Podcasts about QA

Deborah Colombari: The Agile Team Destroyed by a Toxic Feedback Loop 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.   "Whenever we almost started to achieve norming, we went back to storming." - Deborah Colombari   Deborah shares the story of a team of about twelve developers and QA specialists that kept losing people one by one. The early signal was not a technical problem. It was turnover. A toxic direct leader created pressure, blame, and snarky responses whenever the team tried to explain what was happening. Because that leader's own manager behaved the same way, Deborah saw a reinforced pattern rather than an isolated personality problem. The team could not stabilize. Every time it moved toward norming, someone left or someone new arrived, sending the team back into storming. Knowledge walked out the door with the people who left, delivery dates slipped, morale dropped, and a fixed-date call center project was eventually canceled. Deborah's story is a sharp systems thinking reminder for Scrum Masters: sometimes the problem is not inside the team. The team may be showing the symptoms of a system that punishes honesty, overloads people, and teaches them that leaving is safer than speaking.   In this segment, we talk about Russell Ackoff, his systems thinking interview with Haynes Media Works, and the Tuckman model.   Self-reflection Question: What turnover or morale signals are you treating as team problems when they may be system problems? Featured Book of the Week: Russell Ackoff Interview by Haynes Media Works Instead of a book, Deborah recommends an old Russell Ackoff interview from Haynes Media Works. She connects Ackoff's thinking to Donella Meadows and to the practical work of Scrum Masters. Deborah highlights how Ackoff explains that the outcome a system is designed to produce affects how the whole system behaves. Her example is health care: if the system rewards treating sickness, it becomes a disease-care system instead of a health system. For Scrum Masters, the lesson is direct. Teams are systems, companies are systems, and the incentives around them shape what they do. Deborah uses Ackoff's work to remind us to look at feedback loops before assuming people are the problem.   [The Scrum Master Toolbox Podcast Recommends]

Is AI going to replace software testers, or make them more valuable than they have ever been? Jonathon Wright says both, and in this episode he does not back down from either position. Jonathon Wright has spent thirty years in test automation, going all the way back to WinRunner. He now runs thirty AI agents across twelve screens, he sat with Gartner for their research on agentic platforms, and he says the last six months changed the job more than the previous three decades combined. We get into what he calls confidence engineering, why he has not opened a browser in five years, and what happened when he generated thirty seven thousand test scenarios from space agency specifications and put them in front of NASA. He also tells the story of a testing leader who taught his agents everything he knew about accessibility work and then got let go. A few minutes later he tells you your career has another fifteen good years in it. I pushed him on the contradiction. Listen for how he answers it. What you will learn in this episode: Why the flood of AI generated code makes verification the scarce skill, and testers the people who matter most What confidence engineering means and how to apply it to systems that never return the same answer twice Why browser automation was the easy problem, and what is actually hard now How intent driven testing lets you model a whole system before a single line of code exists Why getting the requirements right matters more with agents than it ever did without them The story of an agent taking twenty minutes to press one key, and what it tells you about the hype cycle Three concrete things to start doing Monday to stay valuable as your agent count climbs Whether you are an automation engineer, a manual tester, or a QA leader trying to work out what your team should do differently this quarter, this conversation will give you a clearer read on where the value is actually moving.

Sony has lost Kojima. XBOX has found Kojima. And we go helldiving for crabs....

A year ago, Palo Alto Networks built dashboards to track AI spend. Today those dashboards are useless, and the team that built them thinks that's the whole story.Recorded at FinOps X in San Diego, this conversation brings together Abhinav Lad, who leads cloud and AI finance at Palo Alto Networks, and Kuntal Patel, who runs the cloud engineering function behind it. They explain what happened when agents entered the picture, and AI stopped behaving like a service anyone could forecast.The short version: consumption went from linear to exponential almost overnight. Agents are goal-oriented rather than task-oriented, so they plan, call tools, verify, fail, retry, and keep looping until they hit the outcome, and every iteration is billable. So how do you run finance on top of that? Abhinav and Kuntal walk through the metrics that replaced their old forecasts: adoption rate, cost per user, AI as a percentage of revenue - and the budget limits that let engineering leaders choose between the newest model and a longer runway. They get into the open question of whether a cheaper model saves money or just burns more tokens thinking. They explain why an AI gateway became the control plane for cost and security at the same time, why retry caps belong in the design phase instead of the postmortem, and how FinOps starts to resemble product QA once the bill becomes the clearest signal that something is broken.They close on a warning worth sitting with: cost per million tokens is a number that means almost nothing on its own, and a value story built on it will point you somewhere you don't want to go.Palo Alto Networks: https://www.paloaltonetworks.comAbhinav Lad: https://www.linkedin.com/in/abhinav-ladKuntal Patel: https://www.linkedin.com/in/kuntalpatel35Alex Salkever: https://www.linkedin.com/in/alexsalkeverTimestamps:[0:00] Intro[1:00] Who runs FinOps for AI at Palo Alto Networks[2:10] Last year's AI dashboards are already useless[4:26] Agents turned linear forecasts exponential[7:27] Three traits that make agents expensive[8:34] The hidden bill: RAG, vectors and egress[9:16] Cost per user and adoption rate[11:21] Giving engineering leaders a budget[12:07] Using DORA metrics to prove value[13:53] Where DORA stops fitting AI[16:20] Does the cheaper model actually save money[17:57] Why you need an AI gateway[20:05] Inside Prisma AIRS[21:00] Three cost models for three use cases[22:52] Forecasting lessons from Electronic Arts[24:03] Runaway agents and endless loops[25:59] Capping retries before they burn cash[28:06] Writing cost policy at design time[29:01] When FinOps becomes product QA[32:17] Explaining AI spend to the C-suite[34:51] Valuing AI beyond engineering[37:04] Crawl, walk, run: where they are today[38:20] Why cost per million tokens is meaningless[39:26] Closing thoughts

"You're not going to be replaced by an AI; you're going to be replaced by someone using AI." But there's a layer above that too: what happens when AI is using AI? In this episode of Marketing Speak, I welcomed back serial entrepreneur David Henzel, of MaxCDN, LTVPlus, and now Context Engine, to talk about what it actually looks like to run an "AI-first" company from the inside. This isn't a theory. David's businesses have already cut ten-person QA teams down to two doing a better job, and he walks through exactly how. What we got into: ➡️ Why middle management may be the first real casualty of the AI shift ➡️ How to build a "company brain" that keeps every decision, call, and conversation in one place ➡️ Why David refuses to rely on just one AI vendor, and what that protects you from ➡️ What it really means to go "AI-first" instead of just "AI-enabled" ➡️ How documenting your business could make it more valuable if you ever sell it ➡️ David's free offer: a Context Engine workshop and a year's license for three lucky listeners If you've been wondering how to actually operationalize AI in your business instead of just using it as a faster search bar, this conversation will give you a real blueprint. The show notes, including the transcript and checklist to this episode, are at marketingspeak.com/550.

Kirsten Mann is a board director, product leader and, with this episode, a three-time guest on the podcast. Last time she was here, she was building Vizory, an AI tool that helps company directors get through board packs. This time she's back to explain why she isn't building it any more. Talking to Randy Silver, she walks through exiting Vizory, the friction tests she ran before writing a line of code, and why she now believes distribution, not the build, is where most AI products actually die.We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Key takeawaysBefore writing a line of code, Mann tested for force rather than sentiment: she asked directors for two confidential board packs and an audio recording of themselves reviewing one, and only started building once 10 people agreed to do it. Coffee-shop enthusiasm is cheap; handing over a company's most sensitive document is not.Vizory's most-loved feature in demos, cross-pack search, was barely used once shipped. Directors said they wanted it and reacted strongly when they saw it, but in practice they went straight to an automated triage view instead — a clean case of stated preference diverging from revealed preference.Board software runs on quarterly, not weekly, cycles, and trust compounds slower than value does. Mann modelled two review cycles before directors would rely on the tool; in practice it took three, meaning some customers on quarterly boards needed close to a year before the value became obvious, a timeline no 30-day trial can accommodate.Pricing got caught in a double anchoring trap. Directors first benchmarked Vizory against the $20-a-month ChatGPT and Claude price point, so Mann positioned against the cost of a governance failure instead, then created a second anchor by pricing early pilots at $150 a seat against a $300 target, which made the later increase read as a 100% price rise.Cutting friction is the wrong instinct early on. Mann deliberately added friction, asking for sensitive documents and charging as early as possible, to separate real demand from polite interest, arguing that most founders run tests designed to confirm they're right rather than to find a reason to stop.Mann's main lesson for other founders is that distribution is now the product problem. AI has collapsed the cost of building software but done nothing to the cost of reaching people who trust you, which is why she pushed to add distribution, adoption and willingness to pay as a third discipline in the Makers' Manifesto, alongside building the right thing and building it right.Multi-model products carry a new operational risk. One of Vizory's AI "judges" silently stopped being selected because the underlying model had been deprecated overnight without warning, a reminder that model drift means the QA and evals work never really stops, however fast the initial build was.We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

#387 | Six B2B marketers share tactical plays for using AI to make content that doesn't sound like AI. This Exit Five Live session breaks down a set of specialized agents that lifted blog traffic 700% in six months, a 23-step agent that researches and writes AEO content straight into WordPress, and the winning play: turning original survey data into an ownable narrative. Also on the agenda: a structured review process that replaces vibes-only QA, a Claude skill trained on stakeholder feedback so drafts land pre-aligned, and five rules for using AI to learn faster without outsourcing your thinking. One thing the full group agreed on: the human stays in the loop.Timestamps(00:00) - - Intro and the Exit Five Live Format (03:47) - - Meet the Lineup (07:38) - - Build a Digital Team of Specialized AI Agents (14:33) - - Before the Prompt and After the Prompt (21:42) - - A 23-Step Agent That Writes AEO Content for LLMs (30:11) - - Turn Original Research Into a Content Differentiator (33:20) - - Treat Your Edits as Training Data (35:55) - - Faster to Content and Faster to Internal Buy-In (43:19) - - Rules for Learning With AI (49:16) - - The Winning Play 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:Optimizely - the AI platform for marketers. Build your own AI agents or pull from a directory of 50+ pre-built ones for marketing use cases. Their new Virtual Teammates can join meetings, complete tasks, support campaigns, and keep your website optimized. Learn more at optimizely.com/exitfive.Webflow - A website platform built for the agentic web, letting modern marketing teams build fully custom sites that perform in AI search with no developer needed. Learn more at webflow.com/for/exitfive.Zoom Webinars & Events – The virtual event platform built to help B2B marketers run webinars that actually drive pipeline, with branded registration pages, live engagement features, and built-in tools to repurpose sessions into clips and content. Learn more at zoom.com/exitfive.Compound Growth Marketing - A full-funnel demand gen agency helping high-growth cybersecurity and enterprise software companies show up earlier in the buying journey, combining AEO, modern paid advertising, and a dedicated go-to-market engineering team. Podcast listeners get two free media planning sessions to find out what channels are driving the best ROI. Learn more at compoundgrowthmarketing.com/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

On this week's Compliance Unfiltered, PCI engagement chaos doesn't have to be the norm. Todd Coshow and Adam Goslin explore how smarter compliance workflows can eliminate repetitive evidence collection, QA bottlenecks, and annual audit scrambles. Learn how request lists, automated evidence mapping, and operational mode streamline PCI and multi-framework compliance while reducing human error. From year-round evidence collection to better assessor workflows, discover how the right platform and processes can save time, improve accountability, and make complex compliance engagements far less painful.

This week on EMS One-Stop, I sit down with Liz Harney, almost a year after I first walked into a classroom at EMS World Expo looking for another session to report on and found what I later described as “the keynote I didn't know I needed.” Liz's presentation was built around a part of EMS education we routinely acknowledge but rarely give equal billing: the affective domain — empathy, communication, professionalism, self-awareness and the ability to connect with another human being in what may be the worst moment of their life. But the real power came from Liz herself. Before she was a paramedic, educator and EMS leader, she was the patient. At the lowest point of a decade-long struggle with addiction, one paramedic looked beyond the overdose, the circumstances and the judgement, and treated her as a person who still mattered. Liz credits that interaction not simply with helping save her clinically, but with changing the trajectory of her life. She recovered, entered EMS and ultimately became the kind of paramedic she had encountered that day. It is a remarkable journey from patient to paramedic, and in this conversation, Liz explains why that experience now sits at the heart of everything she teaches about the human side of EMS. | MORE: Liz Harney — the affective domain is the heart of EMS, so why aren't we teaching it? Our discussion also catches up with where Liz's thinking has evolved since. In her recent EMS1 article, “Shiny Happy People,” she takes that same belief in people and applies it to EMS leadership, challenging professionals that is exceptionally good at identifying everything that might go wrong not to let that instinct extinguish every new idea before it gets moving. Together, we connect those themes: the provider who changed one patient's life, the importance of deliberately teaching the affective domain, turning cynics into champions, recognizing burnout through QA and QI, and creating leadership teams with enough optimism and emotional intelligence (EI) to make change happen. At a time when EMS is increasingly consumed by another form of intelligence (AI), the conversation comes back to a proposition I think matters more and more: if we are going to get good at AI, we first need to get much better at EI. Additional resources: Liz Harney: Shiny happy people. The leadership power of optimism Rob Lawrence: The affective domain is the heart of EMS — so why aren't we teaching it? Inside EMS: Tactical empathy: The leadership tool you're not using enough Colby Davis: Can emotional intelligence be taught? Shannon L. Gollnick: The paradox of progress — emotional intelligence as the differentiator in an AI-augmented workforce Key quotes “It truly was a paramedic that not only saved me in a clinical sense, but she saved me in a human sense.” — Liz Harney “Everybody has a story and they're all a little the same, but they're all very different at the same time.” — Liz Harney “It's our role as educators and program directors to be able to ensure that we are instilling, improving the affective domain, just as we are the cognitive and the psychomotor.” — Liz Harney “You created from the cynic, turned them into the champion, and then the champion then took that back to their own service.” — Rob Lawrence “I am the dreamer, probably because I have forged myself out of the depths of hell.” — Liz Harney “Look at me, anything is possible. Like there is never a ‘no' in my language.” — Liz Harney “In order to build, in order to make change, in order to move the needle, you have to have folks that are strong in EI to make that happen.” — Liz Harney “There's always room for improvement.” — Liz Harney “I may not be able to teach everyone to care, but I absolutely think you can teach people what caring looks like in practice.” — Liz Harney “No one's ever complained about the gauge of needle that we used, but they've certainly complained that the medic was mean.” — Rob Lawrence “People underestimate the power of storytelling, but I'm here to tell you that is what creates the change into someone, that changes their perspective.” — Liz Harney Episode timeline 00:00 — Harney on surviving addiction, being written off and the potential EMS providers have to influence another person's future 01:02 — Rob welcomes listeners to EMS One-Stop and introduces Liz Harney 02:14 — How Rob first encountered Harney at EMS World Expo and why he describes her presentation as “the keynote I didn't know I needed” 02:40 — Liz Harney 101: critical care paramedicine, organizational leadership, Baptist Health, Kentucky EMS workforce initiatives and Paramedic Pathways 04:10 — Harney reflects on the rapid growth of her speaking platform and why sharing her story remains difficult but necessary 05:32 — The human side of EMS: affect, emotional intelligence and why clinical competence alone is not enough 08:10 — AI versus EI: why Lawrence argues that increasingly intelligent technology makes human emotional intelligence even more important 09:00 — From patient to paramedic: Harney recounts the interaction with the paramedic who changed the direction of her life 18:33 — Revealing her past to colleagues and students, and how that story changes attitudes toward people experiencing addiction 22:08 — Why simply telling EMS students to care does not work — and how Harney began using clinical rotations and direct patient interaction to build the affective domain 23:18 — “Everybody has a story:” students hear directly from people in addiction recovery 25:23 — The cynic-to-champion story: a student's dismissive attitude toward patients with addiction is transformed through one clinical rotation 31:00 — Educating not simply for cognitive and psychomotor competence, but for the clinician the student is becoming 32:15 — Harney moves from education into leadership and begins seeing the same issues around decision-making tables 33:19 — The case for optimism: Harney describes herself as a dreamer and explains how persistent negativity can kill innovation 34:04 — Lawrence's rule: “If we do what we always did, we'll get what we always got” 36:27 — Why potential barriers matter — but introducing every possible objection too early can destroy an idea before it develops 38:02 — “Shiny, happy people:” the discussion turns to Harney's article on optimism, innovation and assembling teams capable of making change 39:02 — Harney describes Kentucky's EMS workforce committee and what happened when a group of optimistic problem-solvers started building ideas without immediately saying “no” 41:00 — Choosing the right people for decision-making groups: why names, titles and tenure should not outweigh emotional intelligence 41:59 — Harney defines EI: self-awareness, self-regulation, communication and empathy 45:00 — “You can teach people what caring looks like in practice.” 45:15 — Lawrence asks whether EMS can genuinely change the affect and attitudes of a generation of clinicians 46:39 — Protecting the workforce: recognizing behavioral changes, cumulative exposure and the early warning signs of burnout 47:41 — QA/QI as more than clinical oversight: documentation can expose frustration and changes in provider behavior 48:07 — The leadership question: “What can I do for you?” 49:27 — The power of storytelling to challenge judgment and change perspectives 49:49 — Harney returns to her own experience: addiction, being cast off and ultimately demonstrating that recovery and transformation are possible 51:10 — Harney's closing message: “You don't have to understand someone's life to understand your responsibility to them.” 52:41 — Turning QA/QI from a fault-finding function into a mechanism for recognizing providers, celebrating success and checking on people carrying heavy cognitive and emotional loads 54:10 — Lawrence recommends seeing Harney speak: “You will come away emotional but enlightened.” Enjoying the show? Email editor@ems1.com to share feedback. 

How do biotech companies move QA from late-stage gatekeeper into an early, risk-appropriate technical partner? Dr. Andrea Bell, a VP and Global Head of Quality Assurance, breaks down what phase-appropriate quality looks like across the lifecycle and why the barriers to getting there are cultural rather than procedural.Andrea and Nick get into the real cost of late QA involvement, how risk-based rigor differs from uniform rigor, what makes a quality management review actually function, and how QA earns credibility with technical teams.A few of Andrea's key takeaways:Late QA involvement costs money and removes the option to remediate cheaplyMost of the cost of poor quality sits below the waterline in scrap, cycle time, investigations, and reputationEarly means early in the decision forums, not just early in the lifecycleRisk-based rigor isn't looser rigor. Uniform checklists are a weaker position and agencies will challenge themThe barriers to embedding QA earlier are quality culture and reporting structure, not procedureData only makes QA proactive if people are empowered to act on itQA earns credibility by co-authoring the argument you'll make to the regulator, not by signing off at the endAbout Andrea BellAndrea Bell is a Vice President and Global Head of Quality Assurance with more than 25 years in biotechnology building quality and CMC organizations across RNA, biologics, and small molecule programs. She holds a doctorate in global public health, a master's in quality assurance and regulatory affairs, an MBA in international business, and a bachelor's in biology.About The FDA GroupThe FDA Group helps life science organizations rapidly access the industry's best consultants, contractors, and candidates. Our resources assist in every stage of the product lifecycle, from clinical development to commercialization, with a focus on staff augmentation, auditing, remediation, QMS, and other specialized project work in Quality Assurance, Regulatory Affairs, and Clinical Operations. Learn more: https://www.thefdagroup.com/

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

In this week's Data Debrief, the companion show to Driven by Data: The Podcast, Kyle Winterbottom and Catherine Dowden-King unpack the week's main episode with Michael Ross and range far beyond it into the collapse in graduate hiring, the succession planning nobody is doing, and what's really happening at both ends of the data job market.From a record 45% drop in advertised graduate roles, to the experienced leaders who've been out of work for two years, to Michael's case that every average hides an opportunity, Kyle and Catherine make the argument that AI is taking the blame for decisions plenty of businesses already wanted to make, and that the bill for not developing people will land in about five years' time.They also discuss:Why a 45% drop in advertised graduate jobs is the lowest figure ever recorded, and why AI can't be held responsible for all of it.How record university enrolment colliding with a shrinking entry-level market creates a problem unfolding in real time.Why "entry-level" data roles asking for two years of Python or SQL were never really entry-level.What happens to the pipeline when the admin-heavy tasks juniors cut their teeth on get absorbed by agents.Why the real risk isn't AI replacing juniors, but having nobody ready when the current workforce retires.How data roles are shifting towards QA, product management and facing back into the business.Why succession planning has only ever been pointed at the top of the house, and why that has to change.What skills matrices and career pathways expose the moment you ask "and when this bottom layer moves up, then what?"Why some organisations announced AI-driven headcount cuts when the business was simply performing badly.How "we're cutting because of AI" got turned into a PR positive rather than a negative.Why a retailer, a telco and an airline sat at the same table are nowhere near the same stage of the journey.What the senior end of the market actually looks like, and why it gets discussed far less than the graduate end.Why there are more head of, director and VP roles than at any point in fifteen years, even as true CDO roles decline.How being overqualified has become as much of a barrier as being underqualified.Why an entire cohort of data leaders has been tarred with the same brush through no fault of their own.How the failure to prove value from data and analytics now has a direct, downstream human cost.What Michael Ross's epiphany moment says about technical specialists becoming commercial operators.Why de-averaging matters more than any dashboard, and how averages quietly mislead entire teams.How an 80% average occupancy hid the fact that no hotel was anywhere near 80%.Why 100% occupancy might be a pricing failure rather than a success story.What it takes for a CEO to get close enough to the commercial detail of their own business to win.Why putting your head above the parapet takes bravery, and why the cost of not doing it is the situation the industry is now in.Why Dolly Parton's Imagination Library may be the most important thing she ever built.What's left of the Future of Data, AI & BI event, Driven by Data Live on 8 October at Tobacco Dock, and the new roles on the NED Appointment Finder.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stop AI from turning your writing into repetitive slop and replace it with authentic human voice. You will discover why AI drifts into repetitive phrasing and how to stop it. You will learn to measure your unique writing style with simple numbers that lock in your voice. You will apply a structured editing process that transforms machine drafts into polished content. You will gain confidence to command AI tools without wasting hours on endless revisions. 00:00 – Introduction 02:15 – The AI writing frustration 06:30 – Measuring your voice 12:45 – The five performance steps 18:20 – Building your writing blueprint 24:10 – Call to action Take the new course at: https://academy.trustinsights.ai/courses/ai-for-writers Watch this episode to finally break free from generic AI output and start writing with total control. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-ai-writes.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 for writing and for writers. We have seen no shortage of people talking about AI watermarking and all this stuff and how you can tell whether somebody’s using AI for writing or not. And we at Trust Insights have put together a new course, Trust Insights AI for Writers, for how to get AI to write better and not coincidentally, help you as a human also become a better writer. So, Katie, to start off, what are the things that when you are writing with the assistance of AI, what are the things that sort of you wish AI would do better? Katie Robbert: You know, I wish it would listen better. And by that I mean I feel like you can craft a really strong prompt. You can say, here are my writing samples, here are things that I don’t want you to do. And it kind of just like freewheels and does its own thing anyway. And I feel like that is frustrating for a lot of people. So, you know, I really try to write the first draft of things myself as the human and then know, and I’ve talked about this in the newsletter and on pod on our podcast where I bring in AI is to double check with like our ICPs or to use it as an editing tool. But then the editing gets carried away. You know, I’m not an editor and grammar is. I would like to say that the public school system failed me. You know. So I’m like a half decent writer. I have good ideas and I write in a stream of consciousness. And so I need tools or human editors to help me clean things up. And this is where I look to generative AI because the team doesn’t always have time to like fully edit my stuff. But then when I read back what the edits are or what the suggested edits are, I’m like, where did this come from? Or why did you make up a whole anecdote that never happened, but you’re saying it authoritatively? So I feel like the hallucination is the big thing. And the, you know, depending on the large language model, each model has its quirks in terms of the way that it writes or the way that it, you know, quote unquote, articulates thoughts. And so I think one of the things that you’ve shared about Claude, for example, is it likes its, you know, things in threes, like three punchy points. And it’s very much a tell that, like, oh, that’s a Claude thing. And I found that when I have, you know, an AI assistant help edit my stuff, it turns it into a lot of that, like the individual sentences, the three punchy points, like the this and this, then this or this or, you know, those kinds of ways that it writes. And that’s not what I, the human had put in. And I’m just sort of sitting there exhausted, like. But I just, I need this edited and I need it coherent and is it good enough? But did it change too much of my own human writing? So I think that, you know, when I think about using AI for writing, that’s what I’m personally struggling with is, you know, I still want to be the one writing it, but, like, I need someone to help me polish it. And the polish is like subpar. Christopher S. Penn: Why do you think AI does that? Why do you think AI behaves the way it does and turns original writing into something that sounds like slop? Katie Robbert: Oh, gosh, if only there was a course that was going to tell me the answer to this question. Christopher S. Penn: There’s something else that will also tell you the answer to that question. And that happens to be the fifth P. Katie Robbert: I should have guessed that one. Sneaky, sneaky. The framework at Trust Insights is purpose, people, process, platform, performance. Chris, you’re specifically talking about performance. And I think that where a lot of us get caught up in prompting these large language models is we think we’re being clear on the performance, but we’re not as clear as we could be. And that’s where the frustration sets in and that’s where we want to throw up our hands. And so the perfect purpose could be, I need you to edit this, you know, five thousand word essay. I need you to look for spelling and grammar and, you know, a cohesive thread like all those things. People, here’s my audience, here’s my authoritative voice, here’s my samples process. I want you to go through this and just list out the changes. Don’t change it for me, platform. This is going to be published on my blog, which is hosted here, and I’m going to add images in these places. And then performance. We typically think of performance as did we get the polished thing from our purpose? But it sounds like we are missing a lot of opportunity in the performance part of the 5Ps to really spell out what we need. Christopher S. Penn: And that is the premise of the new course. The biggest chunks of the new course that we have are twofold. One, we spend a lot of time on research because good research leads to better outcomes typically. And two, we spend a lot of time on math, which is your average writer is like, But I started with the premise for this course, that writing is code. If I put nonsense words together, you’re like, did you just get hit in the head? Like, what happened? Did you actually put decaf in the coffee maker this morning? If I don’t say words in the right order in a statistically predictable pattern, you have no idea what’s going on. You might say, these tests, coverage, adding branches, empty. Like, what? What does that mean? That’s word salad. Language follows patterns, and those patterns are predictable. And the reason why AI writes the way it does is because it’s choosing the most probable patterns, even when it doesn’t sound like you. So the first thing that we have to do is give AI performance, right? To say, this is what success looks like. And it has to be in a tangible form. The percentage of passive voice that you use in a text when you write as a human, how much passive voice do you use? The number of sentences that begin with a noun or a pronoun. What percentage of your copy is that? Is that the number of EM dashes that you use naturally in your text as a human? What is that? Our friend Anne Hanley says, I use the EM dash because I’m an actual writer, but I don’t use it in every sentence. And where AI typically goes off the rails is when it knows that a construction is probable, like using EM dashes, like using triadic rhythm, like using bicolon or isocolon. And it says, hey, I’m going to use the most probable things. But it has no concept of frequency, so it overuses it. And you get, it’s not this, it’s that in every single sentence or in, you know, Claude in particular loves bicolon. It’s. It sounds like a drum beat. One and two and one and two. And you’re like, could you please vary the beat? Katie Robbert: Right? Christopher S. Penn: When you look at a, like a slide deck Claude generates, everything is bicolon, all the headings, you know, this and this, sharp insight and this. And you’re like, my God, this is so mind numbing to read. If we give AI analysis of how our writing to begin with and say success looks like this set of numbers, now go right, then check your work and compare what you wrote versus what the blueprint is and it will go, oh, I didn’t do this at all. Like, I use 82% passive voice. Yeah, go back and fix it. But the fifth P in the 5P framework by Trust Insights is so important. We have to establish what success looks like for it so that it mathematically can go back and fix its code. Katie Robbert: So let me ask you this question though, because I will give Claude like samples of my writing and say this is what it’s supposed to sound like. Am I doing it wrong? Because I have all of these samples from like literally years and I feel like Claude or a large language model, you know, is trying to evolve my writing so that my writing fits its format, not its editing, to my writing. Like, I feel like that’s where I’m struggling. Christopher S. Penn: You are not doing anything wrong except you are asking it to count and it can’t count. And so even though it will analyze your writing as a language model, it has no clue of how to count. One of the things that’s in this course is probably worth the price of admission alone is a Python script that it has to run on a writing sample you provide that will do that fingerprint mathematically not letting the language model try to count, because language models can’t count. The Python script goes through and it counts in your original sample, this is the percentage of passive voice that you use and it writes it down as a fingerprint, as a file in your language model of choice. It works in ChatGPT, it works in Copilot. It tested in all the systems. It can then rerun that script on its output and say, initial sample, 4% passive voice, my work, 18% passive voice revision loop. I need to go back and keep revising until I hit this number. But it can’t count that by itself. It needs the support of actual code to do it. And that’s what’s in the course is pre baked. Nobody has to be coding, no coding involved. It’s bundled in. But you would drop that into your Copilot or your ChatGPT or your Claude and say, this is how you’re going to measure yourself. Yourself, you’re going to do the fingerprint and you’ll reuse that fingerprint and then you will go back and you will count using this script. And that’s, you know, again, you’re not doing anything wrong. It’s just the average non technical user doesn’t think, hey machine, I remembered you can’t count well. Katie Robbert: And I say, okay, so that’s an interesting distinction because so up until now, you know, I’ve been saying like, hey, this is the sample of my writing. And you’re right, it absolutely, it takes what I give it and it like, is like, oh, you said to do this, let me make sure that I do this. And by making sure that I do this, I’m going to do it 16 times out of the 18 sentences, but I’m also going to do this in 15 of the 18 sentences. And so it’s taking everything it knows about my style of writing and trying to jam it all into one sentence. And I’m like, whoa. Like, yes, grammatically it’s correct, but now it’s garbage because that is not at all what I wrote. And I sort of. I feel, it’s not that I feel like my hands are tied because I can’t fix it myself, but like, the reason I turned to a system for help is because I’m not an expert editor. And so I miss things that an editor would find. And I need that, like, for me, I need that kind of support of like, hey, you started a point over here and then you dropped it halfway through and you made a different point at the end. Like, you gotta pick a story and stick with it. Christopher S. Penn: And this is where these tools, these AI tools simply by themselves cannot do that. Like, they just do not understand. How do I, how do I even count? So I’ll show you an example of one of the things that is bundled in the course. And again, the average user does not need to look at this. The average user is not going to. You will just drop the file and say, machine, off you go. But it will look at thing. There’s functions in this. It says like, look at the rate per word of the this kind of word. For example, it’s often said that good writing relies on relatively few adverbs. Adverbs are words that end in ly, you know, actually, etc. AI loves adverbs, obviously, actually, and stuff like that. Which also sounds condescending. Katie Robbert: Yeah. Christopher S. Penn: And so if your writing style uses almost no adverbs, when you do a fingerprint, it will say, hey, your adverb rate per 1000 words is like 1%. And so when it goes back and counts, it’s revision, it’s edits that you had it make. And it goes, oh, I used 9% adverbs in my revision. But the target, the performance says 1%. I need to go back and fix my work. It’s a diagnostic. And that’s what’s missing from all of our prompts, because it can’t do that. That’s what’s missing from every single AI for Writers course I’ve ever taken or every session I’ve ever sat in. Nobody thinks of writing as a system of measurement, of analytics. And therefore, when AI just follows its own internal process programming the probabilities that it generates, we’re all like, why is this keeps coming out like slop? Why can I not prompt this thing to sound like me? It’s because it can’t count. And so the cornerstone really, of this entire course that we’ve created is let’s give AI the tools it needs to count, let’s give it the measures that it needs to count, and then let’s give it clear guidelines. You know, one of the things that is in the toolkit is a. Again, this is another piece of code the user, you. The user will not use. This is built into a skill that you just install, and there’s instructions on how to install it. But it will say, if you have this cadence, don’t do that. Here’s how you do it instead, right? If you do short, long, short, long cadence over six consecutive sentences, you’re writing us. You’re writing a dead drum beat. Dead. Don’t do that, do this instead. Things like that. So, for example, if you look at AI writing ChatGPT, Claude, Gemini, and you just take a step back and you look at the page, all the paragraphs are about the same length. They’re all. They all kind of look like the same gray rectangles on a page. If you were to step back and stop looking at the letters and just look at the shape. If you look at your human writing, there’s a good chance that it’s. There’s some paragraphs are real short. Maybe it’s even one word, like an emphasis one like, no, don’t do this. Right? And that’s just its own paragraph. That frequency of change in paragraph length is something that you can measure. Machines don’t know to look for that. Machines don’t even think about that. And so if we give them the tools, the counting tools to go. Katie tends to alternate her paragraph length. Sometimes her paragraph length is 11 sentences, other times it’s one. I should replicate that general pattern. Because once you give AI a pattern, it’s like, oh, I know how to do patterns. I can do this. And it goes off and it creates it. Katie Robbert: I mean, I have a lot of thoughts and comments, you know, and so like, the big elephant in the room is, you know, why are we teaching people how to train the models to write when there’s real writers out there? So, I mean, that’s a big question. So I’m just going to like, put that, like, stick it up here for a second. When I think back to like high school and middle school, for example, we generation were taught the five paragraph writing. But for a lot of us, this is like, this is how were taught to write. So the first paragraph is your opening argument. The second, third and fourth paragraph are your supporting reasons for your argument. And the fifth paragraph is your conclusion. And so a lot of us who were, you know, that was like drilled into our heads on our like yellow piece of paper with the green lines, like that’s how you’re supposed to write. And so I think what’s often frustrating for someone who writes their own stuff is because were taught to structure our writing in a certain way. It can come across as well. AI must have written that because of the structure. It’s like, no. My sixth grade middle school English teacher, when slapping rulers on tables was legal, scared the bejesus out of me and told me, this is how you have to write. And so I write the way that AI writes because it was drilled into my brain. This is how you write. And I guess I’m wondering, so when you’re saying like pattern recognition, like it learned these patterns from us. We taught it the patterns it did. Christopher S. Penn: But it is averaged together everything. And that’s why it often comes out so different than the way an individual writes. Because everyone has their own pattern distortions. Everyone has words they like, everyone has words they don’t like. Everyone has words, life experiences that will show up in your writing. AI is averaged all of that together into. And then what it does is it spits out the highest likely probability except for when it’s using watermarking. And so the five paragraph essay and that kind of blocky set. Yeah, it’s going to do that because the majority of writing it has seen on a bell curve is exactly that. If you look at the, you know, an earnings report or a press release, it is exactly that dead metronome of boring writing. The thing about writing to that. And we say this in the beginning of the course and we’ve said this in many different places. Good creative work that’s interesting is low probability. Right. You, the way you write should be surprising and different than what the way that middle school teacher taught you to write. Right? I will. There’s all sorts of expressions I’ve used for this. But if you say, you know, this is a, this works like a Prius and other people the average is this works okay. Right. This works okay. It’s boring. This works like a Prius, has a very specific connotation and A mindset behind it. And so what we want AI to recognize with the tooling in our course is recognize how the. In our individual style looks and replicate that specific pattern, not the general patterns. You’ve been trained on the general patterns that you will generate without these very rigid mathematical guardrails. Katie Robbert: Okay. One of the things that I’ve noticed and I’ve seen, and actually, this is a phrase that you use a lot. And so I guess I’m sort of asking, like, have you adopted this from AI or is it a phrase that people use and AI has adopted it is something that I see a lot in my conversations with a large language model is. And here’s the shape of the thing, and here’s the shape of it, and here’s the shape of the problem, and here’s the shape of the challenge. Well, that’s not the same shape as it was before. And I’m like, why are we talking about shapes? And to be fair, Chris, you say that a lot, but you are not in my instance of a large language model. And so I guess my question is, have you. No, you’re not. Have you brought that from working with large language models? Because that’s not a phrase that you used to say before. But I find that, like, there’s these little ticks and, you know, quirks that these large language models have, and each one has a different one of ways that it phrases things. So you’re talking about, like, the 1, 2, 3, the cadence, but then there’s also these descriptors, and I find that really interesting. And I feel like if a human is not paying attention, that very easily slips into a lot of your work. Christopher S. Penn: It. And it slips into how you think, too. Like, I find myself when I’m writing now, I go, oh, that is negative parallelism. And I will even call that out. Like, in a LinkedIn post I published this morning, I say, and to quote one of Claude’s favorite constructions, it’s not this, it’s that. Because negative parallelism, which is a form of bicolon, is. Is something that the language models use. But writing is a lot like. It’s a lot like nutrition. You become what you eat. So if you are reading AI generated text all the time and. And you start seeing that’s a sharp insight, that’s a sharp angle. It’s going to influence you. And so, yeah, I probably have picked up things because I’ve spent so much time wrestling with these tools and trying to diagnose how they do their constructions that, yeah, some of it is going to influence how I write and speak and even think. And one of the things that linguists, if you read this one, the discourse online, the, one of the things that linguists are very concerned about with AI generally is that it’s sort of a flattening of language, that the language, the English language itself is morphing because of the influence of AI on it. When you, when I saw, I think the stat was two out of three pages on the Internet are now written solely by AI. That is going to have a, a shaping effect on how we read and how. And when you see, you know, I think it was something 10 out of 1, 9 out of 10 new books submitted on Amazon was written solely by AI. That’s going to change your language. That’s going to change how you read and how you write is how you think. Katie Robbert: Yeah, it’s interesting. So let’s say I’m a brand new learner. I want to sign up for the AI writers course. What are some of the big things that I’m going to learn? I believe you’re introducing a new framework which is called craft. Can you speak a little bit about what the craft framework does and if I’m someone who’s trying to help my writing, what that means? Christopher S. Penn: Well, okay, so the CRAFT is a subset of the 5P framework, right? So the whole course is Craft fits in process. It’s a breakdown of process. But fundamentally the whole course itself is actually structured on the 5P framework. I’m the major lessons are literally right out of the 5P framework by Trust Insights because it’s the best framework for pretty much everything. CRAFT is a subset of process, which stands for create. You need to come up with the idea because AI is not going to do a good job. Then you need to do a buttload of research to inform the idea. Then you need to architect the work itself and we’ll talk about process decomposition, which is which we borrow from software development. I basically took the learnings of some software development and turned it into a writing course. Then you have what’s called fabulate, which is basically a machine. Here’s how to go and do this. How you build the. The plan for a tool to go do it. And then the last part, which is the hardest part, is the tuning part where the machine and it’s not hard for you, the user, it’s hard for the machine. You say, hey machine. Remember here’s the fingerprint that we did of how I write. You need to retune your work to match it. And then I as a human editor review and go, you missed this. These constructions are still off. And the machine’s like, okay, I’ll go fix it. You will see that in the course content to watch the tuning process go, oh, I can’t be hands off. I of the human still have a role to play as the editor saying, machine, you missed this. Katie Robbert: And I think that’s an important point because I think there’s a lot of misunderstanding as to where AI for writing fits in to an everyday process and how it can be the most beneficial. And so we’re not teaching you, hey, AI is going to replace your writing. We’re trying to teach you to more smartly and thoughtfully. Clearly I’m not a writer today to more pragmatically use AI in your writing, not just say, hey, AI do my writing. Because that’s where the slop comes from. To your point, these are word prediction machines. And so, you know, you often give examples of like cliche statements. So like, if I say it rains, AI is likely going to say it pours, you know, and so we have to be more creative than that. And that’s really where the human side of this comes in, is being very thoughtful about, you know, how can I break out of that Prius version of writing so that it’s, you know, something more interesting? Like one of my favorite song lyrics that my husband is convinced is just gibberish, which is the comfort of the knowledge of the rise above the sky, but could never parallel a challenge of an acquisition, which is not something that I think AI would necessarily come up with, but it’s a real song lyric. And it’s like that always sticks with me. Not only because when I was a teenager, me and my friends thought it was amazing to be able to recite it on command because we thought were so cool, but because it just strikes me as such an interesting way of saying something very simple. It’s an over complicated statement, but it’s not something that AI would come up with. And so I like, that’s where my brain goes, I’m writing is like, is there a more interesting way to say this other than, well, when it rains, it pours. Christopher S. Penn: And in the course you’ll actually see that in the first two steps of the CRAFT framework. The first is, you know, the creation, you, the human have to come up with the idea. And the second is the research. Because the more better research you do, the more unique the words are going to end up because you will dig deep into Subjects that maybe you didn’t even know about. So in this course I teach about building a short story and I build a 20,000 word short story in there about time travel and the research process for that, for this short sci fi story ended up pulling like five years worth of quantum physics papers and looking at the eight different forms of, you know, how physicists understand the very fabric of space time that works into the story and that creates a much richer story than just winging it. Because the research process digs super deep into low frequency words, things like Cartesian planes and Lorentzian manifolds and all these things. I just read the research outputs and going, wow, I need a PhD just to understand what the research is about. But it creates better writing. And to your point, and you know, kind of the whole point of the course is if we really, if I were to distill it down, this course is about how do you slap better guardrails on AI so that when it’s writing it follows instructions better and it brings to life the things that you want. And so the course will become available on the this coming. Well, by the time you listen to this podcast, it’s out. It will come out yesterday by the date the course is out Exactly. And you can find it at TrustInsights AI for writers. It’s 397 US and with it you get the course. You get the deep research toolkit for doing the research. You also get the writer’s suite which contains a writing planning skill, which by the way is fantastic. It will really force you, the human to think a writing fingerprint skill and a writing QA skill that will take any piece of existing writing and QA it like software against the original requirements document that you wrote for the writing. So Katie, you will see a lot of very strong parallels of the things that you’ve been writing and talking about for years about how do you do great build, great software. If writing is code, we’re going to apply the same practices. Katie Robbert: I think that’s great. And you know, I love more guardrails for AI. So I’m excited to test all of this out. Christopher S. Penn: Exactly. I hope, I hope you do. And if you’ve got some thoughts about how you have been helping AI write better and you want to share and pop by our free Slack group, go to Trust Insights AI analytics for marketers, where you and over 4,700 other marketers are asking answering each other’s questions every single day, including whether pizza sauce should be sweet or not. Katie Robbert: The answer is no. Christopher S. Penn: Wherever does you watch or listen to the show. If there’s a challenge you’d rather have it on, we are probably there. Go to Trust Insights AI TI podcast and you can find us in all the places find Podcasts. Podcasts are served. 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 Robbert 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 Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insight services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and 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. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which 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 mid sized 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.

« L'inquiétude grandit, relève le site Afrikmag, autour de Gaël Mocaër et Sebastian Perez Pezzani, deux réalisateurs français détenus au Togo depuis la fin du mois de juillet. Les deux hommes se trouvaient dans le nord du pays pour le tournage d'un épisode de l'émission documentaire Les Routes de l'impossible. Leur collaborateur togolais, Fred Vedomey, est également privé de liberté. Leurs familles ont publiquement alerté sur leurs conditions de détention et leur état de santé. Elles se disent bouleversées par une situation qui dure depuis plusieurs semaines ». « La justice togolaise a fait une première concession, pointe le site Afriquinfos, en libérant sous contrôle judiciaire vendredi dernier Sebastian Perez Pezzani ». Celui-ci « souffre de douleurs persistantes et doit subir une intervention médicale, précise Afrik.com. Selon son avocat, deux médecins ont attesté de la nécessité de cette prise en charge et l'un d'eux a estimé que les infrastructures médicales disponibles au Togo ne permettaient pas de réaliser l'intervention envisagée. Sebastian Perez Pezzani n'est pas hospitalisé, mais il reçoit régulièrement la visite d'un médecin. D'après son avocat, il souhaite être opéré en France ».  La semaine dernière, Reporters sans frontières avait fait état de « graves problèmes de santé » du réalisateur et demandé son évacuation sanitaire vers la France. Pas les autorisations nécessaires ? Afrik.com revient sur les raisons de l'arrestation des deux réalisateurs et de leur collaborateur togolais : « Leur interpellation est intervenue le 27 juillet dans une zone proche de la frontière avec le Burkina Faso, où la situation sécuritaire est tendue. Les deux réalisateurs français ont ensuite été placés sous mandat de dépôt à Lomé le 17 août. La justice togolaise leur reproche notamment des faits liés à l'utilisation d'un drone et à des prises de vues réalisées dans cette partie du territoire. La justice togolaise a également évoqué la question des conditions dans lesquelles les deux Français exerçaient leur activité professionnelle. Des sources locales ont rapporté un différend concernant leur titre de séjour et l'utilisation d'un visa qui aurait été considéré comme inadapté à des activités journalistiques et à des prises de vues professionnelles ». Le nord du Togo sous la menace jihadiste Il faut savoir, poursuit Afrik.com, que « le nord du Togo fait l'objet de mesures de sécurité renforcées depuis plusieurs années. Depuis la fin 2021, cette région est confrontée à des incursions et attaques attribuées à des groupes armés affiliés à al-Qaïda et à l'État islamique. Un état d'urgence y est maintenu et régulièrement prolongé par les autorités togolaises ». De plus, relève encore Afrik.com, « cette affaire intervient alors que les relations entre les autorités togolaises et plusieurs médias français sont tendues. Les signaux de Radio France Internationale et France 24 sont interrompus au Togo depuis juin 2025. Les autorités togolaises avaient justifié ces mesures en contestant la couverture de l'actualité politique et de manifestations de l'opposition par les deux médias ». La valse des nominations au sein de l'armée togolaise Pour sa part, en marge de cette affaire, Jeune Afrique pointe le récent remaniement au sein de l'armée togolaise opéré par le président Faure Gnassingbé. « Une refonte dictée autant par la menace jihadiste dans le nord du pays que par la volonté du président du Conseil de consolider sa mainmise sur l'armée. » Un nouveau chef d'État-major des armées a été nommé, de même qu'un nouveau directeur de l'Agence nationale de renseignement. « Ce mercato militaire répond aux besoins de la nécessaire réadaptation de la stratégie antiterroriste, précise Jeune Afrique, dans cette région du nord du Togo, confrontée à d'incessantes incursions jihadistes en provenance du Burkina Faso voisin. Selon plusieurs sources proches de l'appareil sécuritaire, le profil des gradés promus à des postes clés traduit aussi la volonté du chef de l'État de resserrer la coordination entre les services de renseignement et les forces engagées sur le terrain. Il s'agit aussi, pour Faure Gnassingbé, de mieux contrôler l'armée en s'assurant de la fidélité de ses chefs ». Jeune Afrique qui précise encore que « le nouveau chef d'état-major sera également un élément central dans la coordination avec les partenaires étrangers dans le domaine sécuritaire ». Comme l'avait révélé le site panafricain dans une enquête publiée début juillet, « des “instructeurs“ turcs sont notamment présents sur la base de Dihiaga, dans le nord du Togo, où, ils seraient chargés de la formation des forces spéciales et seraient actifs lors d'opérations militaires. En outre, depuis le mois de janvier, 300 militaires russes d'Africa Corps sont déployés dans le pays ».

How My Agency Went From 40 Employees to 3 (AI Updates for Agency Owners, Q3 2026) AI won't do your strategy — but it will eat every hour of admin, reporting, and manual work in your agency if you let it. In this solo episode, Jordan Ross (founder of 8 Figure Agency, the first AI consulting and development firm built for agencies) shares what's actually working in marketing agency automation right now — including the inside story of quietly converting an agency in his own portfolio from 40+ employees across the world down to a 3-person AI-native team. You'll learn: - Why systems you build today compound forever — and why the SOP-and-training playbook that ran agencies from 2019–2024 is dead - The exact build process Jordan runs with his engineer: scoping documents, hyperscoped sprints, "golden cases," and the QA loop that turns AI output into a self-sustaining service - Vibe coders vs. 10x engineers — what non-technical founders get wrong about building with AI, and why product management is quietly becoming the highest-income skill an agency owner can develop - The AI SDR that made Jordan rethink his roadmap — and the build-vs-buy question every agency owner should be asking before writing another line of code - The real result: automating admin, reporting, split testing, and data insights saved one ad-agency client 40 hours a week in a single month Chapters 00:00 — There's never been a better time to own an agency 01:56 — The hidden portfolio agency: 40+ employees to 3 03:55 — Doing work vs. building systems (why most founders are stuck on low leverage) 06:04 — Golden cases: how to QA an AI system until it runs without you 08:16 — Why I'm exiting my portfolio (again) — stress, kids, and lifestyle maxing 10:18 — Vibe coders vs. 10x engineers, and the product-manager skill founders need 12:12 — The AI SDR that shattered my brain: build it or buy it? 14:26 — Automating agency admin: the 40-hours-a-week result Links & Resources

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

What happens when customers stop visiting your website and start buying straight from an AI chat?Ryan Gavin, CMO at Slack (part of Salesforce), joins Daniel to break down how AI agents are rewriting the Marketing playbook, from lead routing to content QA to the agent first internet.Plus: how 1 engineering agent saved a quarter of a million hours, how Ryan's team grades every piece of content with AI before it ever hits his inbox, and how every Marketer can shift gears from manual "doer" to strategic orchestrator.If you're a Marketer who wants to future proof your team for an AI and agent first world, this episode is for YOU.Hit follow and leave us a rating if you're loving the pod. It helps more Marketers find us.Follow Ryan: LinkedIn: https://www.linkedin.com/in/ryan-gavin-seattle/ Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketingSign up for The Marketing Millennials newsletter: www.workweek.com/brand/the-marketing-millennialsDaniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit: www.workweek.com

https://directtoconsumer.typeform.com/DTC-Brand?utm_source=podcast-639&utm_medium=podcastTo Subscribe to DTC Newsletter - https://dtcnews.link/signupMedia owns the traffic. Brand owns the site. The page in between belongs to nobody, and it's been sitting in a Notion doc called landing page priorities Q3 since 2022.Eric brings Daniel from Pilothouse back for an all killer no filler on the post-click experience: why it stayed generic for a decade, what changed in the last twelve months, and what the team is seeing in its pilots with Black Crow AI.For media buyers, creative strategists, and founders whose ads are working and whose conversion rate isn't.What you get:The middle child problem. Media assumes brand is loving the page, brand assumes media is, and nobody has touched it since 2022.Why this was never a priority question. Personalizing creative is cheap. Personalizing destinations used to mean five pages through design, dev, QA, and deploy, which took literal months. So teams built one page, pointed everything at it, and updated it once a year.The 65-inch OLED analogy. You walk into a store, tell the salesperson exactly what you want, and they hand you the catalog. That's what a generic PDP does to someone who just clicked a very specific ad.The creative is the brief. The ad unit becomes the input for the storefront: the copy, the image, the targeting, the interests, all of it read and matched.What the pilots are showing: roughly 20 to 21% lift in conversion rates, on storefronts now taking about half the budget rather than one test ad set off in the corner.Where Black Crow adds something a general purpose model doesn't. Persistent ID across sessions means the page knows you're back and can serve a different experience.The technical prerequisites that actually gate this: Shopify, and enough Meta budget to test a difference. Brand and creative prerequisites matter less.Brand safety. These aren't fully dynamic pages. You can lock images and titles and adjust on the fly.Which brands it suits so far: a few concentrated top SKUs rather than a long tail catalog.The third party cookie, revisited. Daniel's verdict on the biggest talking point of 2022: what a nothing burger.Why the strategist now owns this. No IT ticket, no web team queue. That's the difference between now and twelve months ago.Who this is for: performance marketers and DTC founders who have solved pre-click and never touched what happens after.What to steal: treating your best ad as the brief for its own landing page, and the Shopify plus testable budget prerequisite check before you invest in any of this.Timestamps:00:03:00 Why the post-click experience matters00:07:00 Personalized landing pages lift conversion rates00:10:00 AI-powered landing page personalization00:15:00 Matching landing pages to ad creative00:21:00 Using ad creative as the landing page briefSubscribe to DTC Newsletter - https://dtcnews.link/signupAdvertise on DTC - https://dtcnews.link/advertiseWork with Pilothouse - https://www.pilothouse.co/?utm_source=AKNF639Follow us on Instagram & Twitter - @dtcnewsletterWatch this interview on YouTube - https://dtcnews.link/video

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

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

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

Online education has never been easier to access. You can learn almost anything through YouTube, Udemy, online communities, independent creators, coaches, and specialized training programs. Access isn't the problem anymore. Trust is. There's a big difference between spending $10 on a course and investing $2,000, $5,000, or more in a mentor, mastermind, or coaching program. When the investment gets larger, a five-star testimonial on a sales page doesn't cut it. In this episode of Building Better Developers, Rob Broadhead and Michael Meloche talk with Amit Zandberg about the challenge of creating trustworthy AI course reviews and helping people evaluate online mentors and high-ticket educational programs. The conversation quickly moves beyond online courses into a bigger question: Can AI help us establish trust, or does it make the trust problem even harder? About Amit Zandberg Amit Zandberg is an entrepreneur focused on bringing greater trust and transparency to online education. Through his work building a review platform for online mentors, creators, courses, and high-ticket educational programs, Amit is tackling a growing problem in the creator economy: helping potential students determine whether an expensive program is actually a good fit before they invest. His approach combines detailed learner feedback with AI-assisted analysis to identify patterns, pros and cons, mentor quality, learning experiences, and results while also addressing the growing challenge of fake and AI-generated reviews. Amit's work sits at the intersection of online education, entrepreneurship, artificial intelligence, consumer trust, and the creator economy. His experience provides a practical look at both sides of AI: using it to make businesses more efficient while building safeguards around the new problems AI can create. The Problem With High-Ticket Online Education Reviews exist almost everywhere. Before buying software, booking a hotel, choosing a restaurant, or ordering a product, we expect to find them. High-ticket online education works differently. A creator can spend months building an audience on LinkedIn, Instagram, TikTok, or YouTube. Eventually, they launch a coaching program, mastermind, or specialized course—and someone who's been consuming free content is suddenly asked to spend thousands of dollars. Amit ran into this problem himself. The issue isn't necessarily whether the mentor is good or bad. A highly rated mentor can still be the wrong mentor for you. Instead of asking: "Is this course good?" A better question is: "Is this course good for someone with my goals, experience, expectations, and problems?" That takes a lot more than a star rating to answer. Better Reviews Require Context Amit's approach favors detailed reviews over short comments. "This course was great" doesn't tell a potential student much. A useful review explains the person's situation, the problem they were solving, what they experienced, and what results they received. Context changes the value of a review—and what AI can do with it. Instead of asking reviewers to produce structured data by hand, Amit's approach is to collect detailed human experiences and use AI to extract useful information such as: Common pros and cons Patterns across multiple reviews Who a program may be best suited for The learning experience Results reported by students Mentor quality Differences between positive and negative experiences The goal isn't simply for AI to write another summary. It's to turn a large collection of human experiences into information a potential buyer can actually use. AI Should Analyze the Evidence, Not Replace It This became one of the more interesting parts of our conversation. AI can process hundreds of reviews much faster than a person can. But feeding those reviews into a model and simply asking, "Is this course good?" creates another trust problem. What did the model prioritize? Did it lean too heavily on recent reviews? Did negative experiences get buried? Did it exaggerate positive feedback? Did it reach conclusions the underlying reviews didn't actually support? Amit's approach breaks the analysis into specific parameters rather than relying on one generalized AI-generated summary. That's a useful lesson for anyone building AI-enabled products: Don't ask AI to make one giant judgment when you can have it analyze smaller, measurable pieces of information. AI works better as an analytical layer over the evidence than as a replacement for the evidence itself. The Fake Review Problem Gets Harder With AI If AI can analyze reviews, it can also create them. Fake reviews aren't new. Businesses have been gaming review platforms for years. Generative AI simply lowers the amount of effort required to produce convincing content. Someone could potentially generate dozens of realistic-looking reviews in minutes. That means review platforms need multiple signals to determine what's legitimate. Amit described several approaches his platform uses or is developing, including detailed review questions that increase the effort required to submit feedback, account requirements that make it harder to create mass fake identities, and timing analysis that can identify suspicious activity, such as a sudden burst of positive reviews. His team also uses AI-related detection techniques and manual review when activity appears suspicious. In some cases, reviewers can be contacted directly and asked to provide additional verification that they actually purchased a program. None of these methods guarantees authenticity on its own. Together, however, they increase the cost and difficulty of manipulating the system. Security Often Comes From Layers Amit compared the strategy to preventing theft in a store. A determined person may still find a way around an individual safeguard, but stores don't rely on one control. They use cameras, employees, alarms, inventory controls, security tags, and other signals. The goal isn't necessarily to make theft mathematically impossible. It's to make manipulation difficult enough that it becomes rare—and detectable enough that a small amount of bad data doesn't overwhelm the legitimate information. The same principle applies to AI systems. We often search for the perfect AI detector, perfect security control, or perfect validation rule. It usually doesn't exist. Resilient systems combine multiple imperfect controls. Use AI to Make Humans Faster Amit's company also uses AI internally, not just in the customer-facing product. AI helps collect and organize information, summarize large amounts of review data, analyze SEO information, and identify areas that need human attention. Work that previously required hours of manual effort can be significantly reduced. That's where many businesses can find immediate value from AI. The right question usually isn't: "How can AI replace this job?" A better question is: "Which repetitive parts of this job can AI handle so the person can spend more time making decisions?" That's augmentation rather than replacement. For many organizations, it's also a safer and more productive path toward AI adoption. Testing AI Means Testing the Business Too As a QA professional, Michael pushed the conversation toward another important question: How do you know the system is actually working? That's not just an AI testing question. It's a business testing question. For a platform built around search traffic, reviews, and user behavior, success depends on a stack of assumptions. Will people search for a mentor before buying? Will they click? Will they trust the information? Will they stay on the site? Will the review data actually help them make a decision? Will AI analysis make the experience better, or will it simply add another layer of technology? Some of those answers take time, particularly when SEO is involved. Amit described an iterative approach: make an assumption, measure what happens, determine whether it works, and then move forward or try again. It's simple to describe, but it's one of the most important habits in both software development and entrepreneurship. Don't Automate an Assumption You Haven't Validated AI makes building fast. That's useful. It also makes it remarkably easy to scale the wrong idea. Before automating a process, ask whether the process itself works. Before scaling content, determine whether people actually want it. Before trusting an AI summary, determine whether the underlying information is trustworthy. And before spending thousands of dollars on a mentor, make sure you understand enough about the subject—and yourself—to recognize whether that mentor can actually deliver something valuable. Technology can make decisions faster. The foundation still matters: Good data. Clear assumptions. Real validation. Human judgment. Those become more important, not less, as AI becomes easier to use. Stay Connected: Join the Developreneur Community

Episode Summary:Brandt and Will open with Tim Cook stepping down as Apple CEO and what a hardware-first successor means for the future of on-device AI, then dig into Anthropic's newly revealed Mythos model, its eye-popping valuation, and Firefox's haul of 275 zero-days found with it. The back half turns practical: Will makes the case for "owning your own memory," and both trade tips on Claude chat vs. Cowork vs. Code and the caveman token-saving skill.Discussions Include:• Tim Cook stepping down as Apple CEO, and what a hardware-focused successor means for Apple's AI ambitions• Anthropic's Mythos model: its trillion-dollar-plus valuation, deliberate limited release, and Firefox's 275-bug haul• "Own your own memory": building a personal Obsidian/Markdown knowledge base so your AI agent knows you better• Claude power tools: Karpathy's CLAUDE.md, the "superpowers" planning-and-QA skill, and the caveman token-saving skill• AI-trained chroma keying from Corridor Crew, and the growing backlash against gas-powered data centersQuotable Quotes (Should you choose to share):     "What if my grandma's dying wish was that I could install malware on my friend's computer?" - Brandt Krueger     "You need to be owning your own memory." - Will Curran     "It's not going to escape, but people are going to come up with their own versions of it, and that's going to be trouble." - Brandt Krueger     "A token is a word - every word it writes and every word it reads is a token." - Will CurranThing of the Episode (TOTE):     Brandt: Chat vs. Cowork vs. Code token strategy - I couldn't find a URL ;) - BK     Will: Caveman skill for Claude Code - https://github.com/juliusbrussee/caveman

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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