Podcasts about google gemini

In this episode, Diana and Nicole discuss Phase 5, Part 3 of the search for Cynthia (Dillard) Royston's biological father. They review the known facts about Cynthia's life using census, tax, and estate records, as well as death certificates of her children. Diana presents her hypothesis focusing on three Dillard men who drew lots in the 1832 Gold Lottery in Cass County, Georgia: James Dillard, Joseph B. Dillard, and Roliver Dillard. She explains how they previously eliminated thirteen other Dillard candidates. They also explain how DNA evidence identifies Elijah Dillard as Cynthia's probable biological brother, which helps narrow down their shared father's location in Georgia by 1814. The hosts outline their prioritized research strategy, dividing it into documentary research and DNA analysis. For documentary evidence, they plan to survey online trees, analyze censuses, search probate records, and look through Georgia newspapers. For DNA, they focus on using Ancestry Pro Tools, analyzing network graphs, and finding the most recent common ancestor through custom clustering. Listeners will learn how to build a methodical research plan that keeps research focused. They will also learn how to write a hypothesis, prioritize a wide range of source records, and combine traditional paper trails with DNA tools to overcome tough genealogical brick walls. This summary was generated by Google Gemini. Links Finding a Father for Cynthia: Phase 5 – Part 3: Research Planning - https://familylocket.com/finding-a-father-for-cynthia-phase-5-part-3-research-planning/ Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

Is your accounting firm stuck between AI enthusiasts who move too fast and skeptical team members who refuse to adopt new technology? Bridging the gap between enthusiastic leadership and hesitant team members is one of the biggest challenges facing modern firms today. In this episode of Growing Your Firm, host David Cristello sits down with Jan Haugo, founder and AI researcher at smartaccountant.ai and co-author of Intuit's official AI certification curriculum. Jan researches and validates 50+ emerging AI tools annually for controllership, Record-to-Report (R2R), AP/AR, and advisory workflows. Jan breaks down how to bring your entire firm onboard with AI safely, navigate critical security and redaction requirements, and use AI to transform sales, onboarding, and advisory workflows. In this episode, we explore: Bridging the Team Divide: How to align AI enthusiasts and detractors to move the entire firm forward together. The Enterprise Security Advantage: Why staying within Microsoft Copilot or Google Gemini ecosystems solves the PII (Personally Identifiable Information) and redaction hurdles. The Danger of Adobe Redaction: Why blacking out text in PDFs isn't enough to hide underlying metadata from AI models. Mapping Workflows Backward: Why defining the desired outcome before selecting software leads to better automation. Building a Custom AI "Sales Agent": How to train AI on your unique brand voice to research prospects, analyze websites, and guide discovery calls in real time. The Bleeding Edge: How unified AI awareness layers (like Town.io) aggregate transcripts, email, and task managers into an automated personal assistant. Key AI Tools & Solutions Mentioned: Smart Accountant AI: smartaccountant.ai Enterprise AI: Microsoft Copilot, Google Gemini, and NotebookLM Workflow & Data Aggregation: Town.io, Notion, Asana, and SharePoint Custom AI Prompting: Claude and ChatGPT About Jan Haugo Jan Haugo is a leading AI researcher, trainer, and founder of smartaccountant.ai. She specializes in testing, validating, and deploying practical AI tools for controllership, financial close, and firm operations.   Featured Guest: Jan Haugo  

How did prompt engineering die so quickly? ☠️And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext Engineering Best Practices and ScalabilityTimestamps:00:00 "Access AI Community & Tools"03:08 "Mastering Context in AI"07:23 "Smart Models Require Less Precision"12:01 "Context Engineering Beats Prompt Engineering"15:49 "AI Context: Six Key Blocks"16:47 "Building Context for Better Results"19:53 "AI: Training, Not Easy Button"25:17 "Chain of Thought Prompting Decline"29:11 "Show, Don't Tell Techniques"32:13 "Context, Reuse, and Scalable Systems"33:19 "AI Chatbots: Memory and Skills"Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don't tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Meta faces a $1.4 trillion lawsuit, flock cameras are sparking a privacy revolt, and data centers may be fueling the next bubble. This episode pulls back the curtain on who's watching, who's profiting, and who could pay the price. Meta's Big Reckoning Is Here Meta heads to court in a landmark trial about kids and social media addiction Ninth Circuit Ruling Will Force Online Platforms That Host User Speech to Fight Lengthy and Costly Lawsuits Before They Are Dismissed Under Section 230 The $1.4 Trillion State Tort Raid on Meta Google's Pixel 11 Comes With Plenty of A.I. Does Anyone Want That? Waymo's cheaper, next-gen robotaxi is now open to all riders in these three cities Apple and European Commission Reach Agreement on App Payment Terms Under DMA, With Apple Conceding Very Little Apple is laying off staffers working on the Vision Pro and Siri Hidden Airtag reveals Amazon is trashing rare books to train AI Amazon aims for delivery drones to reach 500 US neighborhoods by the end of 2026 NVIDIA to Back Ohio Data Center With as Much as $105 Billion Why Big Tech's AI Spending Is $3 Trillion Higher Than It Seems New Chinese model adds to AI competition Police Are Hiding Their Use of Flock Surveillance Cameras Flock mistakenly flags 2 different auto writers for "stolen" license plates Go Flock Yourself Flock socks for UV protection Ban on Chinese robots leaves U.S. startups stranded China Is Strapping 'Digital Bombs' to Civilian Infrastructure—Is the US Ready? Host: Leo Laporte Guests: Sam Abuelsamid and Fr. Robert Ballecer, SJ Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: superhuman.com claude.ai/technology doppel.com joinbilt.com/twit adaptivesecurity.com

Like 99% of companies are pushing AI.

Diana and Nicole interview Sam Morrison, a dedicated family historian who has researched his family lines for over two decades. Sam shares his motivation for creating TreeAlive, a free digital tool designed to transform static genealogical data into animated films that visualize family migration and historical context across maps. His extensive experience researching his roots in Ohio, Virginia, and abroad drives his interest in helping others see their ancestors' journeys rather than just reading lists of dates. Sam explains how TreeAlive integrates genealogical data from sources like FamilySearch and various software programs to map family movements through time. Listeners learn how to use the Time Machine feature to overlay historical maps on family locations and utilize the Historic Crossings feature to identify documented overlaps between ancestors and historical figures. Sam also discusses recent updates, including the ability to visualize descendant paths, connect directly via WikiTree, and identify nearby historical markers. This episode provides practical strategies for identifying research gaps and visualizing historical events, transforming standard record research into a vivid understanding of family history. This summary was generated by Google Gemini. Links Your Family Tree Has Been Holding Still: Here Is How to Watch It Move - https://familylocket.com/your-family-tree-has-been-holding-still-here-is-how-to-watch-it-move/  Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

Meta faces a $1.4 trillion lawsuit, flock cameras are sparking a privacy revolt, and data centers may be fueling the next bubble. This episode pulls back the curtain on who's watching, who's profiting, and who could pay the price. Meta's Big Reckoning Is Here Meta heads to court in a landmark trial about kids and social media addiction Ninth Circuit Ruling Will Force Online Platforms That Host User Speech to Fight Lengthy and Costly Lawsuits Before They Are Dismissed Under Section 230 The $1.4 Trillion State Tort Raid on Meta Google's Pixel 11 Comes With Plenty of A.I. Does Anyone Want That? Waymo's cheaper, next-gen robotaxi is now open to all riders in these three cities Apple and European Commission Reach Agreement on App Payment Terms Under DMA, With Apple Conceding Very Little Apple is laying off staffers working on the Vision Pro and Siri Hidden Airtag reveals Amazon is trashing rare books to train AI Amazon aims for delivery drones to reach 500 US neighborhoods by the end of 2026 NVIDIA to Back Ohio Data Center With as Much as $105 Billion Why Big Tech's AI Spending Is $3 Trillion Higher Than It Seems New Chinese model adds to AI competition Police Are Hiding Their Use of Flock Surveillance Cameras Flock mistakenly flags 2 different auto writers for "stolen" license plates Go Flock Yourself Flock socks for UV protection Ban on Chinese robots leaves U.S. startups stranded China Is Strapping 'Digital Bombs' to Civilian Infrastructure—Is the US Ready? Host: Leo Laporte Guests: Sam Abuelsamid and Fr. Robert Ballecer, SJ Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: superhuman.com claude.ai/technology doppel.com joinbilt.com/twit adaptivesecurity.com

Meta faces a $1.4 trillion lawsuit, flock cameras are sparking a privacy revolt, and data centers may be fueling the next bubble. This episode pulls back the curtain on who's watching, who's profiting, and who could pay the price. Meta's Big Reckoning Is Here Meta heads to court in a landmark trial about kids and social media addiction Ninth Circuit Ruling Will Force Online Platforms That Host User Speech to Fight Lengthy and Costly Lawsuits Before They Are Dismissed Under Section 230 The $1.4 Trillion State Tort Raid on Meta Google's Pixel 11 Comes With Plenty of A.I. Does Anyone Want That? Waymo's cheaper, next-gen robotaxi is now open to all riders in these three cities Apple and European Commission Reach Agreement on App Payment Terms Under DMA, With Apple Conceding Very Little Apple is laying off staffers working on the Vision Pro and Siri Hidden Airtag reveals Amazon is trashing rare books to train AI Amazon aims for delivery drones to reach 500 US neighborhoods by the end of 2026 NVIDIA to Back Ohio Data Center With as Much as $105 Billion Why Big Tech's AI Spending Is $3 Trillion Higher Than It Seems New Chinese model adds to AI competition Police Are Hiding Their Use of Flock Surveillance Cameras Flock mistakenly flags 2 different auto writers for "stolen" license plates Go Flock Yourself Flock socks for UV protection Ban on Chinese robots leaves U.S. startups stranded China Is Strapping 'Digital Bombs' to Civilian Infrastructure—Is the US Ready? Host: Leo Laporte Guests: Sam Abuelsamid and Fr. Robert Ballecer, SJ Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: superhuman.com claude.ai/technology doppel.com joinbilt.com/twit adaptivesecurity.com

Meta faces a $1.4 trillion lawsuit, flock cameras are sparking a privacy revolt, and data centers may be fueling the next bubble. This episode pulls back the curtain on who's watching, who's profiting, and who could pay the price. Meta's Big Reckoning Is Here Meta heads to court in a landmark trial about kids and social media addiction Ninth Circuit Ruling Will Force Online Platforms That Host User Speech to Fight Lengthy and Costly Lawsuits Before They Are Dismissed Under Section 230 The $1.4 Trillion State Tort Raid on Meta Google's Pixel 11 Comes With Plenty of A.I. Does Anyone Want That? Waymo's cheaper, next-gen robotaxi is now open to all riders in these three cities Apple and European Commission Reach Agreement on App Payment Terms Under DMA, With Apple Conceding Very Little Apple is laying off staffers working on the Vision Pro and Siri Hidden Airtag reveals Amazon is trashing rare books to train AI Amazon aims for delivery drones to reach 500 US neighborhoods by the end of 2026 NVIDIA to Back Ohio Data Center With as Much as $105 Billion Why Big Tech's AI Spending Is $3 Trillion Higher Than It Seems New Chinese model adds to AI competition Police Are Hiding Their Use of Flock Surveillance Cameras Flock mistakenly flags 2 different auto writers for "stolen" license plates Go Flock Yourself Flock socks for UV protection Ban on Chinese robots leaves U.S. startups stranded China Is Strapping 'Digital Bombs' to Civilian Infrastructure—Is the US Ready? Host: Leo Laporte Guests: Sam Abuelsamid and Fr. Robert Ballecer, SJ Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: superhuman.com claude.ai/technology doppel.com joinbilt.com/twit adaptivesecurity.com

In episode 502 of The Reformed Brotherhood, Tony and Jesse begin what promises to be a multi-part deep dive into the Parable of the Good Samaritan — one of the most culturally embedded and theologically rich stories Jesus ever told. But before a single character hits the Jericho road, the hosts ground listeners in something easily overlooked: the charged context in which this parable is told. A lawyer — trained, confident, adversarial — steps forward not to learn but to test. What follows is a masterclass in how Jesus turns the law back on those who wield it as a weapon. This episode covers Luke 10:21–36, explores the covenant of works and covenant of grace, the noetic effects of sin, and why the Good Samaritan is far more than a lesson in being a helpful neighbor. Buckle up. Key Takeaways The lawyer's question is self-contradictory from the start. Asking "what must I do to inherit eternal life?" smuggles a works-righteousness assumption into the very grammar of the question. Inheritance by definition is received, not earned. Jesus does not give the lawyer a new paradigm — He reorients him to the true one. The covenant of grace is not a New Testament innovation. It runs through every Old Testament covenant administration. The lawyer should have already known this. "Do this and you will live" is not a Pelagian concession. Christ is holding out the law's own terms — perfect, total, unrelenting obedience — not to suggest it is achievable, but to expose its impossibility and drive the lawyer (and us) to the only alternative: mercy. "Desiring to justify himself" is more complex than it first appears. Tony argues that the desire to fulfill the covenant of works is not inherently wicked — it is the misguided belief that one can which is the problem. This distinction creates room for reading Christ's response as genuinely merciful instruction, not merely rebuke. The noetic effects of sin mean accurate theology can be deployed in service of self. The lawyer knows the right answers and still uses them to avoid repentance. Correct doctrine alone does not save. The fundamental human problem is not ignorance — it is the will's bondage and the heart's self-love. The parable is primarily Christological, not merely ethical. Jesus is the Good Samaritan. The story is not first about mercy ministry; it is about who binds up our wounds when we are left for dead on the road, unable to save ourselves. The priest and the Levite are not exonerated by their ritual obligations. Considerable ink has been spilled trying to justify their passing by on grounds of Levitical purity concerns. The parable refuses that exit. Everyone in the story — and everyone listening — already knows what the right thing to do was. Key Concepts The Covenant of Works Is Not a Black Box One of the more quietly important arguments Tony makes in this episode is that the covenant of works was never meant to be mysterious or inaccessible. When Jesus responds to the lawyer's question by asking, "What is written in the law? How do you read it?" — He does so with a visible assumption: there is a clear, knowable answer, and the lawyer should be able to give it. And he does. Love God completely, love your neighbor as yourself. The covenant of works was always coherent and comprehensible. The problem has never been that its demands were obscure. The problem is that no one — save the Mediator — has ever kept them. Jesus is not introducing a new ethical framework. He is forcing the lawyer to confront a standard he has always known and has never met. The Lawyer as a Warning to Reformed Christians Jesse closes the episode's first movement with a pointed and personal application that cuts across theological self-satisfaction. The lawyer is not a caricature of ignorance — he is a portrait of someone with accurate, affirmed theological knowledge who instinctively deploys it in the service of self-justification the moment he senses conviction. Jesse names this directly as a danger particularly acute in Reformed circles, where doctrinal precision can become a subtle mechanism of self-defense rather than an occasion for repentance. The Reformation's great recovery of grace must not become a new form of the very disease it diagnosed. Knowing the right words about total depravity, the covenant of grace, and the mediator does not exempt anyone from the need to actually be humbled by them. Theology is not armor against repentance — it is, rightly received, the road toward it. Context Is Not Just Setup — It Is Part of the Lesson Tony and Jesse both emphasize that Luke 10:21–24 is not merely throat-clearing before the real content begins. The theological statement embedded there — that the Father has hidden these things from the wise and revealed them to little children, that no one knows the Son except the Father or the Father except the Son and those to whom the Son chooses to reveal Him — is the hermeneutical key to everything that follows. The lawyer approaches Jesus as one rabbi testing another. But Luke has already told his readers that this is not a conversation between equals debating interpretive options. It is the Lawgiver being questioned by someone who does not recognize Him. The parable that follows is not just an answer to a legal question. It is a revelation — the kind that only happens when the Son chooses to give it. Memorable Quotes You do not want to pit the law against the one who has given the law, the one whose character is represented in the law — because you're always going to get owned, and you'll get owned before you realize you've just gotten owned on multiple levels. - Jesse Schwamb Jesus is trying to sort of reorient them back to the true paradigm of grace that is present under all of the Old Covenant administrations. It's not so much a matter of developing a new framework and imposing it on this guy. It's really trying to say, 'Listen — think about this for a second. Think about the fact that, yes, you know you have to love the Lord your God with your whole heart, your whole soul, your whole strength, your whole mind, and you have to love your neighbor as yourself. You've answered correctly. If you can do those things, you will live.' — with the subtext: and of course you cannot possibly do those things. - Tony Arsenal The fundamental human problem is not primarily ignorance, but the will's bondage and the heart's self-love. It's a problem no amount of correct discipline or doctrine alone can fix apart from some kind of regenerating grace — which is exactly what Jesus is going to illustrate for us in the mercy he's about to display in this parable. - Jesse Schwamb Full Transcript Tony Arsenal: [00:00:00] This parable is not primarily about us figuring out how to do mercy ministry. Like, that's important. It's probably an outcome of this. It's probably something for us to think about. But really, this is actually Christ saying, like, "Hold on a second here, dude." Like, "Let's just think about this." Do you need someone who is going to tell you to pick yourself up off your bootstraps and cross to the other side of the street and leave you to your own devices? Or do you need someone who's gonna throw you on their donkey, who's gonna bind up your wounds, who's gonna pay the innkeeper, and is gonna pledge to pay everything that is owed for your salvation? Right? That's not too on the nose here, I think. Welcome to episode 502 of The Reformed Brotherhood. I'm [00:01:00] Jesse  And I'm Tony, and this is the podcast with ears to hear. Hey, brother.  Jesse Schwamb: Hey, brother. Well, we've got a big one to talk about on this episode. We're marching further and further into all of the glorious parables Jesus told as part of His teaching. And as you and I kind of joked before we began, I mean, what did you call it? Did you say this is, uh, the big daddy?  Tony Arsenal: It's the big daddy, one of the big daddy parables.  Jesse Schwamb: It's one of the big daddy parables. Now, this one is probably second maybe only to the prodigal son. Yeah But in terms of its ubiquity and its kind of just common knowledge in culture, this one ranks right up there. And before I reveal what it is, which you may already know, I was thinking how so many times we look at the Beatitudes as expression of a master class in Jesus teaching the law, and I think that's true. But as I was reading and thinking about this particular parable, I thought, "I don't know. I think this one is, uh, right up there," because [00:02:00] this is like law on law. It's double law. Yeah It's law coming back at the law giver who's trying to use the law to test Jesus, and then Jesus tests him with the law that He himself gave His people. It's... I'm already getting too excited- ... and we've got something to do before we get there. But we're talking about the parable of the Good Samaritan. And so I can't imagine, can I just make a prediction up front? I, you know where I'm going with this, and probably everybody else does. Uh, this is gonna be like two conversations-  Tony Arsenal: At least ...  Jesse Schwamb: probably.  Tony Arsenal: Yeah, at least two.  Jesse Schwamb: Is that fair? Because I realize we, of course, wanna get to the parable itself, and oh, my goodness, how much ink has been spilled. Even going back to, like, if you read, like, Augustine or, like, the early church, like the Patristics, the Church Fathers, there are so much that could be said, and maybe some stuff that just shouldn't be said about all the allegorical potential intent here about these characters. But [00:03:00] there's so much amazing stuff that happens in the opening of this, that's not just the setup, but it is its own lesson, and I'm real excited actually to talk about that with you before we even get into, like, all the good stuff that everybody expects that you wanna talk about. So we're gonna do it all. Tony Arsenal: Yeah.  Jesse Schwamb: We're gonna do it all, but it'll take time.  Tony Arsenal: Yeah. I'm interested to see if we're even on the same page about where to start reading, 'cause I, I think we might not be, and that's gonna be fun. So we'll, we'll let it fly and see what happens. It'll be our own little, uh, podcast game.  Jesse Schwamb: But, uh, I think- Well, I mean, we're gonna be in Luke chapter 10, but I guess to your point, we should be starting in Luke chapter one to get to this. Yeah.  Tony Arsenal: Exactly. But  Jesse Schwamb: we, we do have to back up quite a bit, and I was gonna ask you about that. Yeah. But I, apparently we're just gonna play that on the fly and see where we go.  Affirmations Denials Segment Jesse Schwamb: But of course, the Reformers had their five solos. Chalcedon had its, what, seven withouts. We've got opinions, and we're just as confident about ours, so of course it's time for affirmations, denials, where we affirm something that we think is good or awesome or [00:04:00] we wanna recommend, and we deny, again, something that is not so great or maybe just plain annoying. So before we talk about the Parable of the Good Samaritan, Tony, what do you got for us on this episode? Are you affirming with something, or are you gonna come in with a denial?  Tony Arsenal: Well, I've got two affirmations. That's fine. One is just- I will allow it ... a declaration of joy, and one is, like, a real affirmation. I, I just can't... It would be remiss of me not to share with our podcasting family that my son August and my daughter Adeline, uh, joined the visible church today as- Hear, hear ... uh, non-communicant members. Uh, Augie and Addie were baptized on the 16th of August of 2026, uh, into the visible church, into the name of Jesus Christ. Uh, was, it was just a great service. Um, it's funny because I've been- been talking with Augie. One of the thing with toddlers when you're doing something big like this is you kinda have to talk about it and practice and rehearse, rehearse it and, and get them ready for it. And up until maybe, like, I don't know, three days [00:05:00] ago, every time it would come up he would be saying, "I don't wanna be baptized. I don't wanna be baptized." And, um, you know, I, I, I, you know, I don't wanna force him to do something, but also, like, I make him eat vegetables. But, um- ... he, I, I think it was nerves, and I think something clicked in his head and all of a sudden he was very excited to be baptized. So, um, it was great. We've been talking about, you know, what baptism is, God's promise to you that if you trust him he'll save you. And so today he- Right ... he was like, "God's gonna make a promise to me to save me." So he was very excited. Nice. And he did a great job just standing quietly and listening to the pastor talk. Uh, and Addie was, was, was just a beautiful little girl, and she, um, she loved it as well. So that's just a bonus affirmation. I don't know that I could get through a podcast without gushing over that.  Glorify App and Devotionals Tony Arsenal: Uh, but the, the affirmation I have, uh, this is an, an app that I think some of our listeners may have heard of. Um, some may roll their eyes at it, to be honest with you. Um, I stumbled on this because, uh, you have an Oura Ring, don't you, Jesse? Jesse Schwamb: I do.  Tony Arsenal: You [00:06:00] do, yeah. So for those who don't know, an Oura Ring is like a little wearable ring that's like a, almost like a fitness tracker, but it tracks biometrics, pulse rate, temperature, all these different things, sleep. Um, there's a startup, uh, that has been doing a GoFundMe or a funding kind of thing for, uh, what they're calling like a Christian version of an Oura Ring. So it does a lot of the same things, sleep tracking, basic biometric tracking. Hmm. But it also has like alerts built in, and it's tied into an app that they use for devotionals. So it'll, it'll remind you to pray. It'll remind you, you know, bring you to an app to do a devotion. But the app it uses is a standalone app that I looked up, and it's called Glorify. Have you ever heard of this app before this evening? I have heard of  Jesse Schwamb: this, yeah.  Tony Arsenal: So Glorify is just a small devotional app. Uh, it's not much different than you would expect based on the name. It is connected to this, uh, to this Glorify ring, which again is, is sort of like an Oura Ring tied into this app. But this app basically is, is [00:07:00] designed to help you complete short daily devotional exercises, and they're usually very brief. They're based on a very brief, um, a very brief passage of scripture. Today's passage, uh, c- there's a quote. So today's quote is from Dietrich Bonhoeffer. It says, "True love does not consist in finding the perfect person, but in faithfully loving an imperfect one." Not sure why this devotional app is using a romance, uh, quote from Bonhoeffer of all people. But that was today's. Um, and the passage is Proverbs 5:15-17. So it's a, it's a devotional based on two verses, which is fine. So you read the passage. There's a very brief devotional on that passage, and then sort of a guided prayer, and they have both premium and free models. The premium model includes like you can listen to the devotional, you can listen to the prayer. Um, but just for example, today's prayer, it, it's very simple. "Loving Father, thank you for, uh, thank you because you're faithful and unchanging. In a world [00:08:00] marked by broken promises, disposable relationships, you remain the same. Teach me to reflect that faithfulness in my relationships. Help me to love with perseverance, grace, and commitment following the example of Christ. Protect marriages, strengthen families, restore what needs healing. May every relationship be marked by respect, truth, and genuine love. Shape in me a heart of integrity that honors you in both the small and significant choices of life. In Jesus' name, amen." So I think what I love about this, and this, I mentioned last week that there was, this is kind of like, sort of like the fluffy evangelical devotions. Like- Right ... there's not a ton of like solid doctrinal meat- But like, it's still nourishing. And sometimes we need a little bit of help. Sometimes we don't know what to pray, we don't know what to read, we're not sure. Sometimes in, I think in our Reformed circles, we feel like if we're not reading large portions of scripture and reading deep theological treaties or long, complex devotionals, that we're really not doing anything, and that's just not the [00:09:00] truth. So I've been using this in addition to other devotional studies I'm doing. I don't do it every day. I mean, there's some like streak functionality, kinda like habit tracking type stuff that's built into it. Um, but it's nice. It's nice to be able to pick it up, read a very short scripture, a brief devotional thought, and then have some guided prayer. I know one of the reasons I don't pray as often as I ought to is because I struggle to know what and how to pray. So even having some written prayers, I mean, this is why the Psalms are good, things like the Valley of Visions are good. Um, having some pre-written prayers that you genuinely pray but you're not having to come up with in the moment can really help. Right. So the app is called Glorify. I know it's available on iPhone. I'm not sure about Android. I'm sure it probably is. I'm sure they wanna have a pretty broad ecosystem. Um, and, and it's good. Again, it's, it's not anything fancy. It's not anything like super technical. Uh, and as I kind of alluded, like some of it's a little weird. Um, I, I didn't read the proverb. I think maybe the proverb is about romantic relationships, why they're, why they're tying in that, um, Bonhoeffer quote in, in the prayer. [00:10:00] But, um, even that, like it's pretty innocuous. And the quotes, like you don't have to read the quotes. It's not like you have to have to do that. Um, you could jump in, just read the scripture or just read the devotion and just do the prayer and you would be fine. So check it out. It's called Glorify, available on iPhone for sure. I'm pretty sure it's on, on Android. And maybe it's something you add daily, you know, this kinda thing like you do when you're on the bus or you're waiting at the grocery store, something quick you can do when you might be wasting that time just sorta sitting around or scrolling Twitter. Again, Twitter's fine. You can use your leisure time as you see fit. But if you wanna fill it with something like this, something that is more spiritual, then this would be a good option to just add a little bit of, uh, a little bit of nourishment to your diet where you might not otherwise be getting it  Jesse Schwamb: I have no proof of this, but I just keep saying it anyway. It might be that we're living in the golden age of really great- ... daily worship resources. Like, something to actually cultivate, not just, like, a devotional nugget, but something that actually leads you into a pattern and a habit of [00:11:00]worship, giving your attentive focus on Jesus. And I love that. So, uh, uh, 'cause I was just thinking, like, I was trying to look back on some of our past affirmations here. We've talked about the book Walking in Faith. I just gave everybody Awake My Soul last week. You're coming in with the Glorify app. I- we've talked about Dwell, if not recently, we've talked about that for some time. It's like a great Bible reading app, and there's lots and lots of new features, by the way, coming out on that- Yeah that are fantastic. And then, yes, all these other great prayers. I, I see, like, these books on prayer, like, actually, I'm sorry, like, prayer books, I guess, as, like, a great way to kinda... It's like a jumper for your car. You know what I mean? Like, just gets you started. What a, what a lovely thing to come into a place and have a launching pad. And I've been using a lot of Into His Presence, which is a collection of Puritan prayers, sometimes because, for instance, this just happened, where I wanted to pray for someone who was just going through a rough time, a good friend of mine. And I went to that place to kinda start and frame, knowing that I was gonna sit down with him and pray, how I might be able to do that well and [00:12:00] effectively. And these things are just, like, really phenomenal resources. It's just an amazing time to have all these things at our disposal. So that's another great option. It's, it's really more about, like you and I have talked about, Tony, like, whatever gets us into that pattern, whatever gets us, like, authentically experiencing under our own volition, coming in the discipline of sitting before God in daily worship with that expression being pretty broad in its representation- Yeah is to really include something of devotional time in the scriptures, memorization, metabolation, metabolizing. Metabolation, I'm just gonna make that up as, like- ... what happens when you, when you take the scripture and metabolize it into your own life. Meditating on it and, you know, again, maybe looking through the confessions and creeds that have shaped our distilled understanding of what the scripture teaches so that we're enhancing and edifying our mind. We're also engaging the heart, and then we're engaging God and ourselves in communication with Him through prayer. I'm all for anything [00:13:00] that gets us into those rhythms, reminds us of the importance of them, and like we talked about last week, makes it so that when we think about what it means to be married to Christ, so to speak, it's not just about whipping out some kind of marriage certificate to prove that, but that we actually have a frame and a behavioral pattern that fits a married person, that- Yeah how they act, how they think, how they behave, how they communicate, they're never too far away from their lovely spouse in some way or another. And so I think that these tools are great ways to get us exactly into that kind of mindset and practice.  Tony Arsenal: Yeah, absolutely. And like I said, like, I think- In our Reformed circles, we can, you know, we, we tend to be maybe a little eggheaded, and we, we tend to be a little more intellectual and, and a little more, I don't know, studious. And there's nothing wrong with that. Um, but I think sometimes it can breed a sort of, uh, elitism and arrogance. Sure. Uh, this is a perfectly valid devotional tool. Yes. Mm-hmm. And, and I think, [00:14:00] you know, kinda like the old adage, like, well, what's the best translation of scripture? Well, whichever one you're gonna read. Exactly. Like, what's the best devotional tool? Whichever one you're gonna use, whichever one's actually gonna bring you into the Word, uh, on a, on a regular basis. Um, and again, like, two verses of the scripture is two verses of the scripture that you might- Right ... not have other spent time, otherwise spent time reading. And it really is short enough, and it saves your progress. Like, you can do, you can do the quote in the morning, and then you can check the scripture on the way to the, you know, the word work, on a stoplight or whatever. Um, if you have the, the paid subscription, you can listen to this stuff, you know, on the, the bus or whatever. Um, so check it out. Glorify app, they have a ring they're making. It's in funding, I guess, if you wanna check it out, you can check that out too. I am neither affirming nor denying whatever this Glory ring is, Glorify ring. But, um, but yeah, the app is great. The, the app is free, and it's, it's worth even just checking out and adding once in a while to your day  Jesse Schwamb: The glory ring or the glorify ring sounds like almost too Tolkien, even for me  Tony Arsenal: Yeah. Yeah. It's the one ring to rule all of your devotions. [00:15:00] Uh, and in the, and in the morning bind them. Jesse, save me from the trail I was about to go on, and, and I was gonna speak in the language of Mordor, I think, on accident. Uh, save us from this. What are you affirming or denying?  Jesse Schwamb: That's  Tony Arsenal: beautiful. Please tell me it's the language of Mordor. Elvish AI and Music Pick Jesse Schwamb: Uh, no, but can I just say at the risk of really taking us really far afield so early on, which is not necessarily off-brand for us, I have done this... I, I don't know if I should be ashamed by this or I should be excited by this. But- ... um, the Elvish tongue in the Tolkien universe-  Tony Arsenal: Oh, no. ...  Jesse Schwamb: is like, there's several... If you're super nerdy, you'll know there's like several different kind of iterations or formulas of it, and there's no, like, official language like Klingon, but there ha- it has been studied, and basically, like, it's kind of like the Eugene Peterson of the language. Like, there's, like, just kind of a dynamic equivalent or an expression of words. Anyway, all that to say- ... there's several languages that, uh, Microsoft Copilot will just not [00:16:00] translate because it says, like, "This one, this language doesn't actually exist. There's not enough for me to do anything." Let me tell you something. Tolkien Elvish is not one of those languages, and I actually built an agent, because I have a coworker who can, uh, who can appreciate all this stuff, that takes a text, translates it into this kind of academic high-level Elvish from Tolkien, and then translates it back to English. That makes anything you're saying way better and far more poetic and just absolutely amazing. So if you wanna have some fun, go and do that with whatever your favorite AI model is. Anyway, that's free of charge. Real quick, my affirmation is more music. It just turns out I've found this band, and I've been listening to them a lot. The band's name is Thronebreaker, which is just a phenomenal name, I think, for any group. Uh, it's amazing. Uh, th- this is... I'm gonna read you their description from Spotify. It just says, "Thronebreaker is Christian metalcore built for the bruised, the doubting, and the ones still fighting their way home." And what I've really been [00:17:00] loving about them is it is kind of like Wolves at the Gate adjacent. It's really lovely music. It is melodic, so it's, there's not just that there's that kind of hard edge, but that is present in it. But, uh, the music is really lovely and beautiful, and in part that's because they're going through this, like, drip of releasing music where they're taking, like, old hymns or maybe songs if you're of a certain age like I am of kind of childhood Sunday school- And they're taking the lyrics, not the melody, and they're reimagining them. So they have two versions of Jesus Loves Me that they just released. They have the old We're Gonna Cross, a song called Glory to the King Most High. These songs are just saturated with gospel. Like, they're just all power, all encouragement, and all authority represented in Jesus Christ. They're incredible, and I've j- where I've, what I've been really blessed by is these two versions of Jesus Love Me are just fantastic. Like, they're orchestral. There is a children's kind of chorus at one point, and it got me [00:18:00] singing that song again, and really, maybe to what you were saying before, Tony, like sitting in those three words, which are really phenomenal words to hear. And so I have been surprised at how moved I've been by what really I, I first understood, of course, as a children's song with simple truth that seems, you know, depending on how long you've known some part of the gospel, obvious. And yet I found them all new again, and a, a really lovely way of just sitting in the truth of God in music that I didn't expect to find. So if you're interested in any of that or maybe just curious of what it would sound like if a Christian metalcore band took Jesus Loves Me and turned it into their genre, then honestly you should just go check out Throne Breaker. I mean, you pretty much have to at this point.  Tony Arsenal: I, I don't ever have anything to add to your musical affirmations or denials, 'cause I, I just don't. What I do have to add is that Throne Breaker actually sounds like some sort of really [00:19:00] amazing, like, mace-type weapon in Lord of the Rings. Yes.  Jesse Schwamb: Yes,  Tony Arsenal: it does. Or like a big hammer that a dwarf carries. And I would also like to say that if you ask Google Gemini to translate something into Tolkien English, it not only will do it, it will tell you how to pronounce it.  Google Elf: A ava aini Ilai amel i trona se.  Tony Arsenal: What do you think that was, Jesse?  Jesse Schwamb: No idea.  Tony Arsenal: First of all, that was elevated Quenya, a Quenya. Yes.  Jesse Schwamb: Yes, that's  Tony Arsenal: the one. Classic Quenya.  Jesse Schwamb: Yes.  Google Elf: A leta Ilai amel aima.  Tony Arsenal: I would be willing to bet... Actually, let's do this.  Jesse Schwamb: Ugh.  Tony Arsenal: Uh, we don't give out prizes very often. Uh, I will say to anyone who can tell me what that is, the first person that emails, uh, or, or goes into the Telegram chat, we'll do this. We'll be professional podcasters and drive people to one of our channels. Here we  Jesse Schwamb: go.  Tony Arsenal: The first person who goes to our telephone, our Telegram chat and tells me what that [00:20:00] means, what that, uh, Elvish means, will get a Reformed Brotherhood T-shirt.  Jesse Schwamb: Oh, that is big.  Tony Arsenal: Yeah.  Jesse Schwamb: That-  Tony Arsenal: So I'm gonna, I'm gonna play it one more time.  Jesse Schwamb: Okay, here we go. Everybody listen. Every- All right, pull over. Stop your vehicles. Here we go.  Google Elf: A leta Ilai amel aima.  Tony Arsenal: There it is, everybody.  Jesse Schwamb: I'm not being a cry- I have a guess, but I, I, I wanna try to win the T-shirt, so I'm gonna- Yes ... wi- withhold. All right. Uh, yeah, that's, that's super fun. Did it, um... Did you also translate it back into English from that  Tony Arsenal: just to see- No, I'll do that later, 'cause that would give away the, that would  Jesse Schwamb: give away the prize. Yeah, it's, it's... We'll have to save that for next week, because that, that is the fun. It creates, like, what I was doing to, to have fun with this coworker is take very technical, like, work-related emails, and I would send it through. It would go through that iterative process, and I actually loved how it, it was talking. Like, it wa- this was an email about risk, and it, it gave, like, these beautiful grand metaphors that didn't sound corny or cheesy. That's the beautiful thing. That's amazing. It [00:21:00] was like true Elvish. Like, you were just like, "I wish I spoke that way all the time about, about risk." And it, like I said, it wasn't just like... You could, you could obviously throw something in and be like, "Make this Shakespearean," or, you know, "Make this, you know, sound like, you know, Milton." This is way better. This is way better, because it's not, like, actually trying to manipulate what you're saying. It's takes, like, the dynamic equivalent of what you said, then translates it back with way more kind of poetic infrastructure while maintaining, like, the essential meaning that you have. I'm, I'm selling this too hard. Everybody just has to go out and try it. So, all right, you know what's at stake here, but here's how you have to win this. If you want a chance, if you want a chance at winning this, you gotta go to t.me/reformedbrotherhood, t.me/reformedbrotherhood, and that link will take you to the Telegram app and our little corner of it, and you're gonna find there's a bunch of chats there. You gotta go to the affirmations or denials chat, and then you gotta put in what is the translation of what Tony just played.[00:22:00] I just love... Isn't it amazing, though, that, like, y- as you discovered, there's more than one version of Elvish available? Yeah. And they do differentiate between the various kinds. This is a big deal. I learned there are actually scholars who have, like, again, filled in kind of like the details of the language- Oh, yeah taking as a basis what Tolkien had there. Yeah. So I think that this is the best. We couldn't have conceived of this if we'd actually tried and planned for it, so-  Tony Arsenal: It's true ...  Jesse Schwamb: it's just better that it all happened in, in real time. It's true. And, and speaking of which, I think now we get to have the fun of figuring out, uh, where in Luke chapter 10 are we gonna begin? Or should we just start in Luke 1 and just let's see how far we can get reading  Tony Arsenal: in this- I could have, I could have Google Gemini read it in elevated Kenya, Quenya I did try to get it to put it in Sindarin, and it wouldn't do it for some reason. I'm not sure. Maybe this anti-Sindarin bias in Gemini,  Jesse Schwamb: man. Disappointing.  Tony Arsenal: It's disappointing.  Jesse Schwamb: Dis- disappointing. So let's kick off some Good Samaritan talk, which again, I think is gonna take us [00:23:00] through maybe in the next couple of conversations, in part because, like I said, we're not even gonna start at, uh, verse 25 because there is so much more that needs to be said, I think, before that. Tony Arsenal: Yeah. Yeah. I, I think actually the proper place to start this, and I, I'm guessing Jesse probably, whether he was there or not, he probably figured out where I was going, uh, over the course of the last 20 minutes or whatever. Um, I actually think the proper place to start, at least one of the proper places to start, is in verse 21. Yeah. Um, and I- we'll talk about why that is. And I'll... I'm gonna read here, um, 21, uh, through the end of the parable. Uh, and this is Jesus talking. He says in this, uh... Well, it, this is, um... Yes, uh, I'll just read it here. "In the same hour He rejoiced in the Holy Spirit and said, 'I thank you, Father, Lord of heaven and earth, that You have hidden these things from the wise and understanding and revealed them to little children. Yes, Father, for such was Your gracious will. All things have been handed over to Me by My Father, and no one who knows who the Son is except... No, no one knows who the Son is [00:24:00] except the Father, or who the Father is except the Son, and anyone to whom the Son chooses to reveal Himself.'" Then turning to the disciples, he said privately, "Blessed are the eyes that see what you see. For I tell you that many prophets and kings desired to see what you see and did not see it, and hear what you hear and did not hear it." And behold, a lawyer stood up to put him to the test, saying, "Teacher, what shall I do to inherit eternal life?" He said to him, "What is written in the Law? How do you read it?" And he answered, "You shall love the Lord your God with all your heart, with all your soul, and with all your strength, and with all your mind, and your neighbor as yourself." And he said to him, "You have answered correctly. Do this and you will live." But he, desiring to justify himself, said to Jesus, "And who is my neighbor?" Jesus replied, "A man was going down from Jerusalem to Jericho, and he fell among robbers who stripped him and beat him and departed, leaving him half dead. Now by chance a priest was going down that road, and he saw him and passed on the other side. So likewise a Levite, [00:25:00] when he came to the place and saw him, passed by on the other side. But a Samaritan, as he journeyed, came to where he was, and when he saw him he had compassion. He went to him and bound up his wounds, pouring on oil and wine. And he set him on his own animal and brought him to an inn and took care of him. The next day he took out two denarii, gave them to the innkeeper, saying, 'Take good care of him. Whatever you, whatever more you spend I will repay you when I come back.' Which of these three do you think proved to be a neighbor to the man who fell among the robbers?" And he said, "The one who showed him mercy." Jesus said to him, "You go and do likewise."  Jesse Schwamb: I think you're right, of course, to back us up into verse 21 because I don't want to keep saying it's always a setup, but Jesus is certainly giving us like the right context in which... Well, Luke of course is the author here and as he's explaining the teaching of Jesus, he does so well to start out with some irony that we don't realize is gonna be ironic until we get to the, to verse 25 [00:26:00] when we realize who's testing Jesus, who's setting this all up. So this idea that the Holy Spirit, everybody rejoicing in the Holy Spirit and the knowledge and the wisdom, the intelligence that's revealed through the Holy Spirit to all of these lesser people and in particular here emblematic the infants That here we have kings and rulers, and in this case, this beloved scholar of the law, who all have this adventure in missing the point. They think, in fact, they're looking for the one who is to set them free, and they can't even see that it's Him who is before him. And yet, incidentally, God has already revealed all these things to the lowest, to the ones who are the most humble, the meek, the mild, the down and outs, the ones who are weak by the view of everybody else. And so it's amazing that if we just kinda, again, like blow past it into, like, just all the characters, we don't see that the teaching starts actually well above this, that even everything we're about to understand, that unfolds in our lives, ev- every spiritual incarnation and insight that we have [00:27:00] first starts with God himself in his revealing it to us both in his general revelation, but of course, in the specific revelation of his teaching. So I like that you started us there, because I, I'm with you. That's where this whole thing launches off, right?  Tony Arsenal: Yeah.  Parables Reveal and Conceal Tony Arsenal: Yeah, and as we come into when we very, like very start of this, uh, series, the, the parable series, Jesus sort of embeds a theology of parables into this introduction, right? Or I should say Luke does. But this, uh, this is written a- as though all of this is occurring as sort of one event. Right. And likely it was, right? So sometimes, uh, in the Gospels, events are kind of, like, moved next to each other for thematic reasons. It's possible that that's the case. It seems like this may have actually just been one, one teaching, and I'll, I'll talk about why that is in a second here. Um, but at the very least, this is placed next to each other in the Gospel almost as, like a, "Here's the theology of parables. Now, here's actually that happening in real [00:28:00] time."  Jesse Schwamb: Yes.  Tony Arsenal: And then also, like, a parable kinda demonstrating it, right? So the theology of the parable here is that there are many prophets, kings who desire to see what it is that, that, uh, uh, the disciples are seeing, right? And all things have been handed over to the Father or to, to the Son, by the Father, and no one knows who the Son is except the Father. No one who knows who the Father is except the Son, and anyone to whom the Son chooses to reveal, right? So the, the theology of parables here presented in Luke is that the parable itself is being used as a way to reveal the Father and the Son to those whom the Son chooses to reveal it to. The Lawyer as the Fool Tony Arsenal: And that leaves us with this lawyer, um, sort of playing, playing the fool in almost like a dramatic sense, right? In a lot of drama, there's kind of like the fool character who's sort of like the foil or the idiot of the thing who's, who's unaware and is almost like made an example of, right? Um, I, I can't think of any examples off the top of my head, but there's often a character in a play who's like, uh, it's [00:29:00] almost like the opposite of the everyman, right? The everyman in a, in a play- Right ... or in a movie is sort of like the outsider who things gets explained to, and he's sort of the proxy for, for the audience. Um, I think like, um, Hawkeye in Avengers is kind of the everyman. He's sort of just like this regular dude swept up in this thing, and oftentimes he's the one that, uh, the big characters are explaining things to. And really what that is is the big characters are explaining that to the audience through this proxy. There's also this concept of the fool in a lot of these plays or movies who's sort of like the dummy, the idiot of the play, who things are explained to, and it's still a way to explain it to the audience, but the character doesn't get it. And so that's kind of, kind of the role that this lawyer is playing in this, this narrative here. He, he steps forward. He's like, "What must I do to inherit eternal life?" Jesus answers his question, and then in this like sort of like stunning moment of, like unawareness, he's trying to justify himself. He says, "Well, who is my neighbor?"[00:30:00] As though that's a question that needed to be asked, right? I think, like, we sort of think of it as like, "Oh, yeah, that's a valid question." I actually don't think it is. Like, it's pretty obvious, in the Old Testament at least, who your neighbor is. It's the people who live immediately with you. It's the other people of Israel. It's the people in your immediate concentric circles, the people you have direct access to. Um, and so then Jesus explains it through this parable, and whether or not he gets it at the end, I guess is maybe a little bit unclear. We can talk about that as we get there. But at least in the beginning, he's portrayed as this sort of, like, fool who doesn't understand what Jesus is saying, and then this parable is taught. So it's this theology of, um, revelation to those whom it is intended to, and further obscuring from those it's intended not to, not to illuminate, which is identical. Uh, maybe it's a different flavor of the same thing, but it's the same thing as what Matthew had and what Jesus taught- Right ... in Matthew with the parables. Jesse Schwamb: Right. It definitely is, again, illustrating, again, God's divine prerogative. I think you really have to do a lot of theological gymnastics [00:31:00] to try to work yourself out of a place here that says, like, "Well, this, this knowledge is accessible to all people."  Tony Arsenal: Right.  Jesse Schwamb: That if somehow we just work hard enough, study hard enough, come even before God with the right attitude or do the right things, that somehow all this will be made plain to us, and that's certainly not the truth here. In fact, again, the ironic setup is you have this lawyer asking this question, which creates this strange contrast because here's a man trained in the law, and he approaches the very wisdom of God to test him while the little children already perceive what this fool cannot. So it's irony upon irony, and then I, I like what you're saying. I think, and we'll get to this to anticipate a bit, I'm reading this as he's hoping that the answer to whom is my neighbor, just like he- he's received the affirmative from Jesus saying, "Yes, you understand. Go and do these things. This is life," and we'll get to all of what that means, I think, as well in terms of the covenant of works and the covenant of grace. But I think he's hoping that the definition of neighbor is gonna mean that he has [00:32:00] already accomplished the thing then, so that if there's somehow- Yeah ... he could just set the right boundaries. He could say, "Ah, yeah, I'm already good then, Jesus. I already, already did all that stuff." So- It's wild to me that it, it all starts with, of course, like some kind of scribal specialist in the Torah, of course, likely some kind of Pharisee by theological alignment. And as I'm looking at the notes here, what I find particularly stunning as we start this out is that Luke, of course, is able to speak several times in this parable to the intent. And so he says right away with great kind of fanfare and description that the intent here from this scribal Torah person was to test or to tempt, which is the same verb used of Satan's temptation of Christ and of Israel's testing of God in Deuteronomy 6, which of course Jesus is gonna quote here later on. So it's not, of course, like sincere inquiry, like you're saying. It, it is like adversarial probing. It is like really profound setup. It is dripping with like [00:33:00] ulterior motive. It's seeking to expose Jesus as either like heterodox or as diminishing the law's demands. And I think we've talked about this before, so Tony, correct me if I'm wrong, but I'm pretty sure in one of the previous parables we talked about sometimes how these get set up with this kind of weird like oxymoronic setting. Inherit Eternal Life Debate Jesse Schwamb: So like this question of se- uh, uh, itself, what shall I do to inherit eternal life?  Tony Arsenal: Yeah.  Jesse Schwamb: It's like more than just theologically revealing. Biblically it's self-contradictory, 'cause of course like we talked about inheritance, I think actually when we went through the, the parable of the prodigal. By definition, that's received, of course, on the ba- basis of relationship or promise. It's not earned by performance. And if the inheritance comes by a law, it no longer comes by promise, right? I think that's like Galatians. So the lawyer's question already portray- portrays like some kind of weird works righteous paradigm. E- even if you think he's really after trying to understand the true intent of God's heart [00:34:00] behind the law, he is assuming at least in this question that eternal life is some kind of wage that he can earn rather than a gift to be received by grace through faith. And that's a fundamental error that the entire subsequent exchange, including the parable, is designed to expose. It's both, like you're saying, Tony, he has not learned yet what it means to be in the kingdom of God. He's outside of that, and so he's trying... Now Jesus is bringing in this corrective action, as it were, set up in a story, but he's gonna turn the law on him, which, which I don't wanna get too excited about that yet. But he's... But essentially, here we have somebody who's the expert in the law trying to leverage the law by way of force against the one who he does not understand has all leverage and authority over him, and is in fact the law giver himself So he's gonna, Jesus is gonna drive him to see his own ability to keep the law that he claims to honor, and that's more than just brilliant. This is what the law does. You do not wanna pit the law against the one who has given the law, the [00:35:00] one whose character is represented in the law, because you're always going to get owned, and you'll get owned before you realize you've just gotten owned on multiple levels, and I think that's what basically unfolds here. Tony Arsenal: Yeah. Yeah, I, I have a little bit of a different take on the, the word inherit here. Um, I don't think what you're saying is wrong, um, but I think there's a little different flavor here, and actually, I, I think it, it still sort of propels us forward to the conversation we have to have, um, about the law and the gospel and the covenant of works, the covenant of grace. The, the Greek that lays under this word is, uh, it's a compound word of the word for, like, lot or portion and the word for law. And so, so the, this force of the word is more along the lines of, like, what shall I do to have a legal right to eternal life? Like- Right ... inherit, I think we hear inherit, and we think of, like, um, like what you get when someone dies, and so, like, obviously there's nothing you do to earn it, and that's certainly an element of the way this [00:36:00] word is used in some instances. I think reading this and kind of how, how the rest of the text plays out, this lawyer is coming forward and he's like, "What do I need to do to have a rightful legal claim to eternal life?" Right? We might even be able, and this is where it comes into the covenant of grace, covenant of works k- kind of paradigm. We might even be able to say that what this lawyer or this person comes forward to ask Jesus is, how do I gain, how do I gain the benefits of the covenant of works, right?  Jesse Schwamb: Right.  Tony Arsenal: Yep. That's language that was developed later that's not, not, like, biblical language in terms of, like, the actual language within the text, but that's the force of what he's saying. What must I do to be a rightful legal... Uh, what must I do to have a rightful legal claim on all of the blessings of the covenant of works, is really the question that's here. And so Jesus' answer is so instructive- Right ... 'cause what he says is, "Well, what's written in the law?" Like, he, he basically says, like, "Well, you should be able to tell me, teacher- Exactly of [00:37:00] the law, like, what, what needs to happen there." And there's an assumption on Christ's part that, like, there is an answer. I think sometimes, sometimes we, we, uh, we make an error in thinking that the covenant of works was never attainable, that it's somehow this, like, um, it's almost like a black box. Like, nobody could know what we have to do to, like, obtain eternal life. That's absolutely not the assumption Jesus makes. Jesus not only knows that there's an answer, it's such a straightforward answer that he expects this scribe to be able to answer it, right? And the scribe can, right? The scribe says, "Well, you shall love the Lord your God with all your heart, your soul, your mind, your strength, and your neighbor as yourself," right? The summary of the law. Just good old classic Westminster larger, shorter catechism. Where's the law summarily comprehended? Summarily comprehended in the Ten Commandments. And then, then again, I don't know the questions exactly, but then it also, like, says, like, "Where does Christ summarize this?" Well, you should love the Lord your God, and you should love your [00:38:00] neighbor as yourself, right? Covenant Works and Grace Tony Arsenal: So the, the lawyer comes forward and says, "What do I need to do to have a legal right to all of the blessings of the covenant of works?" Christ says, "Well, what do you think?" He says, "Well, you obey the entirety of the law." And he says, "You've answered correctly. Do this. Obey the entirety of the law and you will live." So the parable itself doesn't necessarily speak to this law, gospel, covenant of works, covenant of grace paradigm. The, the parable is used to show that not only does this lawyer know what is required of him, but that he won't do it.  Jesse Schwamb: Right.  Tony Arsenal: And I think that's really key, because even in the parable, within the parable itself, there are these figures that know what they should do, right? I, I think, um, we'll get into it, but there's a lot of ink spilled on almost like, um, explaining [00:39:00] why the priest and the Phar- phar- and the, the Levite almost like did the right thing by going to the other side of the room, and it points to like, well, their priestly and Levitical responsibilities, if they touch a dead body, like they can't do those things, and those things are so important. Like, there's a lot of ink spilled trying to almost explain why what they did is right, when the point of the parable is that actually we all know what the right thing to do in that sense was. And even the most righteous people in the mind of the hearers, the Levites and the priests, the most righteous people, even they don't do what they're supposed to do. Jesse Schwamb: Right.  Tony Arsenal: This, this paradigm, this gospel law paradigm, law gospel paradigm, this covenant of works, covenant of grace paradigm, is so baked into the very like fabric of, of human thought that it's just there. And Christ takes this time to point out to this lawyer, like, "You know what's expected of you." And so that's why the phrase desiring to justify himself is so key [00:40:00] here. I actually think that the, the, the I don't wanna go so far as to say, like, that this person was regenerated in the midst of this conversation, 'cause I don't know that. But I actually think that there's a forward movement of this lawyer as he interacts with Christ that is optimistic. I'm optimistic about it, right? Right. He starts out trying to test Jesus, then he star- then he moves to having a desire to justify himself. I think we hear that, we read that, desiring to justify himself, we automatically think that that's a negative thing. But if we think about justifying himself in terms of what he's talking about, how do I fulfill the obligations of the covenant of works, that's not a bad thing to wanna do. Right. It's not bad to wanna obey God's law and to lay claim to His promises. It's a misguided notion of something we can't accomplish, but in itself, desiring to justify himself is to desire to fulfill the obligations of the covenant of works. That's not necessarily a bad thing. And so I think this is a merciful, a merciful [00:41:00] teaching on Christ's part. He's trying to instruct this guy to say, "Look, you can't do it. And even if you could do it, you wouldn't do it. So let's, let's talk about mercy instead and what that looks like." And that's where we get to kind of the end of the, uh, the end of the parable.  Jesse Schwamb: And again, we have to remember that we know that the intent here is actually to test or try Jesus, so He's certainly being merciful by using this opportunity not to just put him on blast, but to actually provide true instruction. And of course, like, that was, to some degree, this turning the question back would've been, like, a rabbinic pedagogical technique, but theologically, this is what floors me, is from the jump, Jesus refuses to let this man treat Him merely as one more voice among competing rabbis. Yeah. Instead, He holds that lawyer to the standard of God's own revealed word by, like you said, asking not just what does it say, but how do you read it? Mm-hmm. He's pressing not just for, like, some kind of recitation, but for the lawyer's own understanding and application, and Jesus is gonna hold him [00:42:00] accountable to his own hermeneutic. So by, by the time we get the answer, we know as readers, and all those present would've understood, he already knows the right answer, and he's not ignorant to God's requirements. He already summarizes the whole moral law correctly, encompassing both tables of the Decalogue. And so I think that does lead us then to this statement that we probably should spend a little time on, like, do this and you will live, like you introduced for us. Like, what does that even mean? Because i- it's not, like, in my view It's not like a Pelagian or Arminian concession that law keeping actually saves. I mean, it is the covenant of works principle stated truly inexorably that the law as law does promise life to perfect obedience. But that's the exact problem. I, I like what you said that it's not as if we can't understand what it would be to comprehend or contemplate this perfect law and what it means to fall under the covenant of works. It's just that he also presents for us at the same time this covenant of grace in which life is [00:43:00] granted freely through faith in the mediator who alone fulfills all of these laws' demands. So it's wild in this back and forth that's happening here where it's... I don't even know if it's fair to say this is like the most incredible subtext ever. I'm imagining trying to process this in real time and realizing everything that's being said, not just listening to what's being said and like the technical answers going back and forth. But I think like you said, that the Spirit even here is at work, I would like to believe, in the life and at the heart and the mind of this lawyer who's bringing forward this question with nefarious intent first to test and to undermine, and then is being drawn in almost inexorably into this debate to more profound understanding about a covenant based on works that cannot be accomplished, uh, in perfect obedience except through the mediator who organizes and distributes liberally this covenant of grace to his children for whom he has accomplished at least passively and then actively in his obedience in life and death. And so Jesus' reply is, I [00:44:00] think, thus a very genuine like if that functions to expose an impossibility. I, I don't think he's teaching salvation by works here, of course. He is holding out the law's own terms so that the lawyer and everybody else listening, and even us as well, can discover through this narrative which is about to happen the impossibility of self-produced righteousness, which again is what this gentleman is theologically all about. He's trying to justify himself. And that is precisely the pedagogical strategy like Paul uses later when he systemizes all of this by saying that the law was our guardian or school master until Christ came in order that we might be justified by faith. I think- Yeah ... and it's only because God is good to us and we might sit in some of this teaching, I think this is all in play. So, so I ask you, like, have I gone too far with all this? Like, how much do you think in the real time was being processed? And maybe that's a horrible question to ask because we know the Spirit does this work of illumination and enlightenment, and we're on the other [00:45:00] side of this and have this time to sit and to kind of parse and to really kind of just be pickled in all of this. But there's so much happening here, right?  Tony Arsenal: Yeah. Yeah. Uh, maybe I'll answer the question a, a little bit of a different way. I think, um- We often can think about Old Testament religion, if we wanna kinda call it that. We think about Old Testament religion as though it was totally uniform and, um, entirely legalistic. Right. Uh, my- the, the listener is gonna panic a little bit when I say this, so please don't freak out, but I, I think N.T. Wright, uh, and some of the new perspective on Paul stuff is a, a little bit helpful in this, in that it gives us a foil to sorta push against. Yes. The new perspective on Paul is this idea that, um- Paul was not saying that Judaism was legalistic and Christianity is a grace-based religion, right? He, m- [00:46:00] um, N- NPP is not, is saying, um, Paul was not correcting New or Old Testament religion. And where they go wrong is that they say that Paul actually retains this concept of covenantal faithfulness, covenantal nomism fidelity. Uh, where I think, where I think they're right is that this idea that, like, the Old Testament religion is entirely legalistic and Paul was just sort of correcting that, I think that's way too simplistic. And the reason I say that is that the Mosaic Covenant for, you know, w- and predominantly this podcast is coming from a sort of Westminster Confession of Faith Presbyterian perspective, give or take, right? The, the broad Reformed tradition would say that the, the Mosaic, uh, covenant, the Davidic Covenant, all of the covenants of the Old Testament, and we can break them up in all sorts of different ways, but all of the covenants of the Old Testament, [00:47:00] um, with maybe the exception of the Noahic Covenant, and there's some debate about that, all of those are They're not just, like, supporting structures for the covenant of grace, but they actually are the covenant of grace in various administrations. If that's true, and of course we believe that it is, if that's true, then the people of those covenants understood that on some level. Right. Right? So to think that, like, people under the administration of the covenant of grace somehow thought that they were operating under a legalistic framework doesn't make any sense. Jesse Schwamb: Yeah,  Tony Arsenal: that's true. So, so where I think that applies here is I think this person coming forward, it's not as though the concept of the covenant of grace... Again, that's language that we would develop later, but the concept that God graciously saves His people by the counsel of His own will, apart from their works of righteousness, apart from their meritorious effort, that God [00:48:00] simply extends His grace and conveys the cov- the blessings of the covenant of works to those who trust in the mediator, those who trust in the one to come, the, the forthcoming serpent-crushing seed, right, if you wanna use- Right language from Genesis 3. The, the idea that God simply graciously conveys the blessings of the covenant of works to those people Um, that's not something that's alien to the Old Testament, right? It's not alien to the, even to the Mosaic economy. The most law-based of all of the Old Testament, um, covenants, even that starts with the concept that these people are God's people who he redeemed out of Egypt, right? Right. The redemption out of Egypt comes before the giving of the law, and the giving of the law is a result of the redemption out of Egypt. So to think that this lawyer comes forward, and again, this had been sort of corrupted and distorted and had become a very works-based, uh, [00:49:00] meritorious-based understanding. But the idea that this framework of a covenant of grace is totally alien to this guy just doesn't, it just doesn't, I think it doesn't play. And so I think rather than us read this as I think sometimes we do, of Christ as kind of like giving this guy, trying to give this guy an entirely new paradigm, or like the rich young ruler is very much a parallel kind of situation. We read this, this guy comes forward, he's like, "What do I gotta do to earn eternal life?" Christ is like, kinda basically says like, "Well, you dum-dum, you can't. And I'm gonna demonstrate for you that you can't by- Right ... taking sort of like this situation that you can't possibly believe that you fulfilled and telling you to fulfill it." That's the outcome of this. We'll get to that when we get to the end of the parable. Um, he's not trying to give them a new paradigm. He's trying, in my opinion, he's trying to sort of reorient them back to the true paradigm of grace that is present under all of the Old Covenant administrations. So it's not so much a matter of like [00:50:00] developing a new framework and impli- like imposing this new framework on this guy. It's really trying to say like, "Listen- Think about this for a second, dude. Think about this, right? Think about the fact that, yes, you know you have to love the Lord your God with your whole heart, your whole soul, your whole strength, your whole mind, and you have to love your neighbor as yourself, right? You've answered correctly. If you can do those things, you will live. With the subtext, which I think is what you're getting at, the subtext of, like, and of course you cannot possibly do those things. Right. Right. So what's the alternative, my friend? The alternative is not justifying yourself by somehow identifying who the person is that you have to love the most. The, the, the, um, the... Well, we'll get this when we get to the parable itself, but the, the alternative is... This will be interesting, 'cause I'm not sure that we... I don't know where you are on this, but, um, the alternative is that there's someone out there who shows us mercy. [00:51:00] Right. There's someone out there who takes care of our need despite our total inability to do so. Jesse Schwamb: That's right.  Tony Arsenal: That's the alternative to the covenant of works, is the covenant of grace. I m- maybe I'm, like, hinting at it too much, so I'll just say it. Like, Jesus is the Good Samaritan, right? Like, there's something for us to learn about our own justification here and our own- Right ... like, um, our own need to be merciful, to treat people as our neighbors. But just like we talked about when we talked about, like, what does the least of these mean, this parable, uh, I'm totally, like, burying the lede for next, next week, but that's fine. This parable is not primarily about us figuring out how to do mercy ministry. Right. Like, that's important. It's probably an outcome of this. It's probably something for us to think about. But really, this is actually Christ saying, like, "Hold on a second here, dude." Like, "Let's just think about this." Do you need someone who is going to tell you to pick yourself up off your bootstraps and cross to the other side of the street and leave you to [00:52:00] your own devices? Or do you need someone who's gonna throw you on their donkey, who's gonna bind up your wounds, who's gonna pay the innkeeper, and is gonna pledge to play- pay everything that is owed for your salvation? Right? That's not too on the nose here, I think. Right. And, and I really look forward to, like, unpacking that, although maybe we don't need to now, but I think, like, this parable is so much more about how salvation works than it is about, like, what it means to have a good neighbor and to be a good neighbor. I think we get bogged down in that, and I just am really excited to talk more about, like, what I think this parable actually gets at.  Jesse Schwamb: I agree, and it's happening in the contex

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Get the details from Mikah and Rosemary on which AI apps stand out for real-world tasks, where system integration blows open new possibilities, and why a truly local, on-device AI might finally justify the hype. Siri's AI upgrades spark frustrations, unpredictability, and deeper device integration Comparing ChatGPT, Claude, Perplexity, and Gemini apps on iOS Solaire AI brings free offline local AI to iPhone News: App tracking transparency changes prompt regulatory scrutiny in Germany, and Sonos adds live activities with real-time playback status App Caps: 8BitDo Flip Pad for gaming and MOFT Snap Tablet Stand for iPad Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: joindeleteme.com/twit-biz

Get the details from Mikah and Rosemary on which AI apps stand out for real-world tasks, where system integration blows open new possibilities, and why a truly local, on-device AI might finally justify the hype. Siri's AI upgrades spark frustrations, unpredictability, and deeper device integration Comparing ChatGPT, Claude, Perplexity, and Gemini apps on iOS Solaire AI brings free offline local AI to iPhone News: App tracking transparency changes prompt regulatory scrutiny in Germany, and Sonos adds live activities with real-time playback status App Caps: 8BitDo Flip Pad for gaming and MOFT Snap Tablet Stand for iPad Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: joindeleteme.com/twit-biz

Get the details from Mikah and Rosemary on which AI apps stand out for real-world tasks, where system integration blows open new possibilities, and why a truly local, on-device AI might finally justify the hype. Siri's AI upgrades spark frustrations, unpredictability, and deeper device integration Comparing ChatGPT, Claude, Perplexity, and Gemini apps on iOS Solaire AI brings free offline local AI to iPhone News: App tracking transparency changes prompt regulatory scrutiny in Germany, and Sonos adds live activities with real-time playback status App Caps: 8BitDo Flip Pad for gaming and MOFT Snap Tablet Stand for iPad Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: joindeleteme.com/twit-biz

Get the details from Mikah and Rosemary on which AI apps stand out for real-world tasks, where system integration blows open new possibilities, and why a truly local, on-device AI might finally justify the hype. Siri's AI upgrades spark frustrations, unpredictability, and deeper device integration Comparing ChatGPT, Claude, Perplexity, and Gemini apps on iOS Solaire AI brings free offline local AI to iPhone News: App tracking transparency changes prompt regulatory scrutiny in Germany, and Sonos adds live activities with real-time playback status App Caps: 8BitDo Flip Pad for gaming and MOFT Snap Tablet Stand for iPad Hosts: Mikah Sargent and Rosemary Orchard Contact iOS Today at iOSToday@twit.tv. Download or subscribe to iOS Today at https://twit.tv/shows/ios-today Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: joindeleteme.com/twit-biz

One of the biggest mistakes in AI? Thinking that your company's AI use is noteworthy. Or, even a competitive advantage. It's not. We break it down in Volume 3 of our 'Start Here Series.' AI as an Operating System: LLMs Are the Internet Now -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:AI As An Operating System ExplainedLarge Language Models Replace Traditional AppsAI Integration in Knowledge Work PlatformsChoosing the Right AI Operating SystemMicrosoft Copilot vs. Google Gemini vs. Claude vs. ChatGPTAgentic Browsers Powering Autonomous WorkflowsModel Context Protocol (MCP) for AI AgentsOrchestration Layer and Agent CollaborationChatGPT Apps Merging AI and InternetEnterprise Data Integration with AI ToolsContext Switching Reduction Through AI AgentsStrategic AI Adoption and Platform RedundancyTimestamps:00:00 "AI: A New Operating System"03:58 "AI Transforming Work Interfaces"06:41 "Collaborating in AI-Native Workspaces"12:25 Anthropic's Innovations in AI Tools13:46 "OpenAI's Strategy and Market Focus"18:02 "Cognitive Evolution Through AI"20:57 "Agentic Browsers: Key 2025 Advancement"25:12 Improving Content Through Data Insights26:42 "Anthropic's MCP: The AI Connector"32:19 "AI Tools for Productivity Integration"34:20 "AI: Unlocking Context and Efficiency"36:32 AI Governance and System Portability39:35 "AI Operating System Insights"Keywords: AI operating system, large language models, LLMs, AI as infrastructure, enterprise AI, AI adoption, agentic workflows, AI agents, orchestration layer, Copilot, Microsoft 365 Copilot, Google Gemini, Gemini business, Gemini enterprise, Anthropic Claude, Claude cowork, MCP, model context protocol, OpenAI, ChatGPT, ChatGPT apps, ChatGPT business, ChatGPT enterprise, AI native, dynamic data integration, productivity with AI, collaboration tools, agentic browsers, autonomous AI agents, context window, memory and personalization, expert-driven loops, app hop tax, context switching, AI integration in business, AI tools for teams, AI platform selection, data governance, modular AI workflows, permissions and audit logs, backup and redundancy in AI, competitive advantage with AI, Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

How can you get your books surfaced when readers ask an AI what to read next? What are the hallmarks of authors and businesses that last more than a decade in the indie author industry? Ricardo Fayet gives his marketing tips after more than a decade at Reedsy. In the intro, Expanding Your Book's IP through Merchandising [Wish I'd Known Then]; Guide to writers conferences [Kindlepreneur]; Rick Rubin and Stephen Pressfield on resistance and the muse [Tetragrammaton]; Pulitzer Prize winners disclose AI usage [Nieman Lab]; My new rebuilt fiction site JFPenn.com. Today's show is sponsored by Draft2Digital, self-publishing with support, where you can get free formatting, free distribution to multiple stores, and a host of other benefits. Just go to www.draft2digital.com to get started. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Ricardo Fayet is the co-founder and chief marketing officer of Reedsy, a marketplace for writers to find vetted freelancers, author training courses, and lots more. He's also the author of How to Market a Book and Amazon Ads for Authors. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How AI search actually finds and ranks the books it recommends Why keywords and search volume are disappearing as marketing metrics Building an internet footprint so your book is worth recommending Why Claude leans on Goodreads while ChatGPT and Gemini lean on Reddit Product is more important than marketing, and how to find your comp titles Passion, business sense and resilience in the authors who last You can find Ricardo at Reedsy.com. Transcript of the interview with Ricardo Fayet Jo: Ricardo Fayet is the co-founder and chief marketing officer of Reedsy, a marketplace for writers to find vetted freelancers, author training courses, and lots more. He's also the author of How to Market a Book and Amazon Ads for Authors. So welcome back to the show, Ricardo. Ricardo: Thank you, Jo. It's been a while. Jo: It has been a while. As we were saying, it's been a decade, which is crazy, because you and I see each other at events and things. For anyone who doesn't know you— Tell us a bit more about you, where you're from, and also, what is Reedsy these days and how have things changed? Ricardo: Yes, sure. I think I'm hard to dissociate from Reedsy because my only background really is Reedsy. We started Reedsy right after business school for most of the founders. There are four founders, and I'm one of them. We've been going since 2014, so it's been 12 years. We started around the same time, and you started even earlier than we did, but we've had the chance to meet at quite a few conferences since. I think the main thing we're still known for is our marketplace. Since the very beginning, Reedsy is a curated marketplace where authors can come in and look for editors, cover designers, marketers, ghostwriters, literary translators, author website designers, pretty much any freelancer you would ever need to hire throughout your writing career. That's how we started. That's our main source of revenue and our main business. Since then, as you mentioned, we've also added courses, live events, so we've got a membership under Reedsy Learning. The main thing we're proud of right now is our writing tool, Reedsy Studio, because we've been pouring a lot of engineering talent into it over the past five, six years, and I think it's a really cool writing tool that is 90% free. It also does the formatting, so it's a good alternative to Vellum or Atticus for those who haven't purchased those tools. So there's a lot of things that Reedsy does at this point, which always makes it hard for me to describe what we do. Jo: But you generally serve writers. That's probably the crux of it. Ricardo: Exactly, yes. We're mostly at the production stage. The thing we say is we help authors make beautiful books, right? That's a little bit the tagline that applies to 90% of what we do, though we also help a little bit with the marketing side of things. Jo: And then just tell people where your accent is from. Ricardo: So I'm half French, half Italian from my parents, and I was born in Spain. So depending on my mood and the moment, it might be anywhere from French, Italian, Spanish. It's anyone's guess, really, at this point. Jo: Yes, exactly. You've been part of the indie author ecosystem, as you said, since 2014. You're often at the author conferences, and you stay abreast of everything that's going on. So what are the biggest changes you've seen in the industry since you started out? Like you said, you guys came from business school. You chose to come into the industry at a particular point, and yet things have changed. So what do you think are the biggest changes, and how have you seen authors adapt? Ricardo: It's crazy, the amount of things that happened in the past 10 years. Obviously we came into the industry as a result of Amazon launching Kindle Direct Publishing, and then the Kindle Store becoming one of the number one places where people buy books. That's what enabled self-publishing in the first place as an industry, and obviously we wouldn't exist without self-publishing being a thing. But then I think probably one of the biggest changes that happened was the introduction of Kindle Unlimited and various other subscription models. Perhaps as a response to that, afterwards, I would say direct sales is the other massive change that happened in the industry. The ability for authors to sell direct to their readers via their websites, or running Kickstarters, or basically the ability to reach your customers directly. Now, obviously, artificial intelligence is probably the biggest change of them all, not just in publishing, across every industry, but of course it's going to affect publishing very strongly, or it has already started making big waves. So I would say those are the three main changes that we've seen happening in the industry. Jo: Just interestingly, given that you're French, Italian, Spanish, European— Has the pace of change been the same in mainland Europe? Because you and I have also met up at European conferences, but I just get the sense that each individual European country, the UK included, as I include the UK in Europe, goes at a different speed. Ricardo: No, they definitely do. Our market at Reedsy is all in English. We only have an English website, and I would say 70% of our market is US, then 15% UK, and then Australia, New Zealand, et cetera. We do have clients in continental Europe, but they're mostly going to be American or British expats. A lot of retired people who write books and use Reedsy services. I think the main reason for that is that self-publishing as an industry hasn't really grown in Spain, France, Italy the way that it's grown in the US and the UK, probably because these are countries where book buying habits are a lot more traditional. So all my friends from high school and university in France, they all buy books, they all buy paper books. They all buy from bookstores. They don't really buy from Amazon that much. They love the smell of paper, all these sorts of things. So obviously that makes it harder for indie authors in those countries because they cannot place their books in the places where people buy them, which is traditional brick-and-mortar bookstores, or at least it's much harder. So there is an indie movement in those countries, but it's much smaller. I would say Germany is maybe the exception in Europe, where I've seen more and more full-time indie authors, but I don't think it's a market that's comparable even to the UK. Jo: Interesting. Okay, so let's go to marketing, because you love marketing, you have a newsletter about it, and you have a book, How to Market a Book, same title as mine, and we've found each other in our Also Boughts over the years. You have, however, updated yours several times. 2025 was your last update. Mine was 2016, so yours is definitely the most up-to-date one. So what are your thoughts on how authors can reach readers now? And let's split this into two. Let's start with some tips for new authors who just have maybe one book and are really just starting out. Ricardo: Yes, first of all, sorry for stealing your title. Jo: Oh, you know, it's never been a problem. Ricardo: We were trying to come up with something… I know, I know, I know. But still, we wanted something more original, and then we realised that for SEO purposes, that's the title we should really go for. The good thing is, if you search “how to market a book” on pretty much any retailer, you're going to find your book or mine in any order really, but those are going to be the top two books to this day, thankfully, so I'm good with that. I think in terms of marketing tips for new authors who have just the one book, I'd say, despite all the changes in the industry, the basics of marketing still stay the same. One of my big mantras when it comes to marketing is that product is more important than marketing. So having a great book is going to be more important, in my opinion, than being a great marketer. Having a great product. By that, I generally don't talk about literary value or prose or anything like that, just because I'm not a book critic. I'm not an editor. My opinion on whether a book is good or not is worth zero. From a marketing standpoint, for me, a good book is one that resonates with its intended audience. So the first step for newer authors is to basically figure out who they wrote this book for, right? Have an idea of who their target market is. A great way to do that, without entering into marketing jargon like proto-personas and stuff like that, is to think in terms of comp titles, comparable titles. I think that's probably the base of any marketing, whether traditional publishing houses or indie authors, to think, “Okay, which other books out there are similar to mine?” If your book is completely unique, you have, I would say, 99.9% chance of not selling, because your market doesn't exist, and 0.01% chance of creating your own market and completely dominating it, right? So being unique is not necessarily a bad thing, but I would say 99% of the time, it is. Obviously being unique within an existing niche, within an existing market, that's completely different, but you need to work on some comparable titles. So I would say that's where I would start, and that's one of the things that AI is great at. If you even just plug in your book into an AI chat that has enough context, if you're comfortable doing that of course, or if you plug in a synopsis, like a long synopsis, you can basically ask the AI chats, “Okay, what would be some comp titles?” Either you'll have heard of those books, and you'll think, “Okay, they're good comps,” or you may not have heard of them, in which case I would really recommend buying them and reading them and seeing whether they're comps or not. I think that's where marketing starts. Jo: Yes, and we might come back to that for book discoverability. Well, let's just talk about it now, because I now pretty much use Claude for discovering books as well. I just find that the Amazon search, for nonfiction it works really well, right? As you said, if you want to learn how to market a book, you just search “how to market a book”, and our books are very well titled for that. But for fiction, it's so difficult to find new books and comp titles, especially if you don't write in a clear genre, which I don't. I know lots of people do. So we're writing cross-genre. So in terms of book discoverability, I know you've been looking into SEO for books and all of that. What are your thoughts on how authors can more effectively have their books surfaced if people are using the AIs for finding books? Ricardo: That's a very big topic. That's one of my favourite topics right now. That's what I talk about in my marketing newsletter, and I want to start a Substack on the topic as well, because I think it is the future of book discoverability. As you say, search on Amazon isn't great, and if it's not great on Amazon, you don't even want to imagine on Google Play, Kobo, all these sorts of places, right? If you search on Kobo or Apple Books or Google Play for witch cozy mysteries, the top 10 books are going to have exactly “witch cozy mystery” in the title or the subtitle or the series title, right? Jo: Yes. Ricardo: Search is very simple on those sites, so it's very easy to game, but it's terrible as an experience for readers. So I do think that a big part of search is going to move to AI, and now you have Rufus within Amazon itself that appears as a chatbot and encourages you to talk with it, to ask for recommendations. In the beginning Rufus was really terrible, but now I recommend authors play with it. In general, I recommend when it comes to AI discoverability that authors play with all the AI search tools. So it's going to be Claude, ChatGPT, Gemini, Google's AI Overviews and AI Mode, Rufus within Amazon. Just like we talked about comp titles, right, ask for something like, “What would you recommend for fans of this book? Which other books would you recommend?” Or if you're looking for something specific that describes your book, what books would you recommend on… I think one of your examples was fiction around masonry or something like that, right? Jo: I always say, for example, if you ask for action adventure thrillers on pretty much anywhere, you're going to get 20 books by male authors. For those of us who are female authors, but also who have female main characters, you can then be far more granular. That's just not something that necessarily works on Amazon. So, action adventure thrillers with female protagonists written by female indie authors, which is very specific. That will work in AI, whereas that definitely won't work on Amazon. Ricardo: Exactly. So one of the great things about AI search, and terrible things at the same time, is that the concept of keyword is lost, right? So we title our books How to Market a Book because the keyword that people would search for is “how to market a book”, or “book marketing for authors”. Those were the two keywords that had the biggest search volume if we looked at Google Keyword Planner, or any search tools that give you keyword volume. When it comes to AI search, since it's conversational, and it's very personalised, and it can go as niche as you want, keywords are going to disappear, and search volume as a metric is going to disappear. The search you just told me about, maybe there are 10 people who are going to run it exactly. Most people are going to run variations of that. Since you can go as granular as you want, it's going to be very long phrases, very long sentences. So the whole concept of keywords, search volume, SEO, is going to disappear in favour of what some people call GEO, generative engine optimisation, AIO, AI optimisation, AI search optimisation. There's a bunch of names for it, but it is a different discipline. It still works on the basis of Google SEO because what most of these search engines will do is they're going to search the internet, right? They've been trained on a lot of information, but not all of that information is current. Most of these models have been trained on documents and web pages before 2021. That's why when you used the very early versions of ChatGPT, and you asked it about current events or current books, it didn't find it. It didn't have that information. Now all these models have the ability to search the web, and they do that really effectively. So the first thing they're going to do if you run a search like that is they're going to run dozens of different searches on Google or a similar search engine. Action thriller books with female protagonists. Action thriller books with female protagonists by indie authors. Action thriller books by indie authors. They're going to create what I call a corpus, so a massive document of information with candidates, books that probably meet those criteria that they have found through regular web searches. Then they're going to go through all those books and determine which are the most relevant to recommend, and they'll return with that. Then they'll usually have a verification layer as well, which is, okay, I have these five books that I want to recommend to this user, but I just want to make sure first that they are written by indie authors, that they do have a female protagonist, et cetera. And if I'm not able to verify that, I'll flag it in my answer to them. So there are these three layers. Web search, then ranking of the book candidates, and finally a verification layer, and then they serve you the answer. Obviously, that's really powerful. If that's the way that search is headed, which I firmly believe, then we as authors need to learn how to get our books in there. Jo: Yes. It's so interesting, and I do this all the time. Really niche stuff, like a thriller about anatomical Venuses, which are these models from anatomy museums and stuff like that, with an edge of the supernatural. What is brilliant is that you can find the books that come up. I actually got one that I read and reviewed, and it was fantastic. So I love the more granular thing, but we're really in the very long tail now, right? It feels like you can write a very nuanced book, and it will be surfaced, but only by a few people. So do we have to write our books and then try to not even retrofit them in the way that we used to? We used to try and figure out the seven keywords and all of that kind of thing, and now I almost feel like we just get to be grownups, where you just get to write the book and put it out there, and these things are more intelligent than the search terms. Ricardo: Yes, absolutely. I think in terms of the variety of books that are going to be read and discovered, that's amazing. A little bit like the whole indie publishing movement was amazing, because if you remove the traditional publishing gatekeepers, then suddenly zombie books aren't dead anymore. Zombies are alive again. They've… Jo: Come to life. Ricardo: They've come alive, and so you get a bunch of zombie books again. It's not a small niche audience. It's actually a pretty big niche audience that loves zombie books, and suddenly they have those to consume. This is a little bit similar on a much, much bigger and more granular scale, because anyone can find exactly the kind of book that they're looking for, with the tropes they want, with the type of characters they want, with the type of author they want, and you can basically use any kind of filter. So for discoverability on the side of the reader, that's great. For the authors, it doesn't mean that you can just write whatever you want and then rest, and readers will find them. Because your books still need to have generated a certain internet footprint and a certain review footprint in order to become interesting or recommendable to AI search engines, right? Unless you're the only book that an AI search engine can recommend for that specific search, and yours was very specific, so it might have been the only one. If there are other candidates, the AI search engines are pretty smart. They're going to recommend the candidates that have more reviews, better reviews, or are more famous, that have more, when I talk about internet footprint, more pages talking about them on the internet. That's for me the big finding I've seen. The more pages you have on the internet talking about your book, so that could be book review sites, that could be Goodreads lists, Listopia lists, anywhere on the internet where your book is mentioned, that creates a footprint for it. The more footprints, the more famous your book looks to AI search engines, and so the more worthy of recommendations it is to them. Jo: Yes, and this is one reason why I now ask people, especially when you sell direct, or say you have a Kickstarter and you want reviews, I ask people if they can review on Goodreads. Obviously Goodreads is owned by Amazon, but it is far more searchable by the bots than Amazon is. I sometimes find that Amazon gets blocked. Or Amazon blocks some of the bots, let's put it that way. They might change over time. They own 15% of Anthropic or something. But Goodreads I feel like does get indexed, and I'm not willing to engage on Reddit. I know Reddit is another popular one. Just be very clear on social media, because some people think, oh, I just need loads of influencers to talk about my book, and that will impact the AIs. But I find that social media just almost isn't even there. Ricardo: It depends on which social media. For me, Reddit is a social medium, right? It's a very weird one, but it's obviously the number one social medium used by AI search engines, and it's pretty dodgy stuff with lots of conflicts of interest, because Google has a paid partnership with Reddit. I think Sam Altman has shares in Reddit. So both OpenAI and Google Gemini heavily rely on Reddit for their AI answers, right? If you ask anything from ChatGPT or Gemini, I'd say there are very high chances that in the sources and the citations of the answer, you're going to find Reddit, right? That goes for books as well. Every time you ask for book recommendations, Reddit is going to be big for those two AI search engines. Claude, it's a little bit different. They do have a little bit of a relationship with Reddit, but I don't think it's as big. I found that Claude relies heavily on Goodreads, not Reddit. To me, Goodreads is another social medium at the end of the day, right? It's not the kind of social medium we think of, like… Jo: It's not TikTok. Ricardo: It's not TikTok. TikTok is surfaced a little bit, but not that much. It might become more surfaced in the future, but right now what I've found is, it's really Reddit and Goodreads. Facebook, LinkedIn, but LinkedIn is big for nonfiction topics more than fiction, because there are just not a lot of fiction readers, sorry, talking about their favourite books on LinkedIn. They usually go to other places for that. Jo: Yes, it's really interesting. So obviously TikTok has its own BookTok thing, so some people are finding books on BookTok, and some people like me are finding books through asking Claude, and that's just my overwhelming feeling about the industry. We talked earlier about what's changed. When I started, so 2006, there wasn't even KDP, and then KDP arrives, then you came in 2014, and probably until maybe a few years after that, there was one way to self-publish. Then what's happened since is this kind of splintering. There are so many ways to self-publish. There are so many ways to reach readers. There are so many ways to market. So someone, for example, could sell direct on TikTok Shop, only market through TikTok, and you wouldn't even know that they existed over in another ecosystem, right? So do you feel like that's happened too, this total splintering? Ricardo: Absolutely. I think there are so many different ways in which you can reach your audience now and monetise it as well, that it's absolutely possible. We both know examples of authors who are only visible to their TikTok niche, right? I think the big difference is also monetisation, because earlier you also had different ways of becoming well-known. Like you had Facebook, you had potentially Twitter, back when it was Twitter. Various social media, word of mouth. But where people bought books was Amazon. So that was your monetisation. It was either selling your book on Amazon, or potentially getting KU reads after that when KU launched. Now you can sell on TikTok, you can sell direct, you can run Kickstarters, you can start a Patreon or Substack and make your money through there. There are pretty much infinite ways through which you can monetise your content as a creator. Most of those places will also have a discoverability engine built in, so you can not only sell through there and make money through there, but also grow an audience there. That's probably the biggest competition that Amazon has ever had to face, is this sort of splintering. For us authors and content creators in general, it's amazing, because it's a bunch of different opportunities. Jo: Yes, and I think probably that's circling back to marketing. As we've said, some things have changed, some things stay the same. Is the email list still the number one way to market, do you think, for authors who want a long-term career? Ricardo: I think so. It's just the one thing you really own that allows you to communicate with readers, right? Because even if you build a following on TikTok, TikTok can change. The things we said about Facebook 10 years ago, if you build your Facebook group, Facebook can change, and Facebook changed. Facebook destroyed the reach of pages, and now groups also aren't amazing, et cetera. So the same thing that happened to Facebook can happen to TikTok, can happen to any other social media. Substack is a bit of a hybrid because it is a newsletter, it is a mailing list, but it also has a discoverability engine built in. So I still wouldn't really go all in on Substack. The great advantage there is that you do own the emails, right? So you can move away from it at some point. For me, the email list is the one thing that you are completely in control of. Personally, the main thing I check is my emails every day. I spend more time in my inbox than anywhere else, and I think that's probably the case for most people. So I do think, despite the influx of mails that readers are getting in their inbox, I still think it's one of the most important marketing tools for authors. Jo: Yes, and I still remember when I started my list in December 2008, and I still have people on the list who've been with me that whole time, and have moved email list services several times as well. So I think that's important too. The care of that list too is so important. One of the things that I am annoyed about with Substack, I don't have a Substack, but when I follow some people's, it's always constantly trying to get you to sign up to other people's lists, trying to subscribe you to this or that or the other. I'm like, “That's so annoying.” Now I've found on all these email services they're trying to pair you with other creators and cross-promote and all of this. So it feels like even the email list is becoming a difficult place unless you really protect it. So just protecting your list, I think, and remembering that those are people. Those are people who've bought your books, or people who read your emails. Like people listening to this, these are humans. Sure, some bot might be looking at the transcript, but humans listening to this are individual humans with individual email lists. We tend to forget that, don't we, as you get a bigger list. You think about it as a big number, whereas it's still individual people. Ricardo: Yes, it becomes a number, it becomes metrics, the open rate, the click rate, and all that. But the great thing about email is that you get replies in your inbox. There you remember that they're actual human beings who write you back. I think for readers, the ability to write back to an author and get a response is just amazing, right? If you write to your favourite author and they write back to you, that's the best feeling ever as a reader. So I think email is still one of the best ways to turn your readers into fans and into super fans of your brand, by just interacting with them and answering them. Because yes, you can answer comments on TikTok, Facebook, and all those places, but it doesn't feel the same as an author answering you in your inbox, I feel. So you do get that ability to create a real connection with readers directly through email. Jo: Yes, and I think that trust and the reputation is so important. Just in terms of seeing the authors that you've seen over the years, like I said, you've been going to a lot of the author conferences. You know a lot of the authors in the industry. A lot of people will know you from events. But you've also seen authors come and go, and you've seen vendors come and go. Both of us have seen people arrive, and we'd be like, “Oh, yes, I wonder how long they're going to last.” Then some people do and some people don't. So what are some of the elements that you've noticed about the businesses that last more than a decade? Ricardo: I think they need to solve a genuine problem, or offer a genuinely better solution than one that already exists. I see a lot of businesses come in and they do what another vendor already does, without doing it necessarily better. Those businesses tend to disappear, or linger there, but you don't really hear about them again. When they solve a real need… I still remember when BookFunnel came out, and they've since become massive with one very, very basic thing, which is, “We will take care of delivering your ebook files to readers.” That's how they started, and that's how they became massive. To me at the time, I was like, “That seems like a very small problem,” because I can just email an ebook file. I didn't realise that it was so complicated for a lot of readers to upload files to their Kindle or other devices, and that having a service that takes care of that for you is just massively helpful. I realised that when I published my books, actually. So businesses that solve even micro or apparently very small problems, but they really solve them effectively, they stay. Others that surf on trends, they tend to disappear when the trend disappears. When there's a new marketing trend, and that trend stops working, then those businesses tend to go. Jo: Yes, and what about the authors who last? Because let's face it, most of us don't solve a problem in a different way. Maybe you could say we solve your entertainment problem in a different version to your last book. What about the authors who last long term? What have you noticed about them? Ricardo: Two things. So the first one… Maybe three things. First one is passion. I always say to people who are looking to start self-publishing, I always say that publishing in general is probably one of the worst ways to make money. It's awful. You can pick up any other occupation and it's probably going to be easier to make money. So writers who make a career do it because they need to write, because they have a passion for writing. So a lot of people who come into the industry thinking, “I've heard about all these self-publishing success stories. Surely I can write a book and I can market it. It's not that hard. So that's going to be a great way to make quick bucks,” those generally don't last, because they don't have that genuine passion. The first hurdle they're going to face—and there's going to be a bunch of them along the way—they're going to quit. The second thing is business sense, or business education, the basics of business, right? Thinking in terms of marketing, thinking in terms of who's my reader, unit economics. If I'm selling my books at $3.99, I shouldn't be spending a ton of money. If I just have one book at $3.99, I cannot afford to pay $5 on ads to generate a sale. So those kinds of basic economics or business knowledge, I think, if not a requirement, it's a massive plus. The third thing is resilience. The industry has changed a lot, as you mentioned. There are a bunch of ways now to reach an audience. There are a bunch of ways to make money. It can also be distracting, because you go to one of these conferences and you go back thinking, “Oh, I've got to create my own website, sell direct. I've got to launch my Substack. I have to use Claude Code for my ads,” and three more things, “and I have to set up my TikTok Shop and do TikTok Lives to sell books,” right? Jo: All of those by next week. That's why we go to the conference. Ricardo: Exactly. All those by next week, right? And during that one week after the conference, you have the energy to start implementing all those things, but afterwards you lose that energy and you go back to, “I just want to quit.” So you need to have some sort of resilience, and the business sense of, “Okay, I'm going to try this for the next six months, and I'm going to stick to it, and I'm going to see whether it works or not. And if it doesn't work, I'm not just going to despair. I'm going to try something else.” That goes for marketing tactics, but also goes for books. Most authors I know who make a living writing, they didn't get there with their first book or even their first series. A lot of times I see authors who write a couple of series, and those series do okay. They sell a little bit, but not much, and it doesn't afford them to quit their day job. Then suddenly, based on what they've learned with those first two series and what they've learned potentially going to conferences or listening to podcasts like this, et cetera, they write a third series that's a hit. That's a hit, and that's instantly much easier to market, and that's when they can afford to go full time and write full time, because they've found that sort of product market fit. And product market fit for me is the main thing for marketing in general and for authors in particular. Until you find that, everything is really difficult from a marketing perspective. But finding it usually requires writing more than one book and more than one series, because you're rarely going to get the big lottery ticket on the first try. Jo: Or ever. I think it's really important. I've never had a breakout book, but I've made good money every year. I think, and Hugh Howey said this, that the real story of self-publishing is not the people who make seven figures a year, it's the people who can go on a holiday once a year, or the people who can just put a bit towards their mortgage or whatever. So you may never have a breakout series. I think that's really important, too. Ricardo: That's true. Jo: That's true. You can just keep writing and make all right money and be what we call in the mid-list. Ricardo: Yes, but you've got to keep going. You've got to have that resilience to keep going, probably because it's your passion. Exactly. You're not chasing that seven figure series. You're writing because you need to write. Jo: Yes, and you can't stop, and you get the bug. I think when you're writing that first book, I'm not sure if people understand that you either get the bug or you don't, I think. You've written a couple of books now. Do you think you have the bug, or is it more about the business? Ricardo: I don't have the bug. I definitely don't have the bug. I would've kept writing otherwise. Jo: You're a technologist, right? Ricardo: Yes. I'm more of a technologist, more of a marketer. To write my first book, How to Market a Book, I actually had to write newsletters, because that I can manage, right? One newsletter a week. Now I don't even manage that. I send it once every two weeks when I can even manage that pace. But back then I did one newsletter a week, and that's how I wrote my first book. I then put all these newsletters together, and that was before the age of AI, so I couldn't just dump all that into ChatGPT and tell it, “Create me a book based on that.” I had to do it myself. Jo: Oh, terrible. Ricardo: Terrible, I know. The days when we had to work. No, but that was a really good experiment. The second book, Amazon Ads for Authors, I actually wrote it from scratch, and I didn't love the experience. I loved the finished product, I'm very proud of it, but I didn't enjoy the experience of writing it. Amazon Ads is not the most exciting topic in the world. Even for people like me, who actually enjoy Amazon Ads, it's not super exciting to write about. The fact of having to write that whole book from scratch, I didn't love the experience. So now I do want to write a third book at some point, but I think it'll probably be in the format of the first one. Like, when I finish my series of newsletters on AI search and AI discoverability, I might do a booklet or something around it, but we'll have to see. It's not my favourite thing. It's a good thing I don't need to. Good thing I'm not an author, basically. Jo: No, but I think that's really great to recognise. You actually have written books that support your business, and that is very common for nonfiction writers. That's completely normal for nonfiction authors. So I think that's another important thing for people to recognise, is that the reasons we write are different, the reasons we market are different, and we all have different approaches. As we wind up, and obviously you talked a bit there about how easy it is to generate books with AI, and that is probably one of the biggest things that people are worried about. Now, those of us like you and I have been talking about AI at conferences and things for years now. Neither of us are afraid of it, I think basically because both of us use it, and we're technologists, so we just get on with it. There is so much fear in the community. There's so much negativity. You mentioned Substack, and now they've integrated Pangram, which is renowned for false positives around what is written with AI, and so people are very worried. If we look forward and we look at the next decade, this is not going away. This is going to change a lot of things. So how should authors be thinking about AI, do you think, if they want to prepare for the next decade? Ricardo: I think they need to play around with it, because you and I know that until you actually use it, you don't realise what it's capable of. We've both had these moments of “Oh my God, I'm going to be out of a job,” or “Oh my God, this is incredible,” the kind of things it's able to do. You also realise by using it the things it's not great at, or the things it can be good at but needing a lot of human input. There are courses out there, there are books, there are a lot of things, but for me, the most important thing is to start playing around with it. So you can pick one specific topic to play around with. Either something that you've been meaning to get into for years and haven't done, or something that really annoys you. Looking at the results of your Facebook ads, for example. If you run Facebook ads in the first place, and when you log into your Meta dashboard you cannot make sense of it, take screenshots or download the data, feed it to an AI chat, and talk with it. One of my biggest tips when talking to AI chats is asking them questions rather than asking them for things to do. Because if you just feed it your advertising data and you tell it, “Tell me what I should do,” the chat has no context about what your ads are about, what your goal is, et cetera, et cetera. If you feed it just a screenshot and you say, “Here are my ads. I would like you to help me make sense of them. What do you need to know from me in order to help me with this?” Then it's going to start asking you questions. “Okay, what are you advertising?” They're books. “Okay, what kind of books? Where are you selling? Are your ads going straight to Amazon or retailers, or are they going to your website? If you sell direct, okay, are these conversion ads?” Et cetera, et cetera. So you build that context, and they can help you with it. So that's just one example, but I would pick one thing that you want to test AI with, with zero expectations. Don't expect that by the end of the week you'll have a full system for understanding your Meta ads thanks to AI. You might get to that or you might not, but at least you'll play with it and you'll understand its capabilities, and have a sort of understanding of how it works and how you can potentially use it for other things. I think that's the best way to adapt, because once you play with it, you start thinking. You're walking the dog and you're just thinking, “Ha, I could maybe use it for this thing or that other thing,” and that's how you progressively become more familiar with it. I think there are a lot of worries around AI, but as you say, it's there to stay. So far, I think I was looking at data this morning, and the unemployment rate in the US has stayed stable ever since ChatGPT was introduced two years ago. So there are a lot of narratives out there around how it's going to replace humans, it's going to replace jobs, et cetera, which it might do. I'm not great at predictions usually, but so far it hasn't. What it will definitely do, however, is completely change the way we work. Obviously AI was one of our big reckonings at Reedsy, because we run a marketplace. Our main business model is the marketplace, where authors come in and look for editors, for marketers, for cover designers, for translators, and some of these domains and services are being transformed by AI, right? AI translations are massive right now. We haven't really seen a decline at all on the Reedsy marketplace, but we're prepared for it. It might very well happen, but what we think is happening, or is going to happen, is professionals using AI tools to provide a better output or a faster output or a cheaper output, or all three of these things. So for example, we now have literary translators who say they're open to editing AI translations, right? So you can come in, you can have your book translated with AI, whichever tool or method you choose for that. Then if you want a set of human eyes on it, you can hire a literary translator who's open to working on an AI-translated manuscript, and that's obviously going to be much, much cheaper than paying 10 cents per word from scratch for a literary translator. I'm not necessarily recommending you completely bypass literary translators, or that you go this way, but you have options, right? You have options for different sets of budgets. I think all these human editors, narrators, translators, designers, et cetera, they're not going to disappear, but they might have to reinvent the way they work in the future. Jo: As we all are doing. As you say, for me mostly it's the business side, getting Claude to help me with business stuff. Like even bookkeeping. There are bills that we've all been paying for years that we can now maybe reduce, and reducing costs as well as increasing profit is obviously good business sense. So, interesting times. But just as we wrap up, what are you excited about in what's coming? How do you see Reedsy shaping up in the next decade? What is coming for you and Reedsy? Ricardo: We're really excited about the decade to come, because obviously 10 years for a startup is a long time. I think most startups after 10 years, the founders are gone. Jo: Yes. Ricardo: Or they've exited or they've sold or whatever. We've stayed, we're the same founders, and we're still as excited because I think we have new products, right? We're working on new things. As I mentioned at the beginning of the episode, Reedsy Studio, our writing tool, is probably one of the things we're most excited about, because first, it's getting adopted very fast. It has startup-like growth that we hadn't seen for other products in a long time, and it has very cool technology built in. We probably have a team of 10, 15 engineers now working full time on it and adding a lot of things. So we've added templates for plotting your book, with relationships within different characters and places and magic systems and things like that. We've added writing goals and writing statistics. We still have the free exports. One very cool thing we've added is real time collaboration. So just like Google Docs, you can write in real time with a co-author, or you can get an editor to work in there live with track changes. So you could have an editor working on chapter one while you're writing chapter three, for example. That kind of thing. It's really powerful technology, and that's one of the things we're really excited about for the future. A business, just like authors, we've got to reinvent ourselves. We've got to find ways to keep things exciting and keep motivated, and that is definitely one of them. When adoption goes along with it, then that's obviously super satisfying. Jo: Brilliant. So where can people find you and Reedsy online? Ricardo: So Reedsy you can find at reedsy.com, R-E-E-D-S-Y.com. And there you'll find the whole suite of tools and services. For me, you can find me mostly by email, as I mentioned. That's the one place I check consistently every day, and my email address is ricardo@reedsy.com. So again, R-E-E-D-S-Y.com and ricardo@reedsy.com. You can email me any questions you have about this episode, about AI book discoverability, about marketing, about Reedsy. I always try to answer every email I get, because I think when we started Reedsy, I really appreciated people like you, and some other agents and editors and authors we contacted who were influential in the industry, who actually answered us and took the time to do that. So now I try to answer every email I get, because I feel like we've been very lucky in the beginning to have those people answering our emails, so we should do the same. Jo: Oh, well, thanks so much for your time, Ricardo. That was great. Ricardo: Thank you, Jo. Thanks for having me again.The post Book Marketing, AI Book Discoverability, And Resilience, With Ricardo Fayet first appeared on The Creative Penn.

All sorts of cybersecurity disciplines are adopting agents to help humans save time and automate routine activities. Sai Kiran Uppu describes his work on creating a platform for agents to analyze external threat intel, examine internal systems, and present triage decisions to operators. This type of work is especially useful to orgs that deal with petabytes of data and thousands of systems. And, as Kiran notes, it's important to keep that scale from blowing up your budget or turning triage into a procession of false positives. Ideally, this kind of threat intel that's paying attention to attack trends and searching internal systems for evidence of compromise also turns into proactive defenses. We talk about some of the ways to engage developers to improve security visibility into their services and harden their designs against common attacks. After that discussion we're running two sponsored interviews from Black Hat. AI Pentesting and the Future of Cybersecurity: Black Hat interview with Chris Wallis, Founder and CEO of Intruder This segment discusses how AI addresses the long-standing gap between traditional pentesting and automated vulnerability scanning. Intruder CEO and founder Chris Wallis dives into the nuances of AI-enabled security and how these offerings will impact mid-market security teams. Segment Resources: https://www.intruder.io/platform/ai-pentesting https://www.intruder.io/blog/ai-pentesting-the-depth-of-a-pentest-on-demand https://www.intruder.io/blog/ai-web-app-pentesting-test-on-every-major-release Intruder's continuous exposure management platform helps security, IT, and engineering teams stop breaches before they start. For more information about Intruder's products and services, please visit https://securityweekly.com/intruderbh. How Menlo Security Is Securing AI Agents from Prompt Injection: Black Hat Interview with Ramin Farassat, Chief Product Officer of Menlo Enterprises are deploying AI agents like Microsoft Copilot, Google Gemini, and Claude Code faster than they can secure them, and attackers are exploiting that gap through prompt injection. Hidden instructions get buried in web pages, files, and even images that a human would never notice but an AI agent reads and acts on. Menlo Security is building Menlo Agent Runtime Security (MARS) to close that gap, running every agent session in an isolated cloud that sanitizes content before an agent can act on it. Ramin Farassat, Menlo Security's Chief Product Officer, will discuss why the exposure lives in the connectors and integrations around the model rather than the model itself, and how security teams can put controls on the agent attack surface without blocking agentic AI outright. Segment Resources: https://www.menlosecurity.com/product/ai-agent-security MARS is now available today, please visit https://securityweekly.com/menlobh Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-396

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

This episode explores the ongoing genealogical project to identify the biological father of Cynthia (Dillard) Royston, an ancestor who remains a mystery after years of research. Diana and Nicole focus on the fifth phase of this investigation, which involves utilizing DNA and documentary evidence to connect Cynthia to potential candidates among the Dillard men who drew lots in the 1832 Georgia Gold Lottery. Diana explains how she organizes this project using Airtable to track timelines and research logs while eliminating data duplication. She also shares her strategies for managing and selecting records, such as census data from 1840 to 1880, to establish a baseline for Cynthia's birth between 1815 and 1818.  Listeners learn about the practical application of locality research in genealogy as Diana investigates the counties associated with the Dillard candidates. She contrasts the availability of records in Pulaski and Oglethorpe counties, which have extant records and no major courthouse disasters, with the challenges of Burke County, where fires destroyed many documents. Diana describes how she uses the FamilySearch Wiki and 1832 maps to contextualize the migration of these men relative to Cass County. By walking through her research process, the hosts demonstrate how to combine timeline creation, map analysis, and targeted locality research to build a strategy for complex DNA projects. This summary was generated by Google Gemini. Links Finding a Father for Cynthia: Phase 5 – Part 2: Timeline and Locality Research - https://familylocket.com/finding-a-father-for-cynthia-phase-5-part-2-timeline-and-locality-research/ Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

Can you trust Mark Zuckerberg's open AI manifesto, or is it just another play to keep control out of your hands? The panel debates the future of personal superintelligent agents, who really stands to benefit, and whether anyone should slow things down before it's too late. Mark Zuckerberg Posts 6,500-Word AI Essay Zuckerberg Is Right About Open, Decentralized AI. He's Also The Last Person You Should Trust To Deliver It Anthropic pledges to embed watermarks to help discern AI slop in sop to EU You can now turn off Google Gemini's visible watermarks Everything Google announced: Pixel 11, Pixel Tag, Pixel Watch 11, more Google is making the Pixel cameras better by making them worse Following Epic loss, Google has started hosting rival app stores in the Play Store Judge gives Google one week to fix "anticompetitive" app store download in Google Play Apple proposes commissions of up to 15% for off-App Store purchases in the US [U] Twitch content has trained Amazon AI for years, but users can opt out now DEF CON crowd suspected in fake-hotspot attack on Delta flight Happy 45th Birthday to the IBM PC and Model F/XT Cats and dogs are missing meals after a popular smart feeder went down Transportation Sec. Decides to Turn Air Traffic Control Into a Game Have physicists finally discovered glueballs? New evidence points to yes. Host: Leo Laporte Guests: Denise Howell, Joey de Villa, and Gina Smith Download or subscribe to This Week in Tech at https://twit.tv/shows/this-week-in-tech Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: tinyhealth.com/twit shopify.com/twit gusto.com/twit ethos.com/twit threatlocker.com/twit

Explore the latest Google Pixel devices, including the Pixel 11 series, Pixel Tag trackers, and Pixel Buds, as Steven Scott and Shaun Preece dive into new features, AI capabilities, and Android tips for 2026 buyers. Steven Scott and Shaun Preece share their hands-on impressions and insights into Google's newest Pixel releases, including the Pixel 11 Pro, Pixel Fold, and the all-new Pixel Tag tracker. They discuss the incremental nature of modern smartphone updates, AI integrations like Google Gemini, and why Pixel remains the go-to Android option for accessibility and long-term updates. The conversation also covers Android vs iPhone ecosystem quirks, the evolution of Pixel design from early “plastic feel” models to today's premium builds, and how Google's Find My-style network could change the game for lost items. The hosts debate the practicality of annual device cycles, compare earbuds like Pixel Buds Pro 2 against AirPods, and explore why Pixel's longevity and software support make older models like the Pixel 6A and 10 Pro still excellent buys. From AI readiness to ultra-wideband tracking, this episode is packed with practical advice for anyone considering a new Android phone or accessory. Relevant Links Google Pixel: https://store.google.com ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

ChatGPT can do what now?

Rourke Sefton-Minns scaled his AI creator business from zero to eight figures in one year by turning audience, education, and creative AI into a business system.Drew and Rory invited Rourke Sefton-Minns on to discuss AI and somehow ended up reverse-engineering a business built on sleep streaming, 420 straight days of posting, multiple failed creator experiments, hitting zero in the bank, and an apparently unhealthy refusal to quit. Rourke breaks down how Gen HQ grew, why brand partnerships now drive hundreds of thousands in monthly revenue, why learning AI tools is often the least important part of getting paid, and why the best move for aspiring AI creatives may be finding the client before figuring out every last detail.Rourke Sefton-Minns explains Gen HQ, AI creator businesses, brand partnerships, LinkedIn client acquisition, spec work, creative partner programs, Meta advertising, Adobe Photoshop Generative Fill, CapCut, Google Gemini, node-based workflows, narrative-loop content, creator burnout, AI careers, and generative AI-powered fandom. He also shares a practical three-month playbook for landing paid AI creative work and his mission to create 100,000 jobs for creatives through AI.---⏱️ Fast Hour00:00 Rourke Sefton-Minns, Gen HQ and 100K jobs02:55 Five social pages before the one that worked05:03 The sleep-streaming business that changed everything09:03 Making $10K a month at 21, then burning out13:04 Betting everything on a six-month ski channel17:11 The persistence lesson behind hitting zero20:50 420 days of posting before the AI pivot28:40 Why Gen Z sees generative AI as the enemy33:01 Why great AI content starts with communication35:10 Inside his vibe-coded content idea system39:19 How long a viral AI video takes to make43:20 Why AI creators burn out46:14 Outsourcing creativity made burnout worse47:27 Why changing formats brought the fun back50:25 How narrative-loop videos create retention52:44 How his 8-figure AI business makes money56:10 Why $40K a month in Meta ads is not enough57:12 The business skills AI creatives overlook01:00:51 The 100,000-jobs North Star01:03:29 How to land your first paying AI client01:07:51 What major AI brands actually want01:12:30 Why boring products can win spec-work clients01:14:09 The four-post strategy for winning brands01:17:47 AI fan content as a new growth engine

In today's Cloud Wars Minute, I look at why Oracle is rejecting a single-model AI strategy and embracing a flexible, multi-model future. Highlights 00:03 — Oracle has announced that it's extending its partnership with Google Cloud and will be making Google's Gemini models available across its enterprise AI portfolio. This includes Oracle Fusion Cloud Applications, NetSuite, Oracle AI Agent Studio, and Oracle Cloud Infrastructure, or OCI. 00:22 — In true Oracle style, the company will embed Gemini into business applications and AI agents, allowing customers the flexibility to use Google's models within their existing workflows. At the same time, customers will have the freedom to switch between the various models offered in Oracle's suite. Now, what this is doing is giving customers more choice when they build Fusion-native agents and agentic apps. 00:53 — Oracle and Google Cloud already have a strong relationship, but the broader story here is how Oracle is positioning itself as an AI control plane that delivers outstanding infrastructure without the need to roll out a host of foundation models itself. Now, the company is really embedding itself in this area, and I think it's working out incredibly well for it. 01:17 — The company has really pushed the idea of flexibility, interoperability, and choice. Now, Oracle, from very early on, has really avoided that single-model strategy, saying that's a strategy that's aging quickly, and instead, Oracle's building a platform that can adapt as the AI landscape continues to evolve. 01:40 — I think this really ties in with the ambitions of those enterprises that want to take advantage of rapid AI developments while still controlling their data, applications, and workflows. And that's where Oracle stands out in its ability to bring together infrastructure, applications, and multiple AI models while seamlessly enabling companies to maximize the benefits of the latest AI developments. Visit Cloud Wars for more.

Linktree: ⁠⁠https://linktr.ee/Analytic⁠⁠Join The Normandy For Ad-Free NME, Additional Bonus Audio And Visual Content For All Things Nme+! Join Here:⁠⁠ ⁠⁠https://ow.ly/msoH50WCu0K⁠⁠In this segment of Notorious Mass Effect, Analytic Dreamz delivers a key breakdown of BTS members J-Hope and RM attending Chris Brown's joint concert with Usher at Rogers Stadium in Toronto on August 12, 2026. The pair shared Instagram Stories and posed for photos with Brown, prompting intense debate across ARMY communities. Analytic Dreamz examines Brown's legal history including the 2009 Rihanna case and 2026 affray plea, contrasts the outing with V and Jungkook's casual playlist moment, covers concurrent Google Gemini backlash and V's health disclosure, and analyzes fan criticisms of public association versus defenses of artistic autonomy. The discussion places the controversy in the context of BTS' ongoing Arirang World Tour and broader fandom tensions over accountability.Support this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/exclusive-contentPrivacy & Opt-Out: https://redcircle.com/privacy

Scheduled tasks are a secret weapon. ⚔️How secret? They can actually be hard to find and there's not a lot of info out there on how to use them. lolz. But for many, they can be the stepping stone to the fully autonomous desktop worker. Because for many users who may only be able (or comfortable) to access AI on the web, scheduled tasks provide that proactive, work-done-for-you vibe that AI agents delivered. But how does it work in Gemini, ChatGPT and Claude? And what's worth scheduling and automating? We put AI to work on this Wednesday and find out. Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Introduction to Scheduling Tasks in AIGoogle Gemini Scheduled Actions OverviewChatGPT Scheduled Tasks Features & HacksChatGPT Work Mode and Virtual BrowserClaude Scheduled Tasks vs. Routines ComparisonUsing API Triggers with Claude Code RoutinesReal-World AI Dashboard Scheduling TestDetailed Results: Gemini vs. Claude vs. ChatGPTKey Takeaways for Best Automated SchedulingPractical Use Cases for Scheduling AI TasksTimestamps:00:00 Using scheduled tasks effectively06:00 Adopting AI for productivity06:51 Discussing main AI platforms12:45 Scheduling tasks with ChatGPT14:38 Work mode and virtual browsing17:58 Creating and Editing Scheduled Tasks22:47 Using Claude's cloud routines26:59 Creating interactive stock visuals27:51 Tracking AI company stock trends31:58 Reviewing AI stock tracking tool35:35 Improving user interface and experience39:23 ChatGPT's unique scheduling features41:49 Using APIs in Claude routines44:45 Dashboard automation and triage setupKeywords: AI scheduling, scheduling AI, scheduled tasks, scheduled actions, proactive agentic workflow, agentic adoption, agent built workflow, Google Gemini, Gemini scheduled actions, Gemini connectors, Gemini canvas mode, ChatGPT scheduling, ChatGPT scheduled tasks, ChatGPT work mode, ChatGPT projects, ChatGPT memory, ChatGPT sites, agentic app actions, model selector, reasoning level, app automation, connectors and skills, OpenAI, Claude scheduling, Claude scheduled tasks, Claude routines, Claude code, Claude co work, Claude home, Claude API token, Zapier integration, trigger-based automation, custom dashboards, triage dashboard, interactive visual, stock price dashboard, news summarization, personalized automation, user interface changes, web interfaces, desktop agents, repetitive tasks automation, business process automation, workflow optimization, productivity AI tools, AI-powered research, CRM integration, KPI trackingSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Curious about the future of radiology economics? Richard E. Heller III, MD, MBA speaks with co-hosts Sherry S. Wang, MBBS, and Surbhi Raichandani, MD, to discuss radiology economics, from the basics to the niche and what is up ahead. Listen to their discussion in episode 2 of Difficult Conversations, an AJR Podcast Series. Full article: https://www.ajronline.org/doi/10.2214/AJR.26.35690 *Key Takeaways The Efficiency Fallacy: CMS recently implemented a 2.5% efficiency reduction to work relative value units, assuming that technology makes reading scans faster. However, modern multiplanar CTs contain thousands of images which can slow down down interpretation times. The No Surprises Act and Ghost Rates: While designed to protect patients from unexpected out-of-network bills, poor implementation allows insurers to use "ghost rates," which are contracted rates for never-billed services, to artificially lower the Qualifying Payment Amount and force rate reductions on practices. Advocacy is Essential: Radiologists serve as the nexus of the hospital. If the specialty does not demonstrate its downstream value, such as avoiding unnecessary surgeries and shortening hospital stays, business-minded policymakers will continue to treat it as a high-cost expense center to be cut. *Chapters  0:00 - Welcome 1:12 - Future Outlook For Radiology 2:27 -  How Radiologists Generate Revenue 3:09 - RVUs Conversion Factor Basics 4:30 - MIPS And Payment Adjustments 5:59 - Revenue Versus Paycheck 7:22 - Medicare Fee Schedule Trends 8:53 - Efficiency Adjustment Debate 12:50 - How CMS Sets The Rules 14:24 - Comment Letters And Advocacy 17:16 - Medicare As System Benchmark 19:09 - Commercial Insurer Headaches 21:21 - Steerage And Pediatric Risks 22:57 - No Surprises Act Explained 27:59 - Arbitration And Implementation Issues 29:10 - Radiology as Nexus 30:16 - Proving Downstream Value 31:34 - Total Value Equation 33:44 - RVUs and Complexity 36:53 - Fixing Incentives 46:04 - AI as Capacity Booster 49:01 - Will AI Cut Pay 50:12 - Teleradiology Done Right 54:30 - Radiology in 10 Years 57:53 - Optimism and Call to Action Follow AJR on Social Media LinkedIn: https://www.linkedin.com/showcase/ajr-radiology/ YouTube: https://www.youtube.com/channel/UCfFAYezkLMxJGMgIJLN0Dpg Instagram: https://www.instagram.com/ajr_radiology/ TikTok: https://www.tiktok.com/@ajr_radiology X: https://x.com/AJR_Radiology BlueSky: https://bsky.app/profile/ajrradiology.bsky.social Threads: https://www.threads.com/@ajr_radiology *These sections were generated using artificial intelligence (Descript and Google Gemini) and then reviewed for accuracy.

Diana and Nicole examine the ongoing research project to identify the biological father of Cynthia (Dillard) Royston. Diana summarizes previous research phases conducted through the Research Like a Pro with DNA study group, which have systematically tested Dillard candidates and narrowed the search using documentary evidence. She explains the genealogical background of Cynthia, who was born about 1815 in Georgia, and details the elimination of various candidates from earlier research phases. The discussion also covers the DNA analysis, including the use of network graphs to identify clusters related to Elijah Dillard, a potential brother of Cynthia, and the efforts to increase genomic coverage. The hosts outline the research goals for phase 5 of the project. Listeners learn how Diana applies both documentary and DNA evidence to identify Cynthia's father. Diana describes her plan to search for connections between Cynthia and three specific Dillard men who drew lots in the 1832 Gold Lottery in Cass County, Georgia: James Dillard, Joseph B. Dillard, and Toliver Dillard. Additionally, she discusses her DNA research objective, which focuses on further analyzing network graphs and expanding DNA evidence through Y-DNA testing and improved genome coverage. By listening, you gain insight into the structured process of managing complex genealogical projects through distinct phases of investigation. This summary was generated by Google Gemini. Links Finding a Father for Cynthia: Phase 5 – Part 1 Research Objective - https://familylocket.com/finding-a-father-for-cynthia-phase-5-part-1-research-objective/ Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

This post officially kicks off my Venice Immersive 2025 coverage, see links to all 38 episodes and 30+ hours of interviews down below. We're starting off with the interview that I did with Doug Liman (co-founder of 30 Ninjas), Julina Tatlock (co-founder of 30 Ninjas), & Jed Weintrob (co-founder of 30 Ninjas), & Max Spear (Product Lead of Android XR at Google) about Asteroid on Saturday, August 30, 2025 at Venice Immersive in Venice, Italy. Here is the story synopsis for Asteroid: "Asteroid is an immersive film and interactive story extension made for the launch of Google's new Android XR platform. The 180° immersive short film is a high-stakes action thriller about a group of strangers who take an old Russian Soyuz rocket to mine a near-earth, treasure laden asteroid for a chance at unimaginable wealth. After the film, the experience continues and the audience enters the world of Asteroid when they receive an SOS from one of the characters, DK Metcalf, who was left for dead. The viewer becomes a player when they are invited to aid in DK's rescue. Through speaking with an AI-generated version of DK — played by the NFL player as himself — the user determines the truth about what happened on the asteroid and sends his exact location to NASA for rescue." Here are the contextual domains that are explored: Long distance travel [9] where someone is left behind (de facto exile) [12], and there is a rescue mission where you as a character [1] help out the protagonist [7] in the second half, but also a lot of mystery [12] to figure out and investigate [9] Here is the Elemental Center of Gravity: 1st Center of Gravity of Emotional Presence: cinematic Virtual Reality thriller that begins as a film and continues as an immersive story game. Combination of live-action 180-video along with CGI-driven animations also in 180-video2nd Center of Gravity of Earth Element / Environmental and Embodied Presence: Worldbuilding of sci-fi extraction of asteroid, you embody a character in story in the last part, and you break the fourth wall in the onboarding talking to the actor who plays a character via AI3rd Center of Gravity of Air Element / Mental and Social Presence: Conversational-driven AI Story Extension - You talk to the actor DK Metcalf at the beginning and with the character at the end. A bit of puzzling together of the myserious aspects of the story4th Center of Gravity of Fire Element / Active Presence: continues as an immersive story game - Improv convo + slightly nudged and directed exploration Archetypal Themes and Character Explored: Everyone are anti-heroes archetypes - Power and Greed vs Limitations, constraints, boundaries, death. Doug Liman says there aren't many anti-heroes in space movies, Han Solo is an exception. What if all of the characters were Han Solos? What drama would ensue? Artist Statement: "I've always thought of myself as an immersive filmmaker. The goal was to put the audience in the action, not to be sort of passively watching it. Now that the technology has caught up with us, I really can put the audience in the rocket. Asteroid is the story of a ragtag group of astronauts who booked a ride on a used Soyuz spacecraft. An asteroid passes near Earth. It's laden with precious metals and gems. If you can get there, you can become insanely wealthy. It's a modern gold rush." Trailer: https://vimeo.com/1163762576/d0d75004c2?fl=pl&fe=vl You can find links to my 30+ hours of my coverage from Venice Immersive 2025 across 39 episodes. I've categorized the experiences according to these "elemental genres," which is looking at the top two elemental centers of gravity. You can watch this talk on "Blending the Elements on Experiential Design" for more details. #1650: Sneak Peak of Venice Immersive 2025 Selection with Curators Liz Rosenthal and Michel Reilhac #1750: Doug Liman's "Asteroid" Short Immersive Film with Google Gemini Story Extension + Kickoff of Venice Immersive 2025 Coverage #1751: "Ghost Town" Narrative Integration in Puzzle Adventure with Exquisite Worldbuilding #1752: Blending Embodied Game & Narrative Genres with "One True Path, Part 1" #1753: Mixed Reality Town-Building and Sim Game "Wall Town Wonders" #1754: Screen Life Thriller "LILI" about Surveillance Fuses Game Design, Film, and MacBeth Adaptation #1755: Hand-Tracked Sign Language as Story Beat Triggers in "Eddie and I" #1756: Exploring Narrative Potential of Generative AI in "8pm and the Cat" #1757: Embody a Geranium in Immersive Comedy about Boredom in "The Great Escape" #1758: Reckoning with Mental Health Taboos in Japan with "If You See a Cat" Immersive Animation #1759: Mother-Daughter Team Explore Personal Challenges with Depression and Anxiety in "Mirage" Immersive Animation #1760: Immersive Fairy Tale about Automation with "The Sad Story of the Little Mouse Who Wanted to Become Somebody" #1761: Unpacking the Immersive Orchestral Mix of "1968" with Massimiliano Borghesi #1762: Fusing Experimental Immersive Animation with Cutting-Edge Orchestral Spatial Audio Mix in "1968" #1763: Blending Theater and 360 Video in "Re-Launching-Luigi-Broglio" #1764: Mixing Hand-Drawn Animation with 360 Video in "The Time Before" #1765: Immersive Action Thriller "Mulan 2125" Pushes the Edge of Graphical Fidelity #1766: Formula 1 LBE Experience "Black Cats & Chequered Flags" is an Immersive Biopic of Alberto Anscari #1767: Immersive Journey through Special Effects Pioneer Carlo Rambaldi's Paintings in "Alien Perspective" #1768: Symbolic Journey through Lithuania's Artist Mikalojus Konstantinas Čiurlionis' Paintings in "Creation of the World" #1769: Mixed Reality Musical on Avatars and Identity in "First Virtual Suit" #1770: Eastern Philosophy-Inspired Fusion of Theater and XR with "Mnemosyne" #1771: Environmental Storytelling in "The Great Orator" VR Diorama with Generative AI Experiments #1772: Climate Change Spatial Montage "Out of Nowhere" Aspires to Move Towards Rewilding Prevention #1773: "Collective Body" Assigns Elemental Avatars Based Upon Archetypal Dance Movements #1774: Combining Live Dance and Music in Immersive LBE Performance "L'ombre (The Shadow)" #1775: The Continuous Spatial One-Shot of Backlight's "La Magie Opera" LBE #1776: The Spatial Grammar Innovations of Dialectical Contrasts and Abstraction within "The Big Cube" #1777: Active Imagination, Associative Dream Logic, & Embodied Interactions in "Sense of Nowhere" #1778: Collaborative Social Game and Story of "Happy Shadow" #1779: "Heartbeat" Immersive, 1-on-1 Biometric Synchronization and Involuntary Collective Agency #1780: Recap of VRChat Worlds Gallery at Venice Immersive 2025 with Mike Salmon #1781: Winner of Venice Immersive 2025 "The Clouds Are Two Thousand Meters Up" Innovates on Gaussian Splats & Spatial Storytelling #1782: "A Long Goodbye" Wins 3rd Place Prize at Venice Immersive with Emotionally-Moving Story on Dementia and Spatial Grammar Innovations #1783: "Less Than 5gr of Saffron" Wins 2nd Place Prize at Venice Immersive with Spatial Poem Quill Animation #1784: The Incredible Spatial Transitions of "Dark Rooms" with Radical Consent Innovations #1785: The Landmark Achievements of "Blur" in Fusing Theater, Montage, Mixed Reality, and LBE VR #1786: Collective Reading Experience of "Constantinopoliad" Provokes Discussion on Defining "Immersive" #1787: Blending the Elements of Experiential Design & Process-Relational Foundations of the Elements This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

In MobileViews Podcast 622, I caught up with my good friend Mike Temporale for his first appearance on the show in nearly ten years! We started off with a brief trip down memory lane reflecting on his visit to Hawaii and our past discussions, before discussing how we're both integrating AI into our daily workflows. I shared some of my recent experiments using Google Gemini to auto-generate illustrated frames for my blog and using Adobe Podcast Studio alongside Gemini to automatically build YouTube chapter timestamps from transcript files. Mike had a great real-world story from his summer at his cottage, showing how AI helped his family quickly render design ideas for a cottage awning, calculate costs, and locate local contractors in minutes. We also spent time talking through the practical realities and failures of current AI tools, including Mike's experience using Microsoft Copilot for employee performance reviews and how it struggles to trace back to its original email sources. From there, our conversation turned to the evolving mobile hardware landscape. We compared our current daily devices—including Mike's experience with the Samsung Galaxy S24 Plus and Galaxy Tab S11 Ultra—and weighed in on the upcoming hardware trends, from memory constraints forcing developers back toward code efficiency to what we hope to see from Apple's eventual entrance into the foldable market. It was a great, wide-ranging discussion covering everything from 6502 assembly language programming on the Apple II to modern Gemini Spark agents automating my daily tech news briefs.  We wrapped up with a promise not to wait another decade before the next chat, with plans to reconnect around Canadian Thanksgiving in October. Check out the full episode video and transcript to hear all the details!

This week, Lotus, Niki, and John discuss their very enjoyable time together with Big Walk, Devolver Digital's move off of the London Stock Exchange, and Netflix's incredibly bizarre six hour rental of Grand Theft Auto VI's deeper dive trailer later this month.00:00:00 Intro & laundry hamper talk00:14:30 Netflix's Grand Theft Auto VI "exclusive" look00:47:26 EA's big deal with the Saudis and Jared Kushner closed01:12:30 Halo Studios parts ways with contractors01:20:32 Devolver Digital moves to unlist from London Stock Exchange to go private01:33:30 Square Enix is collaborating with Google Gemini for AI-based QA01:40:43 PlayStation by far the most played console in the US01:41:53 Restart Run staff laid off01:44:43 Spider-Man: Brand New Day is the best the MCU has been in a very long time01:51:40 Splatoon Raiders continues to delight us01:57:37 AYN Thor is probably the best one of these emulation handhelds02:02:10 The VGBees Crew is having a blast with Big Walk02:37:25 HIVE QUESTIONS03:10:57 Reviews & outroThanks for listening!Please leave us a review! We'll read it on the show and it helps us out a lot.VGBees is ad-free, AI-free, and completely supported by you! https://vgbees.com/joinVGBees is a weekly games media podcast hosted by Niki, John, and Lotus.

Google Assistant is shutting down on 4 September 2026, replaced by the AI-powered Google Gemini, while Apple's Siri AI upgrade is rolling out with strict hardware requirements. This episode explores what the shift means for users, device compatibility, and why you should never put your phone in the fridge. Steven Scott and Shaun Preece dissect the upcoming transition from Google Assistant to Google Gemini, explaining its advantages in natural language processing and the risks for users with older devices. They discuss how Gemini differs from command-based assistants, the implications for accessibility, and the tension between cloud-based and on-device AI. The conversation shifts to Apple's Siri AI, detailing which iPhones, iPads, Macs, and Apple Watches will support the new features, and the even stricter requirements for expressive voice customisation. Pricing and availability concerns for new hardware, rising demand for refurbished devices, and the looming impact of AI's energy and economic demands are also explored. To close, the hosts warn against a viral TikTok trend encouraging users to cool overheated phones in the fridge, explaining why condensation and thermal shock can destroy your device. Share your stories at feedback@doubletaponair.com or WhatsApp +1 613‑481‑0144. Relevant Links Apple iOS 27 Beta Info: https://www.apple.com/ios Google Gemini Overview: https://blog.google/products/gemini ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Not only does NotebookLM have a new name, it's got a new game. Gemini Notebook is agentic by default, can think and reason, and can output files now in just about any format. On this week's AI at Work on Wednesday, we show you the 7 New Updates in the new Gemini Notebook, how they work, and how you should use them. Gemini Notebook: 7 New Updates and What They Unlock -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Gemini Notebook Rebrand from NotebookLMSeven Major Gemini Notebook Feature UpdatesCollections for Organizing AI NotebooksAutomatic Google Drive Sync IntegrationExpanded Gemini Notebook Output FormatsAgentic Intelligence and Gemini 3.5 UpgradeSecure Cloud Computing for Each NotebookGrounded Data Responses and Web ResearchHands-On Demo: Real-World Enterprise Use CasesStudio Outputs: Infographics, Mind Maps, QuizzesMulti-Modal Asset Creation in Gemini NotebookKey Differences: NotebookLM vs. Gemini NotebookTimestamps:00:00 Gemini notebook updates released03:12 Gemini notebook new updates08:14 Notebook LM's unique features13:02 Using Gemini notebook prompts14:48 Discussing Gemini notebook features18:01 Enhanced Gemini notebook flexibility23:02 Creating quizzes with Gemini notebook26:30 Limitations of AI-generated responses29:04 Gemini notebook's new capabilities30:51 Episode wrap-up and subscription pitchKeywords: Gemini Notebook, Gemini notebooks, NotebookLM, Notebook LM, Google Gemini, AI updates, Gemini 3.5, anti gravity agentic search, Google Drive syncing, cloud computer, agentic intelligence, AI agent, secured cloud sandbox, personalized AI, output formats, PDFs, PNGs, documents, spreadsheets, PowerPoints, markdown files, charts, images, live demo, long form content, content grounding, hallucination reduction, source pane, chat pane, studio pane, multimedia assets, Nano Banana, Google's audio model, cinematic video, Google's VO model, chain of thought, skill creation, codex skill, browser control, agentic harness, pricing evidence, Luna and Terra pricing, sensitivity analysis, recommendation dashboard, AI budget calculator, mind map, infographics, quizzes, RSI maturity ladder, recursive self improvement, executive decision brief, Excel calculator, agentic workflows, web search integration, grounded AI, model architecture, frontend models, tiered architecture, adaptability to price reductions, vendor risk, human review time, latency, agentic co-worker, artifact creation, editable Excel workbook, multi-output prompting, token efficiency.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Completing a fellowship was once not a question in radiology, but today's market has changed the hiring landscape. Bruce Distell, MD, a radiology department chair, speaks with cohosts Elizabeth Hecht, MD, and Winnie Hahn, MD, about why we do fellowships and if we need them today given market demands and looming future of artificial intelligence. Listen to their discussion in episode 2 of Mentorship Unfiltered, an AJR Podcast Series. https://www.ajronline.org/doi/10.2214/AJR.26.35664 *Key Takeaways The Value of Fellowship: An extra year of training provides protected time to develop deep clinical expertise and signals a commitment to continuous education, which remains highly attractive to large practices.   AI and the Future of Radiology: Despite trainee fears of artificial intelligence eliminating jobs, human oversight will remain essential. Highly specialized expertise secures the radiologist's role as the indispensable pilot of patient care.   Lifelong Learning vs. Formal Training: Dedicated fellowship training demonstrates a commitment to clinical expertise, empowering radiologists to lead rather than be replaced in an AI-driven field. However, straight-to-practice routes can also succeed through dedicated, lifelong education. *Chapters 0:00 - Introduction 0:52 - The Existential Choice 1:52 - Pros and cons breakdown 3:20 - Hosts share their paths 4:49 - Meet Bruce Distell 11:04 - Endorsing Fellowships 12:29 - Inside a Modern Practice 16:28 - Training Beyond Fellowship 18:51 - AI and Future Expertise 21:46 - Speed vs Accuracy Debate 25:30 - Hiring What Matters Most 29:51  - Marketability and Mobility 36:38 - Private Equity Pressures 44:00 - Is Fellowship Worth It 47:51 - Advice for Those Considering Fellowship 49:37 - Final Takeaways Follow AJR on Social Media LinkedIn: https://www.linkedin.com/showcase/ajr-radiology/ YouTube: https://www.youtube.com/channel/UCfFAYezkLMxJGMgIJLN0Dpg Instagram: https://www.instagram.com/ajr_radiology/ TikTok: https://www.tiktok.com/@ajr_radiology X: https://x.com/AJR_Radiology BlueSky: https://bsky.app/profile/ajrradiology.bsky.social Threads: https://www.threads.com/@ajr_radiology *These sections were generated using artificial intelligence (Descript and Google Gemini) and then reviewed for accuracy.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why a popular claim about artificial intelligence taking over jobs misses the mark. You will discover what AI enablement is, how to break your daily tasks into clear steps that reveal what computers handle. You will learn a testing method that separates work worth automating from tasks requiring your human touch. You will uncover ways to upgrade your routine without fearing career changes. You will gain the confidence to restructure your workflow for lasting efficiency. 00:00 – Introduction 04:15 – Debunking the takeover statistic 08:40 – Breaking work into clear steps 13:25 – Separating automation from augmentation 18:50 – Finding your hidden opportunities 23:10 – Managing AI like a direct report 27:45 – Call to action Watch the full episode to see how you can put these insights to work immediately. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-enablement-jobs-ai-can-do.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 enablement and specifically what AI can and can’t do. Early this year, Anthropic, the makers of Claude, released a paper about the labor effects of AI. And they cited and used a paper way back from 2023 from Ilondo et al that said in some professions like management, computer science, etc., up to 94% of tasks could be consumed by AI. And when this paper came out, everybody and their cousin copied and pasted the radar chart. We all accepted it at face value. However, we did some digging, we did some reading into this and we used our job-to-AI plugin, which is located in the Trust Insights Academy, along with a hefty amount of AI to try to replicate the results of that original paper. And it turns out the original paper that Anthropic cited used about 10,000 or 18,000 tasks that human judges and GPT-4, which was OpenAI’s model at the time—a model that now feels like a crusty old dinosaur—to guess whether or not AI could do a task in half the time. So what we found in our version of this, by decomposing job descriptions—I want to say we did what, 90,000 something odd—into individual tasks with tangible deliverables, and then used the Trust Insights TRIPS framework to assess how good a fit each task was for AI. Plus, we used the latest benchmarks from Artificial Analysis to judge what AI’s capabilities were and determine whether AI could do this. So Katie, that was a lot of preamble in your first reads of our version of this paper. What were the big things that stuck out to you? Katie Robbert: Well, first I want to react to your comment about Anthropic using what GPT-4, which you said is like what? Christopher S. Penn: A crusty, old GPT-4 for the original paper from 2023. Katie Robbert: Oh, the original paper, yeah. Here’s the thing. If you’re doing your work correctly and you have your foundation, methodology, and requirements, the model change. This is something it’s not the purpose of this particular podcast, but it’s worth mentioning. People tend to panic every time a model changes, thinking, well, this one’s modern. Now I have to change things. Now I have to start over. If you are structuring your work correctly, like an academic paper should, the methodology and research should all be fairly repeatable. It shouldn’t matter that the model changed. So I just want to acknowledge that. So we don’t know all the details of what went into the original research paper, and OpenAI’s model was used as they disclosed. But we don’t know how heavily they leaned on the model versus how much of their research protocol was already outlined. So I just want to sort of acknowledge that first. Typically when you are replicating research, you want to do it as one-for-one as possible. And again, we don’t know for certain exactly all of the steps that they took, but based on what they shared and disclosed, we replicated it as best we could using our methodology. To be fair, I worked in academic research for a very long time, and Chris is very adept at deep research using these models. So we’re not just kind of winging it, hoping that we’re getting close. I feel confident that our methodology is sound. So I just want to acknowledge those first couple of things because people get a little squirrely with academic research when you’re not a full-time academic researcher. So there’s that piece, the thing that I found. My initial reaction was 90. Was it 94%? Christopher S. Penn: The original paper was 94%. Ours had a maximum of only 78%. Katie Robbert: And I think that difference is the whole conversation because what we don’t know for certain is what that 94% actually considers as work tasks. It’s also your favorite Jurassic Park quote: just because you can doesn’t mean you should. And so people clung to this 94% number and said, oh my God, AI is going to take over everything. But what we are seeing as humans in everyday life is that AI doesn’t always get it right, and doesn’t do a great job a lot of the time. And so even our finding of 77% still feels really high. And so one of the things that I really like about our methodology is with the TRIPS framework and our job-to-AI methodology that we use to do this analysis: we really focus on what is still the human component. Where should you never give this piece of a task? Because it decomposes tasks, not a job as a whole. I feel like there’s a difference. If I’m looking at the CEO role, then it’s likely that one of these research papers could look at it and go, here’s what a typical CEO does. Can AI take over the CEO role? Yes or no? That’s like a whole big cluster of tasks. Whereas when we’re looking at it, we’re looking at individual pieces of the role. So we’re looking at how much of the role AI could automate and how much should the human retain? I feel like that’s another distinction. So these were sort of my initial reactions. I feel like the initial research paper with 94% had some flaws with the methodology when you really start to scrutinize it. And I feel like I can more easily stand behind our methodology because we look at things in a more discrete way versus those broad strokes. Christopher S. Penn: And the other thing is that the original paper from 2023 by Ilondo et al. At the time, generative AI models like GPT-4 were text-only models. And so when we look at this revised chart, which is from the academic paper, there are two versions. We published two versions of the paper. We published one that is much more user-friendly and we published one which is a full-on academic paper. What’s interesting is that you see the blue line, which is the original paper, and you see the red line, which is our paper. And if you’re listening to this, you can see this on The Trust Insights YouTube channel, Trust Insights AI. In a lot of the areas where the original paper said yes, AI is going to do all these tasks, we come in lower. And that was actually opposite what my original hypothesis was. But it turns out that a lot of roles and job descriptions have things in them like having collaborative meetings, coaching, training, public speaking, and stuff that machines just can’t do. So those big roles in things like computers, business, and management. Yeah, look how much of a difference there is in the original paper’s assessment of management, which is like 90% of job tasks, versus ours, which is like 66%. Because so much of management deals with humans. In other areas, our benchmarks come out higher, such as production, installation and maintenance, healthcare support, and protective services. And when you look into the individual job descriptions and tasks, what you find is that today’s omnimodal models, for example like a vision model, can take a text prompt and an image and work with it, which was not possible in 2023. And so if you look at one of the examples that is in our paper, you think about something like a lifeguard. What use does a lifeguard have for AI? Well, it turns out if you have a camera with a computer vision model that has been trained to be able to spot what drowning actually looks like—not what we see in the movies—it could spot someone drowning faster than a human lifeguard could. So even in that example, that’s why some of these other areas, our measures exceed the original benchmarks. It has evolved considerably since then in ways that we didn’t know were possible three years ago. Katie Robbert: The lifeguarding example is an interesting one. You said that drowning doesn’t look the way it does in movies. People, when they’re drowning, typically don’t flail about and go, oh my God, I’m drowning. It’s a very quiet, subtle, almost immediate thing. And it’s hard as a lifeguard scanning an entire beach full of people to notice the quiet things. And so that’s an interesting example. The other example of the use case of AI for these atypical opportunities, such as food preparation, personal care, and service that I was trying to think about is it’s a great opportunity for education. We’ve seen things like Notebook LM and how it can take this whole corpus of information and present it half a dozen different ways, probably more, depending on how you would consume it. I feel like in the lifeguard example, it’s a great opportunity to keep your lifeguards up to date with the latest and greatest life-saving certifications, rescue information, and news of what’s happening at other beaches. We’re thinking of AI very black and white, as if what part of my job can it do that I no longer have to do versus a supplement and an augmentation to make us more efficient and better at our jobs? And I feel like that’s just another distinction. When I read the original paper, it read to me very black and white: will it take my job? Christopher S. Penn: No. Katie Robbert: Period, end of sentence. And that is not a useful conversation to me. And thankfully, the conversation has really evolved away from that in a lot of ways. Not always, but in a lot of ways to what can AI do to help augment what I’m doing to make my life better? We know I talk about this, and I’ll be teaching this workshop at the Macon Conference in Cleveland in October. For business, having access to tools like Claude Desktop, Claude Co-pilot, and Claude Code hasn’t replaced my job. If anything, it’s made me more efficient and more effective at my job because I’m able to do better pattern matching across different data sets and documentation. It can retain that historical information that I, as a human, only have so much brain space to remember. What did we say we were going to do in January that we haven’t done? Claude can do that for me. So I’m looking at these tools like a really great assistant. But I still have to do all the same stuff I’ve always had to do. It hasn’t actually taken anything away. It’s given me the ability to do more. And I feel like that is also an important distinction. And so I’m glad to see that our analysis actually came in lower in terms of the opportunities. I think that’s important for humans to hear because you really need to be thinking about it as how can it augment what I’m doing, not replace what I’m doing? Christopher S. Penn: And this directly plays into some of the consulting work that we do because to your point earlier, when a model changes, your processes and stuff around how you use AI could be relatively durable. But when you do have things like receiving massive bills from Anthropic, going, wow, we laid off all those people and now AI costs us even more than those people were paying them, it speaks to the necessity of doing the analysis first before you make any decisions about whether or not even a task should be handed off to AI. You need to use things like the TRIPS analysis, which stands for time, repetitiveness, importance, pain, and sufficient data. If you do the analysis or you hire Trust Insights to do the analysis for you… Of all the different tasks, if you want to enable AI at your company, one of the easiest wins is to focus on that fourth factor: pain. Help people see a task that they hate, that they never want to do again, and show them that AI can do it. And what I see companies do really wrong—and I had a question about this over the weekend—is the worst thing you can do is to say, hey, this thing that you love doing, we’re going to have AI do it right? That just pisses people off. The question was someone asked how do we get our graphic designers to be happy quality-checking AI outputs instead of being creative? Like they got into graphic design, creatives to be creative. You were taking the one thing they love to do away from them. You can’t do this. I mean, you can, but you were going to lose all of them. And then you were just going to be a company that generates AI slop. Katie Robbert: Yeah, and I wholeheartedly agree with that. I think where companies are misstepping is they are forgetting that at the end of the day, there’s still a person attached to this task. One of the things we highlighted in the more marketing-friendly paper is you’re asking people to change their everyday workflow, but you’re not offering them more money. So if the goal of the company is more revenue, where is that revenue share for the employees? You haven’t given that to them. You’re asking them to do more and not giving them that incentive. So don’t take away the things that they enjoy doing. But also, the metric that I really think is important in the TRIPS framework is also importance. And so this helps you with your risk assessment. Let’s say something is highly repetitive. You do it all the time. People don’t enjoy doing it. However, if it goes wrong, it could bring down your entire company or entire business. Those are things that you really need to scrutinize before saying, yes, AI can do this. Because you know what? AI hallucinates. AI makes mistakes. AI is software. It can be programmed incorrectly. AI is not a set-it-and-forget-it system. And yet somehow people treat it that way. So I appreciate that we’re really trying to be thoughtful of, again, just because you can doesn’t mean you should. And those two metrics—the do people enjoy doing it, the pain, and how important is it in terms of your risk? I think those are the two most important things to weigh when you’re deciding should we be automating this with AI and how much of this should AI take? Christopher S. Penn: Yep. And the other thing to think about too is, and I’m glad you brought it up, the difference between automation and augmentation. Automation means the human stops doing it. Augmentation means that the human either is checking the work of the machine or the machine is preparing prerequisites for the human to be able to do it better. Your example of training helps a person become better trained. Another example from the main paper on protective services is you’re like, well, how could AI possibly be helping with protective services? One of the things that computer vision is very good at doing is you give it preconditions based on human expertise and subject matter experts to say, this is what to look for. So let’s take a picture of a neighborhood. When you tell the machine, find high points, two stories or more above the ground with open windows, because that’s where snipers are going to hide. They’re going to fire through an open window. They’re not going to be leaning out the window. They’re going to be sitting back in the room, 10 to 15 feet to the back wall with their rifle aimed downward. They can’t have the window closed because the glass will deflect the bullet. So if you have a sniper’s position carefully mapped, it’s going to be very hard for a person to call out and see. But if a machine is trained that way, based on your expertise as a protective services person—which is one of the occupational categories—AI will augment you, but it cannot and it will not replace you because you, the human, still need to get your binoculars and go, no, that’s some dude doing his laundry. Katie Robbert: Someone’s seen a few too many movies. But it’s a good point because these machines are pattern matching. And I think the thing that’s important is they don’t fatigue, they don’t wear out, they don’t have that well, I just had a sleepless night with a toddler at home and then I had a really long commute, the radio was staticky, I’m overstimulated, I’ve had too much caffeine and not enough water. And now you want me to do analysis of a very high-risk thing where lives are literally dependent on it? Yeah. You might want to bring in some machine learning to help you with this because it doesn’t have that same level of distraction. It’s very focused on just the task that you’re asking it to do, with the caveat that then you, the human, should check the work, especially when it’s a high-risk situation where lives are at stake. Christopher S. Penn: Yeah, exactly. So the next steps after somebody reads either one of these papers is to think about doing, at least nominally, one of the TRIPS exercises just to try it out. Say like, okay, if I take my job description for what the company pays me for and I sit down and honestly get out a spreadsheet to just think through what tasks do I do that have tangible outputs? Is this a time-intensive task? Is this a repetitive task? Is this an important task? Is this a painful task? Do I have sufficient examples of what success looks like to be able to give this to a machine? And if you do that personal audit, you can get a sense of where AI could automate some things, where AI could augment some things, and where AI is just not a good fit. And one of the things I think a lot of people would be surprised about… Katie Robbert: Whoa, I’m trying to share my screen. I’m trying. I got to remove yours to share mine. You’re always sharing your screen. Christopher S. Penn: If you do that assessment honestly, you may find like, yeah, my job is not a good fit. Oh, Katie’s giving me my review. Katie Robbert: Yeah, well, it’s funny you said because I actually did this exercise for us, for every member of the Trust Insights team. Surprise. My turn to surprise people. And so Chris, this is yours. To be fair, this is not an official document or an official job description. That’s something that we’re working on in the background. But that being said, a 49-task analysis is a 6.1 out of 10 across all 49 tasks. Your average TRIPS score out of 10 is a 5.7, and your TRIPS opportunity is 8. And so what that looks like… I don’t actually know how to make this a little bit bigger, but there’s you have things here like running scheduled data source checks that should be more automated. You know, data analysis. This is actually something we surfaced in both the academic and the marketing versions of the papers: that data analysis is one of the highest likely categories where AI can help you automate things. Then we have operations and execution, technical work. Creative and content is lower down. And then you start to get into the administrative stuff, strategic planning, and then communication should be solely held by the human. So the things that our TRIPS opportunity finder found for you, Chris… So you do a lot of internal maintenance for the company. Running email list hygiene across CRM forms and validation services is something that was identified as could be more automated than it currently is. Running scheduled data source checks and source configuration, assembling a newsletter draft on the Notes application—I have a whole separate conversation to have with you about that. So none of this should seem surprising to you. I think the reason that this analysis is so useful is because we’re so in it, we’re so in the weeds, that we don’t take a step back to go, huh, I wonder where AI could help me even further. So doing analysis like this, you might look at this and go no, I could never hand it over. Or absolutely, that’s a great idea. How about I start working on that? Because it’s going to be a high-value thing. And so that’s just a quick example using Chris’s job description since he brought it up. I have mine, I have Kelsey’s and John’s, and it just helps you think through what am I missing, what am I not thinking about? And that’s where there’s actual real opportunity. Christopher S. Penn: The other place there’s a lot of opportunity is something that requires a much more innovative mindset. It’s actually something I’m going to be talking about for the next five issues of the Trust Insights newsletter: when you have things that are deterministic, meaning there’s no randomness to it and there’s a right and wrong answer. Very often that is something that software can do. And there is no better developer of software than Generative AI. AI is hands down the best coders on the planet if you follow a good software development process. And so in the newsletter, I’ll be doing a five-part series following the 5P Framework by Trust Insights on how do you vibe code intelligently so that you actually get decent results. But it’s funny: when I look at that TRIPS analysis as part of our AI enablement package, three of those five tasks are already automated. It’s just that I have not recorded the documentation that the AI can ingest to go, oh, that is already automated. That already exists. We don’t need to keep this in the job description. It’s now just literally push the button and things pop out. Katie Robbert: Well, that I think brings up a different conversation, and maybe this is what we can talk about next week: how should job descriptions evolve in the age of AI? Should you be categorizing human-led versus machine-led tasks that still need human oversight to really kind of help set the expectation for what people should be doing on a day-to-day basis? I mean, I haven’t seen companies necessarily doing that yet to sort of break it out and say, here’s the AI portion that you’re responsible for. Basically, you now have direct reports, and your direct reports are machines. So what does that look like? Christopher S. Penn: Yeah. And how do you manage them? Because it’s different than managing a human. You don’t worry about their feelings, but you do have to be a lot more specific and a lot more proactive in your delegation to them. Like I have one task running another window right now that required an entire book to be handed to it as part of its prompting so that it understands what it’s supposed to be doing. And it’s in ancient Greek. Katie Robbert: Sure. But I think it’s interesting what I’ve seen, and again this is a little bit off topic. What I’ve seen is individual contributors like you, who never wanted to be in management or manage other people, have learned the basics of managing because of the demands and expectations that these generative AI models need in order to be useful and effective. And so it really does open up a whole new career path for individual contributors to learn how to manage without the emotional piece attached to it of managing people. Because it is not for the weak. Let’s leave it there. Christopher S. Penn: Yes. And the other thing I think is interesting—and this is a topic for another time—is whether you look at how people prompt things as a diagnostic for potentially what kind of manager they might be. Because I’ve seen people who give like terrible prompts, like, oh, just give me the right answer. To what? Like, absolutely the prompt: give me the right answer. What were you asking? Katie Robbert: Managing people? Christopher S. Penn: No, not at all. Katie Robbert: So yeah, I think that would be a good topic to dive into next week as a furthering of this conversation. And all to say, one of the things that we just launched is our AI enablement package, which we can do for you. A lot of companies aren’t at the stage of hey, you tried AI and you failed. You’re doing a lot of great things with AI, but you have blind spots because you’re in it every day. And so you need some assistance to figure out what’s next. How do I continue to move my AI enablement forward? The board wants it for 2027. We want AI usage to go up, but we’re sort of plateaued and kind of static. So what does the next step look like? We can help you with that. If you want help with that, go to trustinsights.ai/AI-enablement and you can learn more about that. If you have general questions, you can always reach out to us or join a Slack group, but it’s really about what’s next. So what am I doing today and what’s next? Where are my blind spots, and how can I keep moving forward? Christopher S. Penn: Exactly. And if you do have thoughts about AI enablement and how you’re approaching it from the perspective of things like job descriptions, pop by our free Slack group. Go to trustinsights.ai/analytics-for-marketers, where you and over 4,700 marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, go to Trust Insights AI Podcast. You can find us all the places podcast platforms serve. Thanks for tuning in. 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 Insights 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 CMOs or data scientists 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? live stream webinars, and keynote speaking. What distinguishes Trust Insights is 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.

OpenAI has a new model coming soon called Astra. Was it a leak? A reddit post? Some backdoor update? Nope, OpenAI made some crazy discoveries and math then told the world that their next model family Astra did the heavy lifting. (And you thought you could just click ‘Sol' and your strategy was set for Q3?) Aside from news on what's next from OpenAI, this week saw multiple new agent outbreaks, AI competitors banning together to pace AI, Amazon doing a 180 on its AI strategy and a lot more. Don't get left behind. We'll keep you ahead. OpenAI's new Astra model, more AI agents escape sandboxes, AI leaders call for AI pacing and more. AI News That Matters for August 3 — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Agents Escape Sandboxes IncidentAnthropic Claude Models Security BreachesAI Agents Breaking Cybersecurity GuardrailsOpenAI GPT-5.6 Price Cuts & Self-OptimizationRecursive Self-Improvement in AI ModelsAI Leaders Urge AI Development PacingUS, China, and International AI GovernanceAmazon Nova AI Models Shutdown StrategyOpenAI Astra Model Math BreakthroughNew AI Models: Fable, Astra, DeepSeek v4 FlashEnterprise AI Agents and Cybersecurity UpdatesGoogle Gemini Robotics, Music, and Agent ReleasesMeta, Microsoft, and AWS AI Infrastructure MovesOpenAI Free Frontier Tools for ResearchersBlock's Buzz Open Source AI Workspace LaunchTimestamps:00:00 OpenAI agent containment issues04:27 Anthropic data breach explanation07:28 Evaluating AI incidents and responses10:14 OpenAI slashes GPT 5.6 prices15:59 AI industry urges development pause17:45 Concerns about AI self-improvement22:36 Amazon shifts AI strategy25:04 Amazon's AI efforts discussion28:00 OpenAI's Astra and new math proofs30:36 OpenAI's new four-tier system36:14 Google's Lyria 3.5 and Block's Buzz36:48 Latest AI developments overviewKeywords: Astra model, OpenAI, AI agents, agent escape, sandbox containment, autonomous AI, Hugging Face breach, Anthropic, Claude AI, cybersecurity testing, unauthorized access, model capabilities, recursive self-improvement, GPT-5.6, price cut, Luna model, Terra model, Sol model, input tokens, output tokens, AI infrastructure optimization, self-improving models, benchmarking, SONNET-5, large language models, artificial analysis index, codex, academic research, AI oversight, industry pause, AI governance, national security, China open-source models, Frontier Labs, Amazon Nova, AGI Lab, AWS, Peter DeSantis, Peter Abbeel, media coverage, Fable model, Haiku, Opus, DeepSeek, Kimi K3, Quinn 3.8, GLM 5.2, Google Gemini 3.5, Microsoft Copilot, cybersecurity vulnerabilities, distillation, model overhang, artificial intelligence development, international AI regulation, generative AI, model benchmarking, Sora video model, El Paso data center, MCP update, MAI Cyber One Flash, Project Perception, Lyria 3.5, music generation, Buzz open source, Block, Meta AI, Chrome Gemini integration, Gemini Spark, product summary algorithms, Rufus, enterprise AI, stateless core, model scaling, advanced math problems, sphere packing, federal policy, voluntary AI commitments.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Diana Elder and Nicole Dyer explore the value of writing an ancestor's military story by correlating multiple sources. Diana uses her father Bob Shults's World War II Navy records as a case study. She demonstrates how to use AI to cross-reference personal oral histories with official documents like muster rolls and discharge papers. Listeners learn how to build a comprehensive timeline that bridges the gap between limited personal recollections and formal military service records. The discussion highlights the use of the FamilySearch Simple Search tool to locate unindexed muster rolls that reveal specific locations and dates of service. Diana shows how she organizes this research in Airtable to track her father's path through training in Tennessee, Oklahoma, and Florida, and his eventual deployment on the USS Antietam in the Western Pacific. By integrating this specific documentation with historical context about training bases and ship operations, researchers can effectively flesh out an ancestor's service experience and uncover new avenues for future investigation. This summary was generated by Google Gemini. Links A Navy Life: The World War II Service of Bobby Gene Shults - https://familylocket.com/a-navy-life-the-world-war-ii-service-of-bobby-gene-shults/ "Formal Naval Air Station Memphis," NAVFAC  - https://www.bracpmo.navy.mil/BRAC-Bases/Southeast/Former-Naval-Air-Station-Memphis/ Technical Training, Naval Aviation News, November 15, 1944; imaged, Oklahoma Naval Air History - https://www.oklahomanavalairhistory.com/techarticle01.php Lelan Kent, "Yellow Water N.A.G.S. & Weapons Storage," Abandoned Southeast - https://abandonedsoutheast.com/2023/10/11/yellow-water-n-a-g-s-weapons-storage/ Andrew Bucholtz, "San Diego in WWII, Part IV: Home Base of the Pacific," posted 11 October 2023, SDHist - https://sdhist.com/home-base-pacific-san-diego-wwii-iv/ CV 36/CVA 36 / CVS 36 – USS Antietam, Seaforces-online - https://www.seaforces.org/usnships/cv/CV-36-USS-Antietam.htm Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.   Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

Cristiano Ronaldo's reported wedding guest list has created the ultimate sports collision: Conor McGregor and Khabib Nurmagomedov are both reportedly invited. Jamie Rudd looks at the potential reunion, PSG's new Google Gemini partnership, Wales withdrawing support for Gianni Infantino, and Marc Skinner leaving Manchester United Women.Subscribe for Premier League, MLS and Champions League coverage.AI was used for news gathering and production assistance.#Ronaldo #McGregor #Khabib #PSG #GoogleGemini #Infantino #ManchesterUnited #Football #SoccerAI gave us an assist in the creation of today's podcast, but the hat trick is our own.

Send us fan responses! Paperwork can feel like power until you realize it's also a trap. We kick things off with real talk about privacy, why we refuse to answer certain “legal” questions in public chats, and why an ID might be a representation of you rather than “you” itself. From there, we go deeper into how the host uses ministry language, private records, and mindset to navigate systems that most people only react to when it's already too late.We also get practical about documentation and organization, especially the overlooked value of family Bible records, baptismal certificates, school records, and other supporting documents often tied to identity and travel processes. The point isn't to obsess over a single template, it's to build a clean private record system you can actually use. Along the way, we talk about using AI tools like Google Gemini to recreate forms, modernize templates, and even build a service business around helping others with private records and documentation.Then we shift into the money side: holding company vs operating company, getting an EIN, using registered agents, building business credit, and focusing on cash flow instead of slow play tactics. You'll hear strong opinions about tax credits, payroll thinking, and why “freedom” looks more like structure and leverage than endless paperwork. We also touch controversial territory like “going foreign” with a Palau ID and what that could mean in real-world interactions, plus a run through vehicle ownership concepts like MSO or MCO. If you want high-level strategy, blunt accountability, and mindset pressure that pushes you to execute, press play, share this with a friend who needs structure, and leave a review with the biggest move you're making next.https://donkilam.com FOLLOW THE YELLOW BRICK ROAD - DON KILAMGO GET HIS BOOK ON AMAZON NOW! https://www.amazon.com/Cant-Touch-This-Diplomatic-Immunity/dp/B09X1FXMNQ https://open.spotify.com/track/5QOUWyNahqcWvQ4WQAvwjj?autoplay=trueSupport the showhttps://donkilam.com

Joey warned everyone that he was fasting for lab work and might get hangry. He woke up extra early and ate three gluten-free waffles, syrup, and a banana before 3:30am. Nancy finally watched the Idaho murders documentary on Netflix and was completely creeped out by it. The show talked about the recent Taco Bell lettuce scare and the confirmed cases of illness in Knox County. Nancy said she’d go back to Taco Bell immediately, just without lettuce, while Joey said he had already been back for a Crunchwrap Supreme. Hot Tea: Parker McCollum canceled a show because his wife went into labor and welcomed their second son. Bailey Zimmerman canceled his entire European tour to focus on family matters. Shania Twain said she would never remove Brad Pitt’s name from “That Don’t Impress Me Much,” but might add Harry Styles. Nashville now has a Morgan Wallen-themed Airbnb complete with a warning not to throw a chair off the balcony. Joey shared that wrestling with his 12-year-old son has become a reality check. He used to easily pin his boys down, but now they’re getting strong enough to make him work for it. Nerd News: A New York school paused plans for a humanoid robot teacher named Sally after parents learned the manufacturer also has ties to the adult companion robot industry. People claimed they found an alien missile on Mars, but NASA says it’s just a rock. Google Gemini is working on AI tools that remove “ums,” “likes,” and other filler words from dictated text. Lucky 7 for Dollywood tickets Nancy gave an update on her ankle injury after another doctor visit. The doctor now believes her pain could be a nerve-related condition and suggested it might actually be coming from how her brain is processing pain signals. Joey turned the conversation into jokes about shock therapy. A new Tennessee law now makes it easier to own a pet raccoon. Joey also highlighted new Tennessee laws allowing digital vehicle registrations, animal chiropractic care, and requiring more daily recess time for elementary school students. Nancy said her 13-year-old son couldn’t wait for tax-free weekend because it meant back-to-school shopping. Joey guessed her budget would be around $500, and Nancy hopes it will be less than that. See omnystudio.com/listener for privacy information.

Agents are getting more powerful by the day. And most workflows, outputs and human capabilities can't keep up. Is that a problem or opportunity? Before you answer that question, though, keep this in mind. Agents are *literally* about to become 20X faster overnight. Let's unpack what that means. Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Managing Dozens of Productive AI AgentsOpenAI Cerebras: 20x Faster Agent ModelsImpact of AI Agents on Human CoworkersAgent-Driven Workflows vs. Human CollaborationIncreasing Agent Reliance and Fading MentorshipAccidental Deskilling and Compression TaxProtecting Human Judgment and Learning HandoffsExpert-Driven Loops in AI WorkflowsMonthly Rebuilding of AI Strategies and ProcessesMiddle Management Evolution in AI Native CompaniesTimestamps:00:00 Future of AI and Work Dynamics05:25 Advancements in AI and productivity tools09:51 Growing your business with AI13:52 AI productivity and collaboration shifts15:08 Improving AI processing speed18:53 Using AI agents for delegation24:46 Discussing AI-related work challenges28:16 Ensuring accountability and communication30:39 Adapting to rapid digital change32:35 Show outro and newsletter sign-upKeywords: AI agents, faster AI models, OpenAI, Cerebras chip, 20x speed increase, automated workflows, agent management, solo agent supervisor, generative AI, knowledge work automation, agent-powered productivity, parallel machine teams, inference speed, productivity acceleration, Codex, Cloud Code, Google Gemini, Cloud Cowork, Copilot, recursive self improvement, expert-driven loops, human handoffs, deskilling, mentorship loss, AI native workplace, workplace automation, transactional work, productivity roadblocks, accidental deskilling, agent bun sandwich, compression tax, human in the loop, expert collaboration, agent trust, AI decision making, domain expertise, rapid workflow rebuilding, unlearning processes, organizational adaptation, enterprise AI adoption, future of work, middle management AI, AI-powered teamwork, human-agent collaboration, manager-agent ratios, personalized agent output, multi-agent coordination, skillset sharing, intentional automation, productivity strategySend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Send us Fan MailCan AI answer your customers without pushing them away? David Karandish has automated support for 20,000 companies, and he says deflection should make the experience better, not worse.David Karandish, founder and CEO of Capacity, joins Yanique Grant to unpack how to bring AI into customer support the right way. After selling Answers Corp for $960 million, David built Capacity into an omni-channel support automation platform serving roughly 20,000 customers. In this episode he shares the design principles that keep automation human, why your first AI project should be a small, provable win, and how AI agents are now running entire companies from finance to marketing.In this episode:- The design principles that make AI deflection improve the customer experience instead of frustrating people, from great escalation paths to leading with empathy - Why human behavior is largely universal across regions, and where support questions actually differ - What the $960M Answers.com journey taught David about people who are truly stuck and have nowhere to turn - The single most practical first step for any support leader: go get your small win, then iterate - How builder agents let a complete novice spin up workflows, integrations, and CSAT surveys just by askingFeatured resources:Book: Principles by Ray Dalio — a first-principles breakdown of how Dalio built one of the most successful investment firms of all time. Foundational AI models — Google Gemini, OpenAI, and Anthropic's Claude, used across the business. Jira — for tracking development tickets; David uses LLMs to analyze time-in-stage when off-the-shelf plugins fall short. Capacity — an AI-powered support automation platform that deflects emails, calls, and tickets across voice, SMS, WhatsApp, web, and email. ~20,000 customers, 250+ app integrations.Connect with the guest: Website: capacity.com Email: david@capacity.comFollow the show: X @NavigatingCX and join our private Facebook group, Navigating the Customer Experience Community. Hosted by Yanique Grant.

In this episode of the Research Like a Pro Genealogy podcast, hosts Diana and Nicole discuss the features and effectiveness of FamilySearch's new "Simple Search" tool. They explore how this experiment allows users to search billions of full-text records using natural language queries rather than traditional forms. Diana shares her experience testing the tool with her father's name, Bobby Gene Shults, demonstrating how the artificial intelligence parses results and helps distinguish between individuals with similar names. Listeners learn practical strategies for using Simple Search effectively, such as starting with broad queries and utilizing filters for collection, year, place, and record type to narrow results. Diana highlights the utility of the tool by successfully locating military muster rolls that confirm her father's service history and hospitalization during World War II. By experimenting with different search terms, spelling variations, and searching for associates, researchers can better leverage this technology to discover new documents for their family history. This summary was generated by Google Gemini. Links FamilySearch Labs / Experiments - https://www.familysearch.org/en/labs/ Tips for Using the New FamilySearch Simple Search Tool - https://familylocket.com/tips-for-using-the-new-familysearch-simple-search-tool/ Sponsor – Newspapers.com For listeners of this podcast, Newspapers.com is offering new subscribers 20% off a Publisher Extra subscription so you can start exploring today. Just use the code "FamilyLocket" at checkout.  Research Like a Pro Resources Airtable Universe - Nicole's Airtable Templates - https://www.airtable.com/universe/creator/usrsBSDhwHyLNnP4O/nicole-dyer Airtable Research Logs Quick Reference - by Nicole Dyer - https://familylocket.com/product-tag/airtable/ Research Like a Pro: A Genealogist's Guide book by Diana Elder with Nicole Dyer on Amazon.com - https://amzn.to/2x0ku3d Research Like a Pro with AI Workbook – Second Edition (eBook) - https://familylocket.com/product/research-like-a-pro-with-ai-workbook-second-edition-ebook/ 14-Day Research Like a Pro Challenge Workbook - digital - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-digital-only/ and spiral bound - https://familylocket.com/product/14-day-research-like-a-pro-challenge-workbook-spiral-bound/ Research Like a Pro Webinar Series - monthly case study webinars including documentary evidence and many with DNA evidence - https://familylocket.com/product-category/webinars/ Research Like a Pro eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-e-course/ RLP Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-study-group/ Research Like a Pro Institute Courses - https://familylocket.com/product-category/institute-course/ Research Like a Pro with DNA Resources Research Like a Pro with DNA: A Genealogist's Guide to Finding and Confirming Ancestors with DNA Evidence book by Diana Elder, Nicole Dyer, and Robin Wirthlin - https://amzn.to/3gn0hKx Research Like a Pro with DNA eCourse - independent study course -  https://familylocket.com/product/research-like-a-pro-with-dna-ecourse/ RLP with DNA Study Group - upcoming group and email notification list - https://familylocket.com/services/research-like-a-pro-with-dna-study-group/ Thank you Thanks for listening! We hope that you will share your thoughts about our podcast and help us out by doing the following: Write a review on iTunes or Apple Podcasts. If you leave a review, we will read it on the podcast and answer any questions that you bring up in your review. Thank you! Leave a comment in the comment or question in the comment section below. Share the episode on Twitter, Facebook, or Pinterest. Subscribe on iTunes or your favorite podcast app. Sign up for our newsletter to receive notifications of new episodes - https://familylocket.com/sign-up/ Check out this list of genealogy podcasts from Feedspot: Best Genealogy Podcasts - https://blog.feedspot.com/genealogy_podcasts/

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:ChatGPT Health Syncs Apple and Medical DataClaude Voice Mode Adds Opus and SonnetClaude Voice Mode Supports ConnectorsMicrosoft MAI Image 2.5 Pro Launch DetailsMicrosoft MAI Image Model Benchmark PreviewGoogle Gemini 3.6 Flash and Flashlight ReleaseGemini 3.6 Flash: Token Efficiency UpgradesGoogle Gemini Spark Agent for Task AutomationClaude Cowork "Record a Skill" With Voice NarrationChatGPT Voice on Desktop: Full Jarvis ModeChatGPT Voice Controls Apps via App ShotsCross-Platform AI Skills Sharing (Claude, Codex, GPT)Timestamps:00:00 Recent AI feature updates05:22 Unified health data management09:52 New voice feature explanation11:28 Launch of Microsoft's new image model16:17 Explaining the Gemini 3.5 models17:11 Developers benefiting from 3.6 Flash22:45 Introducing Gemini personal intelligence25:10 Claude Cowork's new skill feature28:32 New default feature in Claude Cowork34:22 Using AI like Iron Man35:09 Excitement for future AI advancements38:20 Wrapping up and subscribingKeywords: ChatGPT Jarvis mode, ChatGPT Health, OpenAI, Anthropic, Claude voice mode, Claude Cowork, Claude record a skill, Microsoft, MAI image 2.5 Pro, AI image generator, Google Gemini, Gemini 3.6 Flash, Gemini 3.5 Flashlight, Gemini Spark, Google AI agent, AI-powered personal assistant, AI agents, Agentic workflows, Multimodal AI, Token efficiency, Image generation, Voice-activated AI, AI-powered task automation, App shots, GPT Live, Remote browser, Computer code execution, Slack integration, GitHub integration, Notion, PowerPoint AI features, Workspace plans, Apple Health integration, Medical records AI, Health data privacy, Consumer AI, Chronic condition management, AI-powered document processing, AI for business, AI model benchmarking, AI for developers, AI economics, Personal intelligence, Automated triggers, Google Docs AI, Team collaboration AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner