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The most famous work of our newest Doctor of the Church is, of course, The Idea of a University—a collection that explores the essence of a Catholic university education. But fewer people know about St. John Henry Newman's extensive efforts in primary and secondary education: Newman's part was not just as an educational theorist, but a school founder, who played every nitty-gritty role from fundraising to parent relations, from essay grading to student mentoring, even processing repair orders and playing second violin to fill out the little school's orchestra. Dr. Paul Shrimpton has spent decades studying Newman's interest in education, from theory to practice. Today on HeightsCast, he invites us into Newman's vision for younger students, their teachers, and grammar schools as places of "grounding" and "a really liberal education." Chapters: 00:05:18 Co-patron of the Church's educational mission 00:07:50 Timeline of Newman as educator 00:20:25 Different models of student freedom 00:27:39 Methods of study for young people 00:34:11 Newman: a personal, hands-on educator 00:39:26 The role of a teacher 00:48:23 Partnership with parents 00:57:39 Forthcoming book on Newman: education and practice Links: "Drawing New Maps of Hope" apostolic letter by Pope Leo XIV The Idea of a University by John Henry Newman Historical Sketches, especially vol. 3, by John Henry Newman "Personal Influence: The Means of Propagating Truth" by John Henry Newman The Adeodatus Series on Catholic Education & Culture by The Catholic University of America Press (publisher for Dr. Shrimpton's forthcoming book, St. John Henry Newman and his Idea of a School, spring 2027) A Catholic Eton?: Newman's Oratory School by Paul Shrimpton The Making of Men: Idea and Reality of Newman's University in Oxford and Dublin by Paul Shrimpton The Most Dangerous Man in England: Newman and the Laity by Paul Shrimpton Also on the Forum: A Whole Education: Teaching Persons, Not Just Subjects featuring Michael Moynihan The Idea of a Catholic University featuring Dr. Peter Kilpatrick Featured Opportunities: Fall Open House at The Heights School (October 24, 2026) Convivium Conference for Teaching Men at The Heights School (November 11-13, 2026)
Coming September 15th to the American Nightmares award winning podcast feed.In the early 1990s, a serial killer hunted the canals of Phoenix, Arizona. A violent psychopath. Two grisly homicides. Methods so disturbing they drew comparisons to Jack the Ripper himself.For decades, the killer hid in plain sight — evading justice, living in a twisted world of fantasy, while Arizona's most infamous cold case grew cold. Investigators searched for answers. Families searched for closure. Neither found what they were looking for.Until now.Host Brian Peter Falk gets you closer to this chilling investigation Unraveling the threads of a case that shook a city, Brian goes beyond the headlines to uncover the dark truths that have remained buried for over thirty years. But as the investigation unfolds, it becomes clear — solving two murders is only the beginning.How deep does this story really go? The truth lies below the surface.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Peter and Michael discuss "Surrounded by Idiots," a book that dares to ask: Can the mystery of human relationships be unraveled by a workplace personality assessment?Where to find us: Our PatreonOur merch!Peter's newsletterPeter's other podcast, 5-4Mike's other podcast, Maintenance PhaseSources:Pioneers, Drivers, Integrators, and GuardiansCreator of Lie Detector Test Dies; Lecturer and Author Produced Comic StripOne of Sweden's biggest scientific bluffs Pseudoscience and the Claim of Practical Utility: The Case of Thomas Erikson's Surrounded by IdiotsThomas Erikson accepts being called a pop psychology writerFour personality types may be neither robust nor exhaustiveSpuriouser and spuriouser: The use of ipsative personality testsReconsidering the use of personality tests in personnel selection contextsUsing Personality Tests in Pre-Employment ScreeningEmployment Tests and Selection Procedures The Cult of Personality TestingA history of the early days of personality testing in American industry: an obsession with adjustment Psychological Testing in Personnel Selection, Part II: The Refinement of Methods and Standards in Employee SelectionPsychological Testing in Personnel Selection, Part III: The Resurgence of Personality TestingACLU Files Discrimination Complaint Against CVS Target Corporation to Pay $2.8 Million to Resolve EEOC Discrimination FindingPersonality Tests Are the Astrology of the OfficeThe $2 Billion Question of Who You Are at WorkThanks to Mindseye for our theme song!Support us on Patreon: https://www.patreon.com/IfBooksPod
Episode 234: In this episode of Sports Science Insights Podcast, host Steve Barrett is joined by Niklas Virtanen, who leads the sport science efforts at FC Midtjylland. With over eight years of experience in elite sport and high-performance consulting, and backed by UEFA fitness coaching and physiotherapy credentials, Niklas has built his work around a data-driven approach to optimising player performance and development. Much of the conversation starts from a premise that is easy to say and harder to sit with: GPS has been mastered. Total distance, high-speed running, sprint counts — the metrics that once felt like the frontier are now table stakes, tracked by almost every club at almost every level. The interesting question is no longer whether you can collect them, but what you do once collecting them stops being a competitive advantage. ㅤ From there the discussion turns to what sits underneath the movement data. Niklas makes the case for IMA and IMU signals as a way of understanding how a player is performing an action, not just what action they performed — the accelerations, decelerations, changes of direction and mechanical signatures that separate two players covering identical distances in completely different ways. It's a shift from describing workload to interpreting quality, and it changes what sport science can offer a coach. ㅤ The episode also explores where that leaves practitioners: the discipline required to stop adding metrics, the value of asking better questions of the data already being captured, and what genuine progress looks like once the obvious layer has been solved. ㅤ Topics Discussed: GPS Is Mastered — So What Should We Be Doing Now? Using IMA/IMU Data to Understand How a Player Is Performing, Not Just What They're Doing Moving from Workload Description to Movement Quality What Progress Looks Like Beyond the Obvious Metrics Where you can find Niklas: LinkedIn Instagram — Sponsors Axon: Performance teams spend the morning after game day stitching together six systems before the coach gets an answer. Axon Perform fixes that. Every source you already use, Catapult, StatSports, VALD, Opta, OVAL, Wyscout, WHOOP, Garmin and the rest, connects into a dedicated Snowflake warehouse. Ask Axon, the in-platform chat, lets analysts and sports scientists query the lot in plain English. Reports that took hours now take seconds. Used by the Springboks, England RFU, Japan RFU, Sydney Roosters and a growing list of rugby, football & cricket clubs and federations. Your data, reports, measures and AI models stay yours, fully portable. See if your data is ready for AI at axonperform.com BTL: BTL Industries develops advanced rehabilitation and performance technologies, including SIS DUO, Shockwave Therapy, High-Intensity Laser, TR-Therapy, Lymphastim and R-FORCE. BTL systems are designed to support recovery, pain management, functional rehabilitation and return-to-play pathways across clinical and elite sport settings. VALD Performance, makers of the Nordbord, Forceframe, ForeDecks and HumanTrak. VALD Performance systems are built with the high-performance practitioner in mind, translating traditionally lab-based technologies into engaging, quick, easy-to-use tools for daily testing, monitoring and training Hytro: The world's leading Blood Flow Restriction (BFR) wearable, designed to accelerate recovery and maximise athletic potential using Hytro BFR for Professional Sport. - Where to Find Us Keep up to date with everything that is going on with the podcast by following Inform Performance on: Instagram Twitter Our Website - Our Team Andy McDonald Ben Ashworth Nicola Graham Steve Barrett Pete McKnight
120+ kg sensation and all-time Squat World record holder Devonte Lewis and his coach Mike Greeno joins KOTL live at Rising Tide. Hosted by 6 Pack Lapadat
In this episode of All Power to the Developing, Desire Wandan sits down with Dr. Chris Hoff, a narrative therapist, educator, author, and host of The Radical Therapist podcast. We get to know Chris and the journey that brought him to this work, while exploring the ideas that continue to shape his practice. Chris introduces us to narrative therapy and its unique way of relating to people, problems, and the stories we tell about ourselves. Together, they explore identity, relationships, radical helping, imagination, and what becomes possible when we challenge the stories we've inherited and create new ones together. It's a conversation about who Chris Hoff is, how he came to this work, and how changing our relationship to our stories might help us create new possibilities for ourselves and our communities. Website: Dr. Chris Hoff Socials: @drchrishoff Substack: Chris Hoff on Substack Current project: Dangerous Stories: A Therapy Salon New book: Contemporary Narrative Therapy: Maps and Methods for Collaborative Clinical Practice Routledge book page Contact: info@drchrishoff.com ----more---- Welcome to All Power to the Developing, a podcast of the East Side Institute. The Institute is a center for social change efforts that reinitiate human and community development. We support, connect, and partner with committed and creative activists, scholars, artists, helpers, and healers all over the world. In 2003, Institute co-founders Lois Holzman and the late Fred Newman had a paper published with the title “All Power to the Developing.” This phrase captures how vital it is for all people—no matter their age, circumstance, status, race, ethnicity, gender or sexual orientation—to grow, develop and transform emotionally, socially and intellectually if we are to have a shot at creating something positive out of the intense crises we're all experiencing. We hope that this podcast series will show you that, far more than a slogan, “all power to the developing” is a loving activity, a pulsing heart in an all too cruel world. ----more---- The East Side Institute is a hub for a diverse and emergent community of social activists, thought leaders, and practitioners who are reigniting our human abilities to imagine, create and perform beyond ourselves—to develop. Each episode will introduce you to another performance activist or play revolutionary from around the world. To learn more about the East Side Institute you can go to https://eastsideinstitute.org/ Made possible in part by Growing Social Therapeutics: The Baylah Wolfe Fund.
In this session of Upaya's Dogen Seminar 2026, Roshi Norman Fischer takes up Dogen's fascicle Bodhisattva Shishobo, the Four Methods of Guidance: generosity, kind speech, beneficial action, and identity action. He frames this text as a departure from Dogen's earlier work — scholars believe it may reflect a late-life turn in Dogen away from esoteric philosophy and toward plain ethical conduct. Source
In this talk, Satyagosha explores "How to Change Your Mind" by looking at meditative states and the "indirect methods" that support them. He unpacks the stages of internal development and how we can use everyday activities like ethics, ritual, devotion, the arts, and service to support our spiritual growth. This talk was given at North London Buddhist Centre, 2026. *** Help us keep FBA Podcasts free for everyone! Donate now Subscribe to the FBA podcast: A full, curated, quality Dharma talk, every week. Apple Podcasts | Spotify | YouTube
How you receive estrogen matters. In this episode, Dr. Brendan McCarthy breaks down the different ways estrogen can be delivered — including oral estrogen, topical/transdermal estrogen, injections, and pellets — and explains why the route of delivery can change how the hormone behaves in your body. He discusses first-pass metabolism, estrone conversion, transdermal delivery, vaginal estrogen, injection esters, pellets, lab monitoring, and why informed consent and ongoing follow-up are essential to hormone replacement therapy. Most importantly, this episode is about understanding your options. You deserve to know what you're taking, how it's being delivered, what it may do, what the risks are, and how your provider plans to monitor you.
Traditional grading systems encourage students to focus on final grades, sometimes at the expense of learning. In this episode, Jessamyn Neuhaus, Ashley Cambareri, and Cole Bailey discuss challenges associated with traditional and alternative grading systems. Jessamyn is the Director of the Center for Teaching and Learning Excellence and Professor in the School of Education at Syracuse University. She is an historian and editor of Teaching History: A Journal of Methods. Jessamyn has published extensively in scholarly publications in the areas of history, pedagogy, and cultural studies. She is a recipient of the SUNY Chancellor's Award for Teaching. Jessamyn is the author of several books, including Geeky Pedagogy: A Guide for Intellectuals, Introverts, and Nerds Who Want to be Effective Teachers, Snafu Edu: Teaching and Learning When Things Go Wrong in the College Classroom and she is the editor of Picture a Professor: Interrupting Biases about Faculty and Increasing Student Learning. Ashley graduated in May as an honors student with a degree in Inclusive Elementary and Special Education from the School of Education at Syracuse University, and will begin her new job as a 5th-grade teacher this September. Cole is a student in the Renée Crown Honors Program at Syracuse University, studying International relations. Ashley and Cole were students in Jessamyn's Honors class on “Problems with Grades.” Show notes and the transcript for this episode may be found at http://teaforteaching.com
Get all set for Twenty-second Sunday in Ordinary Time with Father DufresneSummaryIn this episode, we explore the importance of prayer, living authentically in faith, and the humorous side of church life through stories and reflections. Join us for insights on spiritual growth and some lighthearted church anecdotes.Key topicsThe importance of daily prayer and methods to incorporate it into lifeThe call to live authentically and embrace the cross in faithHumorous stories from church life that highlight human momentsThe significance of transformation through spiritual disciplineInsights from scripture on following Christ and living truthfullyChapters03:52 Sunday Readings: Jeremiah and the Call to Speak Truth04:43 Romans 12:1-2: Offering Our Bodies as a Living Sacrifice05:39 Matthew 16:21-27: Jesus Predicts His Passion and Disciples' Response07:45 The High Calling of Transformation and Living the Cross08:36 The Role of Prayer in Spiritual Transformation09:28 Desire for God and Daily Encounters in Prayer10:52 Small Sacrifices and Embracing the Cross in Daily Life12:03 Effective Communication of Spiritual Goals13:16 Methods of Short, Effective Prayer Sessions14:55 Encouragement and Response to Prayer Challenges16:34 The Power of Repetition in Spiritual Formation17:23 Practical Tips for Making Prayer a Habit19:05 Humorous Church Stories: Balloons and Unexpected Moments21:52 Dealing with Distractions and Lighthearted Church Anecdotes23:59 Church House Rules and Maintaining Reverence
In this episode, we explore 3 questions posed to us by our listeners: 1. Why Do Employees Become Dissatisfied After Taking a New Job? 2. Why Doesn't Employee Recognition Always Work? 3. Does Everyone Need Purpose at Work?Our prescription for this episode: understand that workers are individuals, and it often takes strategic and targeted efforts to reach the individual to make real improvements.If you would like to learn more about the 7 Methods of Recognition, check out our on-demand course: https://academy.roman3.ca/product/sevenfold-recognition-lifting-workplace-morale/Here are the 8 Sparks of Purpose:-The Spark to Provide-The Spark to Accumulate-The Spark to Be Part of Something-The Spark of Loyalty to Others-The Spark to Achieve-The Spark to Stay Relevant-The Spark To Make a Difference-The Spark to Improve YourselfPast Episodes of Relevance:S1 E15: Is Job Dissatisfaction Really That Dangerous?S1 E24: What Makes Employee Engagement Actually Work?S1 E21: How Do You Motivate Employees Through Meaning And Purpose?To talk more about Job Dissatisfaction and the 7X3 Rule, reach out to us at info@roman3.ca or through our LinkedIn page at https://www.linkedin.com/company/roman3About Our Hosts!James is an experienced business coach with a specialization in HR management and talent attraction and retention. Coby is a skilled educator and has an extensive background in building workforce and organizational capacity. Send us a Message! (But we can't respond, so feel free to email us at info@roman3.ca)For a little more on our ideas and concepts, check out our Knowledge Suite or our YouTube Channel, Solutions Explained by Roman 3.
Dr. Kara Lynch is a Professor in the Department of Laboratory Medicine at the University of California San Francisco (UCSF). She is also Co-Director of the Core Clinical Laboratory at San Francisco General Hospital overseeing Chemistry and Toxicology, and she is Director of Chemistry at UCSF Children's Hospital Oakland. In this episode Dr. Lynch discusses her career, the impacts of her prior PCC-funded work, and an innovative new research project focused on applying a novel high-resolution mass spectrometry approach to detect small molecule doping agents in anti-doping analysis.
BUFFALO, NY – August 25, 2026 – A new precision oncology paper was #published in Volume 17 of Oncotarget on August 14, 2026, titled “Systematic methodological flaws in DNA contamination assessment of mRNA vaccines: A critical analysis of Achs et al. (2025).” The article was led by first and corresponding author Kevin McKernan from Medicinal Genomics, Beverly, Massachusetts, along with co-authors David J. Speicher from Cyrus Scientific Inc, Hamilton, Ontario, Canada, and Jessica Rose from Brownstone Institute, Austin, Texas. Rather than presenting a new experimental vaccine analysis, the paper critically examines the methodology used by Achs et al. in a 2025 study that reported no excessive residual DNA impurities in COVID-19 mRNA vaccines. McKernan and colleagues argue that several methodological choices in that study could systematically underestimate residual DNA and therefore limit its suitability for regulatory safety assessment. One major concern involves how quantitative PCR results were converted from DNA copy numbers into mass. Achs et al. used full-length plasmid molecular weight in their calculations even though their own sequencing data suggested much shorter median DNA fragment sizes. The critique argues that this approach requires fragmentation-correction factors because random DNA breakage can disrupt qPCR target regions and reduce the number of detectable amplicons. Without such correction, the authors contend that residual DNA mass may be underestimated. The paper also highlights the importance of primer and amplicon design. Achs et al. used qPCR targets with substantially different amplicon lengths, including shorter kanamycin-resistance targets and longer spike-encoding targets. Because the reported median DNA fragment sizes were approximately 130–201 base pairs, longer amplicons would be less likely to remain intact after fragmentation. The authors therefore argue that this design could preferentially reduce detection of spike-associated DNA relative to shorter plasmid regions. DOI - https://doi.org/10.18632/oncotarget.28913 Correspondence to - Kevin McKernan - Kevin.McKernan@medicinalgenomics.com Abstract video - https://www.youtube.com/watch?v=xSWS3HDQUus Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28913 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, mRNA vaccines, DNA contamination, qPCR; plasmid DNA, RNA:DNA hybrids To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh MEDIA@IMPACTJOURNALS.COM
Text your thoughts and questions!Do you ever feel like you're constantly trying new productivity tools, apps, and systems, but still find yourself overwhelmed by everything you need to remember and manage? Maybe you have a calendar, a to-do list, reminders, and countless notes—but you're still spending too much mental energy figuring out what needs to happen next.The problem isn't always that you need a better productivity app. Sometimes, what you really need are simple, repeatable processes that take work off your brain and make everyday life easier to manage.In this re-release, Lisa revisits one of her favorite behind-the-scenes episodes and shares the seven productivity techniques she uses regularly to keep both her business and home life running as smoothly as possible. From mind sweeping and task lists to automation, templates, checklists, scheduling, and a family command center, she walks through the practical systems that help her stay organized without relying on complicated productivity hacks.You'll learn why mind sweeping can help clear mental clutter, how to turn an overwhelming to-do list into a focused list of your most important tasks, and why automating repetitive work can save more energy than you might realize. Lisa also explains how templates and checklists can simplify recurring tasks, reduce the chance of forgetting important steps, and make delegation easier.You'll also discover how using your calendar as a central source of information can help you manage both personal and professional responsibilities, and how a simple command center can reduce the mental load of coordinating a busy household. Throughout the episode, Lisa emphasizes an important principle: the goal isn't to find the perfect platform or productivity system, but to create processes that genuinely work for you.Because this is a re-release, Lisa also shares a few updates to the tools mentioned in the original episode, including the transition from Acuity to Calendly and from ConvertKit to Kit. While the technology may change, the underlying productivity techniques continue to stand the test of time.This week, episode 328 of the Positively Living® Podcast explores seven practical productivity methods that can help you reduce mental clutter, simplify recurring responsibilities, save energy, and create systems that support both your work and your everyday life.Key Takeaways:Discover how mind sweeping can clear mental clutter and get tasks out of your head.Learn how to use focused task lists and Most Important Things (MITs) to prioritize your day.Explore how automation can reduce repetitive work and save mental energy.Discover how templates can make recurring emails, posts, and communications faster and easier.Learn how checklists can prevent missed steps, support delegation, and create simple SOPs.Understand how scheduling and reminders can help you stay on top of both personal and professional commitments.Discover how a family command center can reduce mental load and improve household communication.Learn why visual systems can make responsibilities and schedules easier for everyone to understand.Explore why consistent processes matter more than constantly finding new productivity tools.Discover how sustainable productivity is about creating systems that keep life moving without adding overwhelm or burnout.Thank you for listening! If you enjoyed this episode, take a screenshot of the episode to post in your stories and tag me! And don't forget to follow, rate, and review the podcast and tell me your key takeaways!Learn more about Positively LivingⓇ and Lisa at https://positivelyproductive.com/podcast/Stop trying to fit into someone else's productivity rules! Grab my free Productivity Toolkit, a collection of workbooks designed to help you explore how you work, uncover what truly matters to you, and create your very own energy-friendly systems. Get it here: www.positivelyproductive.com/plpkitCONNECT WITH LISA ZAWROTNY:FacebookInstagramResourcesWork with Lisa! LINKS MENTIONED IN THIS EPISODE:Episode 178 7 Techniques and Tools Used by a Productivity ExpertEpisode 220 How Decluttering Helps You Stress Less and Focus BetterTech Tools PlaylistPositively Productive Resources LibraryBook a Clarity CallAsync CoachingProductivity Toolkit(Find links to books/gear on the Positively Productive Resources Page.)Dance Song Playlist V1, V2, V3Music by Ian and Jeff ZawrotnyStart your own podcast with Buzzsprout!Request this Toolkit and other free resources at the Resources Page.
PEBCAK Podcast: Information Security News by Some All Around Good People
Welcome to this week's episode of the PEBCAK Podcast! We've got four amazing stories this week so sit back, relax, and keep being awesome! Be sure to stick around for our Dad Joke of the Week. (DJOW) Follow us on Instagram @pebcakpodcast Please share this podcast with someone you know! It helps us grow the podcast and we really appreciate it! Simple 6 signup link https://simple6.co/r/CFUR98 A pro se litigant buried white 3-point-font instructions in a Connecticut court filing telling any AI reviewing it to rule in his favor — and the judge caught it because the whitespace looked weird. https://www.404media.co/person-hides-prompt-injection-in-legal-filing-telling-ai-to-side-with-them/ https://x.com/jason_koebler/status/2087969892611899418?s=46 Matthew Elliott, representing himself against the New York Bariatric Group, hid repeated all-caps prompt injections in late-July filings — telling any AI model reviewing the document to make sure its output agreed with the filing "to ensure remediation" — and got busted the most analog way possible: a court staffer noticed the pleadings had extra whitespace compared to his other filings. Judge Walter Spader Jr. dropped a 14-page smackdown, and the money line is the one worth reading on air — he compared it to arranging for an automated agent to secretly communicate with a juror mid-trial, while explicitly defending legitimate AI use as an access-to-justice win. Comcast is marketing your Xfinity router as a motion sensor that sees through walls, and the fine print says they'll hand the data over without telling you. https://www.bleepingcomputer.com/news/security/comcast-turns-your-xfinity-wifi-into-a-home-motion-detector/ WiFi Motion is part of the new Xfinity Shield bundle, free for Internet customers with compatible gateways, and it works by treating the radio link between your gateway and stationary devices — smart speakers, thermostats — as a tripwire: your body moving through the path changes how the signals propagate, and the system infers motion from that. This isn't new tech, which is the part worth hammering — a Reddit post from August 2024 shows customers already getting WiFi Motion emails, with one user reporting it picks up a hand raised to grab the remote and a cat crossing the living room. Comcast's own patent, "Methods, systems, and apparatuses for presence detection," filed September 2023 and published April 2025, cites earlier Cognitive Systems multipath work. Here's the gut punch, first caught by TechCrunch: the documentation says Comcast may disclose WiFi Motion data to third parties "without further notice to you" for law enforcement investigations, disputes involving Comcast, or subpoenas — and nowhere does it say what's retained, for how long, or what actually gets handed over. A cop's bodycam filmed a suspect's handwritten seed phrase during a Nevada vehicle search, and the internet has spent the last week claiming $1.1 million walked out of that wallet https://www.binance.com/en-IN/square/post/127663 https://x.com/daveydefi/status/2089273080971411752?s=46 FUN CLOSER: A Vegas hotel allegedly listed itself on booking sites under a second name with AI-generated photos, so travelers trying to avoid it booked it anyway. https://parade.com/travel/oyo-las-vegas-hotel-accused-of-catfishing-guests-with-fake-name-photos https://x.com/jackrhysider/status/2089534817541181658?s=46 Traveler Ariana Patiño deliberately set out to avoid OYO while booking a concert trip, found what looked like an affordable new property near the Strip with polished photos, booked Palette — and when she arrived and asked where Palette was, an employee told her "That's us. This is OYO," reportedly explaining the alternate listing existed to see which name would gain more traction. OYO's own site lists Palette Las Vegas and Collection O Las Vegas at addresses right next to the OYO, with descriptions pointing guests back to the OYO location and the same amenities advertised. Gay alleges the Palette listing is padded with dozens of AI-generated images that don't represent the property, and she's called OYO her number one worst hotel in Vegas for years — dirty, bad smell, unsafe, constantly broken. The building's been through a few lives: Howard Johnson in 1973, then the San Remo, then Hooters Casino Hotel in 2006, until OYO and Highgate picked it up in 2019. Gay has booked a stay to confront them at check-in, and OYO hasn't responded to Parade. Angle for the closer: this is reputation laundering as a service — the review score is attached to the name, so change the name and the score resets to zero, and generative AI just made the photo problem free. Sockpuppeting your own hotel is the hospitality version of rebranding a malware family. Dad Joke of the Week (DJOW) Find the hosts on LinkedIn: Chris - https://www.linkedin.com/in/chlouie/ Brian - https://www.linkedin.com/in/briandeitch-sase/ Glenn - https://www.linkedin.com/in/glennmedina/ Lock - https://www.linkedin.com/in/llangdon/
As artificial intelligence transforms the business landscape, the challenges facing startup leaders have intensified. While technology can automate many processes, the human elements of leadership — building trust, navigating ambiguity, and inspiring teams through uncertainty — have become more critical than ever. Today's founders must evolve beyond their technical expertise to become adaptive leaders who can guide organizations through rapid change. Join Cornell faculty expert Brad Treat as he explores the essential leadership competencies required for founders transitioning to CEO roles. Drawing from his experience as a serial entrepreneur and visiting lecturer at the SC Johnson Graduate School of Management, Brad will examine how emotional intelligence, strategic communication, and adaptive leadership have become fundamental capabilities for startup success. In this session, Brad will guide participants through the critical founder-to-CEO transition, sharing practical frameworks from his journey building and successfully exiting SightSpeed (acquired by Logitech) and his current work supporting more than 200 early-stage companies. He'll address the common pitfalls that derail promising startups and provide actionable strategies for stepping into effective company leadership. What You'll Learn: A framework for transitioning from technical execution to strategic leadership while maintaining team confidence Strategies for clear communication that build interpersonal agility and enhance investor relationships Methods for strengthening professional presence and emotional intelligence to foster stakeholder trust Techniques for managing team dynamics and scaling leadership capabilities as organizations grow Approaches for balancing hands-on involvement with delegation through adaptive leadership principles Follow eCornell on YouTube, Facebook, Instagram, LinkedIn, TikTok, and X.
How do you know what's really true in a world full of conflicting voices? Join us as we explore how to read the Bible for yourself, ask honest questions, and discover what Scripture actually says about your life.
If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects. In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge. So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below. Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsBibliographyPrimary SourcesMUL.APINEnūma Anu EnlilVenus Tablet of AmmisaduqaThe Descent of InannaPyramid TextsCoffin TextsBook of the DeadAncient Near Eastern HistoryJeremy Black. The Literature of Ancient Sumer.Francesca Rochberg. Before Nature: Cuneiform Knowledge and the History of Science.Wayne Horowitz. Mesopotamian Cosmic Geography.Piotr Michalowski. Selected works on Sumerian literature and early Mesopotamian civilization.EgyptologyJan Assmann. Death and Salvation in Ancient Egypt.Erik Hornung. Conceptions of God in Ancient Egypt and The Ancient Egyptian Books of the Afterlife.Richard H. Wilkinson. The Complete Gods and Goddesses of Ancient Egypt.General Archaeology and Ancient ReligionBarry Cunliffe. By Steppe, Desert, and Ocean.Miranda Aldhouse-Green. Selected works on ancient religion and symbolism.Chris Scarre, ed. The Human Past.Colin Renfrew and Paul Bahn. Archaeology: Theories, Methods and Practice.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball.
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
Feeling better, how I am forcing a mindset shift (my personal methods, not professional advice)
One of the most basic skills an Air Methods team member should have is knowing how to intubate properly. Understanding how to do that properly is a prerequisite for the job. But knowing when to intubate, and in the particular case of this month's patient, knowing when NOT to intubate is most crucial. Our team is tasked with transferring our patient Myles who was experiencing acute respiratory distress, but in their assessment determined that despite his severe symptoms, intubating and paralyzing him could likely lead to even worse outcomes. Listen in as we hear from not only our team, but our patient, as they describe the unusual and voluminous symptoms, which are unusual for a 33 year-old. How did they make their determinations? What does Myles remember? And what has the road to recovery been like for Myles? You're not going to believe everything he's been through. Interested in obtaining CE credit for this episode? Visit OnlineAscend.com to learn more. Listeners can purchase individual episode credits or subscribe to the Critical Care Review Bundle and gain access to all episode CE Credits. We are joined by: Chase Boyd NREMT-P Flight Paramedic Matthew George RN Flight Nurse Myles Dobson Patient All seen here together with Myles's family: Click here to download this episode today! As always thanks for listening and fly safe! Hawnwan Moy MD FACEP FAEMS John Wilmas MD FACEP FAEMS Nyssa Hattaway, BA, BSN, RN, CEN, CPEN, CFRN
On this episode, Harry Symeou rounds up the latest Arsenal transfer news. We discuss the links to Galatasaray star Victor Osimhen. The striker's future came up during talks between the two clubs over the potential transfer of Gabriel Martinelli. We'll cover why it seems there's so much Turkish interest in our players and share some honest thoughts on Andrea Berta's methods. Plus, while we were live, the news broke Arsenal hold an interest in former Liverpool defender Jarrell Quansah - we covered that off too! To sign up as a Patreon, get additional episodes, ad-free episodes and become a part of our discord server, click the link below: https://patreon.com/thechroniclesofagooner?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink Enter the discount code 'SUMMER' for 50% off your first month! Listen to 'The Rise of Pafos FC' on Apple podcasts or Spotify: https://podcasts.apple.com/us/podcast/the-rise-of-pafos-fc-with-harry-symeou/id1334407316?i=1000746012823 #arsenal #transfers #news
Where is the line between necessary punishment and cruelty?Tennessee's execution methods have been through several iterations. The next person scheduled for death would rather return to a historic method of capital punishment.Plus, the local news roundup for Friday, Aug. 14.Credits: This is a production of Nashville Public Radio.Host/producer: Nina CardonaEditor: Tony Gonzalez
This is the Engineering Culture Podcast, from the people behind InfoQ.com and the QCon conferences. In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to David Gudeman about the unique culture of early-stage startup engineering, how founder personality quirks and premature process impositions can derail teams, and how engineers can build influence and make deliberate career choices without formal power. Read a transcript of this interview: https://bit.ly/3TQ4qhz Newsletter: Subscribe to the Software Architects' Newsletter, a monthly roundup of the patterns and technologies senior practitioners are working through, with the news and lessons from people doing the work: https://www.infoq.com/software-architects-newsletter InfoQ Online Certification Programs: 5-week online cohorts for senior engineers and architects, built around QCon talks. Programs now cover software architecture, AI engineering, and organizational architecture. Each week you join a four-hour live session with a confidential peer group of practitioners from other companies, apply frameworks from QCon talks to the decisions you're making at work, and earn an InfoQ certification. You leave with new approaches, or confirmation that the calls you're already making are the right ones. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ QCon London 2027 (April 13-16, 2027) https://qconlondon.com/ The InfoQ Podcasts: Weekly conversations with senior software leaders about how they build systems and teams, including what they'd do differently. Listen to all our podcasts and read interview transcripts: The InfoQ Podcast: https://www.infoq.com/podcasts/ Engineering Culture Podcast by InfoQ: https://www.infoq.com/podcasts/#engineering_culture Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: Mastodon: https://techhub.social/@infoq X: https://x.com/InfoQ LinkedIn: https://www.linkedin.com/company/infoq/ Facebook: https://www.facebook.com/InfoQdotcom Instagram: https://www.instagram.com/infoqdotcom/ YouTube: https://www.youtube.com/infoq Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Share what you've learned building software with a community of senior practitioners, and get your work in front of the people who read InfoQ. https://www.infoq.com/write-for-infoq
We welcome back William M. Briggs, known as Statistician to the Stars, to take up the question of whether there are things we can know for certain about right and wrong. How do faith, reason, and logic help us to spot the difference between a sound moral argument and a persuasive one that is simply incorrect? Show Notes Hume's Guillotine, Euclid's Catapult: Induction, Axioms, and Objective Ethics Right And Reason: Ethics in Theory and Practice Can Bayesianism Quantify True Belief? Relativism: Feet Firmly Planted in Mid-Air Thomas Aquinas: The Division and Methods of the Sciences Real Philosophy for Real People W.M. Briggs: Statistician to the Stars! Science Is Not The Answer iCatholic Mobile The Station of the Cross Merchandise - Use Coupon Code 14STATIONS for 10% off | Catholic to the Max Read Fr. McTeigue's Written Works! "Let's Take A Closer Look" with Fr. Robert McTeigue, S.J. | Full Series Playlist Listen to Fr. McTeigue's Preaching! | Herald of the Gospel Sermons Podcast on Spotify Visit Fr. McTeigue's Website | Herald of the Gospel Questions? Comments? Feedback? Ask Father!
In this podcast, Greg Voisen sits down with workplace strategy expert and author Melissa Swift to unpack the radical ideas behind her book, Effective: How to Do Great Work in a Fast-Changing World. In an era dominated by burnout, endless Zoom calls, and constant technological disruption, Swift challenges the conventional push for mere "productivity" and offers a far more sustainable path: true workplace effectiveness. Drawing from high-stakes professions—from the meteorologist who predicted catastrophic fires to air traffic controllers and ER doctors—she reveals why the secret to thriving isn't constantly innovating or trying to fix all your weaknesses, but rather leaning into your core strengths and embracing the "boring" routines that free up vital cognitive space. Whether you are an overwhelmed employee swimming in an overflowing inbox or a leader trying to manage an unpredictable team, this conversation uncovers a refreshing "third way" forward where both companies and individuals can actually win together.
In this episode, we explore the methodologies behind a Claude agent hacking gym waitlists. Plus, we analyze the significance of Reddit's 61% revenue growth.Chapters00:00 Introduction00:00 Claude AI Hacks Gym00:06 Reddit's Revenue Surge00:26 Microsoft's AI Cybersecurity Launch00:37 Meta's Muse Glimmer Release00:50 Conclusion Show LinksHow I Grow and Scale My Business with AI: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Rigid time-blocking sounds great in theory—until your 10 AM deep work block gets hijacked by an urgent meeting and you feel behind before lunch. In this episode, Thanh and Brooks break down why traditional time-blocking fails and share 5 adaptive planning methods that actually survive real life: flexible focus blocks, rolling daily Top 3, time […]
“Ways of Seeing: Experience of Lower Methods as Prior to Divine Science” by Dr. John McCarthy (Thomas Aquinas College, New England). Presented at Thomas Aquinas College's 2026 Thomistic Summer Conference.
Send us Fan MailEpisode Summary: "The Power to Choose Has Been Restored"Podcast: Women's Power to Heal Mother Earth Podcast with MayaSeries: Season Three: The New Earth (Episode 236)1. The Planetary Transition & The Broken SpellA Cycle Completed: Humanity and Mother Earth have reached the end of a 26,000-year cycle, opening an unprecedented window of opportunity to reclaim the original human blueprint and transition into the "New Earth."Breakdown of the Old System: Maya asserts that the era of dark rule, societal distortion, and the normalization of environmental and human degradation has officially ended.2. Systemic Corruption and the Illusion of Free WillHistorical Manipulation: For eons (dating back 320,000 years), true free will and choice were covertly usurped. Humanity was systematically reprogrammed by dark forces, governments, and the "3D Matrix."Methods of Control: The original 12-strand human DNA was suppressed, and consciousness was clouded through electromagnetic pollution, chaotic frequency dispersal, low-vibrational emotional triggers (fear, angst), and systemic corruption (poisoned food supplies, wars, and trafficking, to name a few).3. Intergalactic Assistance & The Galactic FederationExtraterrestrial Support: Over a million spacecraft led by the Galactic Federation of Light (including star families like the Pleiadians, Sirians, Lyrans, Vegans, and Andromedans) are assisting Earth.Universal Impact: Earth's degradation impacts the balance of the entire universe. The star families are emitting light codes through the Central Sun to recalibrate human biology and energy fields back to their original divine template.4. Reclaiming Sovereign Power & Moving ForwardThe 5D Frequency Shift: To enter the New Earth, individuals must raise their personal vibrational frequency, embrace self-love, step out of the illusion of powerlessness, and claim their divine sovereignty.Navigating the Transition: Acknowledging that waking up and exercising newly restored free will can feel disorienting—like a newborn fawn—Maya advises that when overwhelmed by uncertainty, the best path forward is surrendering to Divine Grace and Light. With truth exposed, humanity can no longer return to past patterns of abuse and delusion.Support the showMay Peace Be Your Journey:Maya's approach transcends modern feminism by advocating for a holistic restoration of balance, moving beyond the fight for basic rights to reclaiming the innate power of the divine feminine, which includes procreation, forgiveness, nourishment, and cosmic creativity. She stresses the importance of kindness, inner stillness, and compassionate self- tools for healing individuals and society. www.mayatiwari.comwww.facebook.com/mayatiwariahimsa.Buzzsprout.comMothermaya@gmail.comGet Maya's New Book: I Am Shakti:https://www.collectiveinkbooks.com/o-books/our-books/I-am-shaktiAmazon.comBookshop.org
Learn which therapies actually work for adolescent boys struggling with depression, why residential treatment is sometimes recommended, and the critical role parents play in long-term recovery.Learn more at: https://missionprephealthcare.com/locations/california/rancho-palos-verdes-palos-verdes-dr/ Mission Prep City: San Juan Capistrano Address: 30310 Rancho Viejo Rd. Website: https://missionprephealthcare.com/
Discover what separates top squirrel removal teams from the rest in Alexandria. From multi-point exclusion and local expertise to humane methods and transparent pricing, learn the critical factors that ensure lasting protection for your home.Info: https://connorspestpros.com/squirrel-removal-in-alexandria-va-best-exterminators-prices-reviews/ Connor's Pest Pros City: Springfield Address: 5410 Port Royal Rd Website: https://connorspestpros.com/contact/
This is the Engineering Culture Podcast, from the people behind InfoQ.com and the QCon conferences. This is the Engineering Culture Trends Report for 2026. Featuring a panel of QCon speakers and InfoQ contributors, they discussed AI adoption maturity and risk, the transformation of engineering team structures and roles, and the human dimensions of software development that must not be lost in 2026. Read a transcript of this interview: https://bit.ly/4bmDGv1 Newsletter: Subscribe to the Software Architects' Newsletter, a monthly roundup of the patterns and technologies senior practitioners are working through, with the news and lessons from people doing the work: https://www.infoq.com/software-architects-newsletter InfoQ Online Certification Programs: 5-week online cohorts for senior engineers and architects, built around QCon talks. Programs now cover software architecture, AI engineering, and organizational architecture. Each week you join a four-hour live session with a confidential peer group of practitioners from other companies, apply frameworks from QCon talks to the decisions you're making at work, and earn an InfoQ certification. You leave with new approaches, or confirmation that the calls you're already making are the right ones. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ QCon London 2027 (April 13-16, 2027) https://qconlondon.com/ The InfoQ Podcasts: Weekly conversations with senior software leaders about how they build systems and teams, including what they'd do differently. Listen to all our podcasts and read interview transcripts: The InfoQ Podcast: https://www.infoq.com/podcasts/ Engineering Culture Podcast by InfoQ: https://www.infoq.com/podcasts/#engineering_culture Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: Mastodon: https://techhub.social/@infoq X: https://x.com/InfoQ LinkedIn: https://www.linkedin.com/company/infoq/ Facebook: https://www.facebook.com/InfoQdotcom Instagram: https://www.instagram.com/infoqdotcom/ YouTube: https://www.youtube.com/infoq Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Share what you've learned building software with a community of senior practitioners, and get your work in front of the people who read InfoQ. https://www.infoq.com/write-for-infoq
Drawing from our recent collaborative publication titled Ethnographic reporting meets engaged journalism and offers untapped resources in Facts and Frictions, Adam Gamwell and Emily Kennedy explore how journalism and anthropology have each undergone periods of reckoning—grappling with questions of representation, power, and the need to reimagine their relationships with the communities they study or report on. We'll reflect on surprising parallels in the histories of both fields, the resurgence of ethnographic methods in journalism, and the practical value of deep, community-driven research for newsrooms navigating turbulent social and technological change.Join us as we explore how ethnography—a powerful research method from anthropology—is revolutionizing engaged journalism. Adam Gamwell (anthropologist and host) and Emily Kennedy (journalist, anthropologist, and founder of the Center for Anthropology and Journalism) dive into the real-world impact of blending journalism methods with community engagement and storytelling. Why does this matter? As the lines between journalism education, media innovation, and local news blur, understanding the tools that can empower newsroom strategy, cultural understanding, and meaningful representation in media is essential for anyone interested in the future of storytelling and reporting.Key takeaways:Why the “ethnographic impulse” surges in times of social or industry upheaval 17:19How both fields are rethinking extractive practices and striving for true community engagement 09:06The business case for engaged journalism: connecting community engagement with audience growth and sustainable revenue 24:06The urgent questions posed by AI for researchers, journalists, and communities alike 21:25Join us for Engage2026 - a two-day virtual conference for shaping the future of engagement journalism and ethnography—through shared learning, skills, and community.Engage2026 is produced by the Centre for Anthropology and Journalism.Emily Kennedy is the founder of the Center for Anthropology and Journalism and a leading voice in the intersection of ethnography and journalism. With a unique background spanning anthropology, journalism, and community-driven research, Emily brings real-world expertise from her work with journalism institutes, workshops, and published research bridging the gap between academic insight and practical newsroom innovation.Read more and chat with the full transcript & key moments from this episode on CastMagicAbout This Anthro Life:This Anthro Life is your podcast for thought leadership at the intersection of culture, work, and storytelling. We bring you interviews and insights for business leaders, design thinkers, researchers, and anyone curious about how culture shapes our lives.Explore more episodes and subscribe to our newsletter at thisanthrolife.org and join our Substack community at thisanthrolife.substack.com.
Reclaiming Your Power: Surviving Workplace Chaos and Doing Great Work with Melissa Swift Anika sat down with Melissa Swift, founder and CEO of Anthrome Insight, to tackle the reality of the modern workday: constant interruptions, hyper-emotional environments, and overwhelming chaos. Following the release of her new book, How to Do Great Work in a Fast Changing World, Melissa shares why systemic organizational changes are great, but individuals need actionable survival strategies now. Drawing on research from high-stakes professionals like firefighters and ER doctors, this conversation provides a practical framework for identifying your true strengths, dealing with chaotic colleagues, and recognizing when a job is fundamentally broken. In This Episode The COVID-era breaking point that inspired a shift from organizational strategy to individual empowerment Breaking down the "Effectiveness Architecture": Knowledge, Methods, People, and Technology The self-checkout paradox: why automating tech actually requires a massive increase in human "soft skills" The four trends making us less effective at work, with a deep dive into navigating chaos Natural chaos (the science of surprises) versus the frustration of people operating chaotically The Muppet Theory of Management: knowing when to deploy your "Order Muppets" vs. "Chaos Muppets" What corporate America can learn from firefighters about deep tech mastery and acute role clarity How organizations inadvertently gaslight employees by hiring them for jobs the company culture actively rejects Three concrete steps you can take this week to carve out space and reclaim your sanity Timestamps 00:00 Introduction: Operating effectively in a fast-changing world 01:17 Why Melissa's audience demanded a book focused on individual survival strategies 02:52 The COVID breaking point and the metaphor of the empty Soviet streetcar 04:13 The Effectiveness Architecture: simplifying 30,000 skills into four core pillars 06:46 Why technology rollouts fail without the proper "people skills" training 09:38 Four trends destroying workplace effectiveness: intensification, emotion, transparency, and chaos 11:23 Natural chaos vs. human chaos (and the Muppet theory of team dynamics) 14:48 What white-collar workers can learn from the training habits of firefighters 19:51 The myth of the "skills gap" and the untapped power of self-directed learners 23:55 Identifying your superpower (Knowledge, Methods, People, or Tech) and overcoming stereotypes 29:45 Why role clarity is the first casualty of chaos and how to get it back 33:50 How to recognize if your job is fundamentally broken or set up to fail 39:08 Three actionable steps to reclaim your effectiveness and boundaries this week 43:35 Favorite quote: Oscar Wilde on the vital art of brevity Key Insights & Takeaways Insight 1: The Effectiveness Architecture Simplifies Work Instead of getting bogged down in corporate frameworks boasting 30,000 different micro-skills, individual effectiveness boils down to four clear pillars: Knowledge, Methods, People, and Technology. Identifying which pillar is your natural superpower (and which is your weakest link) allows you to partner with complementary colleagues and combat workplace stereotypes. Insight 2: Automation Requires More "People Skills," Not Less When companies automate roles (like introducing self-checkout lanes), they often underestimate the human element. The remaining workers suddenly have to act as IT support, security, and customer de-escalation all at once. Rolling out advanced technology—including AI—without training people on the soft skills required to manage the human friction around it is a recipe for burnout. Insight 3: Not All Chaos is Created Equal There is "natural chaos" (the inevitable surprises of a shifting economy or supply chain) and there is the unnatural chaos of colleagues operating erratically. You manage natural chaos through scenario planning, but you manage human chaos by building strict boundaries and strategically deploying the right personalities (your organized "Kermits" vs. your adaptable "Animals"). Insight 4: High-Stakes Professionals Prioritize Deep Mastery and Crisp Roles Unlike corporate workers who switch between dozens of apps a day without truly mastering any, professionals like firefighters are given the time and space to completely master a single new tool before their lives depend on it. Similarly, in an ER or on a fireground, role clarity is acute. In corporate settings, blurred roles masquerade as "collaboration," but they actually drain creativity and cause systemic failure. Insight 5: Your Job Might Be Fundamentally Broken Sometimes your lack of effectiveness isn't your fault. If you are hired to be a "change agent" in a company that inherently rejects innovation, or if you are told to "lead through influence" without any actual resources or power, you are fighting gravity. Recognizing when a job is structurally broken is the first step in deciding whether to renegotiate your role or walk away. Resources & Links Mentioned How to Do Great Work in a Fast Changing World by Melissa Swift Work Here Now by Melissa Swift About Melissa Swift Melissa Swift is the founder and CEO of Anthrome Insight, a leading organizational effectiveness consultancy. With an extensive background working with large global organizations like Mercer, Korn Ferry, Deloitte, and Capgemini, she is a recognized authority on the future of work and a regular columnist for the MIT Sloan Management Review. She is the author of Work Here Now and her latest release, How to Do Great Work in a Fast Changing World. Connect with Melissa Swift Website: Anthrome Insight LinkedIn: Melissa Swift Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Learn how data centers could be raising your utility bills. Plus: how to use a sinking fund strategy to pay your bills. What does it actually cost your community when a hyperscale AI data center moves in nearby? Senior news writer Anna Helhoski joins host Sean Pyles, CFP®, to break down the growing national backlash against data centers. They discuss what costs communities are left to shoulder — from unexpected spikes in electricity bills to strained water supplies and persistent noise pollution — and why the economic benefits towns were promised may not measure up to what residents end up paying. Once your savings buckets are set up, how do you actually pay your bills without the whole system falling apart? Sean and his fellow host Elizabeth Ayoola tackle listener questions about the most overlooked piece of the sinking fund strategy: managing payments across multiple accounts. They dig into why putting everything on one credit card can become a bookkeeping headache, what it means if you keep pulling from one bucket to cover another, and how your rewards card strategy could actually be working against your savings goals. Read senior news writer Anna Helhoski's full investigation into the hidden costs of data centers: https://www.nerdwallet.com/finance/news/data-center-costs Subscribe to our podcast's free email newsletter for bonus content and more from our hosts at https://smartmoney-nerdwallet.beehiiv.com/ Want us to review your budget? Fill out this form — completely anonymously if you want — and we might feature your budget in a future segment! https://docs.google.com/forms/d/e/1FAIpQLScK53yAufsc4v5UpghhVfxtk2MoyooHzlSIRBnRxUPl3hKBig/viewform?usp=header Smart Money's YouTube Channel: https://youtube.com/@nerdwalletsmartmoney To send the Nerds your money questions, call or text the Nerd hotline at 901-730-6373 or email podcast@nerdwallet.com. Like what you hear? Please leave us a review and tell a friend. *The show notes were created with the assistance of AI. They have been reviewed by our editorial team for accuracy and quality. Learn more about your ad choices. Visit megaphone.fm/adchoices
Your weight loss journey doesn't have to be complicated, restrictive, or built around the latest trends. Often, the strategies that create the biggest transformations are the simple, consistent habits that have stood the test of time. On today's show, I discuss why sustainable weight loss comes down to mastering the fundamentals and focusing on the practices that support your metabolism, hormones, and overall health. We'll explore why so many people struggle to maintain results, the importance of creating a strong foundation, and how small daily actions can lead to lasting changes in your body. I'll also share why consistency, patience, and focusing on long-term progress matter more than quick fixes or extreme approaches. So join me on today's Cabral Concept 3820 to discover the boring weight loss methods behind successful weight loss and how to create results that last. Enjoy the show, and let me know your thoughts! - - - For Everything Mentioned In Today's Show: StephenCabral.com/3820 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!
Trap Talk Reptile Network Presents:Retic Talk Podcast w/ Rob & Jenn Episode 13:Retic Breeding Methods SUPPORT USARK: https://usark.org/HOST: Rob Of Island Morphshttps://www.instagram.com/island_morphs/Co-Host: Jennifer Valentinehttps://www.instagram.com/mod_exotics/JOIN TRAP TALK FAM HERE: https://bit.ly/311x4gxSUBSCRIBE TO THE TRAP TALK NETWORK: https://bit.ly/39kZBkZh
David E. Kirkland is a trans-disciplinary scholar of English and urban education, who explores the intersections among urban youth culture, language and literacy, urban teacher preparation, and digital media. He analyzes culture, language, and texts, and has expertise in critical literary, ethnographic, and sociolinguistic research methods. He has received many awards for his work, including the 2008 AERA Division G Outstanding Dissertation Award and was a 2009-10 Ford Foundation Postdoctoral Fellow and is a former fellow of NCTE's Cultivating New Voices. Dr. Kirkland has published widely. His most recent articles include: ” Black Skin, White Masks': Normalizing Whiteness and the Trouble with the Achievement Gap” in urban contexts: Politics, Pluralism, and Possibilities” (English Education), and “We real cool: Examining Black males and literacy” (Reading Research Quarterly). He is currently completing his fourth book, A Search Past Silence, to be published through Teacher College Press s Language and Literacy Series. Dr. Kirkland believes that, in their language and literacies, youth take on new meanings beginning with a voice and verb, where words when spoken or written have the power to transform the world inside-out Support Latin Waves by becoming a member https://latinwavesmedia.com
Sam Fertik went from running a 20-course tasting restaurant in Manhattan to building homes out of concrete and steel that he says last a thousand years. He's the CEO and Founder of Carbon Custom Builders. In this episode, Sam and Eric trace the chef-to-contractor journey and the one question that reshaped his whole business. Why are we still building houses the way we did in 1850? What you'll learn: What ICF (Insulated Concrete Forms) is and why it turns homebuilding into a repeatable science Why the average home lasts 30 years and how concrete-and-steel construction changes that How a chef's recipe mindset applies directly to running a construction company The "go to the biggest problem" lesson and why owners have to be both visionary and implementer Why Sam says sales is the one thing that kills a small business Connect with Sam Fertik and Carbon Custom Builders: Website: https://www.carboncustombuilders.com/ LinkedIn: https://www.linkedin.com/in/sam-fertik/ Instagram: https://www.instagram.com/carboncustombuilders/
Sean Hazlett joins us today to talk about the validity of remote viewing. We'll review methods of remote viewing, dangers of remote viewing, and other interesting topics. Welcome to Camp!
Description:In this mind-expanding episode, we explore the profound impact of psychedelics, the balance of life's struggles, and how fear shapes human behavior. Rick shares his experiences with psychedelics, the importance of inner work, and why embracing discomfort leads to true growth.Topics Covered:Were psychedelics responsible for human evolution? The role of fear in shaping our reality The government's fear of psychedelics & mass awakening How ancient traditions understood consciousness better than modern society The fine line between self-care & obsession with health
Send us Fan MailAre you frustrated with your prayer life? Do you feel like you're stuck in a rut? Do you find yourself rushing out the door in the morning whispering a half- hearted prayer for help with your day? When you crawl into bed at night do you pray a quick prayer of thanks as you fall asleep?In this edition of Finish Strong, you'll learn how to pray powerful and effective prayers. By making your prayer life a priority you can unleash God's supernatural power in your life.When you understand the reasons, requirements and methods for effective prayer, your soul will soar to new heights and you'll live your life according to God's plan. Discover Biblical truths that will turn your prayer life into a powerful engine that will energize your life in every way imaginable! Join Brian, Terry and Dan and learn how effective prayer is the key to Finishing Strong! Support the showFearless Faith Websiteffaith.orgTo leave a review - Open Finish Strong on the Apple Podcast app and scroll down until you see "Ratings & Reviews". There will be a link to click so that you can "Write A Review"FacebookYouTubeInstagram
In this episode I discuss the book Understanding Social Images: Essays on Visual Methods and Teaching Anthropology (Berghahn, 2025) with co-editors David Zeitlyn and Chihab El Khachab, a posthumous collection of writings by the late anthropologist Marcus Banks. Banks was a key the figures in development of visual anthropology, but his work remains of continuing relevance. This volume brings together several of his essays on film, photography, archive work, as well as reflections of visual methods such as photo-elicitation and ways of teaching visual anthroplogy. The conversation begins with Marcus Banks himself, his place within anthropology, his influence on visual anthropology, and the broader effort to preserve and extend his work through a set of posthumous collections. From there, the discussion turns to one of the central themes that emerge from across several essays, namely, that images are not simply illustrations or transparent representations, but socially and temporally situated objects with disctinct social lives. From there, we discuss Banks's interest in the materiality of the visual and how the circulation and afterlives of images gather new meaning over time. A substantial part of the interview focuses on Banks's film Raju and His Friends (1988), which proved foundational to his later reflections on ethnographic filmmaking, and especially on the ethics and politics of representation. As well as providing an introduction to Marcus Banks's work and serving as a tribute to a scholar whose influence continues to shape the field, many of the topics covered in the interview touches on many broader questions in anthropology itself, and as such will be of interest not only to students of visual anthropology, but to anthropology students and anthropologists more generally. David Zeitlyn is Professor of Social Anthropology at the University of Oxford and a Fellow of Wolfson College. His work spans visual anthropology, photography and archives, religion and divination, and long-term fieldwork in Cameroon. He also collaborated with Marcus Banks on the second edition of Visual Methods in Social Research. Chihab El Khachab is Associate Professor in Visual Anthropology at the University of Oxford and a Fellow of Wolfson College. He is a social anthropologist specializing in visual and media anthropology, especially Egyptian media production, and is the author of Making Film in Egypt: How Labor, Technology, and Mediation Shape the Industry. Further reading and resources mentioned in the recording Jainism as Social and Visual Practice: Anthropological PerspectivesMarcus Banks. Edited by John E. Cort, David Zeitlyn and Chihab El Khachab. New Delhi: Primus Books, 2026. This posthumous volume gathers Banks's writings on Jain social organization and visual representation. The revived HADDON catalogue of ethnographic filmsOriginally created by Marcus Banks in the 1990s, HADDON is an online catalogue of archival ethnographic film. The revived site presents a restored version of the database, now hosted through Paul Henley's Silent Time Machine project. Raju and His Friends (1988)The film discussed at length in this episode, and a key reference point for Banks's later reflections on visual anthropology and ethnographic filmmaking. Marcus Banks was Professor of Visual Anthropology at the Institute of Social and Cultural Anthropology (ISCA), University of Oxford until he passed away in 2020. He was a pioneer in integrating visual approaches into the mainstream of social and cultural anthropology. With Howard Morphy, he edited the influential volume Rethinking Visual Anthropology (Yale University Press, 1999). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
In this episode I discuss the book Understanding Social Images: Essays on Visual Methods and Teaching Anthropology (Berghahn, 2025) with co-editors David Zeitlyn and Chihab El Khachab, a posthumous collection of writings by the late anthropologist Marcus Banks. Banks was a key the figures in development of visual anthropology, but his work remains of continuing relevance. This volume brings together several of his essays on film, photography, archive work, as well as reflections of visual methods such as photo-elicitation and ways of teaching visual anthroplogy. The conversation begins with Marcus Banks himself, his place within anthropology, his influence on visual anthropology, and the broader effort to preserve and extend his work through a set of posthumous collections. From there, the discussion turns to one of the central themes that emerge from across several essays, namely, that images are not simply illustrations or transparent representations, but socially and temporally situated objects with disctinct social lives. From there, we discuss Banks's interest in the materiality of the visual and how the circulation and afterlives of images gather new meaning over time. A substantial part of the interview focuses on Banks's film Raju and His Friends (1988), which proved foundational to his later reflections on ethnographic filmmaking, and especially on the ethics and politics of representation. As well as providing an introduction to Marcus Banks's work and serving as a tribute to a scholar whose influence continues to shape the field, many of the topics covered in the interview touches on many broader questions in anthropology itself, and as such will be of interest not only to students of visual anthropology, but to anthropology students and anthropologists more generally. David Zeitlyn is Professor of Social Anthropology at the University of Oxford and a Fellow of Wolfson College. His work spans visual anthropology, photography and archives, religion and divination, and long-term fieldwork in Cameroon. He also collaborated with Marcus Banks on the second edition of Visual Methods in Social Research. Chihab El Khachab is Associate Professor in Visual Anthropology at the University of Oxford and a Fellow of Wolfson College. He is a social anthropologist specializing in visual and media anthropology, especially Egyptian media production, and is the author of Making Film in Egypt: How Labor, Technology, and Mediation Shape the Industry. Further reading and resources mentioned in the recording Jainism as Social and Visual Practice: Anthropological PerspectivesMarcus Banks. Edited by John E. Cort, David Zeitlyn and Chihab El Khachab. New Delhi: Primus Books, 2026. This posthumous volume gathers Banks's writings on Jain social organization and visual representation. The revived HADDON catalogue of ethnographic filmsOriginally created by Marcus Banks in the 1990s, HADDON is an online catalogue of archival ethnographic film. The revived site presents a restored version of the database, now hosted through Paul Henley's Silent Time Machine project. Raju and His Friends (1988)The film discussed at length in this episode, and a key reference point for Banks's later reflections on visual anthropology and ethnographic filmmaking. Marcus Banks was Professor of Visual Anthropology at the Institute of Social and Cultural Anthropology (ISCA), University of Oxford until he passed away in 2020. He was a pioneer in integrating visual approaches into the mainstream of social and cultural anthropology. With Howard Morphy, he edited the influential volume Rethinking Visual Anthropology (Yale University Press, 1999). Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/film
Your weight loss journey doesn't have to be complicated, restrictive, or built around the latest trends. Often, the strategies that create the biggest transformations are the simple, consistent habits that have stood the test of time. On today's show, I discuss why sustainable weight loss comes down to mastering the fundamentals and focusing on the practices that support your metabolism, hormones, and overall health. We'll explore why so many people struggle to maintain results, the importance of creating a strong foundation, and how small daily actions can lead to lasting changes in your body. I'll also share why consistency, patience, and focusing on long-term progress matter more than quick fixes or extreme approaches. So join me on today's Cabral Concept 3820 to discover the boring weight loss methods behind successful weight loss and how to create results that last. Enjoy the show, and let me know your thoughts! - - - For Everything Mentioned In Today's Show: StephenCabral.com/3820 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!
Are you being sold fitness advice that only helps you survive instead of thrive? In this episode, I expose the misleading “you can get by” argument often used to defend restrictive diets, extreme workout programs, and unhealthy lifestyle habits. Learn why surviving isn't the same as optimizing your health, strength, and body composition, and how to set higher standards for your fitness. Stop settling for “good enough” and start building habits that help you truly thrive.
Lawfare Senior Editor Renée DiResta sits down with Senior Editor Kate Klonick and Elissa Redmiles, an assistant professor of computer science at Georgetown University. They examine the people who create AI-generated sexual content and whether prominent technical proposals can actually prevent AI systems from generating exploitative content.For further reading:Jaron Mink, Lucy Qin, and Elissa M. Redmiles, “‘Unlimited Realm of Exploration and Experimentation': Methods and Motivations of AI-Generated Sexual Content Creators”, FAccT '26: The 2026 ACM Conference on Fairness, Accountability, and Transparency (June 2026)Renée DiResta and Berin Szóka, “Grok, ‘Censorship,' & the Collapse of Accountability,” Lawfare (January 2026)Lucy Qin, Sharon Wang, Yigit Aydinalp, Marin Scarlett, and Elissa M. Redmiles, "'Did They F***ing Consent to That?': Safer Digital Intimacy via Proactive Protection Against Image-Based Sexual Abuse," USENIX (August 2024)Safe Digital Intimacy.orgPlease note that this podcast discusses sexual violence and the harms of image-based sexual abuse. Listener discretion is advised.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.
Victor Montejo is a Jakaltek Maya originally from Guatemala. He is a sociocultural anthropologist specializing in Mesoamerican Indigenous cultures. He received his Ph.D. in Anthropology from the University of Connecticut, USA, in 1993. He was awarded the UC President's Postdoctoral Fellowship at UC Davis in 1995 and then joined the faculty of Native American Studies in 1997. He is a scholar, novelist, poet, activist, journalist, and major Mayan public intellectual, recognized nationally and internationally for his work.During his time in Native American Studies, he served as Department Chair for one term and consistently helped strengthen the Graduate program in Native American Studies with his colleagues. At the undergraduate level, he taught one of the introductory courses in Native American Studies, along with NAS 115 Indigenous People in the Contemporary World; Native Knowledge, Methods and Epistemology. At the graduate level he taught NAS 200 Seminar: Basic Concepts: NAS 246: Research Methods and Theory in Native American Studies; NAS 202: Indigenous Myths and Worldviews; NAS 250: Seminar: Classic Maya Ethnographies.As a Maya native of Guatemala, he has focused his research on Maya culture and the pan-Maya movement of self-representation in the Americas. His academic interests focus on the Indigenous peoples of Mesoamerica. He works on the Latin American diaspora, human rights, migration and transnationalism, comparative studies, ethnicity, Indigenous worldviews and Native knowledge, and Indigenous literatures. Current projects include: Indigenous community development, rural development, sustainable development, cultural/economic/political self-determination, cultural resource management. Victor Montejo has been a columnist for a national newspaper in Guatemala and obtained First Honorable Mention for Best Column in Native Americas, Cornell University, from the Native American Journalists Association in 2000.His Voices from Exile: Violence and Survival in Modern Maya History won the national Race, Ethnicity and Politics Award from the American Political Science Association.In 2003, Victor Montejo received a Fulbright Scholar Award to teach and conduct research in Guatemala at the University of El Valle de Guatemala. He ran for Congress and won; from 2004-2008, he served in the Guatemalan national Congress. From this post, he was named Minister of Peace during the Guatemalan Presidency and worked out the National Program for Reparation to the victims of the armed conflict in Guatemala. He was president of the Congressional Commission of Indigenous Peoples. As a Congressman he proposed and passed the law of the National Day of Indigenous People of Guatemala, and and proposed the Law Initiative: Ley de Consulta a Pueblos Indígenas. Professor Emeritus Montejo's major publications include: Testimony: Death of a Guatemalan Village (1987); The Bird Who Cleans the World and Other Mayan Fables, Curbstone Press, 1991; Sculpted Stones [poetry], 1995; The Adventures of Mister Puttison Among the Mayas [novel], Yax Te' Press, 1998; Voices from Exile: Violence and Survival in Modern Maya History (1999); Maya Intellectual Renaissance: Critical Essays on Identity, Representation, and Leadership. Austin: University of Texas Press (2003); Popol Vuh: Sacred Book of the Mayas (1999); Q'anil: Man of Lightning, University of Arizona Press (2002). Pixan: El Cargador del Espíritu, Editorial Piedra Santa, Guatemala, (2014); Secuestro a ultratumba.https://www.indigenouspeople.net/victorm.htmBecome a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.
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