Podcasts about QA

Olaitan Fashanu: When the New PO Stops Refining—and the Team Starts Self-Destructing 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.   "If we're actually doing the job of refining this ticket properly, then we will not be creating this tension in the team." - Olaitan Fashanu   The team was working well. They had a strong PO who came to refinement with the problem clearly framed: this is what we want to solve, here's the context, here's the user story, here are the acceptance criteria. The team picked it up, refined it, ran with it. Then change came. A new PO joined—and the routine collapsed. The new PO cared about one thing: hitting the delivery date. Tickets dropped into Jira with no context, no problem statement, no acceptance criteria. Just "this needs to ship by end of month." Within weeks, Olaitan saw the symptoms cascade through the team. Developers asked designers what tickets even meant. QA struggled to maintain quality. Tension built. The diagnosis was clear: refinement had broken. His fix? Bring back the Definition of Ready as a non-negotiable shared standard, and introduce a product trio—business viability, technical feasibility, and design usability collaborating on every story before it reaches the rest of the team.   In this segment, we talk about the Definition of Ready and the product trio collaboration model.   Self-reflection Question: What's the symptom you're seeing in your team right now—and could the real source be how stories are getting refined, not how they're getting built? Featured Book of the Week: The Secrets of Facilitation by Michael Wilkinson Olaitan calls out The Secrets of Facilitation by Michael Wilkinson as the book that shaped how he handles difficult moments. The book teaches the power of asking the right question at the right time—clarifying questions, probing questions, the questions that drive a stuck group forward. "You will understand how, when to ask clarifying questions, ask really powerful questions that will help you drive or probably help you reach your goal in any session you find yourself." For Olaitan, the biggest payoff was learning to manage group dynamics in real time—what to do when something said in a meeting lands badly, when a comment threatens to derail the room. As a Scrum Master, you live in those moments. This book hands you a toolkit for them.   [The Scrum Master Toolbox Podcast Recommends]

Your AI code review tools read the diff. They stare at your code. But they never actually run it. So the bugs that only show up at runtime, the broken user flows, the bad query plan, the duplicate submission, sail right past review and land in front of your customers. In this episode, Joe Colantonio sits down with Evan Marshall, founder of Ito and a fifteen year engineer who spent five years in applied cryptography securing hundreds of millions of dollars for millions of people. Evan is taking that ship fast without breaking things discipline and pointing it straight at testing. Ito is an agentic QA platform that builds and runs your actual app on every pull request, navigates it like a real user, exercises the frontend and backend as one system, and brings back real runtime evidence: video replays, logs, the exact lines responsible, and steps to reproduce, posted right in your PR. You will learn: Why static code review misses the bugs that cause real production incidents How Ito spins up ephemeral environments and tests across UI, API, and database Why QA is not disappearing, it is leveling up into a manager and quality strategist role How to keep your test layer separate from your code generation so your signal stays honest The skills testers and engineers need as AI writes more of the code If you are shipping AI generated code at high velocity and your QA cannot keep up, this one is for you. Try Ito on your own code. Your first ten pull requests are reviewed free, no credit card required. Check it out at https://testgld.link/itoai now. And as Joe always says, seeing is believing.

QA podcast： Can career and bhakti go together？ Kolkata - Chaitnya Charan by Exploring mindfulness, yoga and spirituality

跟著可樂老師幫你安排好的12週學習計劃， 兩週一次就有直播 QA解答， 課程精選113 個關鍵句型， 幫你穩穩打好日文基礎輕鬆朝 N4 前進。 募資期間報名送可樂老師全新《N5句型與聽寫練習寶典 上冊》 把握8/5前的募資優惠，趕快加入！ 報名課程連結： https://shifuclass.com/U0Vhr EZ JAPAN 粉絲專屬折扣碼：japan350 結帳輸入可再折 350 元 博客來： https://pse.is/8z8pz3 誠品： https://pse.is/8z8qbw 金石堂： https://pse.is/8z8qmj 蝦皮：  https://pse.is/8zg4uv - 官方 DISCORD：點進去跟我們互動吧 本集教學內容：教學內容 來IG學更多：EZJapan IG -- Hosting provided by SoundOn

I episode 136 tar vi en fot i bakken på vår egen investeringsreise. Vi ser tilbake på hvordan porteføljene har utviklet seg hittil i 2026, hvilke posisjoner som har vært de største driverne, og hvilke selskaper som har overrasket oss – både positivt og negativt.Før vi går inn i praten, forteller vi også om endringene i StockUp fremover. Vi har gått over til en forretningsmodell som innebærer reklame, og ønsker derfor å lansere et reklamefritt alternativ. Vi har gleden av å annonsere et samarbeid med Supercast med tre supporter-nivåer (Bronze, Silver og Gold) for de som ønsker å støtte podcasten og få tilgang til mer fellesskap og å følge tettere på vår investeringsreise. Supercast StockUp Premium Tiers;- Bronze: Reklamefri lytteropplevelse- Silver: Reklamefri lytteropplevelse, 12timers pre-release og tilgang til investornettverk og innsikt i Faruk & Kenneths tanker på reisen- Gold: Samme som Silver i tillegg av tilgang til porteføljer og 1-3 QA investormøter i løpet av året.Link: https://stockup.supercast.com/Vi forsøker å lære så mye som mulig på vårer reiser, og snakker da også åpent om feil vi har gjort, ting vi angrer på. Et viktig tema er hvordan investeringsfilosofien vår har utviklet seg de siste årene. I tillegg deler vi hva vi er mest spent på i markedet akkurat nå, hvilke temaer vi følger ekstra nøye med på, og hva vi aktivt velger å unngå.Som vanlig blir det også en oppdatering på hvilke gjester og samtaler som har satt spor hos oss, samt bokanbefalinger og ett viktig takeaway vi håper lytterne tar med seg. En episode for deg som liker å følge med på hvordan to aktive investorer tenker, reflekterer og justerer kursen underveis.Vi takker for følge dette første halvår av 2026 og gleder oss til fortsettelsen i August. God sommer til alle sammen.Episoden er spilt inn for informasjons- og underholdningsformål, og innholdet i episoden skal ikke anses som en investeringsanbefaling. Innholdet er ikke sponset. Podcastvertene kan være være investert eller ikke i selskapene som diskuteres. Gjør alltid egen research og konsulter en finansiell rådgiver for råd.StockUp Discord: https://discord.gg/Kmhkb6CgFB Linktree: https://linktr.ee/StockUpPodcast X: https://x.com/HanFarStockUpX: https://x.com/k_astorp Hosted on Acast. See acast.com/privacy for more information.

… The Three Weeks And The Daughters Of Lot (Ep. 333) Rabbi Dovid Orlofsky Download Sponsored 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.

https://youtu.be/b_G8krkwKv8 Ganesh Krishnan, CEO of AiHello, is helping Amazon sellers automate advertising, improve profitability, and scale their businesses using AI. Driven by a mission to give entrepreneurs more freedom and enable them to build businesses around products they love, Ganesh shares how AI can eliminate repetitive work while allowing business owners to focus on strategy, innovation, and growth. In this conversation, Ganesh introduces The AiHello Ads Framework: Tap into the Wisdom of Crowds, Find the Right Keywords, Bid at the Right Level, Dynamically Adjust Bids, and Rinse and Repeat. He explains how AI can leverage historical marketplace data to identify profitable keywords, optimize bids automatically, and continuously improve campaign performance. Ganesh also discusses the dangers of AI hallucinations, why Amazon's incentives differ from sellers' incentives, how AI has transformed his own company's operations, and his vision for building zero-hallucination AI systems capable of advancing toward artificial superintelligence. — Build AI Superintelligence with Ganesh Krishnan  Good day, dear listeners. Steve Preda here, and welcome Ganesh Krishnan, the CEO of AiHello, an Amazon Ads automation company helping you grow your revenues, reduce work hours spent on ads management, and decrease your ad costs. Welcome to the show, Ganesh.  Thank you, Steve. Nice to meet you  Well, it’s great to have you here, and let’s jump right in. And my first question is, what is your personal ‘Why,’ and how are you manifesting it in AiHello?  So it started off with my thesis that we all need to do good towards the planet. A long time ago, I started having my own natural things, selling chemical-free, ecological, sustainable, good-for-the-planet, good-for-your-wallet, good-for-your-health items, and I would sell organic items. And eventually, what I realized was that it was taking a lot of my time marketing, managing it, changing the bids, doing everything. I started working more and more on AI because I’ve worked in AI commercially. I worked in AI in my industry. That was my job. So I said, “Why not use, apply that to my own startup, to my own industry for selling organic things?” And once I started selling it, some of my friends reached out and said, “Can we use your AI for our own businesses?” And I said, “Sure, why not?”  And then I started opening it up. And then one person came through and said, “Okay, let’s release it to the general public, see how it goes.” And then as we started earning money, I realized that I don’t need to do a job. I can have this startup, and I can help different people have their own lifestyle. You could have your own lifestyle. You could sell your own stuff that you like, e-commerce, usually on Amazon, and then we help you have your lifestyle. So this is my personal ‘Why’, is we need more equality. We need more people doing stuff they love rather than doing stuff they hate to do, and they hate to wake up and go to work. So do what you love. We are here to empower you.  Wow, that’s amazing. So you are empowering people to start their own e-commerce businesses on Amazon, and you help them with AI tools to get up to speed and compete with the big boys.  That is correct.  Yeah. I love it. So on your LinkedIn profile, you mentioned that you are, I don’t know what the word was that you used, but something to do with superintelligence, AI superintelligence. So what is it that you are doing, and what is your vision of how AI superintelligence can be tapped into?  It’s a very long topic. But to start off with, we used the old form of AI, which is a lot of regression, a lot of statistics, a lot of big data learning, and a lot of neural networks, if you felt fancy. And then LLMs became a huge thing. And we launched AiHello probably six or seven years ago. LLMs became a big thing two or three years ago. And it was pretty fancy. It was very good. It made life easy for us. But we cannot use it within AiHello to give it to clients, primarily because LLMs start hallucinating once you go past a certain context. The problem with hallucination is that it exponentially becomes larger and larger. Because if the previous thesis is wrong, if your previous hypothesis is wrong, then it builds on top of it, and it builds the wrong things.  Hallucination exponentially becomes worse. And when it comes to finance, when it comes to ads, and when you’re working with sensitive data, this can be catastrophic. So you cannot use these large language models for finance, for situations where you need precise data, and especially when you have lots of context. It’s going to lose the context of the first part. Just because you mentioned something at the start of the conversation doesn’t mean it’s not important. It is critical. As humans, we understand what is the most critical part of a conversation, and then we keep that in mind. But LLMs, because of context limitations, just keep on going and start hallucinating.  So a few months ago, we came up with the idea that we could use something like a large language model, but not based on the transformer model. And we could base it on data so that there is almost zero hallucination. So instead of building weights, we build it based on data. And we launched this. We don’t use it on AiHello, but we decided to use it on an email service because we have a lot of emails. We process a lot of emails for clients. We process a lot of emails for specialists. So we could use the zero-hallucination approach within emails, and if it is successful, then we can put it into AiHello.  And we can, of course, release it as an API as well. So this is going to set the basis of artificial superintelligence because what is stopping us right now from reaching or breaching that wall of artificial superintelligence is this hallucination. And of course, there is also logic. LLMs are pretty stup*d. They don’t understand. You can teach them, they learn, but they do not question what you teach them. They always take it on blind faith.  Yeah. Wow. That is genius. I love it. You are going to un-hallucinate AI. And if it stops hallucinating, essentially it becomes a lot more powerful and scalable. AI becomes scalable, or this whole process becomes scalable. That’s fascinating. So your ‘Why’, your mission, is to empower all these people to run their businesses. Do you have a framework for this that you could describe in three to five steps? How do you get someone up and running with their own business on an e-commerce platform? Or do you have any other framework that you could share with the audience? Something simple that they may be able to benefit from? One of the caveats of using AI is that it needs a lot of data. So if you’re just starting out with your e-commerce business, you need to put more of your human intelligence, more of your gut instinct, more of your thoughts, and more of your emotions into building it out. And once you have built up enough data, then you can put it into AiHello and start automating it. So what I would say, if you’re starting an e-commerce business, is hire a specialist who can help you launch off the ground.  Do a bit of the hypothesis work, do a bit of the analysis, and then come to AiHello and start automating it. You can only start automating once you have a good idea of how things work for you. And finding how things work for you is something you need to do on your own. It’s like you can’t start running, or you can’t start driving a car, until you learn how to crawl and until you learn how to walk.  Okay. So basically, it’s the age-old innovation thing that you have to innovate something on your own, and then you can scale it with AI. That is correct.  Yeah. So let’s say I came up with some kind of formula, concept, or product that is currently not being promoted, and I believe it would work. Or maybe I’ve already tested it and I want to scale it. I want to get on Amazon and sell it there. What can you do for me? What are the steps for me to be successful with AiHello’s help? So the first thing when you select a product, is: what are the keywords for it? What keywords do you use for that product? The second would be: what are the bids for that product? For each keyword, what is the right bid to put up? And then you have other things like budgeting. Do you change the bid depending on the time of day? Do you change the bid in total? Those are the things that you need to keep adjusting continuously.  With AiHello, we automatically harvest the right keywords for your product. We change the bid. We optimize the bid. We also do dayparting, where you can change the bid depending on the time of day. So there are different things that you can use AI for. You could certainly do all of it manually, but it’ll probably take you days or weeks to do what AI can do in a couple of minutes.  So a couple of minutes. But doesn’t the AI also need traffic data to be able to define things?  Yeah. So one of the other things about AiHello is that, because we have the wisdom of crowds, if you come up with a keyword, we know exactly how that keyword is going to perform. As you say, you have the wisdom of crowds. Can you extrapolate what you’ve experienced with other products and other customers onto a new product that doesn’t yet have a lot of traffic? Is this what you mean by the wisdom of crowds? Or what do you mean by the wisdom of crowds?  Let me give you an example. Let’s assume you want to sell coffee, and you go to our platform and say, “This is my product. It’s coffee. Help me sell it.” So what we do is, we know this is coffee. What are the keywords around it that are going to help sell it? Because we’ve sold other coffee products, we know that organic coffee sells well. We know coffee in the morning sells well. Black coffee sells well. Caffeine sells well.  And we also know, based on the previous performance of other keywords, what a good bid is for each keyword. If you don’t know the keywords, then of course you have to spend time researching them. And if you don’t know the bids, then you have to spend time researching what bid to put in. But we do all the research for you, and you put it in. And the second part, the bigger part, is that if the bid doesn’t work out, if you’re not selling, then we increase the bid automatically. If you are losing money, then we decrease the bid automatically. So that bid optimization is a critical part of AiHello.  Yeah. We use Amazon ads to promote my books. And yes, it takes a lot of skill to find the keywords, eliminate the negative keywords, adjust the bids, have the right bids, and avoid overspending or underspending. But Amazon also does much of the machine learning. So what is it that Amazon does, and what is it that you have to do? And why doesn’t Amazon do what you have to do?  The most critical piece of information to keep in mind is that your aims and objectives are the opposite of Amazon’s aims and objectives. Amazon’s aim is to make money, and your job is to make money. You don’t care if Amazon makes money or not, and Amazon doesn’t care if you make money or not. So when you put up a bid, when you run ads, Amazon will maximize that ad spend, whatever it is. In some ways, it’s like a casino.  You go to a casino, and the job of the casino is to win money from you, and your job is to win money from the casino. Ads have become a lot like gambling nowadays. You throw money into it. You expect to make money. Ninety percent of people lose money, and they give up. And Amazon always finds fresh sellers to move on. You cannot depend on Amazon because Amazon is not on your side.  Yeah, that makes perfect sense. Yeah, I always thought that on some platforms it was really difficult to make money with ads. Facebook, I think, is so competitive that it’s probably very difficult to make money. I know a lot of people who have spent a lot of money on Facebook, but I don’t know very many who have figured out a formula that continues to work. Okay. So you’ve helped someone find their keywords, the right bids, and how to adjust those bids. But what we’ve found is that at some point, ads die, and then we have to switch things up. It actually happens quite frequently that you have to create new campaigns and new ads. So what’s the dynamic there? How do you optimize so that you’re not still supporting ads that don’t work anymore, and you switch at the right point?  So when we say ads, it’s not technically the campaigns. A campaign is just a container for all of your ads. You have products inside it, and you have keywords inside it. So a campaign is made up of products and keywords. And the question is, when you say ads die, did the keywords die? Then you need to add new keywords, right? You always have to keep adding new keywords and testing new keywords. It’s a continuous job of trying to find the right keywords for your book or your product, and then optimizing the bids constantly to make sure that you’re profitable.  You have to make sure that your ads don’t die because of a lack of fresh keywords. And of course, there’s always a limit to the number of keywords you can add because each product has a limited number of keywords that people are searching for. Maybe there’s a long-tail keyword that’s going to make money, but there’s not enough search volume. Or maybe there’s a high-volume search keyword, but it’s not profitable for you. So you have to figure out what the right strategy is for you. Eventually, if your product is good, you’ll make money. If your product is not good, you won’t make money. That’s the bottom line. With ads, you quickly find out if your product…  So essentially, it’s a cyclical thing. So you find the keywords, you figure out the right bids, you adjust the bids, and then you have to find new keywords and keep doing this.  Yeah.  So why do keywords go stale? Do people not search for certain things anymore?  There could be multiple reasons for it. One reason is that a competitor has come in and taken your search volume. And you have to know: are you losing search volume? Are you gaining search volume? Has your search volume dropped off? The second reason is that people are not searching for that keyword anymore. Is it out of fashion? The third is: are you underbidding? Is the bid too low? Again, you would know by the number of impressions. Have the impressions dropped off?  If the impressions have dropped off, is it because of a competitor? If it’s not because of a competitor, are people searching less? Are your bids too low? If the search volume is the same, are people clicking less? Why are they clicking less? Is it your images? Is it your product? Is your product no longer in fashion? I mean, I don’t know. Maybe a few months ago, fidget spinners were really in fashion, and nowadays no one uses them. So those things go out of fashion.  Yeah. The spinners, I remember. They’ve been out of fashion for a while.  Yeah.  Yeah, that’s fascinating. So it’s a never-ending cycle of innovation and figuring out what works and what doesn’t work. So let me ask you this: What drives growth in your business?  Most of the growth is… There are different ways to put it. Four years ago, we used to create a lot of blogs. We used to create lots of content. We used to create lots of YouTube videos. And then ChatGPT came along. If you ask kids now, “Do you Google that?” They don’t know what Google is. They really don’t know what Google is. And that’s not a cliché. It’s surprising. They’ll be like, “What Google?” Everything goes through ChatGPT.  So for us, growth went from Google to ChatGPT. And we didn’t spend enough time optimizing for LLMs on our site. So what drove growth before was blogs and YouTube. And what drives growth now is large language models like ChatGPT and Claude. People just ask ChatGPT, “What do I do about this on Amazon?” It recommends solutions, and then we go through them.  So how do you leverage large language models or AI applications?  This was one of the biggest boosts to our company. We managed to set the processes right. We managed to create the templates. We managed to bring structure to our company. Development work has become ten times faster. The turnaround is ten times faster. We’re able to release features quickly. We’re able to find bugs in our existing code quickly. There are a lot of things going on. If I were to say that our company is no longer the same company it was even a year ago, that would not be an exaggeration. It would be the truth. What we were a year ago is not at all what we are right now.  So in what way did you change? Is it coding that accelerated and changed everything? I mean, in what other ways did you change as a company?  So the code is all done with AI first. Our developers use AI. They put in the prompt, they check the results. There is a second developer who checks whether everything is okay and whether everything is done. And then finally there’s QA, and then we push it to staging. We used to do roughly one-month or forty-five-day sprints. Now we do weekly sprints. So it has gone four times faster. The biggest hurdle for us was managing clients and how we manage them. We never had any structure.  So we talked a lot with ChatGPT. We talked a lot about what the right way was to bring structure and accountability into the system. We managed to set up all the software required for accountability. It helped us fix those issues. It created structure. It created accountability for all the people, and then we implemented that. Finally, the last one, which was the most debatable, is that we require a lot of content. We require a lot of graphics. We require a lot of videos for clients on Amazon. I actually went to buy something on Amazon a few days back, and what was puzzling was that when I zoomed in on the images, you could see they were AI-generated because they all had these silly AI mistakes—spelling mistakes, random words.  So almost everything on Amazon right now, all the images, are kind of AI-generated. It’s hard to blame them. We ourselves use AI for a lot of the images. We make sure we don’t have the silly mistakes, but we do use AI as well. So the turnaround time for graphics is faster because of AI as well. Though some clients do complain that they don’t like AI-generated assets. And if a person looks a bit too AI-generated, they just reject it outright. So that is the most debatable part of it. But overall, our company is called AiHello. It’s AiHello. And if we don’t say hello to AI, then we’re not AiHello.  Yeah. Love it. I love the head and the one arm.  Yes.  The hello, and that’s it.  Yeah.  So what is one thing that you’re actively trying to figure out in your business right now? We are a remote-first company, and I’m struggling to bring about accountability among all the team members. We do have a good number of employees. Ninety percent of our employees are good. Ten percent still have accountability issues. And for me, that is a bit of a hurdle. It is a bit of a challenge to push those people who are dragging their feet about AI. Yeah. Because they are not comfortable with AI. They want to do what they are good at and don’t want to do something new.  There is also a bit of hesitation that they might lose their jobs because of AI, although we’re not planning to let go of anyone. Rather, we are hiring more people because we’re able to grow faster. There is an old saying that companies won’t go extinct because of AI, but companies that don’t use AI will go extinct because of AI. Because we are using AI a lot, there is a chance for us to scale, for us to expand significantly. And I want to tap into this advantage and grow. I want to hire more people, and I want to grow. I don’t want to let people go.  So this is a very good opportunity. You hear about Coinbase letting people go. You hear about Facebook letting people go because of AI. And I think those are all nonsensical excuses. Those companies are not growing very well, and they are blaming AI for letting people go, which I think is absolutely nonsensical. There is a very good opportunity for people to grow and for companies to grow using AI and increase their hiring. If you’re letting people go because of AI, it’s just a nonsensical excuse.  So what do you think is the mental hang-up for people? What prevents better AI adoption or faster AI adoption? A long time ago, when computers were being introduced into many industries, I remember there were huge protests because people thought computers would take away jobs. And it did happen. People did lose jobs because of computers. There were many people pushing papers who lost their jobs. And a lot of people refused to learn about computers because they said, “This is nonsensical. I can do it better by hand.” Can you imagine telling people right now that it’s better to do things by hand than to use a computer?  I mean, if you want to do calculations, please don’t use Excel or Google Sheets. Use a pen and paper and tell me you can do it better. It would be absurd to think that way. But at that time, people really did have the mentality that it was better to do things by hand than with Excel. Now, the AI revolution is probably a thousand or a million times bigger than that. And you can drag your feet. There will always be people who drag their feet and say, “I can do it better. AI is just nonsensical.” And sure, some of that is true. But the overwhelming majority of tasks are going to be done extremely well with AI.  And it’s not just large language models. It’s everything. Regression analysis, data analytics, big data analytics, forecasting, calculations. I’m not even talking about transformer models. I’m talking about everything related to AI. So much can be automated and done by AI that if you’re not involved with it, you’ll get left behind, just like the people who didn’t use computers. Do you feel like people have to be highly educated to be able to use AI? Or can people with less formal education benefit from it as well?  I don’t think it has anything to do with education. I think the learning curve for AI is smaller than the learning curve for computers. If you’re already using computers, you can just install a command-line interface and have things running. Actually, you can go to ChatGPT and ask some questions, and you can build something. But if you want to build serious applications, you can use a command-line interface and build them out. I think the learning curve is probably just a couple of hours to become proficient with these tools. I’m thinking more about this: As AI tools develop and take many of the routine, repeatable tasks off our shoulders, doesn’t that mean we will spend more of our time on high-level thinking and orchestration? And won’t that require some kind of mental ability to do that? It requires you to understand context, understand the implications of things, and be able to connect the dots. So that’s what I mean. The people who can really use AI tools have this higher level of awareness and thinking. They can combine ideas and create new things. But are there AI tools that people with less advanced analytical skills can also use? Absolutely. And you’re 100% right. You’re 101% right. This is what I’ve been advocating for a very long time. Don’t spend your time doing mundane, repetitive daily activities that can be automated. Let AI handle them. You should focus on the things AI cannot do right now, which is human-level intelligence: Strategizing. Planning. Working on the bigger-picture tasks. So you’re 100% right, and that’s the direction we should be moving in. And this brings me back to the point I made earlier: You should do what you love. The things you don’t love, the repetitive tasks, should be done by AI.  Yeah. Love it. So what is your vision, ultimately, for AiHello?  So my vision for AiHello goes beyond AiHello. We have something called HalZero, which is the engine we want to put behind AiHello. It’s a zero-hallucination LLM. And we are working toward making it happen. We plan to release an API for it soon. If it does happen, then we would probably have a model that can take in data and answer general-knowledge questions with zero hallucination. And we’re building it based on how the human brain works. The human brain is not one-dimensional. ChatGPT is one-dimensional. Transformer models are one-dimensional.  You give them data, they run it through the transformer model—the encoder and decoder—and then they give you an answer. But the human brain is built in layers. What we call the lizard brain sits at the base, and as you go higher, things become more and more complex. So the brain is information and action, and everything is filtered through it. Then we act on the filtered result. Machine learning models right now do not have these kinds of filters. They have something similar, which is called chain of thought, but that’s really thinking out loud. This kind of reasoning should exist within the latent space of the machine learning model. It should be built into the model itself.  I’ll give you an example. If you had been taught all your life that the sun is green, and tomorrow you woke up in Virginia, went outside, and saw that the sun was yellow, you’d say: “Oh my God, I’ve been lied to all my life. The sun isn’t green.” You would question what you had been taught based on a single observation. But if a machine had been trained for years that the sun is green, and then it saw that the sun was yellow, it might conclude: “The sun is wrong today because I’ve been taught that the sun is green.” The real test of intelligence is this: Can it question its training data? And the answer is no. It won’t, because it has been trained on that data. It has been trained on those tokens.  Yeah. So that’s AI superintelligence? The ability to question the training data?  That is correct. Yeah. So we build it based on connections. How strong is this connection? How many people have stated this fact? What is my own observation? Which observation is stronger? There is always conflict. In the human brain, there is always a conflict between what people say and what we think. Then our logical brain chooses what is usually the best answer. That is how we have a collective consciousness. We also have a personal consciousness. We always have to decide which one is best.  Love it. Well, that’s great. So if you’re running a business and you need to sell a product, and you want to figure out how to be successful on Amazon, how to leverage your ads, and how not to overspend, where should you go? How can people get in touch with you, Ganesh, and your team? And what’s the first step for listeners?  You can send me an email at ganesh@aihello.com. You can connect with me on LinkedIn. I’m always available, and I’m happy to have a chat with you.  All right. So if you’re listening out there and you’re in e-commerce, or you want to get into e-commerce, and you don’t know how to leverage all the tools that are out there, don’t forget: Amazon is in the business of making money, not necessarily making your business profitable. So you can use AiHello to help you. Reach out to Ganesh on LinkedIn and get your team involved. And if you enjoyed listening to this episode, make sure you check back every week because I have successful entrepreneurs sharing their ideas—or at least some of the good ones—with you. So thanks, Ganesh, for coming.  Thank you, Steve.  And thank you for listening. Important Links: Ganesh's LinkedIn Ganesh's website Ganesh's email: ganesh@aihello.com

Au Niger, c'est la deuxième fois de l'année que l'aéroport international de Niamey est visé par un groupe jihadiste. La dernière attaque s'est produite jeudi dernier, et a fait treize victimes selon les autorités : onze militaires et deux civils. Mais cette fois-ci, apparemment, la junte au pouvoir au Niger n'a pas eu besoin de l'aide des Russes pour repousser l'assaut des terroristes. Alors peut-on parler d'un succès ou d'un échec pour les miliaires au pouvoir à Niamey ? Le chercheur nigérien Brimaka Abdoul Azizou Garba enseigne à l'Institut de sciences politiques de Louvain-Europe, en Belgique. Il a été aussi conseiller spécial du président Mohamed Bazoum. Il répond aux questions de Christophe Boisbouvier. RFI : En janvier, les terroristes avaient attaqué l'aéroport de Niamey avec des motos. Cette fois-ci, ils ont essayé de s'y introduire par la ruse en se faisant passer pour des passagers. Pourquoi ce changement de stratégie ? Brimaka Abdoul Azizou Garba : Effectivement, lors de la première attaque, ils seraient arrivés à moto et pour cette deuxième, les informations, en tout cas, font état de l'usage de véhicules, notamment de taxis et de minibus. Donc, ce qui complique la détection et la prévention. C'est vrai qu'après la première attaque, des mesures avaient pourtant été prises pour renforcer la sécurité à l'intérieur et aux abords de l'aéroport. Mais, apparemment, cela n'a pas dissuadé les terroristes qui ont peut-être pu infiltrer la capitale pour mieux observer et tester les dispositifs et exploiter les moindres failles. Depuis quelques semaines, le régime militaire nigérien est en train de détruire un certain nombre de quartiers autour de l'aéroport pour mieux protéger celui-ci. Est-ce pour cela que les assaillants du 18 juin ont tenté de rentrer par la ruse en se déguisant en simple passagers ? Sûrement, parce qu'on a vu que ces mesures ont tendance à un peu dégager, décongestionner l'aéroport, en déguerpissant le quartier mitoyen. Et je pense que tout cela est observé de l'intérieur. Donc, c'est pour cela que je parle d'infiltration. Et ça, c'est typique des conflits asymétriques où l'on utilise l'effet de surprise. En janvier, l'attaque avait été revendiquée par l'EIS, l'État islamique au Sahara. Cette fois-ci, elle est revendiquée par le Jnim. Y a-t-il une coordination ou une compétition entre ces deux groupes terroristes ? Je ne pense pas que ce soit une coordination ou une compétition. Ce qui est sûr, c'est que les deux attaques à l'aéroport, c'est à cause des drones qui s'y trouvent et que les terroristes n'arrivent pas à se mouvoir comme ils veulent à cause de ces drones militaires. Je pense que l'objectif, c'est de tout faire pour détruire ces vecteurs aériens. Je dirais plutôt qu'il faut peut-être explorer un rapprochement entre l'Iswap [État islamique en Afrique de l'Ouest, NDLR] et le Jnim [lié à al-Qaïda, NDLR]. Je pense que, s'il y a coordination, peut-être c'est à ce niveau entre le Jnim et l'Iswap, du côté du bassin du lac Tchad, mais pas au niveau de l'EIS, l'État islamique au Sahara. Et est-ce que le Jnim et l'EIS, l'État islamique au Sahara, ont des ambitions politiques semblables ou différentes ? Je crois que, dans un premier temps, leur objectif, c'est d'affaiblir le Niger parce que c'est le pays quand même le plus solide des trois, où ils n'arrivent pas à prendre, à contrôler un espace. Et l'objectif, a priori, c'est de chercher à affaiblir l'État du Niger sur le plan militaire pour pouvoir s'en prendre facilement aux deux autres. C'est-à-dire qu'au Niger, les jihadistes n'arrivent pas à se tailler un fief comme au Mali ou comme au Burkina Faso ? Oui, les jihadistes n'arrivent pas à le faire parce que le Niger a vécu quand même 12 ans de stabilité politique, 12 ans de sécurité et de développement. Et je pense que le Niger a eu beaucoup d'acquis, y compris sur le plan militaire. Et ça, ça a fait que le Niger s'est largement démarqué des deux autres [pays membres de l'Alliance des États du Sahel, NDLR]. Et ça ne serait pas du tout facile pour les terroristes de pouvoir contrôler un espace au Niger, comme ils l'ont fait au Burkina Faso et au Mali. La résistance farouche des militaires nigériens le 18 juin, est-ce le signe que la junte commence à s'organiser face aux attaques terroristes, ou est-ce à votre avis un acte de désespoir sans lendemain ? Je connais bien nos militaires, je sais que ce sont des militaires qui sont braves. Il suffit de mettre les bonnes personnes à la bonne place pour qu'on puisse voir la différence. On a de très bons militaires, on a de très bons chefs militaires qui sont capables de bien planifier et de bien mener la résistance. Je crois que, dans les mesures que l'état-major a pu prendre, il y a sûrement eu des changements au niveau du dispositif et au niveau de la planification, et c'est ce qui a peut-être donné ce résultat. Au final, cette attaque terroriste repoussée devant l'aéroport ce jeudi 18, est-ce un échec ou un succès pour l'armée du Niger ? Moi, je pense que c'est un succès parce que, lors de la première attaque, on a vu que ce sont les Russes qui sont sortis pour dire : « Bon, on a sauvé le site, sans nous, ils allaient prendre votre aéroport. » Et le général Tiani [qui dirige le Niger depuis juillet 2023, NDLR] l'a dit lui-même dans le discours qu'il a fait : il a remercié les partenaires russes. Et je pense que, cette fois-ci, la riposte est venue des soldats nigériens et ils ont tout le mérite. Et ça montre une fois de plus que l'armée nigérienne est bel et bien capable d'assurer la sécurité de l'État. À lire aussiNiger: le Jnim revendique l'attaque de l'aéroport de Niamey qui a tué au moins 11 soldats et deux civils

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.

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A new boss thought he could save money by cutting a freezer maintenance contract, but the people working inside the cold-storage warehouse knew exactly how dangerous that gamble could be. In this workplace drama, a routine holiday weekend turns into a high-stakes mess involving freezer alarms, spoiled inventory, emergency maintenance, food safety rules, QA holds, and one cost-cutting decision that may have gone way too far. If you enjoy bad boss stories, malicious compliance, warehouse disasters, expensive mistakes, and satisfying tales of managers facing the consequences of their own decisions, this episode is for you.

Join Pramin Pradeep, Co-founder and CEO of BotGauge AI, for a critical look into the unseen risks accelerating behind the generative software boom. For decades, production systems were built by humans and, at least in theory, understood by humans. Today, with engineers heavily utilizing AI coding tools, software is being generated and deployed at a velocity that outpaces our collective capacity to comprehend it. In this episode, Pramin draws from a decade of enterprise QA transformation—including scaling a startup to its acquisition by Sauce Labs—to unpack the rise of "shadow code" and explain why accelerated, autonomous testing has shifted from an operational luxury to a decisive SaaS competitive advantage.

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On episode 56 of Generationship, Rachel Chalmers sits down with Mark Brocato, founder of Mockaroo and creator of Fabricate, to explore the evolution of synthetic data in the age of AI. Mark shares how a simple internal QA tool grew into one of the most widely used synthetic data platforms and discusses how agentic AI is transforming software development, testing, and data generation.

On episode 56 of Generationship, Rachel Chalmers sits down with Mark Brocato, founder of Mockaroo and creator of Fabricate, to explore the evolution of synthetic data in the age of AI. Mark shares how a simple internal QA tool grew into one of the most widely used synthetic data platforms and discusses how agentic AI is transforming software development, testing, and data generation.

Topics covered in this episode: pi + superpowers Terminal: Warp.dev + OhMyZSH {Blink,kitty} + mosh + tmux Claude code MacWhisper or Handy Tailscale Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Six Feet Up is hosting a LinkedIn Live Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Calvin: @calvinhp@sixfeetup.social / @calvinhp.com (bsky) Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: pi + superpowers terminal-first, open-source coding agent Session management is a first-class citizen Extension model is what makes pi special — it's aggressively composable Superpowers brings a structured software development methodology as loadable skills Steps back and asks you what you're really trying to do “hand you the keys to the car” mode vs guardrails might not be for everyone Michael #2: Terminal: Warp.dev + OhMyZSH If you're using the base terminal with default settings, you have so much head-room for improvement. I've been using Warp.dev since Elvis talked me into it. ;) Remarkable terminal but the AI side of things is a bit junky, can be turned off OhMyZSH gives better autocomplete e.g. git branch [HTML_REMOVED] lists all branches in the local repo! Commandbookapp.com is excellent to keep the terminal focused on terminal things and more server commands and other automation in Command Book. Calvin #3: {Blink,kitty} + mosh + tmux Kitty Terminal — GPU-accelerated terminal emulator for macOS, Linux, and Windows with support for graphics, ligatures, and a powerful tiling layout system built right in. Blink Shell — The go-to terminal for iPad/iPhone power users; full SSH and Mosh client with a gorgeous interface built specifically for mobile professional workflows. Mosh — Mobile Shell replaces SSH for remote connections, surviving network switches, sleep cycles, and flaky Wi-Fi with zero dropped sessions — essential for staying connected to long-running agentic jobs. tmux — Terminal multiplexer that keeps sessions alive on your Linux server indefinitely; detach from a Mosh session on your Mac, reconnect from your iPad, and your agent is right where you left it. The combo — Kitty or Blink + Mosh + tmux creates a "persistent remote brain" pattern: your beefy Linux homelab runs the compute-heavy agent sessions 24/7, and any device becomes a thin client to drop in and out at will. Michael #4: Claude code I prefer the IDE experience, the new PyCharm + Claude integration is really good. VS Code too. Why IDE? Because we should still be present with our code and managing context is much easier. Use the best/latest models on high thinking. “Speed” is not your friend, it's just shortcuts. Create skills and agents and use them. Curate your own rules (e.g. Talk Python's Claude.md) Works well on non-coding things. Just create a folder, put a ton of files in there and it's like NotebookLM + Chat + more. Calvin #5: MacWhisper or Handy Transcribes your speech using your choice of Whisper or Parakeet models. All transcription is done on your device, no data leaves your machine. Automatic Speaker Recognition with local models. Handy is more basic, but open source and runs on all platforms. Michael #6: Tailscale No need to open ports at all, Tailscale makes machines inside the same network accessible to each other Works great for laptops, desktops, etc. But also available for servers. Though I still use cloud firewalls for servers. How I use it: My dev database server, preloaded with QA data, is always running on my home mac mini m4 pro. All my apps look for that server before looking locally and tailscale makes them always accessible to each other My local LLMs expose OpenAI API compatible APIs. Tailscale makes these accessible even while traveling or at a coffee shop. Use my mini as an exit node. All traffic is routed outbound from my local fiber network. Great to restricted IPs like accessing my servers without caring about the local IP. Screen share back to my home machines even while traveling. Listen to the Talk Python episode with Alex for a deeper conversation. Extras Calvin: Telescopo great Mac Markdown viewer/editor. Michael: One more: Typora markdown editor. Created formal documentation for many of my open source packages using Great Docs. Via Mark Little: Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Joke: No second date

What happens to QA when AI is writing ten times more code than your team can test? That is the exact problem Ivan Barajas Vargas set out to solve with Amikoo, a purpose-built AI QA agent designed to help testers, SDETs, and even developers move faster without sacrificing coverage or quality. Ivan is no stranger to AI in testing. Before generative AI became mainstream, he co-founded MuukTest, a test automation platform built on symbolic reasoning and expert systems. After six years and thousands of customer conversations, he went back to first principles to build Amikoo from scratch, this time with a harness of 12 specialized agents and 43 tools trained specifically for testing workflows. In this episode, Ivan and Joe dig into the real-world gap between AI code generation and AI-powered testing, why the QA role is being elevated rather than replaced, how Amikoo uses Playwright and page object model patterns under the hood, and where human judgment still has to stay in the loop. Ivan also shares practical advice on what skills QA engineers should be building right now and which test scenarios should never be fully delegated to an agent. If you are trying to figure out where testing fits in an agentic development world, this episode gives you a clear picture of what is possible today and what is coming next. Visit https://testgld.link/amikoo to try the freemium account, and mention you heard this on TestGuild to unlock double the free usage.

Mike & Tommy tackle whether Power BI developers are quietly becoming professional QA testers in the age of Microsoft MCP, weighing in on what separates a true senior developer from a junior when AI writes the first draft.They explore how the developer role is shifting, who owns accountability when AI-generated measures ship broken, and what skills still matter when the tool can do the typing.More on Microsoft MCP for Power BI: https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claudeGet in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

After spending years building unvalidated products that went nowhere, Tibo Louis-Lucas completely changed how he approached startups. In this episode of the ProductLed Podcast, he shares how those early failures pushed him toward a faster, revenue-first way of building, one that eventually led to the success of Tweet Hunter and Taplio, and now powers a growing portfolio of product-led SaaS businesses. Tibo breaks down why revenue is the only validation that really matters, how Tweet Hunter stood out in a crowded market by going deep on a single platform, and the unusual distribution playbook that helped it take off. That included giving a major profit share to a creator-partner and building a network of “creative investors” who amplified the product from day one. The conversation also dives into why selling a company was far less glamorous than it sounds, and why Tibo now prefers building and holding long term. He shares how he thinks about creating an “indie hacker stack” for a specific persona, how AI has changed his day-to-day workflow, and why he now spends less time coding and more time reviewing, iterating, and building systems. One of the biggest takeaways is his operating style: no calls, fast feedback loops through DMs, and a strong focus on staying close to paying users. For founders building product-led companies, this episode is packed with practical lessons on validation, distribution, focus, and building with speed in the AI era. Key Highlights: 02:21 - Why Two Failed Startups Changed EverythingTibo shares the painful lesson of spending years on unvalidated ideas, and how that pushed him to become relentlessly validation-driven.05:38 - Revenue Is the Only Validation That CountsWhy free users can be misleading, how Tibo evaluates startup ideas today, and what made Tweet Hunter feel different almost immediately.09:47 - How Tweet Hunter Won a Crowded MarketThe strategy behind focusing on one platform deeply, serving creators instead of enterprises, and building something clearly better for a narrower use case.12:11 - The Distribution Deal That Fueled GrowthHow Tibo partnered with influencers using profit share and exit incentives, and why aligning distribution with the product was such a powerful lever.15:25 - The Creative Investors Growth EngineWhy he gave small ownership stakes to 17 creators, how that amplified launches and updates, and what made the model work.19:31 - Why Selling Wasn't the Dream OutcomeTibo opens up about the pressure of earnouts, platform risk, and why the acquisition experience made him want to build and hold instead.23:46 - Building an Indie Hacker Software StackWhy Tibo organizes his portfolio around a specific persona instead of a single vertical, and how he thinks about expanding from five products to more.34:54 - No Calls, More DMs, Better FeedbackA look at his no-meeting policy, why DM-based customer conversations work so well for him, and how staying close to users improves product decisions.37:18 - How AI Changed the Way He BuildsTibo explains how AI emptied his backlog, turned him into a QA-first builder, and created a new challenge: resisting feature creep. Resources:

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Kelly Anne Pipe is Head of Developer Experience at Vanguard, and Nicole Scribner is a Director in the firm's Chief Technology Office focused on engineering enablement and advancement.In this session from DX Annual, Kelly Anne and Nicole share how Vanguard is expanding its AI strategy beyond software engineering to the entire product development lifecycle. While the company initially focused on tools like GitHub Copilot for engineers, they found that faster coding alone did not significantly improve delivery speed. Product managers, designers, QA teams, and organizational processes were still operating at a different pace.To address this challenge, Vanguard developed a product team maturity model built around three stages: Augmented, Accelerated, and Autonomized. The framework spans six dimensions, from AI-powered delivery and AI-ready codebases to team autonomy, operations, and responsible AI.Kelly Anne and Nicole explain how Vanguard is applying the model across more than 800 product teams, the behaviors they believe will enable faster delivery, and the lessons they have learned about measurement, organizational change, dependencies, and scaling AI across the product development lifecycle.In this episode, we cover:(00:00) Intro(02:16) The state of AI one year ago at Vanguard(02:54) The engineering bubble(05:05) Building an AI maturity model for 800 product teams(08:24) Dimension 1: AI-powered product delivery(10:00) Dimension 2: AI-ready codebase(12:20) Dimension 3: Autonomous agent utilization (13:00) Dimension 4: AI-augmented operations(14:00) Dimension 5: Team autonomy and enablement(16:11) Dimension 6: Responsible AI(18:15) The people problem: role evolution (20:00) The measurement problem (22:55) Lessons learned from rolling out the maturity model (26:46) What's ahead (30:10) Q&A #1: Getting your codebase ready for AI(32:22) Q&A #2: Audit trails and responsible AI(34:16) Q&A #3: Vanguard's maturity model progress(36:15) Q&A #4: Measuring cycle time across 800 teamsReferenced:• Vanguard• Jennifer St Pierre - Dell Technologies | LinkedIn• Mercari

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Are you falling behind—or getting 10X more productive—with AI? Kevin Surace, the father of the virtual assistant and AI futurist. Here are my 5 biggest takeaways: ✅ If you're not AI-first, someone who is will replace you ✅ AI isn't a tool — it's a workflow multiplier ✅ Productivity is now 10X minimum, not 10% better ✅ Your voice still matters — edit AI, don't fear it ✅ The winners? People who learn faster than AI evolves What's ONE way you're using AI today to boost productivity? Kevin is the father of the Virtual Assistant and a Silicon Valley innovator, serial entrepreneur, CEO, and futurist. He was INC Magazines' Entrepreneur of the Year, a CNBC top Innovator of the Decade, World Economic Forum Tech Pioneer, Chair of Silicon Valley Forum, Planet Forward Innovator of the Year nominee, featured for 5 years on TechTV's Silicon Spin, and inducted into RIT's Innovation Hall of Fame. He has 94 worldwide patents and led pioneering work on the first cellular data smartphone (AirCommunicator), the first human-like AI virtual assistant (Portico), soundproof drywall, high R-value windows, AI-driven building management, Generative AI for QA automation, supply-chain auctions, and the window/energy retrofits of the Empire State Building and NY Stock Exchange. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Kevin Surace:Website: https://www.kevinsurace.com/ X: https://twitter.com/kevinsurace Instagram: https://www.instagram.com/kevinsurace/ LinkedIn: https://www.linkedin.com/in/ksurace/ Facebook: https://www.facebook.com/kevin.surace/ *E - explicit language may be used in this podcast.

RJ Talyor is the Founder and CEO of Backstroke a AI for eCommerce generative content platform for email marketers. Instantly create on-brand, high-performing email subject lines, preview text, mobile push notifications, and SMS messages.Summary of PodcastPodcast introduction and guest backgroundGraham and Kevin introduce the Next 100 Days Podcast and welcome RJ Talyor from Indianapolis. RJ describes Indianapolis as offering the best of a big city with a small-city feel, with about a million people, great sports, culture, food, and good cost of living. He has traveled extensively but always enjoys returning home.Backstroke's AI email generation platformRJ introduces Backstroke.com, which generates performant email campaigns for e-commerce retailers selling clothes, pet food, furniture, and other products online and in-store. E-commerce brands typically expect 20-50% of revenue from email marketing while sending 3-5+ emails weekly, with customers spending 8-12 hours per campaign. Backstroke reduces this to approximately 15 minutes while personalising content so each customer receives a different message tailored to their interests and behaviour.Personalisation through data and engagement Backstroke personalises emails using multiple data layers: subscriber status, past engagement (opens, clicks, conversions), and appended third-party data revealing demographics like age, location, and gender. When additional data is unavailable, the platform uses progressive profiling—analysing engagement patterns to infer preferences. For example, if a customer consistently clicks on men's content over women's content, or prefers dark-coloured shirts over light ones, AI identifies these patterns to drive personalisation, which is more effective than manual analysis.Real-world personalisation: from negative to advocateGraham shares a personal story about Son of a Tailor, a Portuguese apparel brand, where his initial experience was poor—they sent him a shirt too short for his frame. However, the company responded exceptionally well, ultimately creating a monogrammed, high-quality shirt that transformed him into an advocate. RJ explains this is valuable data: AI can flag customers who experienced negative-to-positive journeys as potential super-fans or loyalty advocates, a pattern most marketers miss because they lack time to identify such nuanced customer experiences.AI pattern recognition beyond traditional metricsTraditional RFM (Recency, Frequency, Monetary) models reduce customers to transactional data, but AI can extract signal from unstructured data to identify complex patterns. For instance, AI can recognize when a customer buys different sizes (suggesting purchases for others) or when multiple preferences exist within one account—like RJ's Spotify feed where his children's music preferences mix with his own. AI discerns these overlapping patterns that aren't immediately obvious to humans, enabling more sophisticated segmentation.Team expertise and company historyRJ co-founded Backstroke with his wife Allison, who holds a PhD in deep data analysis and chemical reagents, bringing statistical rigour and predictive modelling expertise. RJ's background includes starting Pattern89 in 2016, an AI company predicting Instagram and Facebook clicks using computer vision and natural language processing, which he sold to Shutterstock. Many Pattern89 team members joined Backstroke, bringing 10 years of AI-based marketing experience, while the team continuously innovates with new foundational models from Anthropic and OpenAI.Implementation results and Surge featureBackstroke achieves an average 30% uplift in conversion rates for new clients. Implementation typically takes about a month for full transformation, but recognising customer demand for faster results, the company launched "Surge," enabling campaigns to launch in 48 hours. This rapid-deployment feature demonstrates predictive capabilities quickly, satisfying customers who want immediate proof before committing to full onboarding.Email variants and human approval at scaleWhile technically capable of generating 10,000+ unique email variants, Backstroke has found that customers require human review of every variant version. Current implementations range from 60-100 variants, with combinations of hero images, subject lines, and templates creating exponential possibilities. The company is building QA agents to enable scaling to millions of variants while maintaining human oversight, recognizing that creative teams ultimately bear responsibility for brand representation.Brand guidelines versus performance metricsA fundamental tension exists between brand teams (who enforce guidelines like "models must face forward" or "only use this colour") and performance marketers (who know "shirts perform better laid on a bed than on a human"). RJ explains this is often gut-feel decision-making based on outdated tests—teams cite tests from a year ago by employees who've since left, creating stale guidelines. AI enables rapid testing of creative variations to identify incremental opportunities, but requires organisational willingness to experiment beyond established brand rules.Customer selection philosophyRather than trying to convince resistant customers to embrace AI, RJ focuses on the "one in 10" truly innovative marketers willing to change. He learned from his previous business that most prospects claim interest but quickly reveal organizational barriers requiring approvals. His strategy is to identify customers genuinely committed to transformation and willing to pay, directing others to resources instead. This approach conserves energy for high-potential partnerships where AI can deliver real impact.Backstroke's core value propositionBackstroke solves the "what" problem: what content, subject line, preview, template, hero image, product display, and offer to send to each person. The platform knows that 46% of clicks occur in the first 400 pixels, so it optimizes that space differently for men versus women, loyal customers versus new ones, and geographic regions. This focused specialization on content optimization is Backstroke's primary value, distinct from solving "when" (send time) or "who" (segmentation) problems.Practical tips for email marketersFor marketers using standard LLMs without specialised platforms, RJ recommends uploading all previous email data and creative assets, then asking the machine to identify winning creative dimensions. This approach reveals patterns in subject lines, imagery, copy length, and offers without requiring subscriber-level analysis, enabling better-than-average results for those without access to specialised tools.Email frequency paradox and engagementKevin raises frustration with receiving excessive emails from companies he likes, asking if AI can enable sending less email while achieving better results. RJ explains that higher engagement with personalised content could theoretically reduce frequency, but email is fundamentally a frequency game—brands send multiple emails weekly to stay top-of-inbox when customers are ready to buy. However, deliverability depends on engagement (opens, clicks), so sending irrelevant content backfires. Backstroke solves the "what" problem, but send-time optimisation and segmentation (the "when" and "who") remain separate challenges.Market focus and customer examples Backstroke focuses exclusively on B2C e-commerce in North America due to language complexity and GDPR privacy requirements in Europe. The platform serves impulse-purchase categories (apparel, furniture, bedding) differently than considered purchases (mattresses, cars), with separate trained models for each. Notable customers include Third Love (women's intimates), Cozy Earth (bedding), Helix (mattresses), and Emile Henry (cookware), representing the apparel and home goods verticals where Backstroke has developed deep expertise.Future roadmap: predictive marketing agentsRJ's 18-month roadmap focuses on building predictive marketing agents that complete marketing tasks generatively while humans serve as brand stewards and strategists. This vision extends beyond email to SMS, apps, and landing pages, with personalisation as a core feature. Graham notes the challenge of making such systems intuitive enough for non-technical users, reflecting the broader industry shift toward AI-augmented rather than AI-replaced marketing roles.European expansion and compliance strategyWhile Backstroke is currently North America-focused, RJ is open to European partnerships but wants to be proactive about compliance. GDPR itself isn't a blocker, but European customers require security documentation and certifications that Backstroke hasn't yet obtained. The company recently achieved SOC 2 compliance (required by enterprise businesses) and plans to secure necessary privacy certifications before entering European markets, avoiding disqualification during sales cycles.Podcast analysis and key takeawaysIn the wrap-up, RJ praises the podcast for getting past fluff into real marketing challenges, appreciating the nitty-gritty discussion of how marketers actually work. Graham and Kevin reflect that the conversation revealed AI's potential to solve the "what" problem while highlighting remaining challenges in "when" and "who" decisions. They note that Kevin's observation about sending less email...

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On this episode of The Association Podcast, Megan Paulini of Associations International, Senior Technical Solutions Specialist and recent AWTC Champion Award winner, shares how she accidentally entered the association world via a temp role and grew from member services into tech solutioning, QA, and deep NetForum work. She explains how an AMC's shared-services model supports multiple association clients across varied tech stacks and why clear definitions, overcommunication, and documentation prevent costly misunderstandings. Megan reflects on recognition through AWTC, her preference for puzzle-style projects, and practical ways she uses AI daily, including having it review requirements for gaps, interview her with follow-up questions, generate test cases, and build a project forecast spreadsheet to reduce QA bottlenecks. The conversation also touches on realistic personalization, data foundations, and balancing customization with guardrails. 00:43 Guest Intro 01:16 Rapid Fire Questions03:23 How Megan Found Associations04:32 From Member Services to Tech07:26 Inside the AMC Model10:06 Patterns Across Clients12:22 Translating Business to Tech15:56 AWTC Award Spotlight20:37 Favorite Projects and Puzzles21:53 Switching to Maybel Setup22:23 Daily AI Wheel Spin22:59 AI for Better Requirements24:27 AI as Interviewer25:51 Forecasting QA Bottlenecks27:10 Choosing AI Tools30:05 Data Personalization Reality32:52 Progressive Profiling Vision33:23 Career Path Marketing35:12 Role Evolution at AMC38:43 Guardrails Over Customization40:27 Office Rundown Analogy41:03 Closing Takeaways

En este episodio Oz conversa con Gina Paola Cárdenas (QA Manager, +12 años de experiencia) y Luis Contreras (Automation Lead en Capgemini, certificado ISTQB) sobre lo que realmente pasa cuando metes agentes de IA al proceso de testing. El 77.7% de los equipos de QA ya se movió a un enfoque AI-first y hay empresas donde el 85% del testing ya no es manual — pero sin criterio técnico, estrategia y gobierno, la IA acelera el caos en lugar de la productividad. Hablan de qué SÍ funciona (análisis de riesgos, self-healing, generación de datos), del peligro de la "pereza de pensar" y la falsa cobertura, de por qué los modelos alucinan entre 3% y 18% y suenan más seguros cuando se equivocan, y de cómo el rol del QA se vuelve más crítico que nunca.00:00 Intro: 77.7% de QA ya es AI-first02:27 ¿Qué cambió con la IA en QA?05:42 El criterio técnico como barrera06:28 El hype vs la realidad: ¿nos reemplaza?09:27 Qué SÍ funciona: análisis de riesgos con IA12:20 Self-healing: scripts que se autocorrigen16:24 Resistencia de los equipos18:18 Scrum ya murió (y los marcos que siguen)20:18 El ejemplo de las velas: evolución de roles22:37 El superpoder del economista en QA26:20 El riesgo: pereza de pensar y falsa cobertura30:56 Pruebas de integración con IA33:52 IA, usabilidad y accesibilidad36:25 Alucinaciones: 3-18% y suenan seguras40:13 "Los de QA no son ingenieros de verdad" + el mercado de $112B41:24 Backlog refinement con criterio crítico43:34 Sistemas críticos: rayos X, carros, vidas46:07 Consejos para QAs: prompt engineering e instinto probador50:01 Cierre✩ CURSOS DISPONIBLES

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BONUS: The Communication Tax — Why Your Team Collaborates Too Much and What to Cut First In this BONUS episode, Roman Nikolaev challenges one of the most deeply held beliefs in the agile world: that more collaboration is always better. As Head of Technology at Cambri, Roman has watched teams burn their best hours in meetings and handoffs that create the feeling of productivity without the outcomes. He shares practical tools — from the vacation test to RFC processes — that help teams find the minimum viable level of collaboration. From Senior Engineer to Accidental Manager "I kind of accidentally ended up in management. I didn't want to lead anyone, I wanted to be just a senior engineer doing my stuff. But somehow, four months in the job, I was already leading a team, and then one year after, I was head of technology."   Roman's career in engineering goes back to the early 2000s. When he changed jobs during COVID, he specifically didn't want a management role — he wanted to code. But within months he was leading a team, and within a year he was running the entire technical organization at Cambri. That unexpected shift from hands-on engineering to leading teams gave him a front-row seat to how collaboration actually works — and how often it doesn't. What he noticed was that the most important differentiator for technical teams isn't technical knowledge — it's communication, and the tax you pay when communication goes wrong. The Communication Tax Is Real "The communication tax is real. The less we need to pay for communication, the more we can concentrate and own things end to end."   Roman describes a pattern most teams will recognize: stakeholders inside and outside the team — product managers, QA, scrum masters, product owners — and at some point, it becomes a game of telephone. The people doing the actual work don't have the context they need. The result? Unnecessary features, wrong implementations, suboptimal technical solutions that don't scale. His argument isn't that collaboration is bad. It's that every handoff, every meeting, every "quick sync" has a cost — and most teams aren't honest about how much they're paying. Handoffs Aren't Collaboration "If you look at a typical software development lifecycle — a ticket created by a product owner, refinement with the team, development, code review, QA, acceptance — there are quite many handoffs. If we can reduce some of this, we get a more effective workflow."   Roman walks through the standard ticket lifecycle and counts the handoffs: PO creates ticket, team refines, developer picks it up, code review with other developers, QA phase, acceptance phase. Each transition is a potential information loss. His provocation: instead of involving more people when someone struggles with a task, give the person working on it the tools and knowledge to complete it independently. The trigger for his thinking was a real team conversation where someone suggested everyone should "jump on the ticket" to help. Roman's response: wouldn't it be better to equip the individual rather than create more dependencies? Async Tools That Actually Work "Instead of gathering a meeting where people come unprepared or with some raw ideas, we have ownership for a task. Someone takes their time, writes down their thoughts, options in a document, and then we assign people to review it."   Roman shares two async practices his teams use at Cambri. First, the RFC (Request for Comments) process on Confluence — one person owns a decision, writes it up with options, and assigned reviewers sign off asynchronously. It turns out to be more effective at finding better technical solutions while spreading knowledge without requiring synchronous deep-dives. Second, his Monday written updates: every week, he spends about 90 minutes writing a detailed post covering all project statuses, what happened last week, what's coming, and business context. The team feedback in skip-level meetings is consistently positive, and he fields far fewer questions about business context and priorities than before the practice started. The Vacation Test "One heuristic would be that if one of the team members goes on vacation, the rest of the team can continue working on their task."   Roman learned this the hard way. He went on a typical Finnish one-month vacation. Before leaving, he explained the architecture and intent for a key task to his team. He came back to discover they'd built the completely wrong thing — wasting one month of a two-month project. He spent the remaining time working weekends, on planes, on trains, just to hit the deadline. The lesson wasn't that he needed more collaboration or synchronous communication before leaving. It was that he needed better communication — and a way to test whether shared context actually exists. His heuristic: if Alice goes on vacation, can Bob continue from where she stopped? If not, you don't need more meetings. You need better async context-sharing. Where to Start: Ownership First, Then Cut Meetings "I would probably first look into if a particular initiative, a feature, or some kind of process has an owner and well-defined roles. Usually, if there is no clear owner, that leads to a lot of synchronous meetings."   For Scrum Masters and team leads looking for a practical starting point, Roman offers a two-step approach. First, ensure every initiative, feature, and process has a clear owner with well-defined roles. Without clear ownership, meetings multiply because nobody is sure who's responsible, so everyone attends everything. Second, look at the team calendar starting with the biggest meetings and ask: can this be an RFC? A message? An email? Then experiment — cancel a meeting, replace it with an async channel, and see what happens. You can always bring it back. In the agile world, Roman argues, we should embrace experimentation with our own processes, not just our products. Recommended Resources Roman recommends Team Topologies by Matthew Skelton and Manuel Pais. The book gave him a clear mental model for independent teams that own their area end to end — teams aligned to value streams that own the customer problem completely. For more of Roman's thinking on collaboration, check out his Substack newsletter: Is Your Collaboration Good or Evil? on High Impact Engineering. About Roman Nikolaev Roman Nikolaev is Head of Technology at Cambri. He's spent his career thinking about how teams actually get work done — and his contrarian view that most teams collaborate too much has sparked real debate in the agile community.   You can link with Roman Nikolaev on LinkedIn.

What if every bug report came with session replay, console errors, and network requests attached automatically? Postman just launched an AI Engineer that runs QA on every pull request. Helpful or hype? Plus, the free Playwright Step Decorator that's blowing up on LinkedIn, what is it? Find out in this episode of the TestGuild News Show for the week of June 8th. So grab your favorite cup of coffee or tea, and let's do this. Time Item URL 0:00 Welcome   0:23 Bugzy https://testgld.link/bugzy.io 1:42 Cypress Schema Validator https://testgld.link/4kiTMBpT 2:17 Playwright Step Decorator https://testgld.link/xyAmfhXV 3:24 Best Agent Skills https://testgld.link/S5XPgllE 4:33 Postman AI Enginner https://testgld.link/S5Ai7iW1 5:52 Follow the Money Harness https://testgld.link/XVQTZZWa 6:39 AWS AgentOps https://testgld.link/R4AbViTQ 7:36 Machine vs human https://testgld.link/6vay5bg8 8:22 Miasma Worm https://testgld.link/ZWLfmkYi  

#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/96j266 －－－－以上為 SoundOn 動態廣告－－－－

Why does it feel like major DayZ updates are arriving with more game-breaking bugs than actual features? This week on the DayZ Podcast, Andy and Dave take an analytical look at the DayZ Experimental branch. We explore the fundamental reason this testing ground exists and ask the difficult questions: Is Bohemia Interactive actually using it to its full potential, or has it just become a glorified preview build?We pull back the curtain on the software development pipeline to look at why BI keeps pushing versions to the stable branch without thorough UAT (User Acceptance Testing). Pulling from our own professional backgrounds—Andy's expertise in IT systems and infrastructure, and Dave's real-world experience in automotive quality and car testing—we break down where the QA process is fundamentally failing. From the recent 1.29 crossplay hacker exploits to vanishing inventory items, we examine the real-world implications these oversight failures have on the player community.Finally, we brainstorm how Bohemia can make Experimental great again. We discuss how to gamify the bug-hunting process, the types of incentives (like exclusive cosmetics or community recognition) that would actually get veterans to stress-test the builds, and how proper feedback loops could save Chernarus from the next broken patch.This channel is your ultimate command center for everything happening in the brutal world of DayZ. Our goal is to break down the mechanics, strategies, and updates that define this hardcore survival game. Whether you are a total fresh spawn hunting for a comprehensive DayZ beginner guide, or a seasoned veteran player looking for high-tier PVP breakdowns, base building blueprints, or deep dives into the latest DayZ news, you're in the right place.What we bring to the community:

Watch the video version on YouTube: https://youtu.be/y6Jck3rU5FI  On this episode of Disability Deep Dive, hosts Jodi and Keith interview Mark Miller, founder and CEO of Inclusion Impact Accessibility and a contributor to the W3C Accessibility Maturity Model, about what it means for state and local governments to meet WCAG 2.1 Level AA and the implications of the DOJ Title II rule with an (now extended) April 24, 2027, deadline for entities serving 50,000+ people. Mark explains WCAG, common barriers across websites, documents, apps, videos, and kiosks; the inefficiency of retrofitting versus building accessibility into design, development, QA, and governance; and why overlays don't deliver "full compliance." The episode also discusses media representation, including CODA, Bridgerton, The Pitt, and a Deep Cut analysis of Todd Browning's 1932 film Freaks, weighing its historical visibility of disabled performers against harmful language, exploitation, and horror framing. Inclusion Impact Accessibility: https://inclusionimpact.co/ Mark Miller: https://inclusionimpact.co/mark-miller/

Setting up a business is a major life decision that should not be taken lightly—it is incredibly painful. The ups definitely outweigh the downs, but the downs can be dark. Having a co-founder makes all the difference. Matthew Duhig, CEO and co-founder of FX Digital, started the business at university with his co-founder Tom, to build a website for his sister's bridal shop for free. Fifteen years later, they've grown from £1.5M to approaching £10M revenue, from 20 people to nearly 80, and they've built connected TV applications for major media and sports companies. Along the way, they had one major near-death experience when a single client became 80% of revenue, then in-housed the work down to 60%—leaving Matt and Tom with no personal wealth or assets, living together, staring at the barrel. But they believed in their proposition, backed themselves against the wall, and won 4 of 5-6 bids they needed to win, which launched them into major tech company work and one of their best years ever.In this episode, Matt reveals his four contrarian beliefs about building businesses: (1) Running a business is incredibly painful and decision should not be taken lightly; (2) Vision comes from consumption (reading, listening, watching—not plucking it from air); (3) Don't make promises you can't control (resentment is harder to overcome than anything else in teams); (4) The job of an entrepreneur is to reduce risk (not take risks). He shares why he's an absolute delegator (sometimes great, sometimes backfires), how he managed to get off the tools when billing five days a week, why he stays in touch with 5-10 people at any given time who might be future hires, and how Barcelona became their second office (Jack the QA lead asked if he could relocate and Matt asked him to set up an office instead).What you'll learn:

Maria Skvortsova: Breaking the Factory Mindset — When a 17-Person Scrum Team Treats Development Like an Assembly Line 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.   "They wait for the story to be pushed to them, then they hand it to QAs and say 'it's not my business anymore.' We have not a Scrum team, but a factory." — Maria Skvortsova   Maria's current challenge is one that many Scrum Masters will recognize: a large distributed team — 17 people, cameras always off, only four months together — that operates like a factory instead of a collaborative unit. In refinement sessions, only the Tech Lead, BAs, and QA speak. Everyone else stays silent. When the sprint starts, developers wait for the Tech Lead to assign stories, work on them in isolation, then toss them over the wall to QA with a "not my problem" attitude. Maria and Vasco explored this challenge through a coaching conversation, identifying information loss as the core issue. Every handoff between developer and tester destroys knowledge and slows the process. Maria had already introduced desk testing — pairing a developer with a QA before deployment to walk through the code on the developer's machine. It worked well in previous teams, but this team keeps forgetting, and in a recent retrospective they even proposed creating a "handover to QA" subtask — the exact opposite of what Maria is trying to build. The experiment that emerged: find a few early adopters willing to try a deeper collaboration model where developers participate in testing and testers participate in design — starting small, measuring what changes, and letting results speak louder than process mandates.   Self-reflection Question: Where are the biggest information loss points in your team's development process, and what experiment could you run this sprint to reduce them?   [The Scrum Master Toolbox Podcast Recommends]

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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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.

Maria Skvortsova: The Team That Gave Up — When Green Reports Mask a Sinking Ship 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.   "They said, 'Yeah, we know, but no one will listen to us.' And they just gave up — waiting for the ship to sink so they could swim away." — Maria Skvortsova   Maria walked into a 20-person migration team where the PowerPoint reports glowed green but the reality on the ground was covered in red flags. Developers were building features against requirements that had already changed — nobody had told them. The scope was impossibly large, and when Maria asked the team why they hadn't raised a red flag, the answer shook her: "No one will listen to us." The team had given up. They were waiting for the project to fail so they could leave. Maria's first instinct was to observe — spend weeks understanding the dynamics, the communication patterns, the culture. But she learned the hard way that when a team is already drowning, there's no time for a slow ramp-up. She needed to act immediately. Her breakthrough came from a simple technique: replacing some daily standups with an async RAG (Red-Amber-Green) status system in Jira. Team members just chose a color for each story — no explanation needed. It gave them psychological safety to signal problems without speaking up in a 20-person meeting. From there, Maria broke the team into smaller cross-functional groups — one QA, one developer, one consultant — so they could actually discuss features instead of hiding behind silence.   In this episode, we refer to Zombie Scrum Survival Guide by Christiaan Verwijs, Johannes Schartau, and Barry Overeem. Also check out the episode with Barry and Christiaan, authors of the book, on the podcast.   Self-reflection Question: When you join a new team and sense that something is deeply wrong, how long do you wait before acting — and is that waiting period serving the team or just your own comfort? Featured Book of the Week: Zombie Scrum Survival Guide by Christiaan Verwijs, Johannes Schartau, and Barry Overeem Maria chose Zombie Scrum Survival Guide because, as she puts it, "Most Scrum Masters learn by the happy path. We all know how it should be. But we rarely think about how it should not be." The book focuses on detecting anti-patterns early — before they become entrenched behaviors that are much harder to break. Maria finds it especially valuable because it provides concrete experiments you can try with your team to shake off the zombie symptoms. Her advice: start here, because understanding what bad looks like is just as important as knowing the ideal.   [The Scrum Master Toolbox Podcast Recommends]

AI coding tools promised to make development faster — and they delivered. But here's the problem nobody talks about enough: when you speed up coding, you don't eliminate the bottleneck in the SDLC. You just move it. And for most teams, it lands squarely in QA. In this episode, Joe sits down with Vilhelm von Ehrenheim, Co-founder and Chief AI Officer of QA.tech, to dig into how agentic AI is reshaping software testing from the ground up. Vilhelm brings serious ML credibility, he helped build Motherbrain, one of the earliest production LLM systems in venture capital, and he's now applying that experience to one of the hardest problems in software delivery: testing at AI development velocity. You'll learn how QA.tech's behavioral knowledge graph gives AI agents the context they need to actually understand your application, why validating user intent beats checking element identifiers every time, how autonomous agents can review PRs, reproduce bugs from Slack messages, and generate targeted tests without a single line of test code ,and what the tester's role actually looks like when agents do the heavy lifting. If you're wondering whether your QA practice can survive the pace of AI-driven development, this one's required listening.

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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 #STRAWSteve Harvey Morning Show Online: http://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

In this episode, Dave and Jamison answer these questions: My parent organization has trust issues: we registered on a recent survey as one of the lowest across the bigger software org (thousands of employees). There are two groups: functional, trustworthy people who get stuff done, and people who are behind, stuck, or just not working. Those struggling say they need better emotional support, but there is consistent, documented evidence that they cannot keep up. I'm perpetually frustrated that there are only two or three people in an org of 30 who can effectively complete tasks and manage the insane workload. I am biased: those in whom I have no trust have repeatedly demonstrated that they cannot be trusted. I believe that the organization would be able to go faster without them. Whats the right answer here? Should I start my own company to abandon this mess? Do we cut scope super aggressively to allow underperformers to be reasonable contributors? One example: one of these contributors was walked through the process, given written documentation of the process, verbally confirmed an understanding of the process, and committed to starting that day. And then two days later identified they hadn't started for those two days because they were blocked by something that was explicitly captured in the document and discussed in the recorded meeting. They do not raise this until they were asked for their progress multiple days beyond the critical start date. Hi Dave and Jamison, First, thank you for the podcast. As someone on the spectrum, it really helps me analyze social situations I struggle with. My question: I work at a software company where management is pushing the use of LLMs for coding, and my team recently started using Spec-Driven Development. SDD often requires strong upfront planning, which has quietly split my small team between developers who plan well and those who get lost in “vibe-coding” loops. One of those people is a close coworker I consider a friend (we hang out after work and have honest chats about everything). He's not strong at planning, so I end up explaining each ticket to him in detail. But he's really great in other ways: communicative, asks a lot of questions in refinement sessions that set a good example for others, does thorough handoffs with the QA team, always responsive in chat, always trying to help. He's also one of the rare people who actually pay attention to alerts. Since we started using SDD, the gap in our speed and output quality has become very visible. One of our tech directors noticed and asked me to teach him planning. We spent several days drawing schemas and working through small features together, and it was clearly painful for him. The TD also had his own sessions with him, but eventually gave up because my friend seemed so discouraged, and the TD decided to “stop the torture” and leave him alone. The hard part is that after all these teaching sessions, he actually seems even slower than before and also more discouraged and down. Since then, our friendship has changed. He stopped talking to me outside work, and I think he now feels jealous or bothered by the difference in our performance. I'm okay with continuing to explain tickets and outlining detailed execution plans for him, but I worry that I'm keeping him in his comfort zone and not really helping his career. And I also miss how things used to be between us