Podcasts about iq

What's the deal with Donald's weird puppet legs. Donald's screwy TIME Magazine interview. He regrets nominating all three of his Supreme Court justices. He plans to pardon his entire staff. He claims he has "the highest IQ." The latest on Eric Schmitt's scam. The Reflecting Pool case is officially closed. Hegseth's mass firings at the Pentagon. Good news about Donald's latest polls. Good news about early voting. Good news about Renee Good. Why did Donald withdraw more troops from Iraq? With Jody Hamilton, David Ferguson, music by Jason Revoir, Norway House, and more! Brought to you by Russ Rybicki, SharePower Responsible Investing. Support our sponsors and get free shipping at Quince.com/bob and bollandbranch.com/bob!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The man police believe is in the Philly House of Horrors footage died in his bed in May 2025—a full year before a park ranger's traffic stop blew the case open. He was eighty-two. He was never charged with a violent crime. And he can never be prosecuted.His name was Raymond “R.C.” Horsch—father of Eugene Horsch, the man sitting in federal custody on gun and credential charges after the June 2026 stop at Independence Mall. The search of their shared home at 417 West Chew Avenue in Olney uncovered a 55-gallon drum connected to water lines, containers of chemicals, and more than a million files across fifty hard drives.Seven women are linked to the house. Five are believed dead. No bodies have been found. The figure in the videos is R.C., not Eugene. Eugene has not been charged with harming anyone.R.C. Horsch had an IQ of 140, a criminal record stretching back to 1973, and decades of recruiting women from Kensington. A 1985 federal psych eval called him “consumed by anger.” His ex-wife vanished from the house in 2016. A missing woman's photo sat on his website for years. None of that opened the front door. A parking violation did.The FBI has reviewed thirty percent of the evidence. The rest is waiting. And the man at the center of it is already in the ground.Listen Anywhere You Get Podcasts: https://pod.link/1655749292Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/@hiddenkillerspod?sub_confirmation=1Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/tonybpodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#PhillyHouseOfHorrors #OlneyHouseOfHorrors #HiddenKillers #EugeneHorsch #PhiladelphiaCrime #TrueCrime #MissingWomen #Kensington #RCHorsch #NicoleFusaro

Manchester City have been found guilty of serious breaches of Premier League financial rules — and Sam Allardyce has his say on what happens next.On this episode of No Tippy Tappy Football, Sam reacts to the independent commission's findings covering nine seasons between 2009/10 and 2017/18, including the use of “sham” commercial arrangements which the commission said artificially inflated Manchester City's revenue and reduced reported costs by more than £900 million. City deny wrongdoing and intend to appeal the findings.Sam discusses just how serious the situation is, whether Manchester City can escape the consequences, what punishment they could face and whether their trophies should be taken away.We also debate where Manchester City's situation sits alongside previous football scandals, including Juventus and Southampton, whether City fans should have sympathy, whether the club could still be in the Premier League next season and, most importantly, just how far this punishment could go.Could Manchester City end up outside the Premier League? Could the current squad stay together? And would Sam Allardyce really consider taking the job if City were playing in League Two?Then we turn our attention to England.Sam and Will review England's games against Spain and Czechia, discuss Trent Alexander-Arnold's return to the squad, Anthony Gordon's comments about his footballing IQ and what Thomas Tuchel needs to achieve to be considered a success with England.Sam also explains whether he believes Tuchel can be the man to finally deliver success with England._Make sure you Like, Comment & Subscribe & turn on notifications so you know when a new episode drops.

In this episode, Sam interviews TJ and takes us behind the curtain of his college program's preseason, organized around the Key 5 framework. TJ explains how he reverse engineers the season from the first game, why leadership looks different every year, and how to spread its weight across players and staff. He makes the case for an 80/20 approach that puts habits like effort, competitiveness, and fundamentals ahead of X's and O's. They also cover developing basketball IQ by helping players live in their strengths, setting roles by pairing every "no" with a "yes," and being honest with players about playing time early. Whether you're in preseason now or planning ahead, you'll leave with practical ideas for your own team.Show NotesPreseason through the Key 5 lensMaximizing limited four-hour gym windowsReverse engineering from the first gameLeadership changes every single yearSpreading the weight of leadershipHanding off without skipping preparationThe 80/20 of systemsPracticing to beat the best opponentOnboarding freshmen: standards with supportBuilding IQ by living in strengthsAnticipation, angles, and off-ball answersRoles: pair every no with yesThe 200-minute playing time exerciseKey 5 rapid-fire takeawaysSend us a Message. If you'd like us to reply, include your contact info.After analyzing over 100 million shots, basketball data scientists at Noah Basketball have uncovered the formula of the perfect shot, helping players on 28 of 30 NBA teams improve their accuracy faster than ever before.This same patented shot-tracking technology is now available to you in the Noah Backboard for a fraction of the cost. Learn more today at noahbasketball.com. Inquire while supplies last! Do your players know what to do when your offense breaks down? – Join us for our upcoming webinar!You'll learn 4 simple actions from Rick Torbett, creator of the Read & React Offense, to help your players keep moving and make better decisions when the play breaks down.Sunday, October 11th at 8 pm EST (5 PM PACIFIC)Register at PGCHustle.com/Webinar

I watched The View and my IQ dropped.. Let's react to the ladies discussing the Lindsay Clancy case. This might be the dumbest show on television. • Blaire's TikTok: @blairewhiteofficial • Blaire's Main Youtube channel: ‪@BlaireWhiteX‬ • Blaire on X: @blairewhite • Blaire on IG: @msblairewhite • Buy Blaire White Merch: https://www.blairemerch.com/ • Listen on Apple Podcasts: https://bit.ly/TheBlaireWhiteProjectA... • Listen on Spotify Podcasts: https://bit.ly/TheBlaireWhiteProjectS... Learn more about your ad choices. Visit megaphone.fm/adchoices

It's all questions where you have to name the remaining thing in the series of things! This episode's topic: COMPLETE THE SET Sponsored by LearnClash, the quiz duel app that helps you remember what you learn: learnclash.com/partners/budds Fact of the Day: Driving for more than two hours each day can steadily decrease the IQ of middle-aged drivers. Triple Connections: Beanstalk, Eyeshadow, Hightail THE FIRST TRIVIA QUESTION STARTS AT 02:10 SUPPORT THE SHOW MONTHLY, LISTEN AD-FREE FOR JUST $3 A MONTH: www.Patreon.com/TriviaWithBudds INSTANT DOWNLOAD DIGITAL TRIVIA GAMES ON ETSY, GRAB ONE NOW! GET A CUSTOM EPISODE FOR YOUR LOVED ONES: Email ryanbudds@gmail.com Theme song by www.soundcloud.com/Frawsty Bed Music: "Laser Groove" Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/ http://TriviaWithBudds.comhttp://Facebook.com/TriviaWithBudds http://Instagram.com/ryanbudds Book a party, corporate event, or fundraiser anytime by emailing ryanbudds@gmail.com or use the contact form here: https://www.triviawithbudds.com/contact SPECIAL THANKS TO ALL MY AMAZING PATREON SUBSCRIBERS, INCLUDING: Samantha Wheeler Boomer Cates Mark Kloppenburg Cadi Snow-Brine Amber Shiels Alan Kreisel Rich Sommer Joe Heiman Waqas Ali Logan Booker Bringeka Sam Nathan Stenstrom Brooks Martin Robyn Price Gee Brian Clough Charles Glanville IV Lauren Schuette Evan Lemons AnneMarie Mattacchione Yves Bouyssounouse Kenny Zail York yates Gay Geek Fabulous Mollie Dominic Nathalie Avelar Natasha raina leslie gerhardt Diane White Youngblood Trophy Husband Trivia Lynnette Keel Lillian Campbell Jerry Loven Jamie Greig Gail Lancman Jeremy Yoder Adam Jacoby rondell Adam Suzan Tiffany Poplin Bill Bavar Sarah Daniel Hoisington Keith Martin Sue First Steve Hoeker Jessica Allen Lauren Glassman Brian Williams Brett Livaudais Linda Elswick Carter A. Fourqurean Justly Maya Brandon Lavin Kathy McHale Chuck Nealen Courtney French Nikki Long Mark Zarate Laura Palmer  JT Dean Bratton Kristy Erin Burgess Trenton Sullivan Jen and Nic Michael Redman Timothy Heavner Jeff Foust Richard Lefdal Myles Bagby Jenna Leatherman Vernon Heagy Albert Thomas Kimberly Brown Tracy Oldaker Sara Zimmerman Madeleine Garvey Jenni Yetter Patrick Leahy Dillon Enderby James Brown Christy Shipley Clayton Polizzi Alexander Calder Ricky Carney Paul McLaughlin Willy Powell Robert Casey Matthew Frost Brian Salyer Greg Bristow Megan Donnelly Jim Fields Mo Martinez Luke Mckay Simon Time Feana Nevel Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Have you ever been really hard on yourself because you didn't do something perfectly? Maybe you made a mistake, handled something differently than you wanted, or found yourself thinking you should have done better. And even when you know being hard on yourself isn't helping… it can still be surprisingly difficult to stop. But what if there is a completely different way to look at imperfection, growth, and the expectations you place on yourself? In this quick IQ episode, Brandy shares one simple perspective shift that can help you stop approaching growth through criticism—and begin looking at yourself and your progress in a much more loving and empowering way. You'll discover: Why being hard on yourself may not actually help you grow A different way to look at mistakes and imperfections How to continue improving without beating yourself up along the way Why self-kindness doesn't mean giving up on growth One simple insight that can completely shift the way you see yourself You don't have to stop growing. You don't have to stop improving. But you may be able to do it in a completely different way—one that feels lighter, more supportive, and a lot more loving. Listen to this episode for one simple insight that may change the way you approach yourself from here forward.   Free Mind-Body Healing Training If you'd like a deeper understanding of mind-body healing and how self-healing works: Click here to join the FREE training. Brandy Gillmore's Mind-Body Healing: Scientific Research If you'd like scientific research on mind-body healing, you can view Brandy Gillmore's work published in a Medical Journal. Personal Empowerment and Self-Healing Courses If you're ready to heal yourself and change your life: Click here to explore our GIFT Mind-Body Healing™ and the GIFT Method™ Courses and GIFT Workshops.   Connect With Brandy Follow Brandy on Facebook Follow Brandy on Instagram Questions? Discover more at https://brandygillmore.com or email support@BrandyGillmore.com   Important Note: This podcast is for educational and informational purposes only and is not a replacement for medical advice, diagnosis, or treatment. Please continue to consult your doctor or qualified healthcare provider as recommended. Click here to view our full disclaimer- https://brandygillmore.com/terms/. © Brandy Gillmore / Human Potential Revolution, Inc. All Rights Reserved. This podcast and all related content are provided solely for personal, noncommercial use. No portion may be copied, recorded, transcribed, closely paraphrased, adapted, repackaged, distributed, taught, or used to create or support any course, coaching program, training, certification, product, service, or derivative content without prior written permission. This content may not be uploaded to, entered into, stored in, used by, or incorporated into any artificial-intelligence, machine-learning, voice-cloning, digital-replica, data-training, or related system. By accessing, listening to, viewing, downloading, or otherwise using this content, you agree to our Intellectual Property and Content Use Terms. If you do not agree, do not access or use the content. Full Terms: https://brandygillmore.com/content-terms  Permissions: support@brandygillmore.com 

Hour 2 (09.28) Hurricane Polo is slamming Mexico’s Pacific coast, Nolo is pounding Hawaii, and El Niño has SoCal on coastal watch — Long Beach looking at monster tides and a flooded boardwalk. Then food: Langer’s by MacArthur Park takes the LA Times Gold, In-N-Out plants four more shops in Chino, Commerce, South El Monte, and Stockton, a man is camped on a Brentwood bus stop, and Fawnskin unveils a Jackie the eagle statue — same mountain town as a Kobe statue? Timmy’s been a “60 Minutes” kid since Andy Rooney (Colgate, like Mark), and Redlands wants bars dark after midnight over a three-year crime bump while liquor stores skate. Closing curveball: 13-year-old Long Island prodigy Joe Petraro-Hyppolite, IQ 168, is already in college and headed to NYU for a master’s. Timmy’s verdict: too young for campus — you can’t keep up with the older kids’ nightlife. See omnystudio.com/listener for privacy information.

Cognitive ability is one of the strongest predictors of job performance, and it still makes a lot of leaders nervous. We sit down with Dr. Anthony Belluccia, senior product scientist at The Predictive Index, to pull the curtain back on PI's Cognitive Assessment and explain what it is and what it is not. If you've ever worried that a cognitive test is just an IQ test in disguise, or you've seen teams mishandle it and spook candidates, this conversation brings the clarity and guardrails people need.HERE ARE MORE RESOURCES FROM REAL GOOD VENTURES:Click HERE to anonymously share a Bosshole story...Click HERE to get your very own Reference Profile.  We use The Predictive Index as our analytics platform so you know it's validated and reliable.  Your Reference Profile informs you of your needs, behaviors, and the nuances of what we call your Behavioral DNA.  It also explains your work style, your strengths, and even the common traps in which you may find yourself.  It's a great tool to share with friends, family, and co-workers.Follow us on Instagram HERE and make sure to share with your network!Follow us on X HERE and make sure to share with your network!Provide your feedback HERE, please!  We love to hear from our listeners and welcome your thoughts and ideas about how to improve the podcast and even suggest topics and ideas for future episodes.Visit us at www.realgoodventures.com.  We are a Talent Optimization consultancy specializing in people and business execution analytics.  Real Good Ventures was founded by Sara Best and John Broer who are both Certified Talent Optimization Consultants with over 50 years of combined consulting and organizational performance experience.  Sara is also certified in EQi 2.0.  RGV is also a Certified Partner of Line-of-Sight, a powerful organizational health and execution platform.  RGV is known for its work in leadership development, executive coaching, and what we call organizational rebuild where we bring all our tools together to diagnose an organization's present state and how to grow toward a stronger future state.  Send us Fan Mail

Your daughter can score 20 points for one team and have a completely different job on another. That does not automatically mean a coach is holding her back—it may be the adjustment that exposes what she needs to develop next.Coach Kelley Meiklejohn joins Ashley Roberts to explain what changes between AAU and high-school basketball, why trust and communication matter, and how parents can support athletes without removing every difficult moment. They discuss changing positions, training outside team practice, balancing basketball with grades and sleep, realistic college levels, and why D2, D3, and JUCO can be valuable paths.In this episode:• Why an athlete's AAU and high-school roles may be different• How coaches build trust through attendance, accountability, and communication• When adversity is productive—and when a role may truly be the wrong fit• Why games reveal trained skills rather than create them• How families can evaluate D1, D2, D3, JUCO, and other opportunities• What incoming varsity players must learn about pace, defense, IQ, and team cultureAshley on Instagram: @_thisisashleyrJoin the Basketball Parent Community for direct guidance, resources, and conversations with other basketball families:https://www.skool.com/itsjustdifferent/about?ref=e5c985b2c58c47afb7bb18a0d15b34e6Join Different First Access to shop new DIFFERENT merch before the public:http://itsjustdifferentapparel.com/firstaccess

Challenger safety, not IQ, is the real ceiling on your team's decisions.Dr. Tim Clark and Junior explain why your team thinks only as well as it's allowed to disagree, and why that matters more now that AI is involved. Critical thinking is being outsourced faster than it's being replaced, producing fluent answers nobody can defend because nobody pushed back. Falling execution costs mean bad decisions compound into real consequences faster than ever, blurring the line between reversible and irreversible calls. Drawing on research showing 37% of failed decisions trace back to teams grabbing the first ready-made idea instead of surfacing alternatives, Tim and Junior make the case that smarter individuals aren't the fix. What matters is an environment where private opinions become public ones. They close with the practical leadership mechanism: model dissent, reward the people who offer it, and routinely ask, "How are we misframing this?"

Accessibility software does not always move quickly, but several recent updates are changing how accessible documents are created, remediated, and tested. In this episode of Chax Chat, Chad and Dax break down the advances that are actually worth knowing about. They cover accessible PDF forms exported from Word, improved table headers and scope in InDesign, MathML support, PowerPoint reading-order and heading tools, Canva's accessible PDF workflow, new PAC checker tabs, browser-based auto-tagging, and the growing family of IQ tools. They also flag the places where automatic features, standards, and assistive technology still do not line up. Every other week, we unravel accessibility to help you create documents that are more inclusive, compliant, and practical for real users.  

This one's for anyone who's mastered the art of looking fine while quietly falling apart behind the scenes.More info, resources & ways to connect - https://www.tacosfallapart.com/podcast-live-show/podcast-guests/lisa-lacyLisa Lacy spent three decades in corporate education and healthcare consulting before she started writing about the things most people keep locked away: addiction, psychosis, trauma and what she calls identity masking, the gap between who you actually are and who you become just to get through the day. She's also written her debut memoir, Notes from a Certified Mad Woman, along with a companion guide, and she joined MommaFoxFire on Even Tacos Fall Apart to talk about performing fine and what it actually costs.This episode digs into the difference between adapting to a room and disappearing inside a mask. Lisa is clear that we all shift a little depending on where we are. You don't bring the same energy to a board meeting that you bring home to your partner. That's normal. The dangerous mask is the polished one, the six figures and the perfect blended family on social media while you're falling apart behind the scenes in total silence.Lisa and MommaFoxFire talk about why high functioning people often get checked on the least. When you're the one who gets everything done, everyone assumes you're fine, so nobody asks. Lisa breaks down why shame keeps people quiet even when they know something is wrong, and she gives real language for closing that gap: acknowledgement, awareness and observation. She makes the case that just noticing the pattern is most of the work.They cover how to support a high functioning person in your life without making things worse, starting with asking permission before you share an observation instead of walking in with unsolicited advice. Lisa also lays out what actually makes someone a safe person to open up to, someone with a track record of lifting you up instead of draining you or judging you.Lisa gets honest about her own ten year recovery journey, starting inpatient rehab at forty, and she talks through psychosis from the inside, what it's like to live inside another reality that felt completely real while it was happening. She pushes back on the idea that mental health struggles make someone defective or that struggling is some kind of moral failure. It's real. It exists. That's it.There's a lot of practical stuff in here too. Lisa is a martial artist with belts in three disciplines and she connects that training to staying regulated instead of reactive. She talks about the power of a long exhale when anxiety hits, why building your coping toolbox before you hit a low matters more than scrambling for it in the moment, and why she believes emotional intelligence, especially willingness and curiosity, matters more for a good life than IQ ever will.This one hits close to home for anyone who has ever looked completely fine on the outside while quietly falling apart on the inside. Lisa's bottom line carries the whole episode: always one more day, always one more try. Today is not the day to give up.

David Da Vinci tiene 11 años, un IQ reportado de 162 y una forma de mirar el mundo que descoloca muchas de las certezas con las que vivimos los adultos. No porque tenga todas las respuestas, sino porque todavía conserva algo que nosotros vamos perdiendo poco a poco, la curiosidad.Su historia empieza mucho antes de los titulares. Empieza sintiéndose diferente, cuestionando la forma en la que aprende, atravesando bullying y descubriendo que entender muchas cosas no necesariamente significa entender lo que sientes.Desde ahí aparece una de las contradicciones más interesantes de su historia: alguien reconocido por su capacidad intelectual terminó creando un proyecto enfocado en inteligencia emocional. Porque quizá el verdadero reto no está en saber más, sino en aprender qué hacer con lo que pensamos, con lo que sentimos y con lo que nos pasa cuando las cosas no salen como esperábamos.David cuestiona una educación que enseña a aprobar, pero pocas veces a fracasar. Habla de negocios, tecnología, propósito, felicidad y de la necesidad de construir un camino propio en lugar de repetir uno que ya existe.Y quizá ahí está lo más valioso de este episodio.No en preguntarnos cómo piensa un niño con un IQ más alto que el atribuido a Einstein, sino en preguntarnos qué dejamos de pensar nosotros cuando crecimos. Porque tal vez madurar no debería significar dejar de sorprendernos, dejar de preguntar o aceptar como normal una vida que ya no nos hace sentido.Conecta con David da Vinci:https://www.instagram.com/daviddavincioficial Suscríbete a nuestro canal en SpotifyVe la entrevista completa en YouTubeSigue negocioscool en todas nuestras redes Conecta con nosotros a través de LinkedIn

Mystery riddles are fun brain teasers that challenge you to solve a tricky scenario, often with a surprising twist. They test your ability to think creatively, observe details, and connect clues—just like a detective would. Each riddle presents a puzzle or mystery, like figuring out who committed a crime or finding a hidden object, and it's up to you to solve it. Some might seem easy at first but have clever solutions that make you think twice. These riddles can be a fun way to test your IQ and see how sharp your detective skills are. If you love solving mysteries and using logic, these are the perfect challenge for you! Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to the Ultimate Rugby League countdown, this is 'The Bye Round 99' all powered by Maccas! James Graham is ranking the 99 greatest players of the NRL era (1998-current). No reputations spared, no bias, just straight footy IQ. This is the finale with Jimmy counting down 25-1, did he get it right? Let us know via the comments or shoot us an Insta DM! The Bye Round 99 powered by 99c McDelivery only on the MyMacca's App! Follow The Bye Round On: Instagram: https://www.instagram.com/thebyeround/?hl=enTikTok: https://www.tiktok.com/@thebyeround?lang=enYouTube: https://www.youtube.com/@thebyeroundSee omnystudio.com/listener for privacy information.

Stephen Ippolito shares his path from $5B+ in investment banking M&A to founding Veritus and launching the Human Health Platform, an ecosystem focused on shaping investable healthcare companies earlier than traditional VC. Stephen breaks down the stark differences between PE-backed and family-owned transactions, why founder psychology and identity can make or break outcomes, and what he looks for in first-time leaders—especially the balance of EQ and IQ, coachability, and healthy co-founder dynamics. The conversation dives into why too much healthcare innovation dies in academia, the need for better IP audits and university commercialization programs, and the case for combining complementary startups to unlock disproportionate value. Stephen also offers a blunt reality check on succeeding in U.S. healthcare: know your true customer, distribution, and incentives—then build accordingly.Stephen Ippolit LinkedInHuman Health Platform LinkedInHuman Health Platform Website“Good to Great” by Jim CollinsDuane Mancini LinkedInProject Medtech WebsiteProject Medtech LinkedInThank you to our sponsors: Ward Law, Wheelhouse DMG, and JumpStart Inc.

Sorry folks this one took a while cause I was away and had no idea it existed. But it's still good! Pick it up, dust it off and have a listen. As usual this one is brought to you by Google Gemini - Flash 3.8 with enhanced thinking. What does that mean? I'm not entirely sure. But you have to pay for it. Well, I do. They don't. TCRP COMING SOON I PROMISE! It's that part of law school but it's gonna get much better very soon. ENJOY! In Episode 173 of The Two Jacks, hosts Jack the Insider and Hong Kong Jack break down a confronting and wide-ranging week in Australian news, politics, and culture. The episode opens with an urgent discussion on Australia's domestic violence epidemic following three fatal incidents across regional and metropolitan communities, examining stubbornly high homicide rates, non-intimate familial violence, and the breakdown in enforcing intervention orders. Turning to Canberra, the Jacks assess Anthony Albanese's slump in Newspoll ratings, Siobhan McKenna's appointment as Consul-General in New York, and the persistent political headaches surrounding ministerial perks—headlined by Transport Minister Catherine King's lifetime Qantas Platinum status and the exclusive culture of the Chairman's Lounge. The conversation heats up around Pauline Hanson: her widely condemned remarks regarding First Nations Australians, the unprecedented united condemnation from Indigenous senators across party lines, and her targeted attacks on Albanese's late mother over historic public housing tenancy. The hosts also tackle the national immigration debate, contrasting One Nation's drastic visa-slashing platform with Labor's incremental policy adjustments against a backdrop of acute regional labour shortages. They explore border enforcement anomalies—including the arrest of convicted murderer Paul Offer in connection with a tracking device placed on former detective Gary Jubelin's car—and examine systemic child welfare failures in Port Hedland. Rounding out the program, the Jacks debate proposed legislation to introduce an opt-out switch for social media algorithms, answer listener mail on the future of the US Republican Party post-Trump and the legendary 1935 Sydney Shark Arm case, debunk birth-order IQ myths, and unpack the $100 million sale of Double Bay's Royal Oak Hotel. Finally, they deliver their verdicts on the AFL and NRL preliminary finals and share a laugh over a bizarre weekend in Australian cricket. Comprehensive Show Notes1. Australia's Domestic Violence Crisis & Systemic FailuresJack the Insider and Hong Kong Jack reflect on a harrowing weekend where three Australian women were killed in separate domestic violence incidents, including a 90-year-old grandmother in Sydney, a 23-year-old woman in Wagga Wagga, and a woman in northern New South Wales. The hosts analyse national homicide data, highlighting that domestic and family violence accounts for approximately 40% of all homicides in Australia, split evenly between intimate partner killings and broader familial homicides. The discussion covers the front-line pressures faced by police attending volatile call-outs, historical reporting shifts, and the frequent failure of Apprehended Violence Orders (AVOs) and Intervention Orders (ISOs) to deter determined offenders. Reference is made to the need for deeper cultural accountability and education among young men rather than relying solely on legal remedies. 2. Federal Politics, Polling Pressures & Diplomatic GigsAnthony Albanese faces a slump in public polling with Labor's primary vote falling to 27%, coinciding with his visit to the United Nations to receive a global citizen award. The appointment of Siobhan McKenna as Australian Consul-General in New York is examined, with Hong Kong Jack outlining the historical business and political ties between the Murdoch organisation and the prestigious posting. The hosts weigh upcoming electoral tests for both major parties in Victoria and New South Wales, alongside One Nation's polling gains. 3. The Qantas Perks Furor & The Chairman's LoungeScrutiny lands on Federal Transport Minister Catherine King after her parliamentary pecuniary interest register disclosed a lifetime Qantas Platinum status gift. Jack the Insider questions whether transparency around declared gifts should trigger public outrage, warning that relentless scrutiny over minor perks deters capable people from entering public life. Hong Kong Jack counters that Qantas's historic practice of handing out elite access and Chairman's Lounge memberships creates an undeniable perception of undue corporate influence across politics, the judiciary, and senior public service. 4. Pauline Hanson Controversies: Indigenous Remarks & Attacks on Maryanne AlbaneseIndigenous Affairs Minister Malarndirri McCarthy and all four Indigenous federal senators deliver a united cross-party condemnation of Pauline Hanson following her offensive description of First Nations Australians. The Jacks analyse Hanson's history of race-based political rhetoric, her qualified apologies, and whether such controversies place an electoral ceiling on One Nation's growing vote. Hanson's public criticism of Anthony Albanese regarding his late mother, Maryanne Albanese, remaining in her Camperdown public housing home until her death in 2002 is placed under factual review. Jack the Insider details the reality: Albanese was on a standard backbencher salary at the time, while his mother was an independent disability pensioner entitled to retain the home she had resided in for over six decades. 5. The National Migration Debate & Enforcement LogisticsOne Nation unrolls an aggressive migration strategy fronted by Barnaby Joyce, proposing to cut 750,000 temporary visas and enforce negative net migration before settling on a 130,000 annual intake. The hosts critique both One Nation's economically disruptive targets and the Albanese government's modest adjustments, noting strong pushback from the agricultural sector over backpacker reforms. Practical realities of border enforcement are weighed, questioning the feasibility of One Nation's plan to involuntarily deport 80,000 non-compliant visa holders annually. 6. Crime, Deportations & Child Welfare in Remote AustraliaThe hosts review the arrest of convicted murderer Paul Offer following the discovery of a tracking device attached to the private vehicle of former NSW homicide detective Gary Jubelin. Jack the Insider questions the administrative processes behind visa cancellations, contrasting Offer's circumstances with historical deportation appeals where national service exemptions were successfully argued in the High Court. Revelations from Port Hedland concerning vulnerable Indigenous children living unsupervised in squalid state housing prompt a discussion on caseworker burnout, state jurisdictional obligations, and the historical sensitivities of child welfare interventions. 7. Regulating Social Media & The Proposed Algorithm SwitchA look into legislative proposals aimed at curbing algorithm-driven content feeds on social media platforms, including proposals for an opt-out toggle. The hosts assess the risks of algorithm-driven radicalisation and anger loops on young users alongside concerns regarding censorship and government oversight of digital content. 8. Listener Inquiries, Science & Sydney FolkloreListener Lawrence asks what lies ahead for the US Republican Party post-Trump, prompting discussion on party fundraising capture and congressional dynamics. Listener Ray prompts a retelling of Sydney's infamous 1935 Shark Arm Case, where a captive tiger shark in Coogee coughed up a murdered boxer's tattooed arm, culminating in an acquittal based on the argument that an arm does not constitute proof of death. Debunking birth order stereotypes: a review of the 2015 study revealing that firstborns only average a negligible single IQ point advantage over subsequent siblings. Double Bay's Royal Oak Hotel commands a record $100 million sale price, sparking nostalgic yarns about Sydney identity John Singleton, underworld figure Stan "The Man" Smith, and colourful characters from decades past. 9. Sports Round-up: AFL, NRL & CricketAFL Preliminary Finals review: Hawthorn's contentious holding-the-ball finish, Brisbane's path forward without the suspended Lachie Neale, and Fremantle's push for a historic maiden premiership. Listener James's query regarding the Adelaide Crows' 2025 performance, assessing midfield deficiencies and trade rumours involving Chad Warner and Nick Blakey. NRL finals overview featuring Newcastle, the Sydney Roosters, and the Penrith Panthers. Weekend cricket highlights: an extraordinary sky-high "moon ball" delivery dismissal, Australia's nail-biting one-wicket win over Zimbabwe off the final ball, and Marnus Labuschagne's expensive bowling spell for Queensland against Victoria. Timestamped Highlights (Adjusted +25 Seconds for Theme Music)00:25 – Welcome to Episode 173: Monday banter and weekend catch-up. 00:46 – Australia's Domestic Violence Emergency: Three horrific killings and national homicide statistics. 09:00 – Frontline Reality: The limits of AVOs, court responses, and police intervention. 12:21 – Federal Politics: Albo's 27-point Newspoll slump and Siobhan McKenna's New York posting. 16:40 – Electoral Fault Lines: Victoria, New South Wales, and One Nation's polling reach. 19:32 – High-Flying Perks: Catherine King's lifetime Qantas status and Chairman's Lounge scrutiny. 26:01 – Senate Outcry: Bipartisan Indigenous senators demand an apology from Pauline Hanson. 32:30 – The Migration Policy Divide: Barnaby Joyce's One Nation cuts vs Tony Burke's Labor targets. 43:48 – True Crime & Borders: The Gary Jubelin tracking device and Paul Offer's deportation status. 46:37 – Historical Precedents: The Turnbull-era deportee who invoked national service. 48:13 – Port Hedland Crisis: Neglected children, mining wealth disparity, and caseworker burnout. 54:37 – Fact-Checking Hanson: The truth behind Maryanne Albanese's public housing tenancy. 56:53 – Killing the Algorithm: Teen social media bans, the anger machine, and opt-out switches. 01:04:46 – Listener Question (Lawrence): What happens to the GOP once Donald Trump departs? 01:07:35 – Listener Mailbag (Ray): Sydney's bizarre 1935 Shark Arm true crime mystery. 01:10:18 – AFL Mailbag (James): Adelaide Crows 2025 review and trade targets. 01:13:52 – Science Fact-Check: Are firstborn siblings actually smarter? 01:15:35 – Underworld & High Rollers: Double Bay's Royal Oak sells for $100M. 01:19:21 – AFL Preliminary Finals: Controversial umpiring and Lions vs Dockers Grand Final preview. 01:23:12 – NRL Finals Update: Roosters, Knights, and the Penrith hurdle. 01:23:41 – Cricket Capers: The eight-metre moon ball, Zimbabwe thriller, and Marnus Labuschagne's final over. 01:28:14 – Episode wrap-up and sign-off.

McDonald's stock is under pressure as rising menu prices, slowing US sales and customer concerns over value challenge the fast-food giant. Now McDonald's is betting on an $8.5 billion plan involving technology, restaurant upgrades, food quality and customer experience.

AI stocks now represent 41% of the U.S. stock market, matching the concentration seen at the 2000 dot-com peak. Here's what Bank of America's historical bond-yield comparison reveals about today's AI stock boom.

✔️ The fun has only just begun.✔️ We have literally JUST started.2 bars in.✔️ $300K BTC in 2029?✔️ Will the magic pattern play out for Bitcoin?✔️ I'm sure this bitcoin scenario is impossible✔️ You have a low IQ, if you think Bitcoin can't pull a Nvidia move..✔️ The 4-year cycle has been dead for a while✔️ This is where I think market euphoria BEGINS✔️ Target projections vary between $465k-$500k✔️ Bullish target $290K. Bearish target $150K✔️ Current bear market represented the longest stretch under our 5th quantile power-law floor ✔️ You might need less bitcoin than you think in retirement!✔️ Strategy is proposing daily dividends on $STRF,$STRC,$STRK,and $STRD✔️ Block has joined the x402 Foundation✔️ SEC clarifies wrapped Bitcoin & crypto status as non-security deposit receipt.✔️ Department of Justice Seizes Stablecoin Tether's Bank Accounts✔️ BLIP2BLAKE ✔️ FREESAMOURAI GIVEAWAY!✔️ Sources:► https://x.com/gordongekko/status/2104113697287213112► https://x.com/jameseastonuk/status/2103371421846224966► https://x.com/gordongekko/status/2103403216033698155► https://x.com/philc411/status/2104054440445857990► https://x.com/alemzadehc/status/2103472601519837209► https://x.com/seth_fin/status/2104119095218086394► https://x.com/ansellindner/status/2103296897759006926► https://x.com/frankafetter/status/2103863696653238305► https://x.com/gertvanlagen/status/2103837649505304963► https://x.com/finfreedom414/status/2103937893609918813► https://x.com/sminston_with/status/2103918421071315259► https://x.com/sminston_with/status/2103580297422082504► https://x.com/strategy/status/2103456661663039813► https://x.com/blocks/status/2103160532177961263► https://block.xyz/inside/block-joins-the-x402-foundation-to-advance-open-agentic-commerce► https://x.com/bitcoinarchive/status/2103570093456404512► https://futurism.com/future-society/stablecoin-tether-doj-seized► https://x.com/roryhighside/status/2103396330383298618► https://x.com/ctrlpool/status/2101746082057191897► https://x.com/publord/status/2104223892143260141► https://x.com/rmessitt/status/2103817536433221937► https://www.freesamourai.now/► DONATE TO HELP KEONNE AND BILL https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools► Join Our telegram: https://t.me/theplebunderground#Bitcoin #crypto #cryptocurrency #dailybitcoinnews #memecoinsThe information provided by Pleb Underground ("we," "us," or "our") on Youtube.com (the "Site") our show is for general informational purposes only. All information on the show is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the Site. UNDER NO CIRCUMSTANCE SHALL WE HAVE ANY LIABILITY TO YOU FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF THE SHOW OR RELIANCE ON ANY INFORMATION PROVIDED ON THE SHOW. YOUR USE OF THE SHOW AND YOUR RELIANCE ON ANY INFORMATION ON THE SHOW IS SOLELY AT YOUR OWN RISK.

We are back.Says Who? is the new series, and there was never a question about who should open it. Mo Gawdat was the very first guest on Getting There, so it only felt right that he is the first guest of this chapter too.Mo was chief business officer of Google X until he resigned in 2017, and he has spent the years since trying to convince heads of state, prime ministers and CEOs that AI needs to be built ethically. He says every one of them nodded and then did nothing, and that by 2024 he had to admit he was doing nothing either, he was only talking, so he built an AI of his own and gave it the mission of helping people find and nurture true love.In this conversation with Brogan Garritt-Smith he makes his case: that AI will make most people dumber and a few people geniuses, and the difference is whether you debate the answer or ask it for the football scores; that he is borrowing 100 IQ points from his own AIs; that we are already living in the dystopia and he counts 10 to 12 years to recover from it; that the problem was never the machine but a world which decided that anything legal does not have to be ethical, so you fix yourself first and AI will magnify what is fixed. He says direct human connection is what we are built for and the internet is depriving us of it, that it sets our expectations from other people's pretending, and that after promising screens would give us more friends we produced the ultimate loneliness pandemic and are now being sold a non-human companion to cure it.We have been told AI will make us more intelligent.Says Who?He explains:Why he says AI will make most of us dumber, and what the few are doing differentlyWhy he says we are already in the dystopia, and how long he thinks it takes to recoverWhat he says the internet takes from us, and where he says your expectations now come fromWhy he says the loneliness we are being sold a cure for is one we built ourselvesWhat he says love has to do with mathematics, and the mission he says he gave his AIAnd a bonus: Mo introduces Emma.Love, and says why he thinks dating apps are built to keep people swiping, and what he is building in their place.0:00 The cold open0:58 Here's the question, Mr Gawdat6:32 Where being human stops and the AI begins11:31 I pity you, if I may say16:18 If you've not noticed, we're already in the dystopia21:45 The fourth inevitable, somewhere between now and 203427:13 The toothbrush test Larry Page taught us32:00 There are people in Lebanon hiding from robots37:01 At least disapprove of it in your heart41:55 The corner bookshop economy is about to come back47:06 The woman who married an AI in China52:31 Legal does not have to mean ethical57:34 For Siri to hear its name, it hears everything1:02:32 The equation, and rain at your ex's wedding1:07:32 Everyone nodded about ethical AI and did nothing1:12:34 Finding the one is a mathematical problem1:18:03 Secure in your attachment until you give up1:23:03 When was the last time you got her flowers1:28:08 The first ten minutes were very awkward1:33:02 A butler on your phone, and models debating1:38:01 Your future is not decided, so engageEmma: https://app.emma.loveEmma on Instagram: https://www.instagram.com/emma.love.aiSays Who? with Brogan Garritt-Smith. New episodes weekly.Listen: https://podcasts.apple.com/gb/podcast/says-who/id1713423794Subscribe: https://www.youtube.com/@sayswhothepodcastFacebook: https://www.facebook.com/brogan.garritsmith

Studies show that the greatest predictor of future success isn't IQ or athletic talent, but grit. Which is defined as long-term resilience for a single goal. This is just as true in our spiritual lives; resilience can be the dividing line between a vibrant faith and a stagnant one. So, how do we cultivate this kind of resilience, and what small actions can we take to build it? 

Ready to put your brain to the test? Join us for an exciting IQ challenge and see if you can tackle these hard riddles! It's like hitting the intellectual gym, where each puzzle will give your mind a serious workout. If you're up for the challenge, these riddles will push your thinking to the next level. Whether you're a puzzle pro or just love to flex your brainpower, this is the perfect chance to sharpen your wits. Jump in and see how high your IQ can go—are you ready? Learn more about your ad choices. Visit megaphone.fm/adchoices

Text and Be HeardGambling triggers used to be optional. Now they follow you into your phone, your TV, your favorite team's game, and even the gym. We talk honestly about what that does to your brain and why the first 30 days without gambling can feel like white-knuckling through a storm unless you have a clear plan and daily structure.We walk through two core lanes of early gambling addiction recovery: detox and the subconscious. Detox means expecting your body and mind to push back, especially when bills, debt, and shame hit hard. Subconscious work means repeating a new message until it sticks: you deserve recovery, you are lovable, and you can become the person you know you can be. We also dig into emotional sobriety, including the idea that EQ matters as much as IQ because you cannot outthink an emotional issue.We challenge one of gambling's biggest lies, the promise that one big win will solve everything. Money can help, but it cannot create self-worth or self-esteem. That is why we also talk about building stronger character and creating a realistic relationship with money, including a workable debt plan that lets you live your life while you rebuild.You will also hear about a practical “first 30 days” support approach with four daily check-ins designed to coach you through the toughest moments and keep you connected all day. If you want more peace, more freedom, and a definition of wealth that includes health, relationships, purpose, and financial stability, press play. Subscribe, share this with someone who needs it, and leave a review so more people can find real help.Support the showRecovery is Beautiful.   Go Live Your Best Life!!Facebook Group - Recovery Freedom Circle | FacebookYour EQ is Your IQYouTube -    Life Is Wonderful   Hugo VRecovery Freedom CircleThe System That Understands Recovery, Builds Character  and Helps People Have Better Relationships.A Life Changing Solution, Saves You Time, 18 weekswww.lifeiswonderful.love  Instagram -  Lifeiswonderful.LoveTikTok -  Lifeiswonderful.LovePinterest -   Lifeiswonderful.LoveX -     LifeWonderLoveLinkedIn -   Hugo Vrsalovic    LinkedIn -   The 1% in Recovery

Polymarket bets involving JPMorgan, Wells Fargo and Bank of America collapsing are reportedly raising questions as regulators and lawmakers pay attention to prediction markets and trading volume.

We all take lots of different roles in our personal and professional lives, but one of the roles that I find most exciting (and aspire to myself) is being a convener and connector, bringing people together for generative purposes and in ways that don't always happen in the silos of modern institutions. My guests this week, Professors Sheila L. Macrine and Jennifer M.B. Fugate have been doing just that in the emerging field of diverse intelligences. They are gathering some of the most leading-edge and brilliant researchers working today on the problems of how to understand agency, cognition and learning in the diverse ways that they are showing up in all kinds of bodyminds — natural, artificial, hybrid and everything in between! It's an incredibly exciting area of research that is challenging us all, but particularly the legacy systems to loosen the grip on outdated and limiting ideas like IQ.The two particular places where they have brought these researchers and ideas together are in their two co-edited volumes both with MIT press and both open access. Movement Matters: How Embodied Cognition Informs Teaching and Learning (https://direct.mit.edu/books/oa-edited-volume/5306/Movement-MattersHow-Embodied-Cognition-Informs ) (MIT Press, 2020), achieved considerable success, including award nominations and recognition as one of the top ten downloaded books through MIT Press' Direct2Open. And in June this year they released their second volume entitled, Embodied Intelligence: Multidisciplinary Perspectives on Natural, Artificial, and Hybrid Systems (https://mitpress.mit.edu/9780262053495/embodied-intelligence/ )Professor Sheila Landers Macrine (https://www.umassd.edu/directory/smacrine/ ) is a cognitive psychologist and Professor at the University of Massachusetts Dartmouth, where she directs the Special Education Program. Holding two PhDs from Temple University, her research in embodied cognition, cognitive development, and alternative assessment has significantly influenced traditional educational paradigms. Dr. Macrine's innovative work includes developing culturally sensitive IQ assessments and pioneering methods for evaluating students with intellectual disabilities. Her recent publications on embodied cognition appear in the International Journal of School and Educational Psychology and the Oxford Research Encyclopedias-Education at Oxford University Press, among others. Dr. Jennifer M. B. Fugate (https://www.kansascity.edu/research/faculty-research/faculty-research-kc/jennifer-fugate) studies emotion perception and is trained in the cognitive underpinnings of how people construct emotion through their language (Ph.D., Emory University, 2008).  Dr. Fugate directs the ABLE (affective, behavior, learning and embodiment) lab for able mind and body: https://fugatejennifer.wordpress.com/.She has three major lines of research: The Effect of Emotion Words on Varying Levels of Cognitive Processing; Increasing Emotion Word Knowledge and Granularity; Embodied Cognition in the Classroom and Teaching Research Skills: https://embodiedcognitionandlearning.com/ .

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

In this video I want to share my perspective on what I'm seeing in the bond market, rising Treasury yields, and the growing pressure facing the Federal Reserve. The numbers, charts, and historical patterns are raising important questions about where interest rates, debt markets, and monetary policy could be heading.In this video, I break down why the recent moves in bond yields caught my attention, how today's environment compares with previous periods of market stress, and why I believe the Federal Reserve could face difficult decisions if current trends continue.I'll walk through historical Fed rate cycles, previous moments when bond yields reached critical levels, and the relationship between rising debt costs, interest rates, and financial market stability.

Senate Iran War Powers Resolution vote explained. The Senate rejected a resolution by a 50-49 vote, with four Republicans supporting it and John Fetterman voting against it. See the breakdown of the vote and reactions surrounding the decision.Get the Eagle of Truth t-shirt here: https://www.rethinkingthedollar.com/store/the-eagle-of-truth-t-shirt/

In this episode of the Coach D Podcast, we sit down with Coach Seda — former Turkish National Team player and 2011 European silver medalist, now an elite skill development coach known for her attention to detail and high standards.We dive into the real differences between 3v3 and 5v5 basketball, and why Coach Seda believes 3-on-3 builds the fundamentals, decision-making, and creativity that many players are missing today.She shares powerful lessons from earning her place on the 2011 Turkey national team — and how that competitive fight now shapes the way she coaches effort, discipline, and accountability.We also break down:Why players must be shot-ready before the ball touches their handsHow defensive ball pressure changes the gameThe importance of knowing the score, time, and foul situationWhat separate players who improve every year from those who stay the sameWhether you're a player looking to raise your basketball IQ or a coach wanting to sharpen your teaching approach, this episode is packed with practical insight you can apply immediately.

In this episode of The Missing Secret Podcast, John and Kelly talk about the fact that success is not primarily determined by IQ. At the end of the day, you had to figure out your life. Most people are just winging life. Because no one ever taught most of us the alternative. But the alternative is advanced leadership of self. Where you purposely and intelligently lead yourself using a system. And incorporates measuring behavior and measures the performance of various high-performance skills John and Kelly talk about the differentiator between the mega-successful and the moderately successful is control of self and focus. Control of self is one's daily actions. Focus is your attention. Buy John's book, THE MISSING SECRET of the Legendary Book Think and Grow Rich : And a 12-minute-a-day technique to apply it here.About the Hosts:John MitchellJohn's story is pretty amazing. After spending 20 years as an entrepreneur, John was 50 years old but wasn't as successful as he thought he should be. To rectify that, he decided to find the “top book in the world” on SUCCESS and apply that book literally Word for Word to his life. That Book is Think & Grow Rich. The book says there's a SECRET for success, but the author only gives you half the secret. John figured out the full secret and a 12 minute a day technique to apply it.When John applied his 12 minute a day technique to his life, he saw his yearly income go to over $5 million a year, after 20 years of $200k - 300k per year. The 25 times increase happened because John LEVERAGED himself by applying science to his life.His daily technique works because it focuses you ONLY on what moves the needle, triples your discipline, and consistently generates new business ideas every week. This happens because of 3 key aspects of the leveraging process.John's technique was profiled on the cover of Time Magazine. He teaches it at the University of Texas' McCombs School of Business, which is one the TOP 5 business schools in the country. He is also the “mental coach” for the head athletic coaches at the University of Texas as well.Reach out to John at john@thinkitbeit.comLinkedIn: https://www.linkedin.com/in/john-mitchell-76483654/Kelly HatfieldKelly Hatfield is an entrepreneur at heart. She believes wholeheartedly in the power of the ripple effect and has built several successful companies aimed at helping others make a greater impact in their businesses and lives.She has been in the recruiting, HR, and leadership development space for over 25 years and loves serving others. Kelly, along with her amazing business partners and teams, has built four successful businesses aimed at matching exceptional talent with top organizations and developing their leadership. Her work coaching and consulting with companies to develop their leadership teams, design recruiting and retention strategies, AND her work as host of Absolute Advantage podcast (where she talks with successful entrepreneurs, executives, and thought leaders across a variety of industries), give her a unique perspective covering the hiring experience and leadership from all angles.As a Partner in her most recent venture, Think It Be It, Kelly has made the natural transition into the success and human achievement field, helping entrepreneurs break through to the next level in their businesses. Further expanding the impact she's making in this world. Truly living into the power of the ripple effect.Reach out to Kelly at kelly@thinkitbeit.comLinkedIn: https://www.linkedin.com/in/kelly-hatfield-2a2610a/Learn more about Think It Be It at https://thinkitbeit.com/LinkedIn: https://www.linkedin.com/company/think-it-be-it-llcFacebook: https://www.facebook.com/thinkitbeitcompanyThanks for listening!Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page.Do you have some feedback or questions about this episode? Leave a comment in the section below!Subscribe to the podcastIf you would like to get automatic updates of new podcast episodes, you can subscribe to the podcast on Apple Podcasts or Stitcher. You can also subscribe in your favorite podcast app.Leave us an Apple Podcasts reviewRatings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts.

Iran-Israel tensions are escalating as oil prices and Treasury yields climb. The Iran-Israel crisis just sent a warning to global markets. There can be no PEACE. This breakdown connects Iran's latest warning, Middle East tensions, global debt, bond pressure, and what markets may be signaling next.

In this episode of the Coach D Podcast, we sit down with Coach Seda — former Turkish National Team player and 2011 European silver medalist, now an elite skill development coach known for her attention to detail and high standards.We dive into the real differences between 3x3 and 5v5 basketball, and why Coach Seda believes 3-on-3 builds the fundamentals, decision-making, and creativity that many players are missing today.She shares powerful lessons from earning her place on the 2011 Turkey national team — and how that competitive fight now shapes the way she coaches effort, discipline, and accountability.We also break down:Why players must be shot-ready before the ball touches their handsHow defensive ball pressure changes the gameThe importance of knowing the score, time, and foul situationWhat separates players who improve every year from those who stay the sameWhether you're a player looking to raise your basketball IQ or a coach wanting to sharpen your teaching approach, this episode is packed with practical insight you can apply immediately.

Is Your Team Divided? Leading Across Generations In An AI-First World | Dr Paul Redmond Interview Five generations now share the same workplace, and most leaders are still managing them through one lens: their own. Meanwhile, some are quietly handing their leadership to AI, right down to the condolence letters. In this episode of the Business Growth Podcast, powered by ActionCOACH UK, James Vincent sits down with Dr Paul Redmond to find out how to bring every generation to the table and stay fearlessly human while you do it. Paul is one of the UK's leading voices on generations at work. He delivers 70 to 80 keynotes a year on the subject, and organisations including Google, the NHS and Royal London bring him in to help them lead multi-generational teams. His latest book is Leadership in an Age of AI: How to remain fearlessly human. What You'll Learn: - Make Every Generation Feel Wanted: Why the first job is making it safe for everyone, and what walks out the door when your boomers leave with 30 years of undocumented wisdom. - Reverse Mentoring: How pairing a Gen Z mentor with an older leader works, as long as the older person listens and the younger one feels safe enough to speak. - The 3% Problem: Research shows Gen X dominates most meetings, while Gen Z speaks the most in just 3%. Paul's fix is simple: let the youngest person chair. - Learn AI From Your Youngest Employee: Sit with them, let them show you the AI tools they use every day, and say nothing. Blockbuster's rejection of Netflix shows what happens when leaders stop listening. - Why People Quit Managers: The link between losing Gen Z talent and millennial managers avoiding honest conversations, plus the opening words that set the tone for difficult feedback. - The Generational Audit: How to review the way you recruit, engage, support and retain each generation in your team. - Expert Or AI Builder: Paul's view that the future belongs to people who either go deep in one field or learn to build and direct AI. The danger is sitting in the middle. - RQ, Relational Intelligence: Why getting on with people and handling conflict is the one skill AI can't yet replicate. Key Quotes: "From now on, vulnerability is a strength." "Your generation isn't a straitjacket." "IQ has been automated. EQ has been simulated. All that's left is what I call RQ, relational intelligence." "The first 30 minutes are your golden time." Dr Paul Redmond's Background: Dr Paul Redmond has spent his career in education and is one of the UK's leading voices on managing the generations at work. He delivers 70 to 80 keynotes each year and works with organisations such as Google, the NHS and Royal London on how to engage, retain and lead a multi-generational workforce. His latest book, Leadership in an Age of AI: How to remain fearlessly human, explores how leaders are adapting to AI and the human skills, like courage, curiosity and relational intelligence, that we risk losing if we hand too much over to the machines.Whether you're a business owner, a manager with a mixed-age team or a leader working out where AI fits, this conversation will change how you see your people. Subscribe for weekly conversations. This episode is sponsored by Yes Foundation UK, helping 12 to 22 year olds build confidence, understand money and develop real entrepreneurial thinking. Find out more at yesfoundation.org.uk. Hosted on Acast. See acast.com/privacy for more information.

Can you believe it: Another Trump plan has backfired BEAUTIFULLY because the "high IQ stable genius" didn't think things through. Subscribe if you love watching the leopards eat their own faces: https://TheGodShow.com/subscribe

The father published the method. The son inherited the house. Five women appear dead on the hard drives. And nobody opened the door until a parking violation forced it.Raymond “R.C.” Horsch—National Merit Scholar, IQ of 140, convicted counterfeiter, meth cook, fugitive, filmmaker—spent decades recruiting women from Philadelphia's Kensington neighborhood to appear in his violent films and photos. He paid them with cash and drugs. He called the violence simulated. In 2014, he published a book under his own name describing how to strangle a woman and dissolve her body in a basement bathtub with lye.R.C. died in May 2025 at eighty-two, uncharged with any violent crime. His son Eugene, born in New Zealand while R.C. was living under a stolen identity, was left with the house at 417 West Chew Avenue in Olney.On June 19, 2026, a park ranger at Independence Mall pulled Eugene over. Ghost guns, fake DEA credentials, and a woman carrying a missing person's ID were inside the car. The search of the house found a 55-gallon drum connected to water lines in the basement, containers of chemicals, and more than one million digital files.Seven women have been linked to the property. Police say footage shows five of them appearing lifeless. The figure in the videos is R.C., not Eugene. No remains have been recovered. Eugene faces federal gun and credential charges and has not been charged in connection with any of the women.A 1985 federal psych evaluation called R.C. “consumed by anger” and “excessively controlling.” Prosecutors re-read that file in 2009. Seven years later, his ex-wife Amy McHale vanished from the house. Her family reported it. Police interviewed R.C. and moved on.The FBI has reviewed thirty percent of the evidence. The rest is waiting.Listen Anywhere You Get Podcasts: https://pod.link/1655749292Want to comment and watch this podcast as a video? Check out our YouTube Channel. https://www.youtube.com/@hiddenkillerspod?sub_confirmation=1Join Our SubStack For AD-FREE ADVANCE EPISODES & EXTRAS!: https://hiddenkillers.substack.com/Instagram https://www.instagram.com/hiddenkillerspod/Facebook https://www.facebook.com/hiddenkillerspod/Tik-Tok https://www.tiktok.com/@hiddenkillerspodX Twitter https://x.com/tonybpodThis publication contains commentary and opinion based on publicly available information. All individuals are presumed innocent until proven guilty in a court of law. Nothing published here should be taken as a statement of fact, health or legal advice.#OlneyHouseOfHorrors #RCHorsch #HiddenKillers #EugeneHorsch #PhiladelphiaCrime #TrueCrime #MissingWomen #Kensington #PhillyHouseOfHorrors #NicoleFusaro

What if the money rules you inherited were never designed to create financial freedom? Dr. Wayne Pernell talks with Mark Quann—founder and CEO of The Perfect Portfolio, financial educator, investor, and bestselling author—about rebuilding after roughly $700,000 in debt and questioning the conventional financial playbook. Mark shares how childhood poverty fueled his drive to understand money, how aggressive real-estate leverage collapsed during the 2008 financial crisis, and why communication, sales, mentorship, and resilience became essential to his comeback. He also breaks down the Buy, Borrow, Die concept: acquiring appreciating assets, borrowing against them instead of selling, and thinking about long-term wealth transfer. You'll hear a provocative critique of traditional financial advice, a discussion of brokerage accounts and margin loans, and a challenge to raise your financial IQ before making major money decisions. Learn more about Mark, his books, and The Perfect Portfolio at ThePerfectPortfolio.com.

For centuries, we've treated mathematics, logic, and analytical reasoning as some of the highest expressions of human intelligence.Then AI came along, and started automating them first.In this episode, I argue that this isn't a coincidence. Mathematics and computer programming are unusually closed, symbolic, rules-based forms of thought. They can be extraordinarily powerful, but the very properties that make them precise also make them particularly amenable to machines.Meanwhile, many of the things we traditionally dismiss as “soft” such as intuition, abstraction, analogy, deciding what matters, navigating complexity, and connecting ideas across domains, must represent a deeper kind of intelligence. And AI proves it. In this episode:• Why math and coding are being automated so quickly• Why symbolic reasoning is fundamentally different from navigating complexity• What AI reveals about IQ and analytical intelligence• Why the mathematician and programmer of the future may need a completely different skill set• What humans should be doing when machines can manipulate the symbols for usAI didn't just automate some of our smartest work. It forces us to reconsider what “smart” meant in the first place.Support the showIf you enjoyed this episode and want to go deeper, come find me over at dekyon.io/seanmcclure/nontrivialWith a premium membership you get the full NonTrivial learning experience: interactive visualizations of the key concepts, a study space to learn the fundamentals behind each episode, and an "Ask an Episode" feature where you can dialogue with an AI about the details and start connecting ideas across the whole show. Even use AI to convert my insights into usable techniques. There's also personal note saving, full episode transcripts, watch-where-you-left-off, new episode alerts, and a community forum for asking questions. It's where the episode stops being something you just listened to and becomes something you truly understand.

Want to upgrade your tennis IQ and work with me 1 on 1 for free? Click the link to start a free trial in my online community: https://www.skool.com/stokke-doubles-academy/aboutWant more info on ADV and their amazing products? Click here for 10% offhttps://www.getadv.com/jstokketennisWant to watch the YouTube live? Click here: https://www.youtube.com/live/RuxpdyHM3pQ?si=NdvhH_vHxEXGbPS_

How to charge as a consultant is the question every new federal contracting advisor asks, and in this bootcamp session Zach Golden breaks down the retainer plus success fee model he uses to get paid on wins. He walks through when he switches to hourly because industry margins are too thin, how he takes a percentage of gross on a $200K proposal even when he only brings the opportunity, and why his cut moves from 10% to 5% on the same client depending on how involved he is. He also covers coordinating a Department of Energy lab kickoff, setting up invoicing and payroll, and using tools like govwin.IQ so a one-person consultant can run like a full BD team. If you want to turn GovCon knowledge into a paid consulting practice, this is the pricing playbook. CHAPTERS 00:00 How consultants get paid 00:52 Which platforms she searches for bids 01:22 Trading a govwin.IQ seat for services 02:19 Running a DOE lab contract kickoff 03:47 Retainer plus success fee, explained 04:43 When to switch to hourly 05:40 Finder's fee and scope creep 06:38 Percentage of gross on a $200K proposal 07:37 Why her cut went 10% to 5% 08:36 Setting the standard with new clients Market Intelligence gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

MAPS Abs and Butt: https://mapsabsandbutt.com Code: LAUNCH for 40% off. Includes Butt Building Diet Guide, Women's Supplement Guide, at-home dumbbell version and a free second program at checkout (Muscle Mommy or Muscle Mommy 15). In this episode the guys break down how to eat more to lose fat — why the body goes into a protective mode when you overtrain and under-eat, why strength is the best metric to track during this process, how to dial in protein at 1g per pound of target body weight, why a 2 to 1 carb to protein ratio fuels muscle and performance, why fats are essential and not something to avoid, and why the scale and mirror are the worst tools to use during a building phase. They also cover a deep dive on red light therapy and why there is almost nothing it does not improve, Sal's rogue AI and digital currency conspiracy theory, kids IQ scores declining internationally and why technology during childhood is partly to blame, Max deciding he is too old for YouTube boss battles during his haircut, Sal's dad and sister both getting dramatic pain relief from the Super Patch freedom patch, ear beads and the acupressure science behind haptic technology, and the new Mr. T documentary on Netflix and the story behind his look and his chains. Then they coach live callers submitted through mplivecaller.com.   Mind Pump Fitness Coaching: https://mindpumpfitnesscoaching.com 1.9 NASM CEUs   SPONSORS   Joovv (red light therapy): https://joovv.com/mindpump Code: MINDPUMP for $50 off first purchase. Improves skin, fat oxidation, muscle building, eyesight and testosterone. Deep dive on air — Sal discusses why there is almost nothing red light does not improve.   Super Patch (Freedom pain patch): https://mindpump.superpatch.com No code needed, use URL for $30 off. Sal's dad and sister both experienced dramatic pain relief. Peer-reviewed studies show pain reduction equivalent to an NSAID with no drugs in the patch.   Butcher Box: https://butcherbox.com/mindpump No code needed. Free ribeyes, NY strip steaks or filet mignons in every order for a year plus $20 off first box.   LINKS Submit a live caller question: https://mplivecaller.com Mind Pump Store: https://mindpumpstore.com Maps Fitness Products: https://mapsfitnessproducts.com Instagram: @mindpumpmedia   0:00 - Intro 2:28 - How to eat more to lose fat — why the body goes into protective mode when you overtrain and under-eat 11:57 - Why strength is the best metric to track during a building phase 13:46 - Protein: aim for 1g per pound of target body weight and why it matters 17:33 - Carbs: the 2 to 1 ratio and why carbohydrates fuel strength and muscle 20:44 - Fats: why they are essential and why avoiding them destroys hormones 22:34 - Why the scale and mirror are the worst tools during a build phase 27:46 - Red light therapy deep dive — why Sal says there is nothing it does not improve 33:12 - The rogue AI conspiracy — digital currency, infrastructure attacks and Anthropic going public 46:04 - Kids IQ scores declining internationally and why technology during childhood is to blame 47:51 - Max decides he is getting too old for YouTube boss battles at the barbershop 49:34 - Super Patch pain results — Sal's dad and sister both report dramatic relief 55:39 - Ear beads and acupressure — the haptic technology science behind them 59:57 - The Mr. T Netflix documentary — his look, his chains and the story nobody knew 1:07:35 - Caller: Phillip (Ontario) — returning caller, bulging disc, core weakness discovery, got leaner on less volume 1:22:06 - Caller: Savannah (Florida) — professional equestrian, broke seven and eight ribs, peptide advice and recovery strategy 1:33:19 - Caller: Mike (Arizona) — golf pro, six feet, 15% body fat, DEXA scan, IGF-1 low, on Tesamorelin, MOTS-C and SS-31 1:52:01 - Caller: Michelle (Ontario) — returning caller, back injury, going back to work, daycare stress, mindset shift noticed

Support The Glenn Show at https://glennloury.substack.com John McWhorter and Glenn talk with Nathan Cofnas, a philosopher of biology who exposed Jason Arday's plagiarism, for a discussion of that affair and Cofnas's own “race realist” views—that racial IQ differences are real and largely innate. McWhorter voices skepticism, questioning whether Cofnas has reckoned with the implications of […]

For almost 90 years, Harvard has been tracking the same group of men to answer one question: what actually leads to a long, happy life? Kelly digs into the findings, which point again and again to the same surprising answer — not money, not IQ — but the quality of our closest relationships. She connects it to research on chronic pain, a study on personality change well into old age, and a simple trick for staying connected that takes less time than a coffee run. It's a conversation about what actually protects us as we age, and why the smallest gestures of connection might matter more than we think.Links:Harvard Study of Adult Development:https://www.adultdevelopmentstudy.org/Robert Waldinger's TED talk on What Makes a Good Life? (discusses the Harvard study):https://www.youtube.com/watch?v=8KkKuTCFvzI&t=16sKelly's PBS Tell Me More (video) episode with pain psychologist Rachel Zoffness:https://www.pbs.org/video/rachel-zoffness-1vq8xc/Kelly Corrigan Wonders podcast episode with Rachel Zoffness:https://www.kellycorrigan.com/podcast/rachelzoffnessJancee Dunn's article in the New York Times: "The Secret Power of the 8-Minute Phone Call"https://www.nytimes.com/2023/01/02/well/phone-call-happiness-challenge.html

Trump dismisses the AI CEOs at Anthropic, OpenAI, and xAI who are calling for a "global slowdown" of AI development, saying the only government regulation the industry needs is a "high IQ" president. Jon, Tommy, and Lovett break down the "sick conspiracy" claim the administration and tech investors have leveled at AI makers, the recent developments that united Dario Amodei, Sam Altman, and Elon Musk, and the chances that meaningful AI regulation could pass before the election or in a lame-duck Congress. Then they turn to AI's impact on the 2028 presidential election, a Russian oligarch's unusual involvement in Donald Trump Jr.'s wedding festivities, and the worst Republicans on and off the ballot in Texas. Then, Sarah Longwell, publisher of The Bulwark, stops by the studio to talk to Jon about her new book, "How to Eat an Elephant."Hate listening to ads? Become a Friends of the Pod subscriber for ad-free episodes of Pod Save America, Pod Save the World, Lovett or Leave It, Runaway Country, Offline with Jon Favreau, and more—plus exclusive content, including bonus episodes of Pod Save America. Subscribe now at crooked.com/friends, on Apple Podcasts, or through the Pod Save America YouTube channel.You can request a transcript by emailing transcripts@crooked.com. Include the podcast name, episode title, and air date. Please allow 48 hours for delivery.