Podcasts about QA

Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8uc3gy —— 以上為 Firstory Podcast 廣告 —— Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

Passing the CWI exam is a huge accomplishment — but it's only the beginning. In this episode of the Arc Junkies Podcast, Jason Becker sits down with veteran welding inspector Daryl Peterson to discuss what new Certified Welding Inspectors really need to know after earning their certification. From ethics and inspection authority to metallurgy, code interpretation, and non-destructive testing, this conversation breaks down the real challenges that many new CWIs face when they step into the role for the first time. Daryl shares insights from 40 years in welding inspection, including the importance of mentorship, understanding QA vs QC responsibilities, and why gaining experience in NDT methods like PT, MT, UT, and radiography can dramatically improve your effectiveness as an inspector. They also discuss common industry myths, welding metallurgy concepts that inspectors must understand, and how unethical decisions early in your career can have serious consequences. If you're a welder thinking about becoming a CWI — or a newly certified inspector trying to figure out your next move — this episode is packed with practical advice from someone who's spent decades in the field. Topics include: What new CWIs should focus on after passing the exam The difference between QA and QC inspectors Why ethics and integrity matter in inspection How NDT experience can accelerate your career The metallurgy concepts inspectors must understand Common welding myths and misunderstandings  

00989B台新美國非投等債（本基金之配息來源可能為收益平準金且無保證收益及配息）用心挑選票息6.5%以上美國企業債採月配息機制，讓你掌握收益機會了解更多

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to measure AI proficiency impact beyond speed. You’ll discover why quality matters more than volume when AI accelerates work. You’ll learn a six‑level framework that lets you map your AI skill growth. You’ll see practical steps to protect your role in fast‑moving companies. 00:00 – Introduction 02:45 – The speed‑only trap 05:30 – Introducing the six‑level AI proficiency model 09:10 – Quality vs quantity in AI output 12:40 – Managing AI access and fairness 16:20 – Actionable steps for managers and individuals 20:00 – Call to action Watch the full episode to level up your AI leadership. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-proficiency-measuring-ai-performance.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI and the way the things that we are measuring in business to measure AIs, the productivity, the benefits that you’re getting out of it. One of my favorite apps, Katie, is called Blind. This is an anonymous confessions app for the business world where people who work at companies—mostly in big business and big tech—share anonymous confessions. They have to say what company they’re with, but that’s it. There were three posts that really caught my eye over the weekend. The first was from a person who works at Capital One bank who said, “Hi, I’m a junior software engineer.” Three years into my career, my co‑workers are pumping out so many poll requests with Claude code and blitzing through jobs that used to take three to five days in less than an hour. I feel like every day at the office is a race to see who can generate more poll requests and complete them than anyone else. The second one was from JP Morgan Chase saying, “I just downloaded Claude coat and wtf. I don’t know what to think. Either we are cooked or saved.” The third was from an engineer at Tesla who said, “I joined recently as a contractor and don’t have access to Claude. I’m slower than the others on my team and it stresses me out.” So my question to you is this, Katie: Obviously people are using generative AI to move very fast. However, I don’t know if fast is the metric that we should be looking at here, particularly since a lot of people who manage coders don’t necessarily manage them well. They don’t. For example, very famously, Elon Musk, when he took over Twitter, fired people who didn’t write enough code. He measured people’s productivity solely on lines of code written. Anyone who’s actually written code for a living knows you want less code written rather than more because there’s a certain amount of elegance to writing less code. So my question to you is, as we talk about AI proficiency—sort of AI proficiency week here at Trust Insights—what would you tell people who are managing people using AI about measuring their proficiency and measuring the results that they’re getting? Katie Robbert: So first, let me answer your question. No, I do not frequent—was it Blind? Yeah. Anyone who knows me knows that I am honest and direct to a fault. So no, that would annoy me more than anything—just say it to my face. But that aside, I understand why apps like that exist. Not every company builds a culture where an open‑door policy is actually true. The policy is: the door is open only if you have positive things to share; the door is closed if you have complaints. I sympathize with people who feel the need to turn to those kinds of apps to express concern, frustration, fear. It seems, Chris, that a lot of the fear over the past couple of years is: “Will AI take my job?” In those environments, leadership decisions about process and output are really pushing for AI to take the job. What I’m not seeing is what the success metrics are. If the metric is faster and more, then you’re missing the third most important one—quality. We don’t know what kind of quality is being produced. Given those short snippets of context, we can assume it’s probably mediocre. It’s probably slightly above the bar, but nothing outstanding—enough to get by, enough to keep the lights on. For some larger companies, that’s fine because you can bury mediocre work in the politics and red tape of an enterprise‑sized organization. No one really expects much more, which is a little sad. So what I would say to managers is, number one, if you’re not clear on what you’re being measured on, or if your success metric is faster and more, head for the hills—run. That is not good. I mean it in all sincerity; that is not going to serve you in the long run because those metrics are not sustainable. Christopher S. Penn: And yet that’s what—particularly at a bigger company—where I can definitely, obviously at a company like Trust Insights, we’re four people. Outcomes are something we all measure because we have a direct line to outcomes. If we sell more courses, book more keynote speeches, get more retainer clients, we all have a hand in that and can see very clearly the business outcome. At a company like JP Morgan Chase, Bank of America, or Capital One, there are hundreds of thousands of employees. Your line of sight to any kind of business outcome is probably five layers of management removed. The front line is way over there—tellers, for example. You write the software that writes the software that manages the system the tellers use. So you don’t have clear outcomes from a business‑level perspective. Because I used to work at places like AT&T where you are just a cog in the machine, your outcomes very often are either faster or more because no one knows what else to measure. Katie Robbert: In companies like that, those outcomes are—quote, unquote—good enough because of the nature of what you produce. Consumers have become so dependent on your company that we often talk about the really crappy customer service at cable and Internet providers. There are only so many of them, and they’re all the same. We have become reliant on that technology and have no choice but to put up with crappy service from the big providers. The same goes for the financial industry. We don’t have a choice other than to rely on these crappy companies because we aren’t equipped to stand up our own financial institutions and change the rules. It’s a big, old industry, and that’s why they operate the way they do. It’s disheartening. When it comes down to humans, you have to make your own personal choices. Are you okay contributing to the mediocrity of the company and never really advancing? Chris, what you’ve been saying—what is the art of the possible? They don’t know, but they also don’t care. They’re not looking to disrupt the industry. No other companies are starting up to disrupt them because they’re so massive; they’re okay with the status quo, changing at a glacial pace, if at all. It’s not a great story to tell. You might have a consistent paycheck, but you might not have a lot of passion for the work you do. It might just be clock in at nine, clock out at five, with two 15‑minute breaks and a 30‑minute lunch—and that’s fine for a lot of people. That works for survival. Outside of that work environment is where you find joy, passion, and the things you’re really interested in. All to say, the advice I would give to managers is: how much are you willing to put up with? Those industries aren’t going to change. Christopher S. Penn: So in the context of AI proficiency, what do you advise them to focus on? Knowing that, to your point, these places are so calcified, faster is one of the only benchmarks that matter, alongside constantly shrinking budgets. Cheaper is built in because you have to do 5 % less every year. How do you suggest a manager or employee who feels the fastest typist wins the day and gets the promotion—even if the quality is zero—handle this? The Tesla engineer example is interesting: they don’t have access to generative AI, co‑workers do, they’re much faster, and the contractor fears being fired. How do we resolve this for team members, knowing that these companies are so calcified that even if a department takes a stand on quality, the other twenty departments competing for budget will say, “Great, you focus on quality; we’ll take your budget because we’ll produce ten times more next year.” Even quality sucks. Katie Robbert: The Tesla example is an outlier. We don’t have context for why that person doesn’t have access to generative AI—maybe they’re brand new. Contractors don’t get access to paid tools, so that explains it. When we talk about levels of AI proficiency, generic training doesn’t work; it doesn’t stick. Companies and individuals need to assess their AI proficiency. We typically do this on a six‑point scale, from Basic to Advanced. Within each level are skill sets: Level 1—editing, correcting grammar, asking it to write code. Level 2—writing code and reading code. Level 3—building QA plans. Level 4—providing business or product requirements, agile cues, or building a project plan. It’s like a career path: today I’m a junior analyst, tomorrow I want to be a senior analyst. The same applies to AI proficiency. My recommendation for managers and individuals stuck in those situations—or anyone looking to level up their AI proficiency—is to look at what’s next, what you don’t know. In the case of Tesla or JP Morgan, they will only produce a limited variety of things. In banking, look at the use cases and how you’re using AI. If you’re building code, how do you automate while keeping a human in the loop? Human‑in‑the‑loop means literal human intervention; you’re not just setting it and forgetting it like a rotisserie chicken. You must ensure a human is paying attention. Perhaps your KPIs aren’t quality of output, but if you start delivering incorrect work, customers complain, and the company loses money, the quality of your output will suddenly matter. It doesn’t matter how fast you’re creating it. For the Tesla contractor who lacks internal AI tools, they can get access to their own tools and build their skill set: acknowledge they’re not as fast as full‑time employees, determine what they need to do to match or outpace them, and work on it in their own time if they care. In that instance, the person is worried about job security, so it’s probably in their best interest to act. Christopher S. Penn: I like how you analogize the six levels to basically the three levels of management. The first two levels are individual contributors; the next two are middle management; the final two are leadership—going from typing the thing to delegating it entirely to someone else. That’s a great analogy. I think after this episode I’m going to revise that chart to help people wrap their brains around it. What does the level of AI performance efficiency mean? It means you go from individual contributor to leader, eventually leading machines—not necessarily humans. The Tesla example worries me because the company is essentially asking contractors to bring their own AI tools—a data‑privacy and security nightmare. Still, when I think about our clients who engage us for AI readiness assessments, we see a hierarchy of people with different proficiency levels outpacing each other. Is it fair to say that people with more proficiency—or who invest more in themselves—will blow past peers who are not? Do those peers need to worry about career viability when a peer becomes a mythical 10× engineer or marketer? Katie Robbert: The short answer is yes, but that’s true in any career path. Unless you’re in a company that promotes someone based on appearance rather than ability, which is another conversation, it’s absolutely true. Levels of AI proficiency run in parallel with organizational maturity. AI proficiency can’t stand alone without a certain amount of maturity within the organization. We often talk about foundations—the five Ps: documented processes, platforms, good governance, and privacy. Those have to exist for someone to be set up for success and move through AI proficiency levels. Otherwise, they’re becoming proficient against creative garbage. That won’t translate to better career opportunities because, boiled down, it’s garbage in, garbage out—you become proficient at moving garbage around, and nobody wants to hire that. Christopher S. Penn: An essay from last year discussed the AI reckoning in larger companies. It said AI is doing what decades of management consulting couldn’t—showcasing as you apply AI to processes. Entire levels of management are unnecessary, doing nothing but holding meetings and sending emails. The essay posited that mid‑level managers may realize they only push paper from point A to point B. In those cases, what should people in those positions think about for their own AI proficiency, knowing that improving it will reveal that they add little value? Katie Robbert: As someone who’s spent most of her career managing, I’ve often had to defend my role. Once, an agency considered dissolving my position because they thought I didn’t bring anything to the table—obviously not true. The team that grew from three people to a $3 million profit center also knows that. Managers need to think about delegation: not just handing off tasks, but ensuring the right people are in the right seats. Coaching is a big part of the job—bringing people up through their proficiency levels. If I’m a middle manager using the individual‑contributor, manager, leadership matrix, how do I get out of that vulnerable middle spot? Maybe I need to create more workflows, find efficiencies, save the budget, identify level‑one champions, and build them up. Those are the things someone in that middle vulnerable section should consider, because they are vulnerable. Many companies have managers who don’t do squat. I’ve worked alongside those managers; it’s maddening. One thing that will evolve with the manager role is that you can no longer be just a manager. You can’t just manage things; you have to bring some level of individual contribution and thought leadership to the role. It’s no longer enough to just manage—if that makes sense. Christopher S. Penn: It makes sense. Over the weekend I was working on something for myself: as technology evolves and I delegate more to it, the guardrails for quality have to get stricter. I revised the rules I use with my Python coding agents—new, enhanced, advanced rules with more guidelines and descriptions about what the agent is and is not allowed to do. This morning my kickoff process broke, so I told the agent to fix it according to the new rules. I realized the previous application sucked, and I fixed it. Now it’s much happier. I think building quality guardrails will differentiate managers who take on AI management—not just people management. Yes, AI can be faster, but there’s no guarantee it’s better. If I’m a manager who gets faster and better results than peers who just hope it works, I keep my job. What do you think about that angle? Katie Robbert: It makes sense. Take the middle‑manager example: the VP says, “Client needs these five things.” The hierarchy follows—manager, then individual contributors. The middle person can step up, create a process, develop a proof‑of‑concept example based on the VP’s input, delegate with quality assurance, and cut down iterations. That saves time, saves budget, gets results faster, and reduces frustration because expectations are clear. Christopher S. Penn: The axiom we talk about when discussing AI optimization is bigger, better, faster, cheaper. Faster obviously saves time and money. We don’t often talk about bigger and better—doing things that add value that wasn’t there before. The value you create should be higher quality. To wrap up AI proficiency, we have three divisions, six levels, and a focus: if you’re worried about someone else being faster, be as fast and be better quality. Cutting corners for speed will catch up to you. If you have thoughts about how people are using—or misusing—AI in terms of proficiency, pop by our free Slack group at trustinsights.ai/analysts‑for‑marketers, where over 4,500 marketers ask and answer each other’s questions daily. You can also watch or listen to the show on any podcast platform or the Trust Insights AI TI Podcast. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach. Trust Insight specializes in helping businesses leverage data, AI, and machine learning to drive measurable marketing ROI. Services span from comprehensive data strategies and deep‑dive marketing analysis to building predictive models with tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, MarTech selection and implementation, and high‑level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL‑E, Midjourney, Stable Diffusion, and Metalama. The firm provides fractional team members such as a CMO or data scientists to augment existing teams. Beyond client work, Trust Insights contributes to the marketing community through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, livestream webinars, and keynote speaking. What distinguishes Trust Insights is a focus on delivering actionable insights—not just raw data. The firm leverages cutting‑edge generative AI techniques like large language models and diffusion models while explaining complex concepts clearly through compelling narratives and visualizations. This commitment to clarity and accessibility extends to educational resources that empower marketers to become more data‑driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever‑evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Depuis le déclenchement des hostilités contre l'Iran, le Mali observe une prudence toute diplomatique. Une prudence que l'on retrouve au travers de la presse officielle. Ainsi, le quotidien L'Essor à Bamako pèse ses mots et évite soigneusement de citer les États-Unis : « ce 11 mars, écrit-il, la deuxième guerre Iran–Israël compte douze jours, égalant déjà celle de juin dernier. Au-delà de ce seuil, l'incertitude sur la fin du conflit ne fera que s'épaissir. (…) Pour une grande partie du monde, attachée au droit international et au système des Nations unies censé garantir la paix, ce scénario paraît ubuesque, s'exclame L'Essor. Le Mali appartient à cette communauté pacifiste, intransigeante sur la souveraineté des États. C'est le message que le Premier ministre Abdoulaye Maïga a porté à l'ambassade d'Iran, jeudi dernier, en signant le livre de condoléances après la mort du Guide suprême, l'Ayatollah Ali Khamenei. Le Mali ne peut rester indifférent lorsque le souverain d'un pays ami périt sous les bombes, quelle que soit la nature du conflit ». Vers une reprise du renseignement aérien ? Aucune mention donc des États-Unis et pour cause, Bamako et Washington semblent esquisser un rapprochement. C'est du moins ce que croit savoir Afrik.com : « selon plusieurs sources au sein de l'administration américaine, les États-Unis, affirme le site panafricain, seraient sur le point de conclure un accord stratégique avec les autorités de transition maliennes. Ce texte ouvrirait la voie à la reprise de vols de surveillance, menés par avions et drones, au-dessus du territoire malien. L'objectif affiché est de renforcer la collecte de renseignements sur l'évolution des groupes jihadistes actifs dans la région. En ligne de mire, la progression du GSIM, affilié à al-Qaïda, qui étend son influence dans plusieurs zones du Sahel. Le dégel a été amorcé de manière concrète le 27 février dernier, précise Afrik.com. Dans un geste politique fort, Washington a levé les sanctions pesant sur le ministre de la Défense malien ainsi que sur plusieurs hauts responsables. Ces officiels étaient jusqu'alors visés pour leurs liens supposés avec des groupes paramilitaires russes ». Ne pas laisser le champ libre à Moscou… D'ailleurs, relève encore Afrik.com, « ce rapprochement est également déterminant dans la compétition pour l'influence au Sahel. Alors que la Russie, via Africa Corps, est devenue le principal partenaire sécuritaire du Mali après le départ des forces françaises de l'opération Barkhane en 2022, Washington ne souhaite pas laisser le champ totalement libre à Moscou. Contrairement aux diplomaties européennes, l'administration américaine actuelle semble prête à tolérer la présence russe si cela permet d'endiguer l'instabilité régionale ». Le site de l'agence Ecofin, spécialisée sur l'économie du continent, note pour sa part que « l'administration Trump a largement abandonné la politique de promotion démocratique de son prédécesseur (Joe Biden) dans le Sahel (…). Cette posture est bien accueillie à Bamako, Niamey et Ouagadougou. Le chemin reste toutefois semé d'embûches, tempère Ecofin : le Mali, le Niger et le Burkina Faso ont institué en décembre dernier un régime de réciprocité face aux restrictions de voyage américaines, signe que les nouvelles relations entre Washington et ses anciens partenaires sahéliens demeurent fragiles ». L'agent français emprisonné à Bamako se porte « bien » Enfin toujours à propos du Mali, Jeune Afrique apporte des précisions sur le sort de cet officier français détenu à Bamako depuis l'été dernier. Un lieutenant-colonel, officiellement deuxième secrétaire à l'ambassade de France à Bamako, mais qui était aussi accrédité auprès des autorités maliennes en tant qu'agent de la DGSE, la Direction générale de la sécurité extérieure, les services secrets français. Il est accusé d'avoir joué un rôle clé dans un complot contre la junte malienne. D'après Jeune Afrique, cet officier français serait « détenu à la base aérienne 100 de Bamako, dans une installation relevant de la Sécurité d'État malienne. Selon une source sécuritaire, l'agent français se porte "bien". Un espace dédié lui permettant de faire régulièrement du sport a été mis à sa disposition. Et "il mange et boit ce qu'il veut", confie la même source. (…) Les autorités françaises ont également pu entrer en contact avec lui ». Jeune Afrique confirme aussi « le fait que, depuis le début de sa détention, il n'a toujours pas eu accès à un avocat. »

How do you ensure software quality when the system you're testing doesn't give the same output twice? Go to https://links.testguild.com/inflectra and start your free 30-day trial, no credit card, no contract required. That's the core challenge facing every QA team building or testing AI-powered applications today and it's breaking all the rules we've relied on for decades. In this episode of the TestGuild Automation Podcast, I sit down with Adam Sandman, co-founder of Inflectra, to get into what non-deterministic AI testing actually means in practice, why traditional pass/fail testing no longer cuts it, and what quality professionals need to do differently right now. We cover: Why AI-generated code is raising the stakes for QA teams while budgets stay flat The fundamental difference between deterministic and non-deterministic systems — and why it changes everything about how you test How to set acceptable risk thresholds for AI systems (hint: it depends on whether you're building an e-commerce chatbot or an air traffic control system) Why testers who embrace AI as a tool — not a threat — will be the ones leading their organizations forward How a live demo failure at a conference inspired Inflectra's new non-deterministic testing tool, SureWire If you're a tester, QA manager, or automation engineer trying to figure out how to keep up with AI-driven development without losing your mind — or your job — this one's for you.

In this episode, host Kalie Moore talks with Bastian Bergmann, Co-founder & COO of Solsten, about the collision between gaming and branding, and why most companies still don't know how to show up in games without feeling like an ad. With 3B+ people playing worldwide and gaming still capturing only ~5% of global ad spend, Bastian argues the opportunity isn't awareness, it's audience strategy. Kalie and Bastian break down why gaming is the only medium that truly spans every demographic, from Gen Alpha to “silver surfers,” and why brands fail when they lead with stereotypes or build empty “brand worlds” instead of experiences grounded in what players actually want.They also explore why gaming should be treated as a real conversion channel, even if measurement hasn't fully caught up yet, and how platforms like Roblox and UEFN will be pushed toward clearer attribution as more dollars move in. Bastian shares standout examples like The New York Times' games-led subscription growth and Chipotle's Roblox activations that drove real-world sales and loyalty signups. For studios and creators, the takeaway is clear: know your audience deeply, design integrations that are brand-agnostic but partnership-ready, and pitch brands with real segmentation and fit, not vague “access to gamers.” The episode closes with what's next at Solsten: Alaris, an AI tool powered by Solsten's psychological dataset, plus an upcoming API layer aimed at unlocking deeper personalization across games, matchmaking, recommendations, and advertising.We'd like to thank Neon – a global payments and e-commerce platform designed to help game publishers earn more money and gain independence from app stores – for making the episode possible. Neon's DTC platform handles everything from webshops and checkout to global payments, tax, and compliance, with full transparency and all-in pricing. Learn more:https://www.neonpay.com/?utm_source=Naavik-Sponsorship-General&utm_medium=Paid-Sponsorship We'd also like to thank modl.ai for making this episode possible! Using a combination of computer vision, reasoning models, and feedback loops, modl:QA+ autonomously explores builds, detects bugs, and generates actionable reports that sync directly with your existing workflows. To learn more, visit modl.ai.If you like the episode, please help others find us by leaving a 5-star rating or review! And if you have any comments, requests, or feedback shoot us a note at podcast@naavik.co.Who's On:Guest - Bastian Bergmann: https://www.linkedin.com/in/bergmannbastian/Host - Kalie Moore: https://www.linkedin.com/in/kaliemoore/ Watch the episode: YouTube ChannelFor more episodes and details: Podcast WebsiteFree newsletter: Naavik DigestFollow us: Twitter | LinkedIn | WebsiteSound design by Gavin Mc Cabe.

台中太平育賢三期好宅招租囉！ 3月開放申請，社宅位於環中東路三段與育才路交叉口 歡迎年滿18歲，名下無自有住宅，符合財稅規定的民眾， 可點擊下方資訊欄連結了解詳情 台中社宅17租：https://sofm.pse.is/8t799g 3/21(六)開放現場看屋，也歡迎到社宅現場參觀！ 以上廣告由台中市政府住宅發展工程處提供 -- 【遠雄樂元】 台中北屯捷運X好市多 雙首排 ➤早鳥首付55萬起 旗艦級新地標21-39坪，台中北屯機捷總站20米，好市多60米，出站即到家。2147坪新世代遊園宅，全齡化公設✦ 早鳥輕入住 https://sofm.pse.is/8t799n －－－－以上為 SoundOn 動態廣告－－－－

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In this CTO Series episode, Daniel Harcek shares how leading engineering teams across radically different scales — from a 7-person fintech startup to a 2,000-person cybersecurity company — taught him that leadership isn't one-size-fits-all. We explore how he builds AI-first organizations, drives agile transformations, and why he believes every person in a company should think like a tech person. What Works at 10 People Breaks at 100 "Leadership is contextual, not absolute. What works with 10 people breaks at 50, at 100." Daniel's career spans from building a 30-person team for a German startup out of Žilina, Slovakia, to leading 70 engineers at Avast's mobile division within a 2,000-person organization, and now running a 7-person team at WageNow. Each scale demanded a fundamentally different approach. At smaller scales, you strip away operational overhead and push ownership directly to the people. At larger scales, you need guardrails, dedicated roles, and structured processes that the smaller team would find suffocating. The lesson: don't carry your playbook from one context to another — rebuild it for the reality you're in. End-to-End Ownership Replaces Specialized Roles "Each engineer owns quality for the task he delivers. And he owns the fact that it comes to production." At WageNow, Daniel runs without dedicated QA people — in a fintech company where quality can't be compromised. Instead, each developer owns quality end-to-end, from code to production. This isn't recklessness; it's intentional design. When teams are small, you set up the system so that it's safe to break things, then trust people with hard tasks. The result: people grow faster, move faster, and care more about what they ship. In larger organizations, you might need specialized DevOps, QA, and platform roles — but the principle of ownership stays the same. The Buddy System and Scaling Without Losing Alignment "The buddy system is one of the easiest things you can do. One buddy for a newcomer for the first 1, 3, 6 months — they often become friends." When scaling fast, Daniel focuses on three things: strong on-boarding guides, well-maintained documentation (now much easier with AI), and a buddy system that pairs every newcomer with a dedicated colleague. The buddy system works because it scales the human side of on-boarding — a tech lead or manager can do one-on-ones, but that's formal, and new people might be scared to speak up. The buddy creates a safe channel for questions, concerns, and cultural integration. Beyond people, scaling also means investing in automation and observability so that as you grow with customers, you grow with failures too — and your incident reporting doesn't burn out the team. Building an AI-First Organization "Every person uses AI. Every person has the capability to use AI. The company builds a second brain so AI can build on top of that." At WageNow, Daniel has implemented what he calls an AI-first organization, inspired by Spotify and other companies pioneering this approach. The concept is simple: before doing any task, ask whether AI can help you deliver the output faster or better. This applies across the entire company — not just engineering. Daniel looks for people in HR, accounting, and UX who understand automation tools like n8n or Make.com alongside AI. The key ingredients: Curate the data: Build a company "second brain" with clean, structured context for AI tools to work with Train the muscle: AI ability is like a muscle — people must use it daily because these skills didn't exist 2-3 years ago Share what works: Exponential AI adoption happened at WageNow once people started sharing their successes and failures with AI tools Respect the guardrails: Data privacy and regulation compliance remain non-negotiable The hidden productivity gains, Daniel argues, lie not in engineering (which gets all the attention) but in operations, accounting, HR, and every other area of the business. Selling Transformation: Financial Arguments for Leaders, Ownership for Teams "For the leaders, it's the financial thing and the cultural thing. For the people doing the work, it's personal development — having more control, having more ownership." At Ringier Axel Springer, Daniel proposed and led a company-wide agile transformation — a 1-2 year effort that required convincing the CEO, product teams, marketing, and sales to change how they operate. His approach: build a dual argument. For leadership, frame the change in financial and cultural terms — more revenue with the same people, better visibility into how work translates to business outcomes. For the people doing the work, emphasize personal growth, increased ownership, and transparency. The transformation breaks silos between engineering and product, creating a shared backlog agreed with all stakeholders. Daniel looks for people with high agency — those who can reinvent and change themselves from the inside, not just wait for a change agent from the outside. Balancing Experimentation with Operational Excellence "The SRE books helped me understand quality as a feature — because quality is basically how reliable you are for your customers." When asked about the books that most influenced his approach as a CTO, Daniel points to the Site Reliability Engineering series from Google — three books that frame quality as reliability, a feature your customers experience directly. Alongside those, he recommends The Lean Startup by Eric Ries, because he believes all tech people should have a sense of business and customer understanding. Together, these books guide how to balance rapid experimentation with operational excellence as the organization scales. About Daniel Harcek Daniel is a technology executive with a proven record scaling engineering organizations across fintech, cybersecurity, and digital media. Builds AI-first teams, operating models, and delivery cultures aligned with product strategy. Led platforms serving 30M MAU, deployed fintech capital pilots, transformed agile delivery at internet scale, and mentors global tech communities and ecosystems worldwide actively. You can link with Daniel Harcek on LinkedIn.

Do commit messages even matter anymore, or did pull requests kill them? Ben works one commit per PR and thinks the commit message is the PR description. Carol and Tim put all the context in the PR and treat commits as disposable breadcrumbs. Adam's somewhere in between — when he's not pushing thirty knife emojis and "nope, still not working" to QA. Meanwhile, Tim's back from emergency eye surgery with a gas bubble floating around his eyeball.LinksBen Nadel's BlogConventional CommitsFollow the show and be sure to join the discussion on Discord! Our website is workingcode.dev and we're @workingcode.dev on Bluesky. New episodes drop weekly on Thursday.And, if you're feeling the love, support us on Patreon.With audio editing and engineering by ZCross Media.Full show notes and transcript here.

Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。 馬上入手

Growth sounds glamorous until the math, the hiring headache, and your calendar disagree. We dig into the real choices pool pros face: stay a sharp, profitable single poler or build a team and chase scale. Along the way, we unpack why the labor market has shifted toward gig work, how rising wages squeeze entry-level hiring, and what that means for route capacity, pricing, and your stress level.I share the quiet obstacles that keep owners from adding a first tech—payroll setup, workers' comp, trucks, scripts for client handoffs—and the fear that a new hire might walk, leaving you with 50 extra pools overnight. Then we pivot to a practical workaround used by savvy operators: partner with builders, service new pools for a year, and sell a partial route at a clean multiple. It's a repeatable way to capture account equity, protect quality, and avoid the overhead of managing staff, all while keeping your book at a healthy size.If your wiring favors systems and leadership, we also map a path to scaling well. That means hiring with a promise—training in a real trade, time off, competitive pay, and a future in repairs—so you can compete with Uber and Amazon Flex. With one solid tech you can double routes, with a second and a repair specialist you unlock higher-margin work and the chance to step back from daily skimming. Still, we're honest about the tradeoffs of a multi-truck operation: more moving parts, quality drift risks, and the need for strong software, QA checks, and culture.Whether you thrive with a tight 60-pool route and a side portfolio of real estate or dream of a 10-truck fleet, the win is clarity. • labor market shifts to gig work and wage pressure• barriers to hiring including payroll, comp and trucks• income math for adding a tech and route capacity• passing on new accounts versus smart partial route sales• builder partnerships and one-year account valuation• when personality fit favors staying small• investing profits outside pool service• how to attract hires with training and benefits• benefiSend a textSupport the Pool Guy Podcast Show Sponsors! HASA https://bit.ly/HASAThe Bottom Feeder. Save $100 with Code: DVB100https://store.thebottomfeeder.com/Try Skimmer FREE for 30 days:https://getskimmer.com/poolguy Get UPA Liability Insurance $64 a month! https://forms.gle/F9YoTWNQ8WnvT4QBAPool Guy Coaching: https://bit.ly/40wFE6y

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Send a textEverything I've learned about AI QA after 2 years of building OttoQA and being one of the first companies to fully automate contact center quality assurance with AI.I'm breaking down the biggest lessons, the biggest mistakes I see people making, and what actually works when you're implementing AI powered QA in a contact center.I cover why you should never reinvent your scorecard on day one, what "false hustle" questions are and why they're killing your QA program, why most AI QA companies won't publish their accuracy rate, how statistical sampling means you don't need to score 100% of calls, why human QA evaluators disagree 30 to 40% of the time and how AI fixes that, how aggregate data turns QA from a scoring exercise into a real coaching tool, and the REDO feature we built at OttoQA that lets agents practice failed calls with an AI customer.30 years of contact center operations. 2 years of building an AI QA platform. This is what I know. Through Expivia Digital, Tom works with contact center leaders on CCaaS platform selection, AI implementations, and NICE Studio and integration services. Same honest, vendor-neutral advice you hear on the Call Center Geek podcast, applied directly to your specific operational challenges. Schedule a consultation at ExpiviaDigital.com to discuss your contact center technology strategy. Click here:expiviadigital.comFollow Tom: @tlaird_expiviaJoin our Facebook Call Center Community: www.facebook.com/callcentergeekConnect on LinkedIn: https://www.linkedin.com/in/tlairdexpivia/Follow on TikTok: https://www.tiktok.com/@callcenter_geekLinkedin Group: https://www.linkedin.com/groups/9041993/Watch us: Advice from a Call Center Geek Youtube Channel OttoQA: try.ottoqa.com Expivia: Expiviausa.com

Handwritten notes aren't old school. They're the edge in a world drowning in automation.In this episode of Uncomplicate It, I sit down with David Wachs, Founder & CEO of Handwrytten (a company using robotic tech to scale real pen-and-ink handwritten outreach), to talk about how brands can bring humanity back into business communication without sacrificing scale.David is a two-time Inc. 500 entrepreneur with decades in marketing. After running a high-volume text messaging company, he realized the most powerful way to stand out wasn't another digital message, it was a note people actually keep.We cover:Why David walked away from mass digital communication and doubled down on handwritten notesThe 5 Cs framework for outreach: content, channel, cadence, choice, and communityWhy most brands over-measure short-term ROI and underinvest in long-term loyaltyThe difference between personal vs personalized (and why mail-merge doesn't build trust)The consumer appreciation drop: 18% in 2022 → 12% in 2025 and what that signalsWhere handwritten notes actually work best in the customer journey (retention > acquisition)Why gimmicky marketing backfires (and the “video screen in a card” story)The numbers: 300% higher open rate than print mail and up to 17x higher response rates in certain industriesHow Handwrytten's system works: handwriting samples, ligatures, randomization, QA via computer vision, envelope stuffing, and stampingThe real rule of automation: scale the logistics, not the sentimentKey Takeaways:The least-used, most undervalued inbox is still the one at the end of your drivewayLoyalty isn't built with coupons, it's built with how you make people feelCustomer service follow-ups are one of the fastest ways to turn frustration into trustGratitude only works when people feel thanked, not when it's just a checkboxWrite to five clients this week. Or call them. That's how relationships compoundConnect with David:Handwrytten - www.handwrytten.comLinkedin - www.linkedin.com/in/davidwachs/Follow Us:

A story about destroying your own work—and creating what lastsThis episode is for sales-led SaaS founders who suspect their product is slowly becoming a custom shop—and don't know how to stop it.Bassem Hamdy, CEO and Co-Founder of Briq, has spent 25 years in construction technology—three software revolutions, three companies.He says Briq found product market fit every 24 months. Each time meant tearing something down to build the next version.Each time, the same thing triggered the rebuild — the company had started solving for individual customers instead of the market.And this inspired me to invite Bassem to my podcast. We explore why the instinct to please your biggest customers creates exactly the kind of fragility that kills companies. Bassem shares hard lessons about killing a product he spent two years building, the moment his QA team exposed how far the company had drifted, and why domain expertise—not platform size—determines who wins in vertical AI.We also zoom in on two of the 10 traits that define remarkable software companies: – Acknowledge you cannot please everyone – Master the art of curiosityBassem's journey proves that remarkable companies refound themselves before the market forces them to.Here's one of Bassem's quotes that captures what happens when a company starts drifting:"Software is like jello. You slap that thing, it's going to shake the hell out of it. So the moment you inject that code, that's client specific, you're pooched."By listening to this episode, you'll learn:Why saying yes to customers can turn your product into something nobody else wantsWhen to check whether your team is building a product or managing client ticketsWhy deep domain expertise matters more than platform size in the age of AIHow one metric—revenue per employee—changes every decision a CEO makesFor more information about the guest from this week: Guest: Bassem Hamdy, CEO and Co-Founder of Briq Website: briq.com

00989B台新美國非投等債（本基金之配息來源可能為收益平準金且無保證收益及配息）用心挑選票息6.5%以上美國企業債採月配息機制，讓你掌握收益機會了解更多

AI is bringing massive changes to our industry, but it's not just about how fast you can write code or use agentic flows. In this episode, I explore how AI is fundamentally shifting the economic bottleneck of software development, and how you can use your systems-thinking engineering mindset to adapt and thrive in this new era.

What does it really take to build a test automation tool that millions of testers rely on, without venture capital, paid ads, or a massive team? In this episode, we explore how SelectorsHub grew into one of the most widely used productivity tools in software testing, reaching over 1.6 million testers worldwide. You'll discover: How to build test automation tools that solve real QA pain Why community-driven development beats chasing funding How to prioritize features when you have thousands of users Whether AI testing tools will replace selector-based automation How to choose between Playwright vs Selenium using automation analysis What founders and QA leaders can learn from scaling without VC If you're an automation engineer, QA lead, DevOps professional, or tool builder looking to scale smarter, this episode delivers real-world insight without hype. Whether you're building frameworks internally or launching your own automation product, you'll walk away with a clearer strategy for solving problems testers actually care about.

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctdu —— 以上為 Firstory Podcast 廣告 ——

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctal —— 以上為 Firstory Podcast 廣告 —— Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8scttc —— 以上為 Firstory Podcast 廣告 —— Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctgs —— 以上為 Firstory Podcast 廣告 —— 加入會員，支持節目： https://richlife.firstory.io/join 留言告訴我你對這一集的想法： 歡迎您用一杯咖啡支持我持續創作 : https://pay.soundon.fm/podcasts/a11a2120-4bc4-4fb2-813b-135bd96e5868 「布姐的交誼廳。陪你聊人生聊職場」Line 社群 https://reurl.cc/36NWEL(密碼：love) 本集重點： 如果您要將這集內容上架至 Firstory 或其他 Podcast 平台，以下為您整理的十個精華重點，適合放進節目資訊欄中：節目精華點：巴黎演講、AI 思辨與歷史偵探巴黎演講的「溯源」哲學：楊斯棓醫師分享為何每場演講都要回溯緣分，並記錄那些導向今日的珍貴相遇 。 與謝志偉大使的 25 年情誼：一段從徵文比賽評審到「院長辦公室」投遞回郵信封的往事，如何在巴黎舞台上化作驚喜火花 。 熱血回饋，拍賣 4,100 歐元：主動捐出珍藏的 1996 年總統大選旗幟，不僅熱絡現場氣氛，更將拍賣所得全數回贈大會 。 自媒體的深度累積：早在 2001 年就開始建立個人數位資料庫，透過長期的寫作與記憶訓練，建立對人名與史實的精準駕馭力 。 活用 AI 抓出邏輯謬誤：教導聽眾將言論丟入 Gemini，請 AI 區分「事實」與「觀點」，並針對觀點進行邏輯謬誤檢測 。 不盲從的選民素養：建議「蓋住政黨標籤」來閱讀內容，主張選民不應愛上政治人物，而應對資訊保持超然的審視態度 。 全台最懂杜聰明獎學金的人：分享他如何從祖父的叮嚀出發，成為連歷史系教授都致謝的杜聰明考據專家 。 五萬元的歷史見證：為了確認一段「綠島十年」的歷史真相，不惜高價標下 1975 年的絕版醫科畢業紀念冊 。 資源流動的共好精神：推崇「騎士精神」，將珍貴的收藏品（如畫作與古書）送往最能發揮價值的地方，而非私藏 。 公民的自我負責：強調國家的強大來自於每個人為自己的責任負責，包括健康管理與思辨素養，而非一味依賴制度 。 來賓 楊斯棓／閱讀引路人 　　暢銷書《人生路引》《要有一個人》作者。醫師，家醫科證號0005758。自2019年10月起卸下臨床業務，生活步調以照顧父母、勤讀史料為主。 　　自2015年始，他每月舉辦讀書有獎徵文活動，並親自包裝、寄贈禮物給讀後感最觸動他的讀者。他謙稱禮物是隨興而至的準備：顯微鏡、三萬哩程、茶葉醇酒，近來則是根據投稿篇數，捐出同等數量給丹鳳高中圖書館，充實館藏，嘉惠眾人。他希望更多人願意在離開校園後讀書，甚至希望參加者也去舉辦相關活動。他自謙所作所為僅是無畏布施，主張：「這種活動，任何一個人有心要辦，都辦得成。」他深信，終身閱讀可以持續改變一個人，而很多人若都如此改變自己，就會改變一個國家的命運。 楊斯棓醫師FB:楊斯棓醫師的咀嚼肌 https://www.facebook.com/bloggerYangSzuPang

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctda —— 以上為 Firstory Podcast 廣告 ——

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctn7 —— 以上為 Firstory Podcast 廣告 —— Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

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《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctsw —— 以上為 Firstory Podcast 廣告 —— Samsung Galaxy Buds4 Pro震撼登場！全新時尚設計，加寬低音雙喇叭呈現完美Hi-Fi音質，搭配最佳化ANC降噪，沉浸更升級。支援頭部動作控制、語音指令與即時翻譯，開啟AI新聲代。馬上入手

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctj4 —— 以上為 Firstory Podcast 廣告 —— 粵語歌曲翻唱國語原創曲第二彈上線！這一集要來聽聽那些被「性轉」翻唱的歌，男歌女唱或是女歌男唱，這一集依舊是大牌滿滿，包括還在聽合歡山黃鶯鶯、張宇、王菲、歌神學友、黎天王、林憶蓮、玉女掌門人周慧敏等好歌盡出，更可以從其中聽到台灣音樂人旺盛的創作力。加入還在聽訂閱會員 https://open.firstory.me/join/stilllistening本集YouTube歌單 https://www.youtube.com/playlist?list=PLGntcMwsgCxBecvUDXoqU1MXvqigyVc5k還在聽Facebook https://www.facebook.com/podcast.stilllistening還在聽YouTube頻道 https://www.youtube.com/@stilllistening還在聽IG https://www.instagram.com/still.listening.pod還在聽Threads https://www.threads.com/@still.listening.pod Powered by Firstory Hosting

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《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctq9 —— 以上為 Firstory Podcast 廣告 —— 加入會員，支持節目： https://the7linepod.firstory.io/join 留言告訴我你對這一集的想法： 這一集我們討論到了： 紐約暴風雪 台灣隊投打守狀況 Skubal經典賽只投一場 主持人 GG 前職棒/中華隊防護員 Ozzy 前駐美記者 現布魯克林居民 資深棒球迷 我大奕哥-江奕昌 前職棒/中華隊體能教練 Eleven Sports中華職棒賽事講評 IG: https://www.instagram.com/ychiang100 專欄-江江隨筆: https://www.sportsv.net/authors/ychiang100 工作洽詢: keith0156@yahoo.com.tw 開頭音樂 "Cool As Blue - intro A'' Music by Jay Man | OurMusicBox Website: www.our-music-box.com YouTube: www.youtube.com/c/ourmusicbox Powered by Firstory Hosting

Send a textWant to see your PDFs think like a database and your chatbots answer with context, not guesses? We sit down with technologist and entrepreneur Max Riggsbee Jr., co-founder of Gadget Software, to unpack how compute-ready documents—what he calls semantic twins—turn unstructured content into structured, queryable knowledge that both humans and machines can trust. Max explains why simply chunking PDFs into a chatbot loses meaning, and how deep descriptors, QA pairs, and entity maps let you navigate ideas, not just pages.We go inside directed AI, where you choose the exact slices of content a model can touch, then generate summaries, outlines, or tables grounded in that selection. Max shares results from work with Signal65, Dell, and Broadcom showing fewer hallucinations, faster token throughput, and better energy use when AI is fed structured, contextual data. From there, we get practical about agentic workflows: the validator checks you need before any output ships downstream, and why human-in-the-loop review still matters. Max's “Georgia” test—person, state, country, or font—reveals how ambiguity explodes without metadata. He also breaks down a real failure in a political cartoon pipeline where an agent inferred a story from SEO slugs instead of reading the article, and how guardrails caught it.Our conversation widens to legacy and Black history. Grant traces family records from enslavement to land ownership, underscoring how caricatures like Jim Crow distort truth when they calcify into the story we're told. Maxwell introduces Then Is Now, the audio diary project he runs with his 90-year-old father, using authentic voice recordings and AI to frame the surrounding historical context. We talk about scanning non-digital originals like the Pentagon Papers as a stepping stone to microfiche, county archives, and the overlooked documents that can restore names, places, and property to the record. On the technical edge, Max shows how rich textual descriptions can stand in for heavy images, enabling vision models to re-render diagrams on demand, making insights lighter and more scalable.If you work with unstructured data, lead AI projects, or care about preserving the story with accuracy, this conversation gives you a roadmap: structure your sources, validate your agents, and keep humans in charge of meaning. Subscribe, share this episode with a friend who needs better answers from their data, and leave a review to help others find the show.Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com. And don't miss Grant McGaugh's new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/ See you next time on Follow The Brand!

《美麗說法》不只談美，更要教你如何「自保」。我們會深入剖析那些沒人敢說的醫美陷阱與必知 QA，拆解麻醉、合約到術後恢復的關鍵細節。變美不該是一場冒險，讓我做你的把關者，確保你在追求自信的路上，安全下莊，安心變美。https://fstry.pse.is/8sctxt —— 以上為 Firstory Podcast 廣告 —— 加入會員，支持節目： https://xxymovie.firstory.io/join留言告訴我你對這一集的想法： 【電影的故事】過年必看電影#3 《九品芝麻官》 我只是跟鄉民進來湊熱鬧的！怎麼就變迷因啦！ 清朝的官場現形記，其實也是以古鑑今？

I am Rolf Claessen and together with my co-host Ken Suzan I welcome you to Episode 172 of our podcast IP Fridays. Today's interview guests are Co-Founder & CEO of Inception Point AI, Jeanine Whright, and Mark Stignani, who is Partner & Chair of Analytics Practice at Barnes & Thornburg LLP. https://www.linkedin.com/in/jeaninepercivalwright https://www.linkedin.com/in/markstignani Inception Point AI But before the interview I have news for you: The Unified Patent Court (UPC) ruled on Feb 19, 2026, that specialized insurance can cover security for legal costs. This is vital for firms, as it eases litigation financing and lowers financial hurdles for patent lawsuits by removing the need for high liquid assets to enforce rights at the UPC. On Feb 12, 2026, the WIPO Coordination Committee nominated Daren Tang for a second six-year term as Director General. Tang continues modernizing the global IP system, focusing on SMEs, women, and digital transformation. His confirmation in April is considered certain. An AAFA study from Feb 4 reveals 41% of tested fakes (clothing/shoes) failed safety standards. Many contained toxic chemicals like phthalates, BPA, or lead. The study highlights that counterfeiters increasingly use Meta platforms to sell unsafe imitations directly to consumers. China's CNIPA 2026 report announced a crackdown on bad-faith patent and trademark filings. Beyond better examination quality, the agency will sanction shady IP firms and stop strategies violating “good faith” to make China’s IP system more ethical and innovation-friendly. Now, let's hear the interview with Jeanine Whright and Mark Stignani! How AI Is Rewiring Media & Entertainment: Key Takeaways from Ken Suzan's Conversation with Jeanine Wright and Mark Stignani In this IP Fridays interview, Ken Suzan speaks with two repeat guests who look at the same phenomenon from two angles: Jeanine Wright, Co-Founder & CEO of Inception Point AI, as a builder of AI-native entertainment, and Mark Stignani, Partner and Chair of the Analytics Practice at Barnes & Thornburg LLP, as a lawyer advising clients who are trying to use AI without stepping into a legal (or ethical) crater. What emerges is a clear picture: generative AI is not just “another tool.” It is rapidly becoming the default infrastructure for creative work—while the rules around ownership, consent, and accountability lag behind. 1) What “AI-generated personalities” really are (and why that matters) Jeanine's company is not primarily “cloning” real people. Instead, Inception Point AI creates original, fictional personalities—characters with backstories, ambitions, and evolving arcs—then deploys them into the world as podcast hosts and content creators (and eventually actors and musicians). Her key point: the creative work still starts with humans. Writers and creators define the concept, tone, audience, and story engine. What AI changes is speed, cost, and iteration—and therefore what is economically feasible to produce. 2) The “generative content pipeline” isn't a magic button A recurring misconception Ken raises is the idea that someone “pushes a button” and content pops out. Jeanine explains that real production looks more like a hybrid studio: A creative team defines character, voice, format, and storyline. A technical team builds what she calls an “AI orchestration layer” that combines multiple models and tools. The “stack” differs by format: the workflow for a long-form audio drama is different from a short-form beauty clip. This matters because it reframes AI content not as a single output, but as a pipeline decision: which tools, which data sources, which QA, and which governance steps are used—and where human review happens. 3) The biggest legal questions: origin, liability, ownership, and contracts Mark doesn't name a single “top issue.” He describes a cluster of problems that repeatedly show up in client conversations: Training data and “origin story” Clients keep asking: Can I legally use AI output if the tool was trained on copyrighted works? Even if the output looks new, the unease is about whether the tool's capabilities are built on unlicensed inputs. Liability for unintended harm Mark flags risk from AI content that inadvertently infringes, defames, or carries bias. The legal exposure may not match the creator's intent. Ownership and protectability He points to a big gap: many jurisdictions are still reluctant to grant classic IP rights (copyright or patent-style protection) to purely AI-generated material. That creates uncertainty around whether businesses can truly “own” what they produce. Old contracts weren't written for AI A final, practical point: many agreements—talent contracts, author clauses, data licenses—predate generative AI and simply don't address it. That leads to disputes about scope, permissions, and—crucially—indemnities. 4) Are we at a tipping point? The “gold rush” vs. “next creative era” views Jeanine frames AI as “the world's most powerful creative tool”—comparable to previous step-changes like animation, special effects, and CGI. For her, the strategic implication is simple: creators who learn to use AI well will expand what they can build and test, faster than ever. Mark's metaphor is more cautionary: he calls the moment a “gold rush” where technology is sprinting ahead of law. Courts are getting flooded with foundational disputes, while legislation is fragmented—he notes that states may move faster than federal frameworks, and that labor agreements (e.g., union protections) will be a key pressure point. 5) Democratization: more creators, more niche content, more experimentation One of the most concrete themes is access. Jeanine argues AI will: Lower production barriers for independent filmmakers and storytellers. Reduce the need for “hit-making only” economics that dominate Hollywood. Make micro-audience content commercially viable. Her example is intentionally niche: highly localized, specialized content (like a “pollen report” for many markets) that would never have made financial sense before can now exist—and thrive—because the production cost drops and personalization scales. 6) Likeness, consent, and “digital performers”: what happens when AI resembles a real actor? Ken pushes into a sensitive area: what if someone generates a performance that closely resembles a living actor without consent? Mark outlines the current (imperfect) toolbox—because, as he emphasizes, most laws weren't built for this scenario. He points to practical claims that may come into play in the U.S., such as rights of publicity and false endorsement-type theories, and notes that whether something is parody or “too close” can become a major fault line. Jeanine explains her company's operational approach: They focus on original personalities, designed “from scratch.” They build internal checks to avoid misappropriating known names, likenesses, or recognizable identities. If they ever work with real people, the model would be licensing their likeness/voice. A subtle but important business point also appears here: Jeanine expects AI-native characters themselves to become licensable assets—meaning the entertainment economy may expand to include “celebrity rights” for fully synthetic personalities. 7) Ethics: the real line is “deception,” not “AI vs. human” The ethical core of the conversation is not “AI is bad” or “AI is good.” It's how AI is used—especially whether audiences are misled. Mark highlights several ethical risks: Misuse of tools to manipulate faces and content (“AI slop” and political misuse). Displacement of creative workers without adequate transition support. A concern that AI often optimizes toward “statistical averages,” potentially flattening originality. Jeanine agrees ethics must be designed into the system. She describes regular discussions with an ethicist and emphasizes a principle: transparency. Her company discloses when content or personalities are AI-generated. She argues that if people understand what they're engaging with and choose it knowingly, the ethical problem shifts from “AI exists” to “Are we tricking people?” Mark adds a real-world warning: deepfakes are now credible enough to enable serious fraud—he references a case-like scenario where a synthetic video meeting deceived an employee into authorizing a payment. The point is clear: authenticity and verification are no longer optional. 8) The “dead actor” hypothetical: legal permission vs. moral intent Ken raises a provocative scenario: an actor's estate authorizes an AI-generated new performance, but the actor opposed such technology while alive. Neither guest offers a simplistic answer. Jeanine suggests that even if the estate holds legal rights, a company might choose to avoid such content out of respect and because the ethical “overhang” could damage the storytelling outcome. She also notes the harder question: people who died before today's capabilities may never have been able to meaningfully consent to what AI can now do—raising questions about how we interpret legacy intent. Mark underscores the practical contract problem: many rights are drafted “in perpetuity,” but that doesn't automatically settle the ethical question. 9) Five-year forecast: “AI everywhere,” but audiences may stratify Ken closes with a prediction question: in five years, how much entertainment content will significantly involve AI—and will audiences care? Jeanine predicts AI becomes the default creative layer for most content creation. Mark is slightly more conservative on the percentage, but adds an important nuance: the market will likely stratify. Low-cost, high-volume content may become saturated with AI, while premium segments may emphasize “human-made” as a differentiator—especially if disclosure norms become standard. Bottom line for business leaders and creators This interview lands on a pragmatic conclusion: AI will change how content is made at scale, and the competitive edge will go to teams that combine creative taste, operational discipline, and legal/ethical governance. If you're building, commissioning, or distributing content, the questions you can't dodge anymore are: What's the provenance of the tools and data you rely on? Who is responsible when output harms, infringes, or misleads? What rights can you actually claim in AI-assisted work? Do your contracts and disclosures match the new reality? Ken Suzan: Thank you, Rolf. We have two returning guests to the IP Friday’s podcast. Joining me today is Janine Wright and Mark Stignani. Our topic for discussion, how is AI transforming the media and entertainment industries today? We look at the issues from differing perspectives. A bit about our guests, Janine Wright is a seasoned board member, CEO, global COO and CFO. She’s led organizations from startup to a $475 million plus revenue subsidiary of a public company. She excels in growth strategy, adopting innovative technologies, scaling operations and financial management. Janine is a media and entertainment attorney and trial litigator turned technologist and qualified financial expert. She is the co-founder and CEO of Inception Point AI, a growing company that is paving new ground with AI-generated personalities and content through developing technology and story. Mark Stignani is a partner with Barnes & Thornburg LLP and is based in Minneapolis, Minnesota. He is the chair of the data analytics department with a particular emphasis on artificial intelligence, machine learning, cryptocurrency and ESG. Mark combines the power of artificial intelligence and machine learning with his skills as a corporate and IP counsel to deliver unparalleled insights and strategies to his clients. Welcome, Janine and Mark to the IP Friday’s podcast. Jeanine Whright: Thank you. Thank you. Thank you so much for having me and fun to be back. It feels nostalgic to be here. Ken Suzan: That’s right. And you both were on the program. So it’s fantastic that you’re both back again. So our format, I’m going to ask a question to Janine and or Mark and sometimes to both of you. So that’s going to be how we proceed. Let’s jump right in. Janine, your company creates AI-generated actors. For listeners who may not be familiar, can you briefly explain what that means and what’s now possible that wasn’t even two years ago? Jeanine Whright: Sure. Yeah, we are creating AI-generated personalities. So new characters, new personalities from scratch. We design who these personalities are and will be, how they will evolve. So we give them complex backstories. We give them hopes and dreams and aspirations. We every aspect of them, their families, how they’re going to evolve. And in the same way that, say, you know, Disney designs the character for its next animated feature or, you know, an electronic arts designs a character for its next major video game. We are doing that for these personalities and then we are launching them into the world as podcast hosts, content creators on social platforms like YouTube, Instagram and TikTok. And even in the future, you know, actors in feature length films, musicians, etc. Ken Suzan: Very fascinating. Mark, from your practice, what’s the single biggest legal question or dispute you’re seeing clients wrestle with when it comes to AI and media creation? Mark Stignani: Well, I think that, you know, it’s not just one thing, it’s like four things. But most of them tend to be kind of the origin story of AI data or AI tools that they use because, you know, but for the use of AI tools trained on copyrighted materials, the tools wouldn’t really exist in their current form. So a lot of my clients are wondering about, you know, can I legally use this output if it’s built upon somebody else’s IP? The second ask, the second flavor of that is really, is there liability being created if I take AI content that inadvertently infringes or defames or biases there? So there’s the whole notion of training bias from the training materials that comes out. The third phase is really, you know, can I really own this? Because much of the world does not really give IP rights into AI-generated inventions, copyrighted materials. It’s still kind of a big razor. Then at the end of the day, you know, if it’s an existing relationship, does my contract even contemplate this? So everything from authors contracts on up to just use of data rights that predate AI. Ken Suzan: And Janine and Mark, a question to both of you. How would you describe where we are right now in the AI revolution in media and entertainment? Are we approaching a tipping point? And if so, what are the things we need to watch for? Jeanine Whright: Yeah, I definitely think that we’re at a phase where people are starting to come to the realization that AI is the world’s most powerful creative tool. But that, you know, storytelling and point of view is what creates demand and audiences. And AI doesn’t threaten or change that. But it does mean that as people evolve in this medium, they’re very likely going to need to adopt, utilize and figure out how to hone their craft with these AI-generated content and these AI-generated toolings. So this is, you know, something that people have done certainly in the past in all sorts of ways in using new tools. And we’ve seen that make a significant change in the industry. So you look at, you know, the dawn of animation as a medium. You look at use of special effects, computer-generated imagery in the likes of Pixar. And this is certainly the next phase of that evolution. But because of the power of the tool and what will become the ubiquity of the tool, I think that it’s pretty revolutionary and all the more necessary for people to figure out how to embrace this as part of their creative process. Ken Suzan: Thank you, Janine. Mark, your thoughts? Mark Stignani: Yeah, I mean, I liken this to historically to like the California gold rush right now, because, you know, the technology is so far outpaced in any of the legal frameworks that are available. And so we’re just trying to shoehorn things in left and right here. So, I mean, the courts are beginning to start to engage with the foundational questions. I don’t think they’re quite there yet. I just noticed Anthropic got sued again by another group of people, big music group, because of the downloaded works they’ve done. I mean, so the courts are, you know, the courts are certainly inundated with, you know, too many of these foundational questions. Legislatively, hard to tell. I mean, federal law, the federal government is not moving uniformly on this other than to let the gold rush continue without much check and balance to it. Whereas states are now probably moving a lot faster. Colorado, Illinois, even Minnesota is attempting to craft legislation and limitations on what you can do with content and where to go with it. So, I mean, the things we need to watch for any of the fair use decisions coming out here, you know, some of the SAG-AFTRA contract clauses. And, you know, again, the federal government, I just, you know, I got a big shrug going as to what they’re actually going to come up with here in the next 90 to 100 days. So, but, you know, I think they’ll be forced into doing something sooner than later. Ken Suzan: Okay, let’s jump into the topic of the rise of generative content pipelines. My first question to Janine. Studios and production companies are now building what some call generative content pipelines. This is where AI systems produce everything from scripts to visual effects to voice performances. What efficiencies and creative possibilities does this unlock for the industry? Jeanine Whright: Yeah, so this is quite a bit of what we do. And if I could help pull the curtain back and explain a little bit. Ken Suzan: That’d be great. Jeanine Whright: Yeah, there’s this assumption that, you know, somebody is just sitting behind a machine pushing a button and an out pops, you know, what it is that we’re producing. There’s actually quite a bit of humans still in the loop in the process. You know, we have my team as creators. The other half of my team is the technologists. And those creators are working largely at what we describe as the the tip of the sphere. So they’re, of course, coming up with the concepts of who are these personalities? What are these personalities, characters, backgrounds going to be a lot of like rich personality development? And then they’re creating like what are the formats? What are the kind of story arcs? What is the kinds of content that this this character wants to tell? And what are the audiences they’re desiring to reach and what’s most going to resonate with them? And then what we built internally is what we refer to as an AI orchestration layer. So that allows us to pull from basically all of the different models and then all of these different really cool AI tools. And put those together in such a way and combine those in such a way that we can have the kind of output that our creative team envisions for what they want it to be. And at the end of the day, what you what the stack looks like for, say, a long form audio drama, like the combination of LLMs that we’re going to use in different parts of scripting and production and, you know, ideating and all of that. And the kinds of tooling that we use to actually make it and get it to sound good and have the kinds of personality characteristics that we want to be in an authentic voice for a podcast is going to be different than the tech stack and the tool stack that we might use for a short form Instagram beauty tip reel. And so there’s a lot of art in being able to pull all of these tools together to get them to do exactly what you want them to do. But I think the second part of your question is just as interesting as the first. I mean, what is what possibilities is this unlocking? So of course you’re finding efficiencies in the creative production process. You can move faster. You can do things were less expensive, perhaps, and you were able to do it before. But on the creator side, I think one thing that hasn’t been talked about enough is how it is really like blown wide the aperture of what creators can do and can envision. Traditionally, you know, Hollywood podcasting, many of these businesses that become big businesses have become hit making businesses where they need to focus on a very narrow of wide gen pop content that they think is going to get tens of millions, hundreds of millions in, you know, fans and dollars in revenue for every piece of content that they make. So the problem with that is, is that it really narrows the kinds of things that ultimately get made, which is why you see things happening in Hollywood, like the Blacklist, which is, you know, this famous list of really exceptional content that remains unpredited, unproduced, or why you see things like, you know, 70 to 80% of the top 100 movies being based on pre-existing IP, right? Because these are such huge bets that you need to feel very confident that you’re going to be able to get big, big audiences and big, big dollars from it. But with AI, and really lowering the barrier to entry, lowering the costs of production and marketing, the experimentation that you can do is really, really phenomenal. So, you know, my creative team, if they have an idea, they make it, you know, they don’t have to wring their hands through like a green lighting process of, you know, should we, shouldn’t we, like we, we can make an experiment with lots of different things, we can do various different versions of something. We can see what would this look like if I placed it in the 1800s, or what if I gave this character an Australian accent, and it’s just the power of being able to have this creative partner that can ideate with you and experiment with you at rocket speed. With the creators that are embracing it, you can see how it is really fun for them to be able to have this wide of a range of possibility. Ken Suzan: Mark, when you hear about these generative pipelines, what are the immediate red flags or concerns that come to mind from a legal standpoint? How about ethics underlying all of this? Well, Mark Stignani: that was not, that’s the number one red flag because I mean, we are seeing not just that in the entertainment industry, but it literally at political levels, and the kind of the phrase, to turn the phrase AI slop being generated, we’re seeing, you know, people’s facial expressions altered. In some cases, we’re seeing AI tools being misused to exploit various groups of individuals and genders and age groups. So I mean, there’s a whole lot of things ethically that people are using AI for that just don’t quite cover it. Especially in the entertainment industry, I mean, we’re looking at a fair amount of displacement of human workers without adequate transition support, devaluation of the creative labor. I mean, the thing though that I’m always from a technical standpoint is AI is simply a statistical average of most everything. So it kind of devalues the benefit of having a human creator, a human contribution to it. That’s the ethical side. But on the legal side, I see chain of title issues. I mean, because these are built on very questionable IP ownership stages, I mean, in most of these tools, there has been some large copying, training and taking of copyrighted materials. Is it transformational? Maybe. But there’s certainly not a chain of title, nor is there permission granted for that training. I mentioned SAG-AFTRA earlier, I think there’s a potential set of union contract aspects to this that if you know many of these agreements and use sub-licenses for authors and actor agreements, they weren’t written with AI in mind. So that’s another red flag. And also I just think in indemnification. So if we ultimately get to a point where groups are liable for using content without previous license, then who’s liable? Is the tool maker the liable group or the actual end user? So those are probably my top four red flags. But I think ethics is probably my biggest place because just because we can do something from an ethical standpoint doesn’t mean we should. Jeanine Wright: Yeah, if I can respond to both of those points. I mean, one from a legal perspective, just to be very clear, I mean, we are always pulling from multiple different models and always pulling from multiple different sources. And we even have data sources that we license or use for single source of truth on certain pieces of information. So we’re always pulling things together from multiple different sources. We also have built into our process, you know, internal QAing and checking to make sure that we’re not misappropriating the name or likeness of any existing known personality or character. We are creating original personalities there. We design their voice from scratch. We design their look from scratch. So we’re not on our personality side, we’re not pulling or even taking inspiration from existing intellectual property that’s already out there in creating these personalities. On the ethical side, I agree. I mean, when we came out of stealth, we came out of stealth in September. There was certainly quite a bit of backlash from folks in my—I previously co-founded a company in the audio space. I mean, there’s been many rounds of layoffs in audio and in many other parts of the entertainment industry. So I’m very sensitive to the feedback around, like, is this job displacement? I mean, I do think that the CEO of NVIDIA said it right when he said, you’re likely not going to lose your job to AI, but you will lose your job to somebody who knows how to use AI. I think these tools are transforming the way that content is made and that the faster that people can embrace this tooling, the more likely they’re going to be having the kinds of roles that they want in, you know, in content creation and storytelling in the future. And we are hiring. I’m hiring AI video creators, AI audio creators. I’m hiring AI developers. So people who are looking for those roles, I mean, please reach out to me, we would love to work with you and we’d love to grow with you. We also take the ethics very seriously. For the last few months or so, I’ve met regularly with an ethicist, we talk about all sorts of issues around, you know, is designing AI-generated people, you know, good for humanity? And what about authenticity and transparency and deception, and how are we in building in this space going to avoid some of the problems that we’ve seen with things like social media and other forms of technology? So we keep that very top of mind and we try to build on our own internal values-based system and, you know, continue to elevate and include the humanity as part of the conversation. Ken Suzan: Thank you, Janine. Janine, some argue that AI content pipelines will level the field for filmmaking, giving independent creators access to tools that were once available only to major studios. Is that the future you envision? Jeanine Wright: I do think that with AI you will see an incredible democratization of access to technology and access to these capabilities. So I do think, you know, rise of independent filmmakers, you won’t have as many people who are sitting on a brilliant idea for the next fantastic script or movie that just cannot get it made because they will be able to with these tools, get something made and out there, at least to get the attention of somebody who could then decide that they want to invest in it at a studio kind of level in the future. The other thing that I think is really interesting is that I think, you know, AI will empower more niche content and more creators who can thrive in micro-communities. So it used to be because of this hit generation business model, everything needed to be made for the masses and a lot of content for niche audiences and micro-communities was neglected because there was just no way to make that content commercially viable. But now, if you can leverage AI—we make a pollen report podcast in 300 markets, you know, nobody would have ever made that before, but it is very valuable information, a very valuable piece of content for people who really care about the pollen in their local community. So there’s all sorts of ways that being able to leverage AI is making it more accessible both to the creator and to the audience that is looking for content that truly resonates with them. Ken Suzan: Mark, let’s talk about the legal landscape right now. If someone creates an AI-generated performance that closely resembles a living actor without their consent, what legal recourse does that actor have? Mark Stignani: Well, I mean, I think we can go back to the OpenAI Scarlett Johansson thing where, you know, if it’s simply—well, the “walks like a duck, quacks like a duck” type of aspect there. You know, I think it’s pretty straightforward that they need to walk it back. I mean, the US doesn’t have moral rights, really, but there’s a public visage right, if you will. And so, one of the things that I find predominantly useful here is that these actors likely have rights of publicity there, we probably have a Lanham Act false endorsement claim, and you know, again, if the performance is not parody, and it’s so close to the original performance, we probably have a copyright discussion. But again, all of these laws predate the use of AI, so we’re going to probably see new sets of law. I mean, we’re probably going to see “resurrection” frameworks, we’ll probably have frameworks for synthetic actors and likenesses, but the rules just aren’t there yet. So, unfortunately, your question is largely predictive versus well-settled at this point. Ken Suzan: Janine, your company works with AI actors. How do you navigate the questions of consent and likeness compensation when creating digital performers? Jeanine Wright: I mean, if we—so first of all, if we were to work with a person who is an existing real-life person or was an existing real-life person, then we would work with them to license their name and likeness or their voice or whatever aspects of it we were going to use in creating content in partnership with them. Not typically our business model; we are, as I said, designing all of our personalities from scratch and making all of our content originally. So, we’ve not had to do that historically. Now, you know, the flip side is: can I license my characters as if they’re similar to living characters? Like will I be able to license the name and likeness and voice of my AI-generated personalities? I think the answer is yes and we’re already starting to do that. Ken Suzan: Let’s just switch gears into ethics and AI because I find this to be a really fascinating issue. I want to look at a hypothetical. And this is to both of you, Janine and Mark: an AI system creates a new performance by a beloved actor who passed away decades ago, and the actor’s estate authorizes it, but the actor was known to have expressed opposition to such technology during their lifetime. Is this ethical? Jeanine Wright: This feels like a Gifts, Wills, and Trusts exam question. Ken Suzan: It sounds like it, that’s right. Jeanine Wright: Throwing me back to my law school days. Exactly. What are your thoughts? It’d be interesting to see like who has the rights there. I mean, I think if you have the legal rights, the question is around, you know, is it ethical to go against what you knew was somebody’s wishes at the time? I guess the honest answer is I don’t know. It would depend a lot on the circumstances of the case. I mean, if we were faced with a situation like that where there was a discrepancy, we would probably move away from doing that content out of respect for the deceased and out of a feeling that, you know, if this person felt strongly against it, then it would be less likely that you could make that storytelling exceptional in some way—it would color it in a way that you wouldn’t want in the outcome. And I feel like there’s—I mean, certainly going forward and it’s already happening—there are plenty of people I think who have name, likeness, and voice rights that they are ready to license that wouldn’t have this overhang. Ken Suzan: Mark, your thoughts? Mark Stignani: Yeah, I mean, again, I have to kind of go back to our property law—the Rule Against Perpetuities. You know, from a property standpoint to AI rights and likenesses—since most of the digital replica contracts that I’ve reviewed generally do talk about things in perpetuity. But if it’s not written down for that actor and the estate is doing this—is it ethical? You know, that is the debate. Jeanine Wright: Well, gold star to you, Mark, for bringing up the Rule Against Perpetuities. There’s another one that I haven’t heard for many years. This is really taking me back to my law school days. Ken Suzan: It’s a throwback. Jeanine Wright: The other thing that’s really interesting is that this technology is really so revolutionary and new that it’s hard to even contemplate now what it is going to be in a decade, much less for people who have passed away to have contemplated what the potential for it could be today. So you could have somebody who is, perhaps, a deceased musician who expressed concerns about digital representations of themselves or digital music while they were alive. But now, the possibility is that you could recreate—certainly I could use my technology to recreate—that musician from scratch in a very detailed way, trained on tons of different available data. Not just like a digital twin or a moving image of them, but to really rebuild their personality from scratch, so that they and their music could be reintroduced to totally new generations in a very respectful and authentic way to them. It’s hard to know, with the understanding that that is possible, whether or not somebody who is deceased today would or would not agree to something like that. I mean, many of them might want, under those circumstances, for their music to live on. These deceased actors and musicians could live forever with the power of AI technology. Mark Stignani: Yeah, I really just kind of go to the whole—is deep-faking a famous actor the best way to preserve them or keep them live? Again, that’s a bit more of an ethical question because the deep fakes are getting good enough right now to create huge problems. Even zoom meetings in Hong Kong where a CFO was on a call with five synthetic actors who all looked like his coworkers and they sent a big check out based upon that. So again, the technology is getting good enough to fool people. Jeanine Wright: I think that’s right, Mark, but I guess I would just highlight the same way that it always has been: the ethical line isn’t AI versus human, the ethical line is about deception. Like, are you deceiving people? And if people know what it is that they’re getting and they’re choosing to engage with it, then I think it isn’t about the power of the technology. In our business, we have elected—not everybody has—but we have elected to be AI transparent. So we tell people when they listen to our show, we include it in our show notes, we include it on our socials. Even when we’re designing our characters to be very photo-realistic, we make an extra point to make sure that people know that this is AI-generated content or an AI personality. Like, our intention is not to deceive and to be candid. From a business model perspective, we don’t need to. I mean, there’s already people who know and understand that it is AI, and AI is different than people. Because it is AI, there’s all sorts of things that you can do with it that you would not be able to do with a real person. You know, we get people who ask us on the podcast side, we get all sorts of crazy funny requests. You know, people who say, “Can I text with this personality? Can I talk to them on the phone? Can they help me cook in the kitchen? Can they sing me Happy Birthday? Can they show up at my Zoom meeting today because I think my boss would love it?” You know, all sorts of different ways that people are wanting to engage with these characters. And now we’re in the process of rolling out real-time personalities so people will be able to engage with our personalities live. It is a totally different way that people are able to engage with content, and people can, as they choose, decide what kind of content they want to engage with. Ken Suzan: Jeanine and Mark, we’re coming to the end of this podcast. I would love to keep talking for hours but we have to stay to our timetable here. Last question: five years from now, what percentage of entertainment content do you predict will involve significant AI generation, and will audiences care about that percentage? Jeanine? Jeanine Wright: I mean, I would say 99.9%. I mean, already you’re seeing—I think YouTube did a survey—that it was like 90% of its top creators said that they’re using AI as material components of their content creation process. So, I think this will be the default way that content is created. And content that is not made with AI, you know, there’ll be special film festivals for non-AI generated content, and that will be a special separate thing than the thing that everybody is doing now. Ken Suzan: Mark, your thoughts? Mark Stignani: Yeah, I go a little lower. I mean, I think Jeanine is right that we’re seeing, especially in the low-quality content creation and like the YouTube shorts and things like that, you know, there’s so much AI being pushed forward that the FTC even acquired an “AI slop” title to it. I do think that disclosure will become normalized, that the industries will be pushed to say when something is AI and what is not. And I think it’s very much like, you know, do you care about quality or not? If you value the human input or the human factor in this, there will be an upper tier where it’s “AI-free” or low AI assistant. I think that it’s going to stratify because the stuff coming through the social media platforms right now—I can’t be on it right now just because there’s so much nonsense. Even my children, who are without much AI training at all, find it just too unbelievable for them. So, I think it will become normalized, but I think that we’re going to see a bunch of tiers. Ken Suzan: Well, Jeanine and Mark, this has been a fantastic discussion of an ever-evolving field in IP law. Thank you to both of you for spending time with us today on the IP Friday’s podcast. Jeanine Wright: Thank you so much for having me. Mark Stignani: Appreciate your time. Thank you again.

Scrubs is getting a TV reboot, and that revival has a lesson for podcasters who want listeners to come back. Today, we look at how the Scrubs rewatch podcast keeps fans engaged and what it teaches us about building a show people stick with over time. We also talk about when a rewatch format works and how to borrow the idea for your own podcast. Then I share the exact AI prompt I used to pull a tight highlight from a clip we played from that Scrubs companion show, trimming it down into something clean and post-ready. We wrap the week sharing wins and celebrating our accomplishments. Episode Highlights: [03:21] Monday's Obsession Worthy preview[04:47] Scrubs reboot clip and pop culture context[19:51] Reviewing your own back catalog for improvement[21:48] Coaching, feedback, and self-critique[30:39] Learning to love your own voice[35:14] Building or finding an uplifting community[36:27] Cutting clips with ChatGPT: the 3-minute to 32-second workflow[42:12] Hallucinations, human QA, and quality control[46:49] Wins of the weekLinks & Resources: The Podcasting Morning Chat: www.podpage.com/pmcJoin The Empowered Podcasting Facebook Group:www.facebook.com/groups/empoweredpodcasting⁠Book A Free Call With Me: https://calendly.com/ironickmedia/freestrategycallJoin The Empowered Podcasting Facebook Group:www.facebook.com/groups/empoweredpodcasting⁠Application To Submit Your Show For Evaluation: https://docs.google.com/forms/d/e/1FAIpQLSc8-Xv6O6lrNPcPJwj3N0Z5Osdl-5kHGz_PiAU45U57S-XgoA/viewform?usp=headerRiverside: https://www.riverside.fm/?via=0676daMy Content Editing Chat GPT Prompt: https://chatgpt.com/share/69a18136-06b0-8004-9ce7-736c5d43232eNo Such Thing As a Fish: https://www.youtube.com/watch?v=ZB6NvXuWk4kRemember to rate, follow, share, and review our podcast. Your support helps us grow and bring valuable content to the podcasting community.Join us LIVE every weekday morning at 7 am ET (US) on ⁠Clubhouse⁠: ⁠⁠⁠ https://www.clubhouse.com/house/empowered-podcasting-e6nlrk0w⁠⁠Live on YouTube: ⁠http://podcastingmorningchat.com/joinusBrought to you by⁠ ⁠iRonickMedia.com⁠⁠ Please note that some links may be affiliate links, which support the hosts of the PMC. Thank you!--- Send in your mailbag question at:⁠ https://www.podpage.com/pmc/contact/⁠ or ⁠marc@ironickmedia.com⁠Want to be a guest on The Podcasting Morning Chat? Send me a message on PodMatch, here: ⁠https://www.podmatch.com/hostdetailpreview/1729879899384520035bad21b⁠

Stop working for "exposure"—it doesn't pay for the weed. In this episode of Thoughts Off The Stem, Justin Barone explores the darker side of society and culture where everyone wants something for nothing. Using a blend of stoner philosophy and relatable humor, Justin exposes the "prospecting pimps" trying to scam creators with "partnerships" that are actually just free labor. From a tin joint holder "collab" to an AI company wanting an unpaid QA department, we're calling out the Choosing Beggars of the industry with the kind of absurd humor and observational comedy that defines true cannabis culture.Visit us at TOTS420.com for reviews, playlists, and more high-grade content.CANNABIS EDUCATION: WEED FACTS from 420magazine.com Stop falling for the THC % lie! Potency is a byproduct of cultivation, not a universal badge of quality. In our cannabis education segment, we break down the 4 pillars of top-shelf bud so you can stop smoking "Campfire Smoke":Smell: Pungent & bold vs. musty & straw-like.Look: Vibrant trichomes vs. "brown sugar" dirt weed.Feel: Sticky & spongy vs. brittle PGR flower.Structure: Light Sativas vs. dense Indicas.DUDE, FOR REAL: CANNABIS COMEDY Boredom busters from BoredPanda.comWe're diving into the internet's most delusional requests. This is cannabis comedy at its peak—reacting to "professionals" who think a photographer should be paid in garlic or that "exposure" pays the rent. Its high thoughts met with the cold, hard reality of the modern hustle.Episode Breakdown: - The "Something for Nothing" Trap - Story 1: The Tin Joint Holder "Opportunity"- Story 2: The AI Software "Beta" Scam - Weed Facts: How to Spot Quality Flower- Dude, For Real: Choosing Beggar Reactions- Closing Thoughts & Stoner PhilosophyKeywords: Cannabis Podcast, Comedy Podcast, Society and Culture, Cannabis Culture, Stoner Philosophy, High Thoughts, Cannabis Education, Cannabis Comedy, Relatable Humor, Observational Comedy, Absurd Humor.Light ‘em if you got ‘em. Subscribe to Thoughts Off The Stem and never settle for mid-grade deals.

In this thought-provoking episode, top industry trainer and QA expert Trey Briggs breaks down First Principles Thinking, a powerful problem-solving method, and how it can revolutionize quality assurance scoring and training. Trey offers industry-leading solutions based on a unique level of experience, knowledge, and expertise. Check it out!

Measuring AI marketing ROI has become one of the most uncomfortable conversations in tech and marketing teams. Everyone knows AI is "important." Fewer teams can explain what success actually looks like. Even fewer can tie adoption to real outcomes rather than experimentation for its own sake. For developers and technical leaders, this isn't a tooling problem — it's a decision-making problem. The teams that win are the ones that slow down just enough to define value before they ship. About Meeky Hwang Meeky Hwang's journey resonates with entrepreneurs, technical leaders, and anyone navigating the intersection of technology and business. As CEO and Co-Founder of Ndevr, a digital solutions development agency, Meeky brings over 20 years of experience building resilient, scalable platforms for organizations including Johnson & Johnson, Pfizer, Forbes, PMC, and Bloomberg. Her work goes beyond website development—she focuses on long-term digital solutions that improve performance, streamline workflows, and align technology with business strategy. Equally important is Meeky's perspective as a woman leading in a male-dominated industry. She has navigated the challenges of technical leadership, entrepreneurship, and scaling a services business while building credibility and strong teams along the way. Her experience offers an honest look at what it takes to grow as a leader without losing sight of innovation, people, or purpose. Follow on LinkedIn and her Website. Measuring AI marketing ROI when the hype is louder than the data AI adoption today often starts with pressure instead of purpose. Tools arrive before goals. Budgets get approved before success criteria exist. That's the first red flag. If you can't articulate what improvement AI is supposed to create — conversion lift, content velocity, operational savings, personalization accuracy — you're not measuring ROI. You're chasing momentum. Measuring AI marketing ROI by defining outcomes before tools The most effective teams reverse the typical process. They define outcomes first, then ask which capabilities might support those outcomes. That discipline alone filters out most bad investments. Before selecting tools, answer three questions: What problem are we solving? How will we measure improvement? What happens if this fails? If those answers feel vague, that's your signal to pause. Measuring AI marketing ROI with clear baselines and success metrics ROI requires comparison. Without a baseline, every result looks impressive — or disappointing — depending on expectations. Establish: A pre-AI performance baseline A specific success threshold A review window short enough to stop bad bets early This turns AI from a belief system into an experiment with guardrails. Measuring AI marketing ROI without wasting budget on "maybe" features Not every feature deserves implementation just because it exists. Time and money are always the real constraints. Teams that succeed evaluate AI features the same way they evaluate architecture decisions: cost, risk, effort, and impact. When those tradeoffs are visible, priorities clarify quickly. Measuring AI marketing ROI while Google, SEO, and platforms keep shifting AI doesn't exist in isolation. SEO changes, platform updates, and algorithm shifts constantly reshape the playing field. That makes flexibility more valuable than novelty. Incremental improvements that survive change often outperform bold implementations that lock teams into fragile solutions. Measuring AI marketing ROI alongside compliance requirements and regional rules Global websites introduce real constraints — privacy, consent, accessibility, and regulatory differences. AI features that ignore compliance increase risk faster than they increase value. Measuring AI marketing ROI with a repeatable compliance checklist A checklist-driven approach ensures new features don't break trust or regulation: Regional consent and privacy rules Accessibility requirements Data handling expectations This protects ROI by preventing costly rework. Measuring AI marketing ROI through discovery, QA, UAT, and launch checklists Strong discovery reduces downstream chaos. Structured QA and UAT validate assumptions. Launch checklists prevent avoidable mistakes. AI doesn't replace these fundamentals — it amplifies their importance. Measuring AI marketing ROI as a founder: delegate, stay lean, and still scale Technical founders often delay hiring because they can do the work themselves. That works — until it doesn't. Sustainable ROI requires delegation. Growth depends on trusting others to execute while leaders focus on direction, not tickets. Callout: AI ROI Scorecard Define outcomes, baselines, and review windows before implementation Decide early whether to pilot, pause, or proceed Callout: Website Launch Checklist (Minimum Viable) QA, UAT, accessibility, and responsiveness checks Hosting, CDN, and integration validation Callout: Delegation Rules for Technical Founders Decide what you keep vs. hand off Train once, so execution scales later Conclusion Measuring AI marketing ROI isn't about skepticism — it's about clarity. When teams define value first, use disciplined checklists, and resist hype-driven decisions, AI becomes a multiplier instead of a distraction. If you want better outcomes, start with better questions — and build from there. Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Online Communities and Marketing Creating your Marketing Site Branding and Marketing Fundamentals with Kevin Adelsberger Develpreneur - Forward Momentum Podcast Videos – With Bonus Content

#333 | Dave is joined by Clare Schmitt, a seasoned marketing leader and a member of our CMO community, to walk through what it actually takes to lead a rebrand at a mid-market B2B company. Clare shares how she partnered with her CEO to drive a full rebrand, from hiring a naming agency and running an RFP, to managing a small decision-making council, rolling out the new brand across every department, and measuring success post-launch. If you're a marketing leader thinking about a rebrand, this episode is a practical, top to bottom playbook from someone who just did it.Timestamps(00:00) - Why rebrands come up and what this episode covers (03:01) - Clare's role at Piedmont Global and how the rebrand got started (05:16) - Should you hire a naming agency? What it costs and what they actually do (08:44) - Running an RFP and why they chose Focus Lab (09:30) - Why the CEO has to own the rebrand go-to-market (11:06) - Keeping the decision-making council small and who was in it (18:24) - How to get CEO buy-in: framing a rebrand as infrastructure, not a marketing initiative (21:01) - Timeline: how long a rebrand actually takes ($50M+ companies) (22:43) - The rollout: project management, execution, and building the website internally (24:40) - Measurement, post-launch QA, and tracking whether your narrative is sticking Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Knak - A no-code, campaign creation platform that lets you go from idea to on-brand email and landing pages in minutes, using AI where it actually matters. Learn more at knak.com/exitfive.Optimizely - An AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Get a free, personalized 45-minute AI workshop to help you identify the best AI use cases for your marketing team and map out where agents can save you time at optimizely.com/exitfive (PS - you'll get a FREE pair of Meta Ray Bans if you do). Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.  ***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

How much will the photographer that captured Andrew Mountbatten-Windsor slumped in the car have made off that single picture? How are figure skating fees worked out? Is Breaking Bad's crystal meth accurate? Richard Osman and Marina Hyde chat about the former Prince Andrew, book recommendations and ice skating in another excellent QA episode. The Rest is Entertainment is brought to you by Octopus Energy, Britain's most awarded energy supplier. Join The Rest Is Entertainment Club: Unlock the full experience of the show – with exclusive bonus content, ad-free listening, early access to Q&A episodes, access to our newsletter archive, discounted book prices with our partners at Coles Books, early ticket access to live events, and access to our chat community. Sign up directly at therestisentertainment.com For more Goalhanger Podcasts, head to www.goalhanger.com Video Editor: Max Archer Assistant Producer: Imee Marriott Senior Producer: Joey McCarthy Social Producer: Bex Tyrrell Exec Producer: Neil Fearn Learn more about your ad choices. Visit podcastchoices.com/adchoices

Dr. Kevin Ko on Biomarkers, Oral Dysplasia, and the Limits of H&E DiagnosisChristine interviews Dr. Kevin Ko (DMD, MD), a pathologist at the BC Cancer Agency with training in oral and maxillofacial pathology, anatomic pathology, and dermatopathology. They discuss his ASDP 2025 lecture on using p53 in oral dysplasia as a potential new approach and the broader problem of diagnostic discordance and over-diagnosis when relying on H&E alone. Dr. Ko shares examples from practice, including recognizing oral porokeratosis (previously followed as dysplasia for years) and a chemotherapy-related lip lesion initially suspected to be severe dysplasia but supported by wild-type biomarker results and clinical history, resolving after stopping chemotherapy drugs. He emphasizes the need for reproducible biomarkers and possibly molecular-based classification to improve consistency and patient outcomes, while also describing the pressure to be near-perfect in pathology, the risk of burnout, and efforts to build sustainable systems (QA sessions, colleague consultation, protected time). The conversation closes with his approach to presentations as storytelling, interest in prospective multi-center research, and a final message about balancing perfectionism with rest while remaining open-minded to new diagnostic methods to improve patient care.00:00 Welcome & Meet Dr. Kevin Ko (DMD/MD, Dermpath at BC Cancer)01:00 The Controversial Idea: Using p53 Biomarkers in Oral Dysplasia01:18 Oral vs Skin Pathology: Discovering Porokeratosis in the Mouth02:07 Diagnostic Error & Overdiagnosis: Why Reproducible Biomarkers Matter05:19 Case Study: “Severe Dysplasia” vs Toxic Erythema of Chemotherapy —Context Changes Everything06:36 The Perfectionism Trap in Pathology (and Why 95% Isn't Good Enough)08:04 Burnout, QA Systems, and Building Sustainable Workflows09:14 Work–Life Balance, Kids, and Choosing Priorities (Family vs Research)11:14 How to Build a Great Talk: Storytelling, Cases, and Future Studies11:38 Final Takeaways: Balance, Open-Mindedness, and Better Diagnostics

AI test automation is evolving fast — but most tools still generate brittle code that breaks with every UI change. See it for yourself now: https://links.testguild.com/Thunders In this episode of the TestGuild Podcast, Joe Colantonio sits down with Karim Jouini, founder of Thunders, to explore a radically different approach to AI testing: executing test automation in plain English without generating Selenium or Playwright code. Instead of "auto-healing selectors," Thunders interprets natural language directly — allowing teams to: Ship twice as fast Achieve 10x test coverage with the same resources Reduce regression cycles from weeks to days Eliminate massive automation maintenance overhead Karim shares real-world case studies, including: A European bank that reduced a 3-year core banking upgrade testing effort to 4 months A SaaS company that transitioned from a traditional QA team to AI-assisted product-led testing We also discuss: Whether AI test agents replace QA roles How QA managers must shift from individual contributors to AI managers The risks of adopting AI without a defined success metric The future of shift-left testing in the AI era If you're a software tester, automation engineer, QA lead, or DevOps leader trying to understand what's hype versus real ROI in AI testing — this episode breaks it down. Try it for yourself and see how AI testing fits into your pipeline. Get personal demo: https://links.testguild.com/Thunders  

This Wednesday's QA shiur is generously sponsored by Bernie Samet. In loving memory of his father, Yaakov ben Rachel, whose yahrzeit is on the 29th of Shevat; in memory of his mother, Chaya Sarah bas Gittel, whose yahrzeit is on the 26th of Shevat; in memory of his beloved wife, Baila bas Zlata, a"h; whose yahrzeit was on 13th of kislev and in memory of his sister's granddaughter, Rachael bas Rivka Tova, a"h, who was niftar on the 17th of Shevat. May the learning of this shiur serve as an aliyah for their neshamot.

#338: Every company adding AI coding tools runs into the same wall. Developers produce more code, but features don't ship any faster. The bottleneck just slides downstream -- to QA, to security, to legal, to whoever comes next in the pipeline. And the team that got faster? They don't even realize the people upstream could be feeding them more work. Viktor's take: the fastest possible setup is one person carrying a feature from idea to production. Not one person doing everything alone -- a system designed so nobody waits. Tests run in CI. Deployments happen through Argo CD. Security scanning is automated. There's a real difference between wiring up a light switch and hiring a butler to flip it for you. None of this is new. The same thing happened with punch cards, client-server, cloud, Kubernetes. One group adopts the new thing, everyone else says it doesn't apply to them, and the market eventually forces their hand. Meanwhile, every team in every company says they'd love to change if only the rest of the organization would get on board. Every team says this. So who's actually blocked?   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

Is traditional performance testing becoming obsolete? In this episode, performance engineering expert Akash Thakur shares why AI is fundamentally transforming load testing, scripting, observability, and shift-left strategies. With 17 years of real-world enterprise experience, Akash explains how AI-augmented tools are already reducing scripting time by 30%, improving analysis speed, and helping teams move from reactive performance testing to predictive intelligence. You'll learn: How AI is accelerating performance scripting and analysis Why shift-left performance testing is finally becoming realistic The role of structured data in predictive QA models How to test AI applications (LLMs, GPUs, inference throughput) differently than traditional web apps What the future role of performance engineers looks like — architect, not script writer If you're a performance tester, SRE, QA leader, or DevOps engineer wondering how AI will impact your role — this episode gives you practical, actionable insights you can apply immediately.

Prabhleen Kaur: How AI Is Changing the Way Agile Teams Deliver Value 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.   "AI's output is not the final output—it's always the two eyes we have that will get us the best results." - Prabhleen Kaur   Prabhleen brings a timely challenge to the coaching conversation: the impact of AI on teams and how Scrum Masters should navigate this transformation. She frames it as both a challenge and an opportunity—teams are now capable of delivering faster than consumers can absorb, fundamentally changing expectations and dynamics.  Prabhleen has observed her teams evolve from uncertainty about AI to confidently leveraging it for practical benefits. Developers use AI for writing and understanding code, particularly helpful for onboarding new team members who need to comprehend existing codebases quickly. QA professionals find AI invaluable for generating test cases based on story and epic context already captured in JIRA.  The next frontier? Agentic AI, where AI systems communicate with each other to produce better outputs. But Prabhleen offers an important caution: AI is learning from many conversations, not all of which are reliable. The human element—critical thinking and verification—remains essential.  For Scrum Masters, this means facilitating conversations about how teams want to experiment with AI, exploring edge cases in testing that AI can help identify, and helping teams navigate the evolving landscape of possibilities while maintaining quality and judgment.   Self-reflection Question: How are you helping your team explore AI as a tool for improvement while ensuring they maintain critical thinking about the outputs AI produces?   [The Scrum Master Toolbox Podcast Recommends]