use technology to replace the inexperienced people.  The bar has definitely gone much higher. And again, we’ve experienced this as well, where, like, we’ll have a task, and we can ask an intern to do it, and they’ll kind of, you know, grudgingly figure it out and do it. But if it’s a critical task that needs to be done, that same exact task can be handed off to an LLM, and it’ll just get it done. Yeah. It won’t be amazing quality the way that, you know, a senior developer will do it, but it’ll be good enough, and it’ll be probably better than what the intern can do.  So if you’re only looking at the task from a purely productive lens, well, I’ll get a better result from the LLM, so then the intern has no purpose. We’re having to reshape the way we think about this, where if we are truly building an internship program, at that point, it’s not about the productivity, it’s about the learning that the intern gets out of it. So it becomes a teaching thing. So we have to be okay with handing off this task to the intern, knowing that an LLM could probably do a better job.  But they need to learn how to do it first so that they can rise above it. And I think what we’re seeing right now is that companies are not mandated to teach. They don’t care about the learning of the intern. They care about the production of the thing. And in the past, it was cheaper to get the intern to do it because there was no other option. But I think companies are realizing, like, well, we can just have the LLM do it, and it’ll be better and faster. Why do we need the interns? And that’s why these opening early positions are not being handed out anymore. The problem with that is that you don’t get senior-level people without training them up as juniors, and we’re giving all of that learning opportunity to machines.  Yeah.  So what does our future look like with that gap? What does the future look like for these kids coming out of school? Like, these are all obviously much bigger questions than my company can ever answer. This is a bigger societal question, but this is what we’re seeing kind of on the front lines, having a very strong internship program. We’re struggling with this idea, like, where is this next generation coming from?  That’s scary. That’s very scary. So who are the ideal clients that, if they listen to this podcast, you want them to contact you? Yeah. So basically, our biggest successes are with brands that are very strong on the wholesale side and are already in retail stores. So a lot of times what happens is you have a company that does an amazing product. They’re already in Walmart, they’re already in Costco, they’re already at Winners, and they have built up a very successful wholesale B2B business. They then launch a Shopify store, and they want to start getting the brands and start building a relationship directly with their consumers.  And then they’re realizing, like, “Oh, this is a different game, and we’re struggling.” And then at that point, it’s easy for us to come in and help because they have a strong offering already. They have a wholesale side that can drive their growth. And our job is to make sure that we start help building out that direct-to-consumer side by making sure that all of the data is mapped out properly. We like working with companies like that because they already have a very good idea of their marketing, why their product is successful.  They know which products are successful and not, so we can skip a lot of the learning part, and we’re just putting a megaphone on their existing brand and their existing offering in the most efficient way possible to make sure that, you know, right away out of the gate, that their D2C business is going to be just as strong as their wholesale business.  Yeah. Oh, that’s fascinating. So essentially, you pick those companies that have a good product already because it’s tested in wholesale channels and the Costcos and Walmarts and the Amazons of the world, and you help them tap directly the consumers through very targeted advertising.  Exactly, exactly. And then once we have that basic foundation set up, then we start doing interesting things like, how do we get the newsletters out? How do we help them get their marketing team to have more of a direct relationship with their end customer? Because when you go to a Costco or you go to a Walmart and you pick up a product, what happens is that the retailer holds the relationship, right?  It’s a way to help them offset that. And it’s kind of a tightrope that we have to walk because we recognize that the relationship with the wholesaler is still key. So whatever we do, we have to be conscious that we’re not going to upset that balance.  Yeah.  And that everybody’s winning, everybody’s making money, everybody’s growing, nobody’s getting upset with one side or the other. So because we’ve had experience working with national brands, we are positioned to understand what that relationship looks like. We know from our client side what it’s like when they’re talking to the Costco rep, what it’s like when they’re talking to the Walmart rep, what those subtle differences are in culture, and what we need to be aware of because, you know, we want to make sure that nothing that we do on our side jeopardizes that relationship.  Yeah. And then the big companies—what they want is they want to commoditize you, and they want to control the relationship, the brand. They want their white-label brand on top of your product. Yeah.  Which is their right. I mean, they’re the retailers. They’re the big guys.  Yeah. If you don’t build your own brand, then you’re going to slip into the white label because you have no leverage over the big, big stores. But if you have a strong brand, you have leverage, and then the whole relationship works better. So it’s very interesting. Okay.  So if people would like to learn about how they can do that, how they can go from wholesale to D2C, and would like to learn about how you can help them, where should they go and how can they contact you?  Yeah, so there’s a lot of information on our website, so drivemarketing.ca, so the Canadian extension. Yeah. And then I think I’m the only Sheldon Poon that’s really active on LinkedIn. I think there’s one other one that’s maybe in Hong Kong somewhere, but that’s not me. He’s, I think, an older guy. So if people look me up on LinkedIn under Sheldon Poon and just message me, I’m pretty active there and I try to answer everybody.  And what I love is helping people out. Even if people have no intention of hiring us, I’m like, “That’s totally fine.” Send me your questions because I like to be able to help and answer and know what people are struggling with because it helps our work. It just gives me an opportunity to have conversations with people.  Awesome. So if you run a brand that has already cracked the wholesale channels, but you want to build your direct-to-consumer, you know, hit up Sheldon on LinkedIn, Sheldon Poon, and have a chat with him. As you can see, he’s a great person to chat with. And if you enjoyed this show, then stay tuned because every week we have a couple of entrepreneurs who are building great companies who come and share their frameworks with you. So Sheldon, thanks for coming and sharing your wisdom, and thanks for listening.  Yeah. Thank you. This was great. It was my pleasure. Thank you so much for having me on.  Important Links: Sheldon's LinkedIn Sheldon's  website

In this Kitchen Side episode, Alex Birkett and Allie Decker unpack what actually makes content valuable in the age of AI, moving past the tired debate over human versus AI-produced content to the deeper question of how companies should measure quality at all. Drawing on decision theory and the idea that almost anything can be measured, they argue that production method matters far less than whether a piece teaches something new and lands with the people it is meant for. They explore AI visibility as a kind of digital brand recall, why sentiment and context matter more than raw share of voice, and how incumbents and startups face very different battles in shaping their narrative across the web. The conversation also digs into why brands no longer own their own story, the compounding risk of building exposure in the wrong category, and why a human QA layer grounded in real customer understanding is the one part of the content system that should never be automated. Key Takeaways The debate over AI-produced versus human-produced content misses the deeper question of how to define and measure content quality in the first place. Almost anything can be measured if a decision carries uncertainty and risk, including fuzzy concepts like brand affinity or customer experience, by breaking them into concrete, quantifiable components. Measurement is only worth doing when the cost of collecting it is lower than the value of reducing uncertainty, and when you will actually act on the result. AI visibility works like a digital brand recall survey, but raw visibility is meaningless without understanding the context and sentiment in which a brand is mentioned. Segmenting prompts by ICP and buying stage reveals visibility gaps that aggregate scores hide, such as a brand appearing at 40 percent overall but only 4 percent for enterprise queries. Brands no longer own their own narrative, because if the rest of the web contradicts a company's website, LLMs treat the external consensus as the truth. Incumbents with high domain authority have an SEO advantage but face a harder AEO battle, since years of press, mentions, and third-party content are slow and expensive to re-steer. Startups can win by getting specific for narrow prompts and buying-journey stages, but building heavy exposure around one positioning creates risk if they later pivot. AI content programs fail because of weak systems, not the AI itself, so the durable model is human strategy up front, AI in the middle, and a human QA layer at the end. Customer understanding is the last defensible advantage, and the one part of the content system that should stay human is the empathy to judge whether a piece will actually land. Show Links Connect with Alex Birkett on LinkedIn and Twitter Connect with Allie Decker on LinkedIn and Twitter Connect with Omniscient Digital on LinkedIn or Twitter What is Kitchen Side? One big benefit of running an agency or working at one is you get to see the "kitchen side" of many different businesses; their revenue, their operations, their automations, and their culture. You understand how things look from the inside and how that differs from the outside. You understand how the sausage is made. As an agency ourselves, we're working both on growing our clients' businesses as well as our own. This podcast is one project, but we also blog, make videos, do sales, and have quite a robust portfolio of automations and hacks to run our business. We want to take you behind the curtain, to the kitchen side of our business, to witness our brainstorms, discussions, and internal dialogues behind the public works that we ship. Past guests on The Long Game podcast include: Morgan Brown (Shopify), Ryan Law (Animalz), Dan Shure (Evolving SEO), Kaleigh Moore (freelancer), Eric Siu (Clickflow), Peep Laja (CXL), Chelsea Castle (Chili Piper), Tracey Wallace (Klaviyo), Tim Soulo (Ahrefs), Ryan McReady (Reforge), and many more. Some interviews you might enjoy and learn from: Actionable Tips and Secrets to SEO Strategy with Dan Shure (Evolving SEO) Building Competitive Marketing Content with Sam Chapman (Aprimo) How to Build the Right Data Workflow with Blake Burch (Shipyard) Data-Driven Thought Leadership with Alicia Johnston (Sprout Social) Purpose-Driven Leadership & Building a Content Team with Ty Magnin (UiPath) Also, check out our Kitchen Side series where we take you behind the scenes to see how the sausage is made at our agency: Blue Ocean vs Red Ocean SEO Should You Hire Writers or Subject Matter Experts? How Do Growth and Content Overlap? Connect with Omniscient Digital on social: Twitter: @beomniscient LinkedIn: Be Omniscient Listen to more episodes of The Long Game podcast here: https://beomniscient.com/podcast/

« Plus de trois ans après son arrestation, le chroniqueur et activiste Mohamed Youssouf Bathily, alias Ras Bath, a enfin vu son procès s'ouvrir lundi à Bamako, relève Afrik.com. À la barre, amaigri mais toujours combatif, il a rejeté les accusations qui pèsent sur lui, notamment "association de malfaiteurs", "atteinte au crédit de l'État, de ses institutions et de ses gouvernants par le biais des technologies de l'information et de la communication", ainsi que des infractions à caractère racial, régionaliste ou religieux. » Hier, pointe encore Afrik.com, « le ministère public a requis dix ans de prison ferme et 240 000 francs CFA d'amende contre Ras Bath. À l'encontre de sa coaccusée Rokia Doumbia, dite Rose Poivron, militante connue pour ses prises de position contre la vie chère, le parquet a demandé dix ans de prison, dont cinq avec sursis, ainsi qu'une amende de 240 000 francs CFA. La défense doit désormais présenter ses plaidoiries. » À lire aussiMali: à l'ouverture de son procès, l'animateur radio Ras Bath rejette les accusations d'atteinte aux institutions Important dispositif de sécurité Pas de commentaire dans la presse malienne qui se contente de rapporter les réquisitions du ministère public. Le site Maliweb indique toutefois que le procès se déroule « sous haute surveillance. Un important dispositif de sécurité a été déployé autour de la cour d'appel de Bamako, dont l'accès a été strictement filtré. (…) Même le stationnement des motos aux abords de la Cour a fait l'objet de restrictions. Cette mesure intervient, précise Maliweb, après des mouvements de foule et des tensions enregistrés autour du tribunal lundi, au premier jour du procès. Malgré les restrictions, plusieurs partisans de Ras Bath et de Rokiatou Doumbia étaient en effet présents aux abords de la Cour. » « Debout après trois ans de prison » Le site Sahel Horizon, site d'opposition à la junte réalisé par des auteurs en exil, le site Sahel Horizon dénonce un simulacre de procès et met en avant le courage de Ras Bath : « À la barre, après trois ans de détention, il a prononcé cette phrase : "Je ne soutiendrai jamais des militaires qui ont pris les armes contre les institutions de la République ; je suis un anti-putsch et c'est clair." Il faut mesurer ce que cette phrase renverse, s'exclame Sahel Horizon. Elle n'est pas une provocation de prétoire, elle est une profession de foi constitutionnelle. (…) Cet homme a été poursuivi hier sous un pouvoir civil qu'il combattait ; il est jugé aujourd'hui sous un pouvoir militaire qu'il refuse. Il n'a pas changé de position quand la rue a changé d'humeur, et il n'est pas devenu le chantre de l'ordre nouveau quand tant d'autres, avocats hier de la démocratie, sont allés chercher un fauteuil dans les antichambres. C'est précisément cette cohérence qui gêne, pointe encore Sahel Horizon, bien plus que les mots eux-mêmes. Un opposant que l'on peut acheter n'est pas dangereux, il est utile. Un opposant qui reste debout après trois ans de prison est un problème politique. » Fiction et réalité Enfin, toujours à propos du Mali, cette charge à lire sur le site guinéen Ledjely contre les autorités militaires, intitulée « Le bilan et le mirage sécuritaire » : « Au Mali, il y a d'une part la fiction que les autorités ont tendance à servir. Et de l'autre, l'implacable réalité que le pays et les populations endurent au quotidien depuis cinq longues années, affirme Ledjely. La fiction s'incarne notamment dans le discours que le Premier ministre Abdoulaye Maïga a prononcé jeudi dernier, pour dresser un bilan forcément élogieux des cinq années de transition que le pays vient de vivre. La réalité, elle, s'est manifestée trois jours plus tard, à travers l'attaque dont le camp de San, au centre du pays, a été la cible de la part des djihadistes du Jnim, affilié à al-Qaïda — une attaque qui a coûté la vie à 12 militaires maliens et permis aux assaillants d'emporter de nombreux équipements. » Et Ledjely de poursuivre avec ce commentaire : « Quand les responsables, en totale déconnexion avec la réalité, pavoisent au sommet, les populations, elles, triment et trinquent à la base. Si l'on veut dresser un bilan, il est précisément dans ce décalage, dans cette distance infinie qui sépare des autorités qui carburent au souverainisme pompeux et des Maliens d'en bas, tenaillés par un quotidien fait de larmes, de sang et de sueur. » À lire aussiMali: les jihadistes du Jnim attaquent un camp militaire de l'armée dans le centre du pays

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In this episode of the Programmatic Digest, Hélène Parker sits down with Dr. Augustine Fou, founder of Fou Analytics, to unpack the realities of ad fraud, attribution, AI, and programmatic optimization. Dr. Fou challenges some of the metrics and tools our industry has relied on for years, explaining how bots can manipulate signals like CTR and how algorithms can unintentionally optimize toward fraudulent inventory. The conversation also explores the limitations of traditional fraud detection, the importance of independent verification, and how traders can use more granular data, including placements and Seller IDs, to better understand where their budgets are actually going. At the heart of the conversation is a simple question: Are we optimizing for metrics, or are we optimizing for real business outcomes? Key Takeaways Question vanity metrics: High CTR and other engagement metrics don't always mean better campaign performance. AI is only as good as its signals: If algorithms optimize toward manipulated metrics, they can unintentionally send more budget toward fraudulent inventory. Look beyond standard fraud reports: Fraud percentages only reflect what verification technology is able to detect. Audit your placements: Regularly review where your ads are running and whether those environments make sense for your audience. Pay attention to Seller IDs: Seller-level data can provide another layer of transparency when evaluating programmatic supply. Consider independent verification: Post-bid data can help validate what actually happened after an impression was served. Quality matters more than volume: Fewer, higher-quality impressions reaching real humans can be more valuable than maximizing cheap reach. Keep the human in the loop: Automation can make traders more efficient, but critical thinking, QA, and active optimization remain essential. Optimize toward business outcomes: Ultimately, campaign success should connect back to real results, not just what looks good in a DSP report.   About Us: We help historically excluded individuals break into programmatic media buying and land jobs they love. Through our Reach and Frequency® program, coaching, and community, we make learning programmatic clear, practical, and welcoming. Join once, stay learning forever. Past workshops, paid gems, and every new recording + resource added as we go. This library isn't a one-time thing, it's the Foull vault plus all Fouture trainings, recordings, and guides. Access it here: https://www.heleneparker.com/library

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Most vendors at Black Hat USA 2026 have something to say about agentic AI. Jeremy Powell, CISO at Sumo Logic, spends this conversation on the harder proof, which is what happens when a company runs its own product in production at scale. Sumo Logic has been doing that for roughly ten to eleven months. Powell calls it customer zero, and it shapes how he answers almost every question here. The Sumo Logic SecOps team ingests seven exabytes a day globally, which Powell puts at roughly half a billion 8K movies. Against that volume, the team reports 100 percent first level triage handled through automation and about 25 hours saved per analyst per week. Everything learned in production feeds back into the product organization in real time. Security tooling is notoriously hard to use, especially in the enterprise, and Powell is candid that the realization drove a concerted engineering and product effort to fix it. One result showed up at Black Hat this week in the evolved version of Mobot, the conversational interface inside the product. Users can now prompt their way into an investigation, see the audit trail behind it, and get to an answer without configuring their way there first. So how do you trust a decision an agent made? Powell points to an audit trail and a log trail behind every decision the SOC Analyst Agent produces, traceable back through every conceivable log to the root decision. He describes it as human on the loop rather than in the loop. People keep the decisions. Execution and delivery get automated. That changes the shape of the job. Powell describes the SOC becoming something closer to an agile QA organization, where analysts assess the fidelity of what the agent did instead of grinding through first level alerts. On the question of whether automation costs analysts their jobs, he uses a phrase he borrowed from someone else: pay attention to the tension. His answer is that the work gets better and more interesting and the analysts get more capable. The same logic carries up to the board. Powell argues a security leader's job at the executive level is to measure risk transparently and report it accurately, and that boards will build a trend line out of three data points. Telemetry becomes the raw material for informed risk decisions communicated in an executive-friendly way, with the full reasoning available on request. As he puts it, the auditor cares, and the board cares if you fail the audit. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Jeremy Powell, CISO, Sumo Logic LinkedIn: https://www.linkedin.com/in/executivembajeremypowell/ RESOURCES Sumo Logic: https://www.sumologic.com/ Sumo Logic at Black Hat USA: https://www.sumologic.com/events/black-hat Dojo AI agentic security and cloud operations: https://www.sumologic.com/blog/dojo-ai-agentic-security-cloud-operations Building an AI-first SOC, the customer zero story: https://www.sumologic.com/blog/building-ai-first-soc-customer-zero See Mobot in action: https://youtu.be/ZZLXaft7tYM View all of our Black Hat USA 2026 coverage: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS Jeremy Powell, Sumo Logic, Sean Martin, brand story, brand marketing, marketing podcast, brand spotlight, Black Hat USA 2026, agentic AI, SOC analyst agent, security operations center, human on the loop, first level triage, security automation, telemetry, exabyte scale, customer zero, Mobot, conversational interface, CISO, board reporting, risk communication, SIEM, AI governance Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailWe catch up as the full crew reunites, starting with summer ramp misery and the real-world limits heat puts on flying decisions. Then we dig into the Synaptic A220 release, why it sparked so much drama, and what it teaches about aircraft nuance, developer QA, and how airlines and engines shape what we see in the sky. • summer heat, humidity, and density altitude affecting real flying motivation and safety margins • first impressions of the Synaptic Airbus A220 in Microsoft Flight Simulator, including performance and handling feel • why streamer-first rollouts can confuse users when a complex aircraft is both new and buggy • learning the A220's procedures, automation quirks, and ILS behavior instead of relying on habits from Boeing or Airbus staples • why stable releases come from strong QA programs and broad beta testing across hardware and add-ons • marketplace-first strategy, piracy concerns, patch cadence, and how perception fuels Discord meltdowns • real-world A220 route presence, fleet replacement questions, and the “in-between” role versus regional and A320-family aircraft • Pratt & Whitney PW1500G geared turbofan contamination issue and the ripple effects of engine shortages • GSX jetway and ground equipment realism frustrations and what still breaks immersion • Vector 787 alpha notes and why airline economics keep pivoting toward fuel efficiency • Alaska Airlines-style milk runs as a perfect sim use case for scenery and procedures • Black Square Commander 114 impressions and why cockpit variety matters • Leonardo Mad Dog nostalgia, mechanical design advantages, and the quirks that still trip us up Keep a lookout at flighttepexpo.com as well as visit our our website and our socials for updates. if anybody's interested, they can join CTP Aviation on JetcardWebsite: www.closedtrafficpodcast.comFacebook: @ClosedtrafficpodcastFollow us on Patreon: https://patreon.com/closedtraffic

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【可樂旅遊17瘋搶日】 每月限定3天搶限量優惠！ 1號訂房，享9折優惠，最高折千元 7號票券，享7%優惠，最高折300元 17號機票，享1.7%折扣，最高折千元 https://sofm.pse.is/9fgyu2 -- 出國前最怕匯率一直漲?中國信託銀行挺你聰明換匯 設定匯率到價智慧通知，匯率相對低點不錯過 再領優惠券享美金最高減3分等優惠 立即設定：https://sofm.pse.is/9g5f3m 投資外幣如幣別轉換可能產生匯兌損失，應評估涉及自身情況審慎投資。 完整注意事項詳見網站資訊。 -- #高雄 正義站&黃線捷運計劃，平面車位3房全新完工 實品屋預約鑑賞中。正義站通勤南科，未來捷運串連衛武營、Lalaport。正義公園，風景入門廳 。陽明國中自由學區07-7801988 洽澄清路227號 https://sofm.pse.is/9fzb8e －－－－以上為 SoundOn 動態廣告－－－－

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#高雄 正義站&黃線捷運計劃，平面車位3房全新完工 實品屋預約鑑賞中。正義站通勤南科，未來捷運串連衛武營、Lalaport。正義公園，風景入門廳 。陽明國中自由學區07-7801988 洽澄清路227號 https://sofm.pse.is/9fzbak -- 留友看❗️女性保養選對關鍵成分與劑量才是重點❗️

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Your developers just got supercharged by AI coding agents. Your test coverage did not. So how do you keep quality high when product code is shipping faster than any test team can follow? In this episode Andrew Knight, the Automation Panda and Senior Director of Product and Engineering at Cycle Labs, shares how his six person team is running the biggest release quarter in company history using AI coding agents, spec driven development, and Playwright. You will discover: How Playwright turned itself into an AI automation platform with the MCP server, the planner, generator, and healer agents, and the new CLI and skills approach that cuts your token usage way down. Why Andy uses Spec Kit to codify his testing strategy once, in markdown, so quality standards get baked into every single pull request instead of being caught in review. How to decide which AI generated tests are actually worth running when compute time and budget are finite. What AI slop looks like from a manager's seat, and how to build a team culture that catches it before it ships. Why Andy believes AI coding tools are the new compiler and markdown is the new programming language, plus my pushback on what that means for everything testers were trained to care about. What Andy really thinks about token costs, subscription tiers, and what happens when the AI subsidies run out. Plus the one piece of advice he gives to any tester still sitting on the sidelines of the AI shift. Whether you are an automation engineer, a QA lead, or an engineering manager trying to figure out where testing fits in an AI first workflow, this episode gives you a practical playbook you can start using this week.

#高雄 正義站&黃線捷運計劃，平面車位3房全新完工 實品屋預約鑑賞中。正義站通勤南科，未來捷運串連衛武營、Lalaport。正義公園，風景入門廳 。陽明國中自由學區07-7801988 洽澄清路227號 https://sofm.pse.is/9fzbje -- 留友看❗️女性保養選對關鍵成分與劑量才是重點❗️

(裕富數位 | 吉速袋) 急用周轉 為你應援  線上申辦3步驟  資料齊全最快 5 分鐘  專人依序審核，最快當日撥款 前往了解更多: https://sofm.pse.is/9ewlph (警語) 總費用年百分率約16.45%-23.97%。利率15.75%~15.99%，手續費1~2.5千元。詳參裕富官網。實際撥款時間與條件視審核而定。請謹慎理財。 -- 【可樂旅遊17瘋搶日】 每月限定3天搶限量優惠！ 1號訂房，享9折優惠，最高折千元 7號票券，享7%優惠，最高折300元 17號機票，享1.7%折扣，最高折千元 https://sofm.pse.is/9ewlp7 －－－－以上為 SoundOn 動態廣告－－－－

Kako izgleda put od sela kraj Kragujevca i popravljanja polovnih računara, do pozicije senior QA inženjera u Norveškoj – samo zato što si ušao u život otvorenog srca?

AI is everywhere, but are property management companies asking the right questions before implementing it? In this episode of the #DoorGrowShow, Jason Hull sits down with Mo Hussain to discuss why successful AI adoption has far less to do with technology and far more to do with operational clarity. Instead of chasing the latest AI tools, Mo introduces his Measure, Map, Automate framework to identify operational bottlenecks, uncover hidden profit leaks, and build workflows that actually improve business performance.  Together, they explore why clean data is the foundation of automation, how undocumented processes create costly inefficiencies, and why AI should enhance human decision-making rather than replace it.   You'll Learn [00:00] Meet Mo Hussain and the Measure, Map, Automate Framework [03:20] Why Most Companies Ask the Wrong AI Questions [08:10] The Role of Clean Data in AI Success [12:45] Mapping Workflows Before Automating Them [15:30] The Process Myth and Better Operational Systems [21:10] Building Accountability Into AI Workflows [25:15] Designing AI Agents That Actually Perform [27:45] Turning Operational Data Into Business Growth [29:15] Final Advice for Property Management Leaders Quotables "AI value really truly is workflow value." Mo Hussain  "AI depends on trusted operational data." Mo Hussain  "The winners are not gonna be the companies that have the most amount of data, but they're the ones that can convert data into consistent operating actions." Mo Hussain  Resources DoorGrow and Scale Mastermind DoorGrow Academy DoorGrow on YouTube DoorGrowClub DoorGrowLive Transcript Jason Hull (00:00) welcome everybody. I'm Jason Hull, the founder and CEO of DoorGrow, the world's leading and most comprehensive coaching and consulting firm for long-term residential property management entrepreneurs. For over a decade and a half, we have brought innovative strategies and optimization to the property management industry.   At DoorGro, we are on a mission to transform property management business owners and their businesses. We want to transform the industry, eliminate the BS, build awareness, change perception, expand the market, and help the best property management entrepreneurs win. Now let's get into the show. And my guest today is Mo Hussain, and we're going to be talking about how property management companies can stop drowning in data and start turning it into real operational growth. In this episode,   Mo is breaking down the measure, map, and automate framework that he has built and approach an approach to uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio.   right.   is Mo Hussain. Mo, welcome to the show.   Mo Hussein (01:01) Hey Jason, happy to be here.   Jason Hull (01:03) So today we're going to be chatting a little bit about how property management companies can stop drowning in data and start turning it into real operational growth. And Mo's going to break down the measure, map, and automate framework, his approach for uncovering hidden margins, reducing manual oversight, and getting more value out of every door in your portfolio. So cool, measuring is important. We'll get into that. So before we get into that, Mo,   Can you give people a little bit of background on yourself? How did you get into entrepreneurism? How did you get connected to property management? And help everybody understand who Mo is. Yeah.   Mo Hussein (01:42) Yeah.   great question. So I I've been in this industry now for probably coming up on 20 years at this point. I I worked at some of the prop tech and software providers that are prevalent in the space. Namely, I worked at both YARTI App Folio, which are both kind of headquartered in in Santa Barbara. and a little bit over ten years ago, I started a consultancy and accounting CPA practice that specifically focuses on   Jason Hull (01:56) Namely, I worked at both YARDIE and at Folio, which are both kind of headquartered in in Santa Barbara. a little bit over ten years ago, I started a consultancy and accounting TPA practice that specifically focuses   on prop tech and real estate. So we offer consultations with implementations, custom reporting, operationalizing around technology, which is which is now the buzz around kind of AI and automation at this point.   Mo Hussein (02:11) Prop tech and real estate. So we offer consultations with implementations, custom reporting, operationalizing around technology, which is which is now the buzz around kind of AI and automation at this point. and then   we've also built products for the space to help with automations, help with you know accounting compliance and bringing visibility and custom reporting capabilities to operators. So kind of leveraging all the experience.   Jason Hull (02:25) And then we've also built products for the space to help with automations, help with you know, accounting compliance and bringing visibility and custom reporting capabilities to operators. So kind of leveraging all the experience   Mo Hussein (02:40) from working as a consultant and also as an accountant and even working as some of these tech providers now being a actual supplier in the industry.   Jason Hull (02:41) from working as a consultant and also as an accountant and even working as some of these tech providers now being a aqua supplier in the industry. Very cool. Very cool. So you're a little bit nerdy.   Mo Hussein (02:52) A little bit. Data. I love data. Right.   Jason Hull (02:53) Okay, so am I. So am I. All right. So   cool. So let's talk nerdy to me, Mo. All right. So let's let's chat about this. So let's get into it. So t tell us about this. Wha why is this wh how'd you come up with this framework? Why is this important? I love frameworks because frameworks are usually where we take something that we notice a pattern in, there's some complexity involved, and we make it simple. So explain to us.   Mo Hussein (02:59) Yeah.   Jason Hull (03:18) Where does the measure map and automate framework kind of come from?   Mo Hussein (03:22) Right, right. And this is this kind of stems from a conversation you probably have with plenty of your your clients and even prospects when you start engaging, you know, the the the very popular question now of how do we use AI? I want to streamline and automate. And it's a very loaded, it's a very loaded, fairly ambiguous question, right? How do we use AI? We want to implement AI into our operations, right?   Jason Hull (03:23) And this is this kind of stems from a conversation you probably have with plenty of your   You know, the the the the very popular question now, how do we use AI? It's a very loaded, fairly ambiguous question, right? How do we use AI? We want to implement AI more.   Mo Hussein (03:48) when conversely, like you know, operators and property managers should be starting with a different qu set of questions, right? Like how like where are we losing things like NOI, margin, time, control, or even consistency, right? AI really only matters when it connects and automation really only matters when it connects to a to a revenue lever or some type of a cost lever or productivity gain or or risk reduction, right?   Jason Hull (03:50) Conversely, like you know, operators, property managers should be starting with a different set of questions, right? Like how like where are we losing things like NOI, margin, time, control, or even consistency, right? AI really only matters when it connects in automation really only matters when it connects to a to a revenue lever or some type of a cost lever, productivity gain or or risk reduction,   right? Yeah. there's there's a couple   Mo Hussein (04:14) and there's there's a couple of key components   Jason Hull (04:16) key components in even conversations that you've probably even had with with property managers today is that firstly like you know operators today they already have a lot of data. They probably have access to a lot of different data sets across, you know, operations, but it's probably, you know, disconnected and disjointed and different reports and disconnected systems, hidden in spreadsheets and and dashboards that probably don't drive much much action, right? and everybody wants   Mo Hussein (04:17) in even conversations that you've probably even had with with property managers today is that firstly, like, you know, operators today, they already have a lot of data. They probably have access to a lot of different data sets across, you know, operations, but it's probably, you know, disconnected and disjointed and different reports and disconnected systems hidden in spreadsheets and and dashboards that probably don't drive much much action, right? and everybody wants to   Jason Hull (04:43) to automate and execute   Mo Hussein (04:43) automate and execute an   operational kind of workflow. But the hard part is not whether, you know, AI can really do something, but the hard part is whether a company even knows where value is leaking and who owns that action and and whether these workflows are even clear enough to to be able to automate. And that's kind of the premise of this framework is to kind of measure what that pain is, you know, map that workflow, automate that repetitive work and manage   Jason Hull (04:45) an operational kind of workflow. The hard part is not whether you know AI can really do something, but the hard part is whether a a company even knows where value is leaking and who owns that action and and whether these workflows are even clear enough to to be able to automate. And that's kind of the premise of this framework is to kind of measure what that pain is, you know, map that workflow, automate that repetitive work, and   manage ideally performance through some type of closed loop accountability. We just put an actual word to it, right? A framework to it, I'm sure   Mo Hussein (05:07) ideally performance through some type of a closed loop accountability. We just put an actual word to it and a framework to it, but I'm sure very   similarly to the conversations that you're probably having also even with customers.   Jason Hull (05:15) Very similarly to the conversations that you're probably having also with customers.   Yeah, yeah, got it. Yeah. it's interesting because we're now seeing a lot of these tech companies or tech forward companies that are kind of backtracking on AI a little bit. They were giving out basically blank checks to use AI as much as they could. Some were even creating sort of a contest internally, incentivizing like who could use the most tokens.   Mo Hussein (05:29) Mm.   Right.   You're right.   Jason Hull (05:41) Which is a little bit insane   to just give people a blank check as if that always the more tokens you burn, the more productivity is being created, right?   Mo Hussein (05:51) Right, right, right. And we're seeing, yeah, and you know, as we're seeing these newer models that are coming out, whether it's, you know, through Cloud, Anthropic or even these other these other LLMs, the token utilization is becoming more and more expensive, especially with these newer models. And so now the question of just like, hey, how is that utilization actually translating to actual business value? Right. And this was a question that eventually would have been would have been pushed, right?   Jason Hull (05:54) Yeah and you know.   Of just like, hey, how's that utilization actually translating to actual business value? Right.   Yeah. Yeah. Yeah. I love it. Like how to use AI. Yeah. Bad question. A better question is how do we actually make sure we're creating more profit? How do we actually make sure we are lowering costs? Like   And that's the the idea, they think, well, AI must be so much cheaper than people. And what's interesting, I've also seen some reports lately showing the amount of money these different LLMs are losing right now. They're spending a massive amount of money to deliver AI to us at a super cheap price right now. And but they're losing money. Every time we're chatting, they're losing money.   Mo Hussein (06:47) Mm-hmm.   Right.   Right.   Jason Hull (07:01) And that's that's a wild business model. They're obviously hoping to win some sort of AI race. They're hoping to get us maybe in the future. And there's a lot of talk lately as well of people thinking we gotta shift to local models. Like we gotta I gotta run this AI stuff on my own computer and not be giving all my money to anthropic or open AI you know, open AI or whatever. So okay.   Mo Hussein (07:15) Mm-hmm.   Right, right.   Right.   Jason Hull (07:26) Cool. So let's continue on. Me measure, map and automate. Yeah. Yeah.   Mo Hussein (07:29) Yeah. Yeah. And   by the way, going on your point, Jason, it's you know, you you also, you know, creating automation and leveraging these models locally, it there's definitely value in that. But you know, now more than ever, f you know, teams are kind of distributed, right? And so ideally, if you've built automations and leveraging these L LMs and   Jason Hull (07:35) Yeah, y you also you know   Locally it is definitely that   Teams are kind of distributed, right? Yeah. Ideally, if you've built automations and leveraging these LLMs   and   Mo Hussein (07:52) And things of that sort.   You probably want to have like some type of an interface that's like cloud based, right? Or for folks to be able to kind of collaborate in some type of a ideally like a safe environment, right? and so measure, map and and automate. So you know, there's there's kind of those three components to be able to actually fully ideally leverage leverage AI. But   Jason Hull (07:55) some type of a an interface that's like cloud based, right? Or for folks to be able to kind of collaborate in some type of a ideally like a safe environment, right? yeah. So measure, map and and automate. So you know there's there's kind of those three components to be able to actually fully ideally leverage leverage AI but   there's there's a couple like kind of key core components that feel like   Mo Hussein (08:20) There's there's a couple of like kind of key core components that I feel like   is very important for folks to to really understand before they can they they can even take advantage of of AI, right? so one is you know AI, AI value really truly is workflow value. And so like the most the biggest opportunities when it comes to automation leveraging AI is things that are repetitive.   Jason Hull (08:25) is very important for folks to to really understand before they can they they can take advantage of of AI, right? so one is, you know, a AI AI value really truly is a workflow value. And so like the most   automation leveraging AI as things that are   repetitive, you know, judgment heavy, ideally high volume workflows. Think about things like you know, leasing follow-up, delinquency, turns, maintenance, triage, variance explanations. another another key thing to understand is you know AI depends on trusted ideal operational data. And so if you don't have accurate or clean property unit, resident, vendor,   Mo Hussein (08:44) you know, judgment heavy, ideally high volume workflows. Think about things like you know, leasing follow-up, d delinquency, terms, maintenance triage, variance explanations. another another key thing to understand is, you know, AI depends on trusted ideally operational data. And so if you don't have accurate or clean property unit, resident vendor   payment data and it's and it's inconsistent, you know, AI just   Jason Hull (09:10) payment data and it's and it's inconsistent, you know,   AI just helps accelerate the wrong answer, right? This notion of like hallucinations also kind of exist. and you know insights without ownership is is just is really just theater. And so although AI may identify a problem and recommend an action, things need to be routed, right? And asci you know action needs to be assigned, there needs to be some accountability that gets created there and then a measurement of of of   Mo Hussein (09:13) helps accelerate r the wrong answer, right? And the this notion of like hallucinations also kind of exist. and you know insights without ownership is is just is really just theater. And so although AI may identify a problem and recommend an action, things need to be routed, right? And as I you know action needs to be assigned. There needs to be some accountability that gets created there and then a measurement of of of a   of of a of a result.   Jason Hull (09:40) of of a of a   result. and then lastly like humans humans control still matters, right? Things that have a very high potential opportunity cost. you know, operators should be very careful on how they utilize AI. So, you know, things around fair housing, sensitive sensitive decisions like screenings, evictions, legal communication, you know, employee decisions and maybe even large payment loopholes and so   Mo Hussein (09:42) and then lastly like humans, humans control still matters, right? Things that have a very high potential opportunity cost. you know, operators should be very careful on how they utilize AI. So, you know, things around fair housing, sensitive sensitive decisions like screenings, evictions, legal communication, you know, employee decisions and maybe even large payment approvals. And so   Once we   have these kind of these table stake table stake items, if you will, kind of address, then you know we can move on to kind of you know the our framework of kind of measure, map, and automate. And so in each of these different components have different purposes, you know. The whole point of the measure step is is to quantify where pain exists and to validate kind of being buying versus buy like building. And so you want to ask things like where   Jason Hull (10:09) Once we have these kind of these table stakes stakeheads, if you will, kind of addressed, then you know, we can move on to kind of, you know, the our framework of kind of measure, map, and automate. And so and each of these different components have different purposes, you know. The whole point of the measure step is is to quantify where pain exists and to validate kind of being buying versus buy like building. And so you want to ask things   like where   Mo Hussein (10:36) where   time, where margin, where service quality or accountability is lost today, right? Examples can be things like, you know, days vacant, you know, delinquency rate, maintenance response times. These would be kind of like outputs like invoice coding time, reporting hours, renewal conversions, right?   Jason Hull (10:37) Where time, where margin, where service quality or accountability is lost today, right? Examples can be things like, you know, days vacant, you know, delinquency rate, maintenance response times. These would be kind of like outputs like invoice coding time, reporting hours, renewal conversions, right?   Yeah. Got it. Yeah, that that makes a lot of sense. So you've got to be you have to have good data.   Which the crux of th where their data is all probably housed is inside of their property management software.   Mo Hussein (11:06) Right.   Jason Hull (11:07) And so hopefully that software is kinda tracking some of this stuff. But, you know, everybody's had a CRM that the team didn't put enough notes in. And then it becomes kind of useless, right? So you're like, what happened with Fred on that call earlier, you know, or previously? I I think I think we talked about this. Can't remember. Why aren't you putting in notes? And so then the flaw becomes the human in the loop in a lot of instances. But then you're saying, you know, also humans matter. Like   Mo Hussein (11:14) Right.   Right.   Jason Hull (11:34) Related to fair housing. We've got to have the human in the loop making decisions. I don't think it would go fair very well to be standing in front of a judge and say, Well, the AI messed this up. It wasn't me.   Mo Hussein (11:43) Right. Right.   Right. Yeah, that's very that's very correct. the the the other thing is is that you know software is a tool, right? So they you know, for like that example that you just gave of like, hey, you know, I had a conversation with Freddie or an owner or what have you, and you know, the notes weren't captured. And so if there's if if if if there's there needs to be also a cultural   Jason Hull (11:46) Yeah, that's very that's very   Yeah, they you know, put like that example that you just gave of like, Hey, you know, I had a conversation   And you know, the notes weren't captured. And so if there's if if if if there's there needs to be also   Mo Hussein (12:06) shift within the organization to become more performance kind of driven, right? And using, you know, places of truth. You know, I, you know, we use Salesforce in our own internal kind of CRM and you know, there's this old ad like this old saying of just, you know, hey, if it didn't happen to Salesforce, it didn't happen at all. In other words, if your system of record hasn't been updated and things haven't been added to it   Jason Hull (12:06) cultural shift within the organization to become more performance kind of driven, right? And using, you know, places of truth. You know, I you know, we use Salesforce in our own internal kind of CRM and you know, there's this this old like this old thing of just, you know, hey, if it didn't happen in Salesforce, it didn't happen at all. Right.   Mo Hussein (12:29) to to ensure that it is correct and accurate and up to date, then   Jason Hull (12:29) to it to to ensure that it is correct and accurate and up   to date, then the organization sees it as, you know, as it didn't happen. And somebody, you know, using anecdotal feedback like, well I did this, but I just didn't update this. And so that's it's very important that, you know, whatever system you're using to kind of measure different KPIs and metrics, that that, you know, that behaviors within the organization are shifting towards that. And it's it's something it's a cultural shift that needs to also   Mo Hussein (12:32) the organization sees it as you know as it didn't happen. And somebody, you know, using anecdotal feedback of like, well I did this, but I just didn't update this, it means it didn't happen. And so that's it's very important that, you know, whatever system you're using to kind of measure different KPIs and metrics, that that, you know, that behaviors within the organization are shifting towards that. And it's it's some it's a cultural shift that needs to also cascade   also from from leadership down as well.   Jason Hull (12:57) Cascade also from leadership down.   Yeah, the advantage we have nowadays with all the AI stuff that's come out is now pretty much everything gets transcribed everywhere. So calls get transcribed, notes can be created automatically. You can also go back and ha check the transcription on a call or a zoom call or recording, figure out what happened. So that you know, not leaving notes in the CRM is a little bit less of a problem than it was in the past.   So we've so we've chatted a bit about measure. What is what's important about mapping or map? Yeah. So this is this goes back to my previous point about like you know AI value being it it is workflow value. Yeah so you know you've measured you've identified you know your measurements and KPIs. So whatever those KPIs may be. Next what you need to do is map what the actual   Mo Hussein (13:30) The mapping. Yeah. So this is this goes back to my previous point about like, you know, AI value being it is workflow value. And so, you know, you've measured, you've identified, you know, your measurements and KPIs, you know, days vacant, delinquency, whatever those KPIs may be. Next, what you need to do is map what the actual what   the actual workflows that are happening, not how leadership or staff thinks it's happening.   Jason Hull (13:53) what the actual workflows are happening, not how leadership or staff thinks it's   happening. There's a very key kind of a distinction is that, you know, a lot of operators and teams kind of assume, hey, you know, we have a set process, but it may not be happening the way that they are envisioning or the way that they're assuming that this is happening. Yeah. And and map that entire workflow end to end.   Mo Hussein (13:59) The very key kind of distinction is that, you know, a lot of operators and teams kind of assume, hey, you know, we have a set process, but it may not be happening the way that they are envisioning or the way that they're assuming that this is happening. And and map that entire workflow end to end.   identify what systems are involved, where handoffs occur, where approvals are required.   Jason Hull (14:21) identify what systems are involved, where handoffs occur, where approvals are required,   Mo Hussein (14:27) where judgment calls are are are are kind of made. And so, you know, every company has, you know, things like experienced managers and accountants and maintenance folks and and they usually know what good looks like versus what bad looks like. And so AI here is to help kind of convert that tribal knowledge ideally into a repeatable operating model. And so examples of how that mapping   Jason Hull (14:28) where judgment calls are kind of made. And so every company has you know things like experienced managers and accountants and maintenance folks, and and they usually know what good looks like versus what bad looks like. And so AI here helped kind of convert that tribal knowledge ideally into an overviewable operative model. So examples of   that mapping would be is you know, hey, what is the entire need to lease workflow?   Mo Hussein (14:50) would be is, you know, hey, what is the entire lead to lease workflow? You know,   Jason Hull (14:54) You know, what is the work order to completion, you know? what is our renewal offer to sign and executed actual renewal? And so and actually and again documenting that, a a lot of organizations have some notion of what that workflow kinda looks like. but   Mo Hussein (14:54) What is the work order to completion? You know? what is our renewal offer to signed and executed actual renewal? And so and actually, and again, documenting that. A a lot of organizations have some notion of what that workflow kind of looks like. but you know, they   haven't actually done they may not have documented, or if they did, it's not updated and they have an out of date SOP or a process diagram.   Jason Hull (15:12) you know, they haven't actually gotten any INOT documents in or if they did, it's not updated and they have an out of data so P or a process diagram.   Mo Hussein (15:22) And that's that's and that's that's that's a very important kind of key aspect of kind of this process.   Jason Hull (15:22) and that's that's and that's that's that's a very important kind of key aspect of kind of this process. Yeah, yeah. Well I a lot of times I end up talking with clients and I've noticed kind of this pattern or trend in the industry of I call it the process myth where everybody thinks if we just had better processes   all of our hopes and dreams would come true when it comes to the off side of the business and we would be more profitable. And especially see this in the two to four hundred door range in single family or small multi-residential property management. And so the challenge there is th that it's impossible to create enough processes, KPIs, and systems to make mediocre people be great. But they pe that doesn't stop business owners from trying. They they're like   Mo Hussein (15:59) Right.   Right.   Jason Hull (16:04) They they they wake up in the morning, they're like, I want to play an impossible game today. And they they still try. And I call it the process myth because if you have really great people, even if your processes are garbage, that I've seen these businesses still perform well. But the reverse is not true. You have mediocre people, you could have insane amounts of systems and processes and stuff, and the business still has a lot of headaches and problems.   Mo Hussein (16:16) Mm-hmm.   Jason Hull (16:29) And so I've kind of noticed this pattern. I call it the three levels of process. And level one is documentation. It's just like writing stuff out. But that's kind of like the owner's manual in the glove box of the car. Nobody looks at it, it doesn't get updated. You know, it's like it's it's it's gathering dust, and people don't actually, that's not actually how the processes are run. And over time, things gravitate towards ease or grace or what the flows best for the person doing the job.   Mo Hussein (16:39) Mm-hmm, mm-hmm.   Jason Hull (16:57) Not for what's best for the job sometimes. And so it gravitates a little bit towards chaos or being worse. Then there's this level two, which is checklists. This is where people are using things like Asana or Process Street or Lead Simple or they some sort of checklist space system where now they're verifying the works getting done in a certain way. But checklist has its own problems in that it's very linear and not every process is linear.   Mo Hussein (16:59) Right, right.   Read simple. Mm-hmm.   Mm-hmm.   Jason Hull (17:24) There's decisions   and splits and merges and sting things happening concurrently in property management. And so the challenge with checklist also it can tend to slow things down. It's not as efficient. So the next level and the problem I had with checklist, we used to use process street, is that it it if anytime a process got complicated, I had to build logic and you know, if-then sort of situations into it.   And it usually got to the point where I didn't even understand it. Like a year later, I'm looking at a process. I'm like, I had to retranslate this back into something that made sense to my brain. And I always, and the nerd had to be the one that did all the updates on it because nobody else could understand it. So then we eventually graduated to level three. So level three is visual workflow. This is for humans.   Mo Hussein (18:01) Right.   Mm-hmm.   Jason Hull (18:13) And so, and with with this, my tip to everybody listening, if you have a system, whether it's checklist or it's any of these three levels, you know, documentation, checklist, or visual workflow, that you your first two processes you make as an operator or as a business owner is how to create a process in this system is number one. And number two, how to QA.   Mo Hussein (18:26) Did   Jason Hull (18:37) A process that is made in the system to know it's actually a good one. If you just make those two, you don't have to do any of the other stuff. Everybody else can do it. You just make those two. That's my tip for all you business owners. And now with AI, you can start adding AI. Once you have things visually mapped out, it's you've got the map like you're talking about. Now you can figure out all right, where can AI take over some of this stuff? And where do we still need the human in the loop? Right. So yeah.   Mo Hussein (18:43) Mm.   And automation. Mm-hmm. Yep.   Yeah.   Right,   Jason Hull (19:05) So any tips for those listening to this that are already geeking out with AI, they're doing a little bit of this measuring and mapping and automating. What are some of the biggest challenges you've noticed where this kind of breaks down or people are making mistakes?   Mo Hussein (19:19) It's it's honestly it's the it's the you know, AI value. it's it's a lot of the small individual decisions that are made in a in a repetitive fashion and that that are made a lot that really are gonna unlock like true value for for any operator. And so like, you know, having very clean data, standardized, you know, systems of truth by what we mean by that is that, you know, hey, you know.   Jason Hull (19:20) It's it's honestly it's the it's the you know, AI value it's it's   like true value for for any op   clean data, standardized, you know, systems of truth. But what we mean by that is that, you know, hey,   you know, you know, whatever work order system that you're using, for example, has accurate, you know, work order data. People, you know, you're making a segment for actually closing out the work order when they complete it. Hey, the end of the week, I'm gonna now try to remember what I did earlier in the week.   Mo Hussein (19:47) you know, whatever work order systems that you're using, for example, has accurate, you know, work order data. People, you know, your maintenance technicians are actually closing out the work order when they complete it. Not just, hey, the end the week, I'm gonna now try to remember what I did earlier in the week.   Close it out. So the data is   the data can't be trusted, then AI is just going   Jason Hull (20:04) data the be trusted and AI   Mo Hussein (20:07) to cause additional kind of confusion. And so having accurate systems of record. And I gave that example of of of a work order when a technician kind of closes that, right? the process map, I think the you know, the three buckets are like three level that you kind of gave, I think is a great, great.   Jason Hull (20:20) Yeah, yeah. Yeah, that makes sense.   yeah.   level that you kinda gave I think it's a great,   great anecdote and framing of how processes should be kind of looked at. And and I think one thing that a lot of operators usually tend to overlook or assume is you know how things are being done versus how they actually are being done within the schemes, right? So an owner somebody at some point said, okay hey this is a process we're gonna take and then over time that just kind of got changed.   Mo Hussein (20:29) anecdote and framing of how processes should be kind of looked at. And and I think one thing that a lot of operators usually tend to overlook or assume is you know how things are being done versus how they actually are being done within the teams, right? It's an owner, somebody at some point said, okay, hey, this is the process we're gonna take. And then over time that just kind of got changed. And there   may be, you know, two different property managers   Jason Hull (20:55) And there may be, you know, two different property managers   Mo Hussein (20:58) operating in two different regions in the same company that are doing leasing renewal differently, right? That going back to that point that you mentioned about systematizing and having accountability loops and task base or like checklist items and ensuring that those things are actually done in that same quality and that same fashion is very, very key. And so getting data, like getting the right data, accurate data,   Jason Hull (20:58) operating in two different regions in the same company that are doing these things renewal differently. Right. That point that you mentioned about synthesizing and having accountability loops and task based or like checklist items and ensuring that those things are actually done in that same quality, in that same fashion is very, very key. And so getting data, getting the right data, accurate data   Mo Hussein (21:23) and then also like your process mapping and your   Jason Hull (21:24) And then also like your process mapping   and your processes kind of documented. I think I think the visual representation is a great way to have that. And those are the two key things that ninety percent of folks that are trying to leverage AI and automation and even the folks that are starting to try to jump into this space and try to automate and use AI for these things like usually we're like where where they're really struggling with. got it. Yeah, I think   Mo Hussein (21:26) processes kind of documented. I think I think the visual representation is a great way to have that. Those are the two key things that ninety percent of folks that are trying to leverage AI and automation and even the folks that are starting to try to jump into this space and trying to automate and use AI for these things like usually we're like we're where they're really struggling with.   Jason Hull (21:50) I was just on a webinar recently and they were talking about building AI agents and they were talking about if you want to make really effective AI agents, you need to give them a really good job description, just like a human. And what what's really funny is if you we coach clients on this a lot, but if we tell the clients to to go, we coach clients on   Creating job descriptions. We call our version of them R docs because each section starts with an R, like role, responsibility, et cetera, all the typical stuff. But then we have some additional sections that we found really paramount. So what we'll tell them to do is go ask your team members, give them this framework, and have them create their own R Doc. And then you take a look at this and see if that's what you would have created. Because it's never like what they think their job is. It's usually very different than what the business owner thinks their job is.   Mo Hussein (22:25) Mm-hmm.   Right.   Jason Hull (22:36) And maybe even different what the manager, the ops person thinks the job is, but then you can actually literally get on the same page with them. You can be like negotiate this and be like, this is what we think your priorities should be, and what your outcomes should be, and what we want you to be able to accomplish. And this is helpful for them to know what they're aiming for so that they can please you because your team members want to please you if they're good. But usually there's a big disconnect, like you're saying, between what   Mo Hussein (23:00) Mm-hmm. Mm-hmm.   Jason Hull (23:05) the the employee thinks their j role and job is versus what their manager thinks they should be doing versus what the business owner thinks everybody should be doing. And so nobody's on the same page. And then you're everybody's roles are a little messy. And then you're going, let's give them processes now to work on. And they're not even clear on what their job is or what their role is. Yeah. And so same thing if you were going to build an AI agent and you were like, I want you to try and be good at everything. And then suddenly it's like really   Mo Hussein (23:29) Right.   Jason Hull (23:34) Hallucinating a lot and it's messing everything up and yeah. And it's not a realistic creature, you know, just like some people give create job descriptions that are for like four different personality types. Right. And then they hire somebody that maybe can actually do all four things, and we call those really highly adaptable, weird creatures entrepreneurs. And then they wonder why that property manager left and stole all their clients.   Mo Hussein (23:34) Horrible.   Right.   Yeah.   Jason Hull (23:57) Instead of finding somebody that's like really good at being one thing. Right. Yeah. And that's how you should see Asia.   Mo Hussein (24:00) Right. That that that that role clarity is very, very, very important, right? And that's how you should see agents as well, is that   like, hey, it's like a trained employee. And so you should exp you should expect the same level of, you know, investment involvement, if you will, and trying to and try to help them be the best of like, you know, whether it's a leasing agent, a maintenance coordinator, or whatever that their role may be. And I I think another aspect is and I'm curious how like how   Jason Hull (24:12) you should expect the same level of you know investment involvement if you will and trying to and try to help them be the best of like you know whether it's a leasing agent a maintenance coordinator or whatever that their role may be and I I think another aspect is and I'm curious that   like how you know when you guys are having conversations with clients around role descriptions stuff it's the concept of ownership like hey what you know how to how to align ownership to and lining that up to hopefully the mental business   Mo Hussein (24:28) you know, when you guys having conversations with clients around role descriptions and stuff, it's the concept of ownership. Like, hey, what, you know, how to how to align ownership to and lining that up to hopefully an eventual business outcome or KPI   or something so that, you know, their performance drives also the business performance, right? How have you guys had this conversation or how do you talk about kind of that concept? I can kind of allude to it without kind of explicitly calling it out.   Jason Hull (24:42) Kate guy or something so that you know their performance derives also the business performance, right? Yeah. How do you talk about kind of that concept? You kind of allude to it without kind of explicitly calling   it out. Yeah, I think well, sometimes I'll just totally call a business owner out on things. But I think what I think will be interesting is people are building starting to build agents. I think that they should.   They should have an understanding of personality types. I think they should have an understanding maybe or a conversation with AI about what Myers Briggs type might be good for this agentic role. And because like somebody that's really good at like strategy and the strategist role, which would be like an INTJ in Myers Briggs, might be good at some operational stuff, but they would be really terrible at customer service.   Mo Hussein (25:19) Mm-hmm.   Jason Hull (25:33) Because a lot of INTJs don't even like humans, right? And so they're logical thinkers and they're really judging and they're practical and they're in you know introverted and they're really bad at understanding how the other person feels or even expressing that. And so you're you you don't want to create these try and create AI AI agents that are multiple split personality types, because I don't think they're gonna be as effective. And you can't also, just like you wouldn't want somebody building the process.   QE QA QA of the process. You don't want them both. You don't want AI to be checking itself. Right. Right. The the brain that had problems doing the messing things up, maybe, or didn't do it totally right. You don't want them checking their own work. Right. And so, yeah, so I think this is going to be interesting that people are going to be building agents and they usually think just logically here's the context it needs, here's the role, whatever. But I think also maybe give it the personality that it.   Mo Hussein (26:08) Right, right.   Right.   Jason Hull (26:29) What's the disc assessment for this person, this agent? What's the Myers Briggs type for this agent? And then if especially if they're communicating with humans or doing a task that you want them to be somewhat human like, they're going to be much better at doing this if you give it you create them in the right way. Just an idea.   the other thing to know as a business owner, you need to know who you are so that you can build your dream team around you. So your advisors, whether they're agentic or human, your advisors, your team members, it should be built ultimately around you thriving and being healthy in your own business so that you've got the tea the tools and the resources that fit you. But most business owners make the mistake.   Of trying to build the business around the business and then wonder why they're miserable and why they're kind of a slave to their own business. Right.   Mo Hussein (27:16) Right. Right. Right.   Jason Hull (27:20) So anyway, Mo, measure, map, automate, MMA. Doesn't involve fighting too much. You know, like mixed martial arts. It's a little bit on the, you know, less physical side of things. fun chatting about.   Mo Hussein (27:26) No.   Jason Hull (27:34) all the the AI stuff that's going. How can people anything else that you want to add to our conversation here about yeah this model? And then could you tell us a little bit about what you do and how maybe you help property managers with this stuff? Yeah. Yeah. so I guess just to put it succinct, kind of a a sandwich kind of takeaway. So yeah, operators need to wait for a perfect AI.   Mo Hussein (27:48) Yeah. Yeah. so I guess just to put it succinctly, kind of a a a sandwich kind of takeaway. So, yeah, operators don't need to wait for a perfect AI strategy.   Start by identifying, measuring where value exists, where things are leaking, then mapping workflows and then deciding what can be safely automated and measuring whether those actions improve performance. and so   Jason Hull (28:00) Identifying, measuring where value exists, where things are leaking, then mapping workflows, and then deciding what can be safe and automated, and measuring whether.   Mo Hussein (28:10) like you know, over time we'll see that you know the winners are not gonna be the companies that have the most amount of most amount of data, but they're the ones that can convert data into consistent operating actions across how they've operated every door. if you we help clients with you know putting together SOPs, also mapping their technology needs, where where they're where they're having operational leaks in the business can be   Jason Hull (28:10) So like you know over time we'll see that you know the winners are not gonna be the companies that have the most amount of most amount of data, but they're the ones that can convert data into consistent operating actions across how they've operated every door. if you we help clients with you know putting together SOPs, also mapping their technology needs, where where they're where they're having operational leaks and the business   can be optimized.   Mo Hussein (28:37) Optimized further using   Jason Hull (28:38) Further using technology and automation, we have a platform that we've built, Prop Strata, to actually connect and help with that automation type effort. and we're also we also do a lot of accounting and and CPA work. you can reach us at www.balanceasset solutions.com, and my emails mo at propstrata.com, or you can reach out to our team at info at balance asset.   Mo Hussein (28:39) technology and automation. We have a platform that we've built, Prop Strata, to actually connect and and help with that automation kind of efforts. then we're also we also do a lot of accounting and and CPA work. you can reach us at www.balanceasset solutions.com and and then my email is mo at at propstrata.com or you can reach out to our team at info at balanceasset solutions.com.   Jason Hull (29:03) Cool. So they could take a look at this at propstrata.com.   Mo Hussein (29:07) Correct. W dot propstrata.com.   Jason Hull (29:11) Okay, cool. Very cool. All right. yeah, check that out, everybody. It sounds interesting. All right. Well, Mo, I appreciate you coming out and hanging out with me here on the DoorGro show and sharing everything.   All right.   So if   If you have ever felt stuck or stagnant in your property management business and you want to take it to the next level, reach out to us at doorgrow.com. We are the world's best at creating high-growth property management companies in the single-family residential space or the small multi-space. And if for a free training or how to get unlimited leads for free, text the word leads to 512-648-4608. That's 512-648-4608.   Also, join our free community just for property management business owners at doorgrowclub.com. And if you want tips, tricks, and ideas to learn about our offers, subscribe to our newsletter by going to doorgrow.com slash subscribe. And if you found this even a little bit helpful, don't forget to subscribe and leave us a review on whatever channel you saw or heard this on. We'd really appreciate it. And until next time, remember the slowest path to growth.   is to do it alone. So let's grow together. Bye everyone.

What is a Quality Unit and how do Quality Assurance and Quality Control fit within it?In this episode, I explain the practical differences between the Quality Unit, QA, and QC across pharmaceutical, medical-device, and combination-product companies. We'll look at the Quality Unit's major responsibilities, why QA is generally system- and process-focused, why QC is generally testing- and product-focused, and why the organizational structure varies between companies.CHAPTERS00:00 What is a Quality Unit? 01:18 The Quality Unit as the umbrella function 01:55 Three major Quality Unit responsibilities 03:08 What functions may sit within Quality 04:08 Establishing, operating, and releasing 04:37 Quality Assurance vs. Quality Control 05:38 Common QA responsibilities 06:16 Common QC responsibilities 06:53 Why pharma and device companies look different 07:32 Drug vs. device terminology 08:06 How QC supports product release 08:26 Final QA vs. QC summary KEY TAKEAWAYS• The Quality Unit provides overall quality oversight. • QA primarily focuses on systems and processes. • QC primarily focuses on inspection, measurement, and testing. • QC testing provides essential evidence supporting product disposition. • Exact responsibilities and reporting structures vary by company and industry.How are QA and QC organized at your company? Share your experience in the comments.#QualityAssurance #QualityControl #QualityUnit #Pharma #MedicalDevices #qualitymanagement Subhi Saadeh is a quality professional, consultant, auditor, and trainer who specializes in drug-device combination products, medical devices, pharmaceutical quality systems, supplier quality, and lifecycle management.Through Let's Combinate, he helps pharmaceutical and medical device teams bridge the gap between drug and device quality, regulatory expectations, and practical execution.Learn more about Let's Combinate:https://www.letscombinate.comSchedule an introductory call:https://calendly.com/letscombinate/let-s-combinate-intro-sessionConnect with Subhi on LinkedIn:https://www.linkedin.com/in/subhi-saadeh-1169aa21Listen to Let's Combinate on Apple Podcasts:https://podcasts.apple.com/us/podcast/lets-combinate-drugs-devices/id1589285792Listen on Spotify:https://open.spotify.com/show/71wYadhCrfLsdYTVachpD2

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Nandini Srinivasan has spent 25 years in the quality industry and leads a global QA organization of over 150 engineers across the US, Canada, India, and Pakistan. In this episode, she breaks down exactly how she built a QA AI acceleration charter, ran a train-the-trainer model, and used a phased proof-of-concept approach to separate real AI from what she calls "powerful automation dressed up as AI.  We get into: How she frames quality metrics for executives Using language around revenue protection Risk mitigation Feature velocity instead of test coverage percentages. She talks about the four pillars she uses to present her team's value: quality, scalability, performance, and availability. Nandini also shares her take on the future of QA hiring, why the judgment layer will always require a human, and what skills testers need to stay relevant as AI agents take over the more mechanical parts of automation. She is also writing a five-part LinkedIn series called "The Voice of QA in the AI Era" if you want to follow along.

In this episode of Future Finance, host Paul Barnhurst and co-host Glenn Hopper are joined by guest Alex Brower, co-founder and CEO of QFlow.ai, to explore why forecasting problems often begin with the data layer. They discuss data plumbing, semantic alignment, Model Context Protocols (MCPs), vibe coding, and how finance and go-to-market teams can work from shared definitions to improve forecast quality and analysis.Alex Brower is the co-founder and CEO of QFlow.ai, helping finance and go-to-market teams connect data, improve forecast accuracy, and boost analyst productivity by up to five times. He previously led finance and marketing at high-growth tech firms like AppTelligent (acquired by VMware) and Cloud Academy (acquired by QA).In this episode, you will discover:How data hygiene and shared definitions improve forecastingWhy unified data alone does not solve business alignment issuesMCP vs semantic layers for reliable analysisThe risks of untested AI-generated analysisWhy AI cost and governance matterAlex explains how QFlow helps companies prepare data, run planning workflows, and turn analysis into board-ready stories.Follow Alex:Website: https://qflow.ai/LinkedIn: https://www.linkedin.com/in/alexbrower/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.ai:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Introduction[02:15] – Data & Forecasting Challenges[06:43] – MCP vs Semantic Layers[09:55] – AI Coding Risks[15:52] – QFlow Data Preparation[18:10] – Planning & Business Insights[20:51] – Closing Thoughts

Le magazine Jeune Afrique propose un portrait du chef d'état-major du Front de libération de l'Azawad, « Mbareck Ag Akli, le "ministre de la Défense" du FLA qu'Africa Corps a cru avoir tué », titre le magazine qui a pu « entrer en contact avec lui le 16 juillet ». Il continue de mener des opérations dans le nord du Mali, aux côtés des jihadistes du Jnim, le groupe de soutien à l'islam et aux musulmans. Africa Corps, les mercenaires russes alliés de Bamako, avait fait état le 5 juillet de sa mort après des combats dans la zone d'Anéfis, mais « moins d'une semaine plus tard, l'homme apparaissait dans une vidéo de propagande […] haranguant ses troupes, leur assurant bravache que la victoire était à portée de main », malgré la perte d'Anéfis, reprise par les Forces armées maliennes. Allié du Jnim « Cette "résurrection" de Mbareck Ag Akli est le nouvel épisode dans la bataille des récits qui se joue parallèlement à la guerre qui oppose les FAMa et leurs alliés russes d'Africa Corps à la coalition que forment les indépendantistes du FLA et les jihadistes du Jnim, écrit Jeune Afrique. Il a mis en lumière la figure de celui qui se présente désormais comme le "ministre de la Défense" du FLA. » « Déserteur de l'armée malienne, vétéran de la rébellion indépendantiste du nord du Mali, il occupe une place centrale dans l'organigramme du FLA », poursuit le magazine et assume « sans complexe » l'alliance avec la branche sahélienne d'al-Qaïda. « Pour moi, tous les habitants de ce territoire sont, à un moment ou à un autre, contraints de s'engager dans la lutte, car notre seul problème est celui qui nous oppose à l'État malien, à son armée et aux mercenaires russes qu'il emploie », explique-t-il à Jeune Afrique. « Nous n'avons aucune difficulté à cohabiter les uns avec les autres », assure-t-il, reconnaissant simplement des « différends d'ordre politique » avec ses nouveaux alliés. L'AES veut accueillir la CAN Le Mali, le Burkina Faso et le Niger sont officiellement candidats pour accueillir la CAN en 2032. « Les trois pays de l'AES, l'Alliance des États du Sahel, ont officiellement obtenu la validation de leur déclaration d'intérêt par la CAF », écrit Afrik.com. « La reconnaissance de la déclaration d'intérêt ne garantit pas l'attribution de la compétition, précise le média en ligne. Les trois fédérations devront désormais élaborer un dossier technique solide, démontrer la capacité de leurs infrastructures à accueillir un tournoi continental et convaincre les instances africaines de la viabilité de leur projet. » Cette candidature commune marque « une nouvelle étape dans la coopération entre les trois États membres de l'AES, qui entendent désormais porter ensemble de grands projets sportifs continentaux », analyse Sene.News. Le format envisagé par l'AES ne serait pas une première sur le continent : « en 2027, la CAN doit déjà réunir trois pays organisateurs, le Kenya, l'Ouganda et la Tanzanie, un précédent qui pourrait servir de référence au dossier sahélien », indique La Nouvelle Tribune qui précise qu'aucune date n'a encore été fixée pour l'attribution définitive de l'édition 2032. Une nouvelle espèce de singe En RDC, une nouvelle espèce de singe identifiée dans le parc national de la Lomami. Ce « petit primate au pelage noir et aux lèvres rose-orangé aura longtemps échappé aux radars de la science, écrit le Monde Afrique. Connu localement sous le nom de "likweli", il vient d'être reconnu par la communauté scientifique comme une nouvelle espèce de singe », le Colobus congoensis de son nom scientifique. Une découverte rare, indique le journal, « c'est seulement la cinquième nouvelle espèce de singe identifiée en Afrique au cours des 75 dernières années. » Mais elle pourrait déjà être classée comme « espèce en danger ». En cause : « la chasse, la petite taille de sa population » et son habitat « touché par le changement climatique ».

Send us Fan MailAriel Assaraf is the CEO and co-founder of Coralogix, a leading observability platform that most recently raised $115 million at a unicorn valuation. He started the company in 2014, and today Coralogix serves more than 4,000 customers, monitors more than 500,000 applications, and processes over 3 million events per second.Before co-founding Coralogix, Ariel served in Israel's Elite Intelligence Unit 8200, where the sheer scale and complexity of data he worked with made one thing clear: existing architectures weren't built for what was coming. He later joined Varent Systems, a homeland security company, leading automation, integration, and QA.In this episode, Ariel draws on more than a decade of building at the frontier of data infrastructure to argue that observability is no longer just a tool for preventing downtime. It is becoming the most truthful, real-time source of intelligence any company owns.In this conversation, we discuss:The evolution from the data collection era ("oil phase") to a landfill crisis, and now the AI-driven "brain phase" where telemetry has become the most valuable raw material for business decision makingHow Coralogix separates the data plane from the control plane, storing data in open format on the customer's own infrastructure to enable data ownership, infinite retention, and freedom from vendor lock-inWhy Ariel invested over $100 million in R&D to build query engines that always return answers, not constrained by predefined schemas that agents will quickly exceed How SREs evolve from reactive incident responders into autonomous operators as agents like Ollie, Coralogix's AI agent, take over incident triage, root cause analysis, and narrative generationThe emerging role of the AI-forward product manager who sits between customer needs and autonomous agents, reshaping how software gets built, priced, and sold in real timeHow Ariel thinks about linear versus exponential impact as a leadership principle, and why the intuition to prioritize exponential value is something agents will never replicateExplore more in the conversation:00:00 Welcome & AI's societal impact paradox01:53 AI Fun Fact: New data on productivity and employment04:15 Introducing Ariel Assaraf and the Coralogix origin story05:15 Evolving data architecture: from oil to landfill to brain phase07:28 Coralogix's data ownership and open format advantages13:27 From telemetry data lake to autonomous, agent-driven analytics20:00 Building scalable, answer-guaranteeing query engines for complex data25:44 The future of natural language interfaces like Olly for SREs and DevOps28:15 Orchestrating multiple AI agents for better decision-making30:20 Responsibility, autonomy, and the evolving role of customer success35:37 How Coralogix turns telemetry into strategic business decisions38:18 Linear vs. exponential value in your careerResources:Subscribe to the AI & The Future of Work NewsletterConnect with Ariel on LinkedInAI fun fact articleOn How Personalized Healthcare Is Being Transformed Through AI and the Human Microbiome

AI content has collapsed the distance between bad and passable. David Ebner, President and founder of Content Workshop and Chatter AI, brings 13 years of enterprise content experience and a classically trained storytelling background to explain why differentiation now determines who wins. The conversation covers a brand story framework that positions the audience as hero rather than the founder, strategic placement of human creativity across the content workflow instead of relegating it to a final QA gate, and a content ideation model that surfaces real-time, relevance-driven topics to build audience trust—particularly in high-scrutiny YMYL verticals like cybersecurity.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What happens when wholesale distributors realize “CRM” is no longer the real conversation, growth, data, AI, and sales enablement are?In Episode 198 of Around the Horn in Wholesale Distribution, Kevin Brown and Tom Burton unpack the shift they saw firsthand at the Affiliated Distributors Functional Success Summit: distributors are moving beyond traditional CRM adoption questions and toward connected data systems, AI-enabled sales strategy, and future-proofing distribution. The episode connects macroeconomic uncertainty, supply chain risk, AI governance, human judgment, and enterprise growth strategy to the real decisions facing wholesale distribution teams today.What You'll Learn:Why distributors are moving past traditional CRM conversations and focusing instead on how to help sales teams create more value, become more consultative, and grow revenueHow CRM-ERP integration, data warehouses, e-commerce platforms, marketing automation, and disconnected point solutions can limit customer visibility unless they are unified into a true enterprise growth platformWhy inflation measurement, interest rate uncertainty, and prediction markets matter to revenue leaders in distribution making investment and growth decisionsHow Strait of Hormuz disruption, Suez Canal risk, oil volatility, plastics, fertilizers, helium, and global logistics instability can ripple through manufacturing and wholesale distributionWhy AI still needs human judgment, oversight, and strategy, and why many companies miss expected ROI when they assume full automation instead of building a realistic digital transformation planEpisode Highlights:01:16 – Lessons from the Affiliated Distributors Functional Success Summit and why sales enablement is replacing traditional CRM talk03:55 – Why disconnected data across ERP, marketing automation, e-commerce, and point software creates risk for distributors06:37 – Episode 198 begins: how Around the Horn connects the economy, supply chain, M&A, sales, marketing, AI, and robotics to wholesale distribution08:30 – LeadSmart's Meridian 360 Enterprise Growth Platform and the move from Smart CRM to broader business growth engines12:32 – Inflation cooling, gas prices, and why energy volatility still affects distributors, manufacturers, fertilizers, heavy minerals, and supply chains15:23 – Kevin Warsh, Fed measurement reviews, CPI, PPI, the 2% inflation target, and the need for more real-time data20:25 – Prediction markets, PolyMarket, Kalshi, and whether betting markets can offer useful economic signals27:17 – Strait of Hormuz risk, Iran, shipping disruption, oil exposure, the Suez Canal, Houthi rebels, and what it means for global supply chains43:02 – AI watchdogs, model testing, DeepMind, Fable, Mythos, and the role of government QA for high-powered AI systems48:38 – Human judgment in the age of AI, digital twins, job displacement fears, and why AI ROI depends on data readiness and realistic automation expectations56:00 – APR Supply's sales tool adoption gains and how better sales technology can support outside sales revenue growthTools, Frameworks, and Strategies Mentioned:LeadSmart TechnologiesMeridian 360 Enterprise Growth PlatformSales CompassSmart CRMSales Co-PilotAI Co-Pilot for SalesCRM-ERP IntegrationCRM Data Enrichment with AIHidden Revenue DetectionSales Automation Without Losing the Human TouchFuture-Proofing DistributionHybrid Selling ModelsConsultative CommerceData warehouses and data lakesMarketing automationE-commerce data integrationERP data unificationAI-enabled business intelligencePrediction marketsPolyMarketKalshiCPI and PPI measurement reviewsAI model QA and AI watchdog conceptsHuman judgment in AI strategyAPR Supply sales tool adoptionMaster of Distribution Management programClosing Insight:The episode's central message is clear: the future of distribution is not about buying more disconnected technology. It is about connecting data, people, process, and AI into a strategy that helps teams sell smarter, serve customers better, and make better decisions under uncertainty.Leave a Review: Help us grow by sharing your thoughts on the show.Learn more about the LeadSmart AI B2B Sales Platform: https://www.leadsmarttech.com/Join the conversation each week on LinkedIn Live.Want even more insight to the stories we discuss each week? Subscribe to the Around The Horn Newsletter.You can also hear the podcast and other excellent content on our YouTube Channel.Follow us on Facebook, Twitter, Instagram, or TikTok.

AI content collapses the gap between passable and genuinely valuable. David Ebner, President and Founder of Content Workshop and Chatter, applies 13 years of enterprise brand storytelling across scrutinized YMYL verticals like cybersecurity to help teams differentiate at scale. The conversation covers building brand story around audience outcomes rather than founder origins, positioning human creativity as a deployable agent at multiple process gates instead of a single QA checkpoint, and shifting measurement away from sales-qualified leads toward trust-driven signals as traditional attribution paths fragment.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Future Finance host Paul Barnhurst and co-host Glenn Hopper are joined by Alex Brower, co-founder and CEO of QFlow.ai, to unpack why forecasting often breaks down even when teams have plenty of data. The discussion focuses on how finance and go-to-market teams can work from shared inputs, improve planning accuracy, and use AI without losing the structure needed for reliable decisions.Alex Brower is the co-founder and CEO of QFlow.ai, helping finance and go-to-market teams connect data, improve forecast accuracy, and boost analyst productivity up to five times. He previously led finance and marketing at high-growth tech firms like AppTelligent (acquired by VMware) and Cloud Academy (acquired by QA).In this episode, you will discover:Fix data plumbing before system consolidation.Use data dictionaries for consistent definitions.AI helps, but semantic logic is still needed.Vibe coding can create hidden costs if untested.Start with key unanswered questions, then plan solutions.Alex shares lessons from leading finance, operations, and marketing teams at high-growth companies, including what he learned from living the forecast tension on both sides of the table.Follow Alex:Website: https://qflow.ai/LinkedIn: https://www.linkedin.com/in/alexbrower/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.ai:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[03:44] – Finance-to-marketing journey & QFlow's solution[07:10] – Data plumbing vs. alignment[11:37] – MCPs, AI, and logic layers[14:50] – Risks of vibe coding & maintainability[21:56] – How QFlow handles data, planning, and storytelling[30:57] – Alignment before strategy & reducing fact-hunting[35:39] – Where CFOs/FP&A should start modernizing[44:03] – Closing & sponsor sign-off

It's time to play the music, it's time to… get Aji and Sally together again. This week's episode is a radical one, and a listener suggestion no less; let's Muppet-cast a software development team! Who would you hire as the content designer? Which of the Muppets would do their best work as the QA? And the most important question of all - which role would be played by the human?! — Your hosts for this episode have been thoughtbot's own Sally Hall and Aji Slater. Don't forget you can now also watch episodes of The Bike Shed over on YouTube! If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Write to our hosts: hosts@bikeshed.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - YouTube - LinkedIn - Mastodon - BlueSky © 2026 thoughtbot, inc.

Today, we're joined by Manuel Schönfeld, Chief Strategy Officer (CSO) and Chief AI Officer (CAIO) at Qu POS, Inc. the pioneer of the restaurant industry's first unified commerce platform. We talk about:Fundamentally changing a company to achieve an eight X multiplierHow AI can help maintain softwareHow getting AI to work on your code is a double-edged swordAI enabling, “one person teams” for product, coding, QA, etc.The mindset change needed to understand how to build maintainable code

Your marriage can feel fragile when the pressure is constant but the problems are hard to name. We sit down for QA 5.0 and take on five questions that show up in real homes: resentment when one spouse is the only paycheck, the “invisible” work of running a household, and what it means to contribute when life doesn't go according to plan. If you've ever thought, “I'm carrying everything,” we talk about how to bring that into the light without turning it into a scorecard.Then we go straight at a modern relationship killer: the phone. If your partner is beside you but never really with you, you're not crazy for feeling lonely. We share why constant scrolling breaks connection, what boundaries can look like, and how to ask for attention in a way that protects closeness instead of starting a fight. We also talk about the fear and grief that can hit when a spouse no longer believes in God, and how to navigate different faith journeys with prayer, compassion, and steady love rather than pressure.We close with two big questions: what to do when your spouse admits they are unhappy, and whether love can survive when you want completely different futures. We talk about happiness vs lasting joy, priorities, shared direction, and the hard truth that love alone is not a full game plan without commitment and alignment. If any of these topics sound like your house right now, you'll feel seen and you'll leave with a clearer next step.Subscribe for more Q&A, share this with a friend who needs it, and leave a review so more couples can find the show. What question do you want us to answer next?

AI content collapses the gap between passable and good. David Ebner, President and Founder of Content Workshop and Chatter, brings 13 years of enterprise content strategy across high-stakes verticals like cybersecurity to explain why brand storytelling now separates differentiated brands from homogenized AI output. Learn how to define brand story as audience transformation rather than origin narrative, where to insert human creativity as discrete process gates rather than a single QA checkpoint, and how to structure through-line messaging across multiple buyer Personas in complex enterprise decision trees. Ebner also unpacks why trust—not sales-qualified leads—should anchor content measurement as recency-driven LLM experiences reward brands publishing against real-time audience relevance.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is hiring a person still your default answer to a problem? For a growing number of startups, it shouldn't be.In this episode, Yaniv Bernstein is joined by returning guest Matt Cook, co-founder of Scouut, one of Australia's most respected engineering recruiters for early-stage startups, to unpack what's changed in engineering hiring since Matt's last visit. They cover ‘team debt' (the AI-era sequel to tech debt), why AI is acting as an appetite suppressant for headcount, why the best engineers are now worth $400K+, and why the ‘software factory builder' is becoming the most sought-after hire in tech.In this episode, you will:Understand "team debt", and why teams built to solve today's problems are already becoming obsoleteLearn why "removing the crutch" (like scrapping a QA gating function) forces the kind of AI-native change that timid, incremental adjustments never willDiscover why most startups were already overstaffed before AI, and why AI now acts as an ‘appetite suppressant' for hiring rather than just an efficiency toolHear why hiring should now be treated as a last resort, and what that actually means in practice for foundersUnderstand why compensation banding and headcount-based budgeting are breaking down, and what smart companies are replacing them withLearn about the rise of the ‘software factory builder': the rare, highly-paid engineer who uses their judgment to build airtight, AI-based infrastructure the rest of the team can lean onTimestamps00:00 Coming Up...00:58 On Today's Show: Matt Cook on 'Team Debt'02:36 What is Team Debt?05:12 Refocusing on AI Native Solutions10:40 Unlearning Old Hiring Ladders12:04 Why Companies Are Oversized14:59 AI, the 'Corporate Ozempic'19:25 Layoffs and Team Composition24:02 Disrupting Yourself at Series B28:12 The Salary Race for AI Engineers34:01 Limitations of Headcount Budgeting38:16 How Downsizing Can Help You Grow41:13 ClickUp's 'Hail Mary' Pivot44:26 Software Factory Builders and 'Forward Deployed' Automation49:14 Maintaining Safe Internal Software54:25 Closing ThoughtsResources mentionedScouut (Matt Cook's engineering recruitment firm for early-stage startups): https://scouut.com.au/Matt Cook on LinkedIn: https://www.linkedin.com/in/matthewmarkcook/ 'Corporate Ozempic' by Scott Galloway — the essay behind the AI/hiring analogy Yaniv references: https://www.profgalloway.com/corporate-ozempic/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us on YouTube for full video episodes: https://www.youtube.com/@startup-podcast Give us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean and memorable name, go to https://⁠get.tech/tspThis episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠ The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

Aliu Adewale: The Counterintuitive Fix—How Collapsing the Jira Board Sparked Collaboration Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "Collaboration is the foundation of a successful Scrum team." - Aliu Adewale   Aliu walked into a team where the daily standup was theater. Developers delivered their tickets and forgot about them. QA picked up "their" column. Front-end, back-end, senior architect, junior developer—everyone was a champion of their own silo. Nobody engaged during refinement. Nobody called anything out. The board had close to ten columns, one per specialty, and it was working exactly as designed: as a handoff system. Aliu's diagnosis is sharp—the foundation of the team's problem wasn't the people; it was the tool. The Jira board was visualizing silos and the team was living up to it. The fix was counterintuitive: he collapsed the board from nine columns to three—To Do, In Progress, Done. Once "In Progress" was the only place where work lived, nobody could hide. A QA needing to know when a ticket would be ready had to talk to the developer. A stakeholder asking for status meant everyone on the ticket had to communicate. The team had no choice but to collaborate. As Vasco frames it in the episode: by creating the smaller problem of "hiding status," Aliu solved the bigger problem of "no collaboration." Sometimes you have to make things a little worse so they can get much better.   In this segment, we refer to the Agile value individuals and interactions over processes and tools, and to the recognition that the tools we choose shape the behaviors we get.   Self-reflection Question: Is your Jira board designed to enable collaboration, or to enable handoffs? Featured Book of the Week: Surrounded by Idiots by Thomas Erikson For Aliu, the book that most inspired him as a Scrum Master is Surrounded by Idiots by Thomas Erikson—a book he discovered through a recommendation on this very podcast. The provocative title pulled him in; the content gave him something he could use every day. As a Scrum Master, you work with people from different backgrounds, different communication styles, different ways of seeing the world. The book maps four personality patterns and, as Aliu puts it, "it might not be a hundred over a hundred, but at least ninety over a hundred about personality and human relation." For someone already working on his emotional intelligence, it became a tool for understanding why a message that landed clearly with one team member completely missed another—and what to do about it.   [The Scrum Master Toolbox Podcast Recommends]

Amit Rawat is an agentic engineer who spent two decades in QA before shifting fully into building AI agents. He's the creator of PromptWright, a desktop tool that turns natural language prompts into automated Playwright browser tests, complete with screen recording, Gherkin scenario generation, and self-healing locators. In this episode, Amit and Joe get into what it actually takes to work with AI agents at a high level, starting with why the planning phase matters more than the prompt itself. Amit breaks down his own workflow, brainstorming with AI, building a detailed plan in HTML before ever executing, and why curiosity and technical depth still matter even as AI gets more capable. They also cover why Amit believes QA professionals, more than developers or DevOps engineers, are best positioned to thrive in the agentic era, how he tracks the ROI on his $200-a-month Claude subscription, the "Chief of Staff," "Chief Health Officer," and "Chief Financial Officer" AI agents he's built to help manage different aspects of his personal life, and how he uses a memory layer so those agents understand his preferences and become more useful over time. If you're a tester, automation engineer, or QA leader trying to figure out where AI agents fit into your workflow and your career, this conversation is a practical look at what's already working today.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Jennifer Gaddis. Interview Summary Show: Money Making Conversations MasterclassHost: Rushion McDonaldGuest: Jennifer Gaddis – Senior Quality Assurance Engineer, Educator, Founder of Road to QA 1. Purpose of the Interview The primary purpose of the interview is to inspire and educate everyday people—especially those without college degrees or traditional tech backgrounds—on how to pivot into technology careers, specifically Quality Assurance (QA), and to reframe fear around AI, layoffs, and automation into opportunity. Jennifer’s story is used as proof of concept that: You do not need a college degree to succeed in tech Transferable skills already qualify many people for QA roles AI does not eliminate jobs—it creates new opportunities Strategic career pivots can result in life-changing income and freedom Rushion positions Jennifer not only as a success story, but as a new blueprint for wealth-building through skills, not credentials. [ 2. Interview Overview (High-Level Summary) Jennifer Gaddis shares how she: Pivoted into tech in 2021 with no degree Went from $40K to six figures within 90 days Built a $400K+ remote household income with her husband Created Road to QA, helping 200+ people land tech jobs Accidentally built a multi-million-dollar education business Used personal hardship, COVID, financial stress, and family responsibility as fuel—not limitations She explains what Quality Assurance engineering is, why it is resistant to AI replacement, and how regular users of apps are already doing parts of QA work without realizing it. 3. Key Takeaways A. You’re Already More Qualified Than You Think Jennifer emphasizes that everyday digital behavior translates into QA skills: Using apps Identifying bugs Expecting software to “work correctly” Navigating systems as an end user This insight forms the core of her teaching philosophy. B. The Faster You Add Skills, the Faster You Increase Income Jennifer repeatedly notes: “The difference in your paycheck is your skillset.” By stacking skills (manual QA → automation → AI testing), professionals increase their market value, not just job security. C. AI Is a Career Accelerator, Not a Threat Rather than fearing AI, Jennifer encourages people to: Work alongside AI Become the humans overseeing AI systems Move into hybrid QA + automation + AI roles She stresses that human oversight is still required in tech deployment. D. Entrepreneurship Can Be Accidental—but Scalable Jennifer did not initially plan to build a company. Her business emerged from: Instagram stories A $97 beginner e-book Real student outcomes Her willingness to: Raise prices Build systems Hire specialists Learn financial discipline Allowed Road to QA to grow sustainably. E. Representation and Access Matter Jennifer openly discusses: Being a Black woman in tech Coming from financial insecurity Navigating family obligations Redefining success for future generations Her story challenges stereotypes about who “belongs” in tech careers. [ 4. Notable Quotes from the Interview “I landed my first year in tech within 90 days.” [ “The difference in your paycheck is your skillset.” “You’re already a software tester—you just don’t know it yet.” [ “I didn’t set out to build a company. I said yes to myself.” [ “AI still needs human oversight.” “My journey was already different, so I had to build something different.” 5. Overall Message Jennifer Gaddis’s interview reinforces a central theme of Money Making Conversations: Income growth follows skill alignment, not traditional credentials. Her journey reframes: Fear → strategy Job loss → skill expansion Limited access → self-investment The interview serves as both motivation and roadmap for anyone seeking financial mobility through tech—without gatekeeping. #SHMS #BEST #STRAWSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Jennifer Gaddis. Interview Summary Show: Money Making Conversations MasterclassHost: Rushion McDonaldGuest: Jennifer Gaddis – Senior Quality Assurance Engineer, Educator, Founder of Road to QA 1. Purpose of the Interview The primary purpose of the interview is to inspire and educate everyday people—especially those without college degrees or traditional tech backgrounds—on how to pivot into technology careers, specifically Quality Assurance (QA), and to reframe fear around AI, layoffs, and automation into opportunity. Jennifer’s story is used as proof of concept that: You do not need a college degree to succeed in tech Transferable skills already qualify many people for QA roles AI does not eliminate jobs—it creates new opportunities Strategic career pivots can result in life-changing income and freedom Rushion positions Jennifer not only as a success story, but as a new blueprint for wealth-building through skills, not credentials. [ 2. Interview Overview (High-Level Summary) Jennifer Gaddis shares how she: Pivoted into tech in 2021 with no degree Went from $40K to six figures within 90 days Built a $400K+ remote household income with her husband Created Road to QA, helping 200+ people land tech jobs Accidentally built a multi-million-dollar education business Used personal hardship, COVID, financial stress, and family responsibility as fuel—not limitations She explains what Quality Assurance engineering is, why it is resistant to AI replacement, and how regular users of apps are already doing parts of QA work without realizing it. 3. Key Takeaways A. You’re Already More Qualified Than You Think Jennifer emphasizes that everyday digital behavior translates into QA skills: Using apps Identifying bugs Expecting software to “work correctly” Navigating systems as an end user This insight forms the core of her teaching philosophy. B. The Faster You Add Skills, the Faster You Increase Income Jennifer repeatedly notes: “The difference in your paycheck is your skillset.” By stacking skills (manual QA → automation → AI testing), professionals increase their market value, not just job security. C. AI Is a Career Accelerator, Not a Threat Rather than fearing AI, Jennifer encourages people to: Work alongside AI Become the humans overseeing AI systems Move into hybrid QA + automation + AI roles She stresses that human oversight is still required in tech deployment. D. Entrepreneurship Can Be Accidental—but Scalable Jennifer did not initially plan to build a company. Her business emerged from: Instagram stories A $97 beginner e-book Real student outcomes Her willingness to: Raise prices Build systems Hire specialists Learn financial discipline Allowed Road to QA to grow sustainably. E. Representation and Access Matter Jennifer openly discusses: Being a Black woman in tech Coming from financial insecurity Navigating family obligations Redefining success for future generations Her story challenges stereotypes about who “belongs” in tech careers. [ 4. Notable Quotes from the Interview “I landed my first year in tech within 90 days.” [ “The difference in your paycheck is your skillset.” “You’re already a software tester—you just don’t know it yet.” [ “I didn’t set out to build a company. I said yes to myself.” [ “AI still needs human oversight.” “My journey was already different, so I had to build something different.” 5. Overall Message Jennifer Gaddis’s interview reinforces a central theme of Money Making Conversations: Income growth follows skill alignment, not traditional credentials. Her journey reframes: Fear → strategy Job loss → skill expansion Limited access → self-investment The interview serves as both motivation and roadmap for anyone seeking financial mobility through tech—without gatekeeping. #SHMS #BEST #STRAWSee omnystudio.com/listener for privacy information.