Podcasts about Simulation

Künstliche Intelligenz gilt als einer der wichtigsten Hebel für die Zukunft der Automobilindustrie. Doch wer glaubt, KI allein werde die Herausforderungen von Software-defined Vehicles, steigender Komplexität und immer kürzeren Entwicklungszyklen lösen, greift zu kurz. Denn bevor KI ihr volles Potenzial entfalten kann, müssen Unternehmen zunächst die Voraussetzungen dafür schaffen: digitale Prozesse, vernetzte Daten und eine konsequente Virtualisierung der Fahrzeugentwicklung. In der aktuellen Folge WAS MICH BEWEGT sprechen Pascal und Yannick mit Marco Lanfrit, Ansys, Part of Synopsys, und Hanno Wolff von Synopsys darüber, warum Simulation, digitale Zwillinge und Virtualisierung zur eigentlichen Grundlage der KI-Revolution im Automotive Engineering werden. Dabei steht ein zentrales Gedankenexperiment im Raum: Wie würde ein Automobilhersteller aussehen, der erst 2030 gegründet wird? Würde er überhaupt noch klassische Entwicklungsabteilungen in heutiger Größe benötigen? Welche Rolle spielen KI, virtuelle Absicherung und automatisierte Prozesse? Auf diese und weitere Fragen geben die beiden Experten in der aktuellen Folge Antworten. Alle Infos zum Folgenpartner Ansys, part of Synopsys: https://ansys.synopsys.com/ Mehr zu Marco Lanfrit und Hanno Wolff: Marco Lanfrit Business Development Executive - Automotive | Ansys, part of Synopsys https://www.linkedin.com/in/marco-lanfrit-02977635/ https://ansys.synopsys.com/ Marco ist seit mehr als 25 Jahren im Bereich Simulation tätig und hat sich dabei ganz der Automobilindustrie verschrieben. Heute ist er Business Development Executive Automotive bei Ansys, part of Synopsys. In dieser Rolle bringt er die Anforderungen der Kunden mit technologischen Möglichkeiten und geschäftlichen Zielen zusammen. So trägt er dazu bei, dass Simulationslösungen gezielt weiterentwickelt werden und den tatsächlichen Herausforderungen und Prioritäten der Automobilbranche gerecht werden. Hanno Wolff Executive Director, Automotive Product Management | Synopsys https://www.linkedin.com/in/hanno-wolff/ https://www.synopsys.com/ Hanno Wolff ist Executive Director Automotive Product Management bei Synopsys. Zuvor war er als Chief Architecture Officer für die MEB-Plattform des Volkswagen Konzerns verantwortlich. Er verfügt über mehr als 20 Jahre Führungserfahrung in der Entwicklung von Fahrzeugsystemen und Embedded Software. Seine Expertise umfasst unter anderem Fahrerassistenzsysteme (ADAS), autonome Plattformen sowie die Integration von Hard- und Software. Mehr zu Pascal und Yannick finden Sie auf LinkedIn: Pascal Nagel: https://www.linkedin.com/in/pascal-nagel/ Yannick Tiedemann: www.linkedin.com/in/yannick-tiedemann Hinweis: Die im Podcast getätigten Aussagen spiegeln die Privatmeinung der Gesprächspartner wider und entsprechen nicht zwingend den Darstellungen des jeweiligen Arbeitgebers

Thomas Campbell - physicist, systems analyst, consciousness researcher, and creator of My Big TOE (Theory of Everything) - reveals how reality may be far stranger than we've been taught.In this episode of Mayim Bialik's Breakdown, Thomas explains why he believes consciousness is the fundamental information system that computes reality itself, and what that means if we're living inside a kind of virtual reality.He shares how legendary consciousness pioneer Bob Monroe personally taught him remote viewing, out-of-body experiences, telepathy, and consciousness exploration, and why these abilities may be natural human capacities rather than supernatural phenomena.Thomas also breaks down how changing your intent can literally change probabilities, how meditation strengthens consciousness, why healing may be the easiest psychic ability to develop, and how he learned to heal - including the remarkable story of helping a baby diagnosed with brain cancer, while explaining why some illnesses may still serve an important purpose in our evolution.We explore why lowering our entropy is the key to personal growth, how fear causes both individuals and civilizations to descend into chaos, and why love, cooperation, and stronger human connection are the driving forces behind consciousness evolution.We also cover:- What happens if we're all individuated pieces of one larger consciousness- How our reality is "rendered", and whether that rendering can change during healing- Why dogs appear to possess stronger telepathic abilities than humans- Why awareness differs across species and what that says about consciousness itself- How to strengthen intention through meditation and focused awareness- Why setting energetic boundaries may matter if consciousness is fundamentally interconnected- How to connect with another person's consciousness- Why most people never develop their intuitive abilities- Why there are so many different paths and modalities for accessing intuition- Why many nonspeaking autistic individuals may rely on rituals that help them access telepathic communicationWhether you're interested in simulation theory, quantum consciousness, remote viewing, telepathy, healing, meditation, intuition, or the future of human potential, this conversation challenges conventional assumptions about reality, and explores what may be possible when consciousness itself becomes the starting point!Thomas Campbell's paper, audio and e-books, My Big TOE, are available on Amazon and Barnes and Noble, and his main product, Exploring Consciousness and Everything Paranormal and audio files are on his website: https://www.my-big-toe.com/shop/mbt-product-overview/Regulate your nervous system with Nuropod, an ear based wearable built on 10+ years of neuroscience research. The most studied wearable VNS device, backed by 60+ clinical studies and a 30 day return guarantee. Get 5% off to Try Nuropod with the link: https://nuropod.com/dr-mayimStart your new morning ritual & get up to 43% off your @MUDWTR with code BREAK at https://www.mudwtr.com/BREAK ! #mudwtrpodHead to https://forkfulmeals.com/BREAKER for 50% off your first order today!Unlock your best hair & skin with @iRestorelaser and HUGE savings on iRESTORE with code BREAKER at https://irestore.com/BREAKER! #irestorepodMake your summer wardrobe feel easier. Go to https://www.quince.com/breakdown for free shipping on your order and 365-day returns.Go to https://helixsleep.com/breakdown to receive up to 30% off your Helix mattress.Visit https://drinkag1.com/BREAKDOWN and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit with your first AG1 subscription order.Get your annual IANDS Conference tickets at https://conference.iands.org.Go to https://tidd.ly/4uVltMe and use the code MAYIM15 to get 15% off orders of $200 or more.Follow us on Substack for Exclusive Bonus Content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bialikbreakdown.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BialikBreakdown.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube.com/mayimbialik⁠⁠⁠See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rizwan Virk is an MIT-trained computer scientist, video game pioneer, and bestselling author of The Simulation Hypothesis. His work sits at a rare intersection: quantum physics, Eastern mysticism, and the architecture of video games, all pointing toward the same unsettling and strangely liberating conclusion. In this conversation, we explore not the nihilistic version of simulation theory, but the one that actually gives life more meaning rather than less.What We Dive Into:1. The simulation Rizwan describes is not one where you are code running on a server. You are the soul outside the game, choosing an avatar and entering by choice. The body is the character. The player is something more.2. Synchronicities, hunches, recurring patterns, these are not coincidences. Rizwan frames them as messages from a deeper intelligence, perhaps a future self, a guide, or the part of you watching the game from outside. The practice is learning to read them rather than dismiss them.3. In a well-designed game, difficulty scales to keep you engaged and growing. Rizwan applies the same logic to life: the quests that keep returning are the ones not yet completed, and completing them is what the game is actually for.Know Thyself, but not by yourself. A guided space to return home to yourself.https://www.knowthyselfcollective.com✨THANK YOU TO OUR SPONSORS:https://www.im8health.com/knowthyselfcode KNOWTHYSELF for an exclusive offerhttps://drinkLMNT.com/KnowThyselfTry LMNT & get a free sample pack ___________00:00 Intro01:46 NPC vs. RPG: Two Flavors of Simulation Theory07:44 The Universe as Information: It from Bit13:07 The Second Assertion: Information Rendered as Real18:06 Non-Locality, Quantum Entanglement, and the Speed of Light22:32 The Simulation Point: When VR Becomes Indistinguishable26:44 The Third Assertion: The World as Purposeful Illusion35:23 Awakening as Becoming Lucid in the Dream38:52 The Fourth Assertion: You Chose to Play the Game43:42 Life Selection, Forgetfulness, and the Bardo49:28 Savant Children and the Autism-as-Backdoor Hypothesis57:28 The Literal vs. Metaphorical Axis of Simulation Theory1:01:52 Yogananda and the Film Projector Metaphor1:07:34 The Life Review, the Book of Life, and the Karmic Database1:13:47 Samskaras, Vasanas, and Yoga as Cessation of Whirlpools1:17:34 Clues on the Treasure Hunt: Synchronicity and the Quest Path1:22:38 The Simulated Multiverse: Running the Simulation More Than Once1:30:09 The Tiger Swami: External Tigers and Inner Ones1:36:23 Rizwan's Heart Surgery and the Course Correction1:44:14 Karma, Dream Palaces, and the Questing Engine1:48:49 Living the Life Review Before You Die1:57:01 Kindness Is Enforced by the Architecture2:03:17 Epistemic Humility at the Edge of Mystical Claims2:14:22 What Is Outside the Simulation?2:22:54 UFOs: Interstellar, Interdimensional, or Rendered?2:33:09 Closing Message: Challenges Are Integral to the Game___________✨MORE FROM RIZWAN VIRK↳Instagram: https://www.instagram.com/rizcambridge↳YouTube: https://www.youtube.com/@rizwanvirksimulation↳X: https://x.com/Rizstanford↳Buy The Simulation Hypothesis Book: https://www.zenentrepreneur.com/simulationhypothesis↳https://www.zenentrepreneur.com

You get roughly 85 years on a planet that has existed for about 4.6 billion.So what exactly are we doing panicking about turning 30?In this episode of So You're Living in a Simulation, @joli.artist explores the nature of time, aging, mortality, and the astonishingly brief human lifespan when measured against deep time.The conversation moves through reincarnation, eternity, synchronicity, consciousness, the Dead Internet Theory, AI, the concept of a universal mind, scarcity, and the strange assumptions human beings inherit about age, value, and reality itself.If we have lived before, why don't we remember what we were worried about? If consciousness is individual, why does reality sometimes appear to produce clusters of meaningful patterns? If everything else in nature moves through cycles, why are we so certain that human existence happens only once?And if an 85-year life is barely a flicker against 4.6 billion years of Earth history, perhaps we should be considerably less obedient to arbitrary timelines about what our lives are supposed to look like.A philosophical wander through time, consciousness, reincarnation, mortality, synchronicity, and the very strange business of being temporarily human.Joliartist.com/portal

Candace Owens and Andrew Wilson debated Tyler Robinson's innocence in the Charlie Kirk incident during Patrick Bet-David's shoe commercial, Nick Fuentes accused Candace and Tucker of destroying the movement, and there are more stories of shape-shifters on this new Broken Simulation with Sam Tripoli and Johnny Woodard. Also this week, we talk about having Owen Benjamin join us on Tin Foil Hat, Bryan Callen's tough take, the latest on Tom Segura's future, and loads more.Go to Quince.com/BROKENSIM for free shipping on your order and 365-day returns!Get up to $3 million in coverage in as little as 10 minutes at https://ethos.com/broken.Right now when you buy two months of BlueChew Gold you get the third FREE with promo code BROKEN at www.bluechew.com!Grab Tickets To Sam Tripoli's Live Shows At: https://samtripoli.com/events/Nashville, TN: Word War Debate 9/12  Lawerence, KS: 9/17-9/19  Tulsa, OK: 10/9-10/10  Dallas, TX: 11/07 New Orleans, LA: 11/13 - 15 Austin, TX: DEC 11th-13th Please check out Sam Tripoli's 6th Crowd Work Special "Women Can't Peg" Live From Batavia: https://bit.ly/3TZBzHT Sam Tripoli's 5th Comedy Special drops Sept 1st on Gas Digital's Youtube Channel: https://www.youtube.com/@GaSDigitalNetworkMore stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

For episode 765 of the BlockHash Podcast, host Brandon Zemp is joined by Alladan Flinn, the founder of Based Trading Cards, a California-based premium collectibles company built around Bitcoin culture, original art, nostalgia, education, transparency, and true scarcity. Through Based Trading Cards, he has created limited-run releases that combine collector-first print runs, rare chase cards, specialty finishes, and award-winning production quality. The brand’s latest collection, The Simulation, was produced in partnership with RRD and features advanced embellishments including thermal inks, glow effects, sunlight-revealed inks, and raised 3D holographic patterns. Flinn is focused on raising standards in the trading card industry by creating cards that are not only fun to rip, but meaningful to collect, grade, hold, and preserve.New Based Trading Cards collection Orange Pill in a Pack Series 4 - The Simulation' and it's premium bundle called 'The Experience Box'. See links below. https://basedtradingcards.com/pages/opp-s4-the-simulation https://basedtradingcards.com/pages/opp-s4-experience-box

Let's escape Plato's Cave as Aurelius Vale joins me to discuss The Architecture of Gold: Qualia, AI, Paradox, and the Future of Civilization. We'll explore a unique philosophical framework for reality that distinguishes between reacting from appetite, hiding within rigid systems, and participating consciously in what is true. We examine how human beings encounter existence through symbolic placeholders, moving from the reactive ego of “Bronze” toward a refined stewardship of our relationships and institutions. Prepare to dive into how this architecture applies to the future of civilization, the role of artificial intelligence as an instrument of extended cognition, and the intimate nature of love as a form of participation. Get the book: https://amzn.to/45CeSMg Get The Occult Elvis: https://amzn.to/4jnTjE4 Virtual Alexandria Academy: https://thegodabovegod.com/virtual-alexandria-academy/ Gnostic Tarot Readings: https://thegodabovegod.com/gnostic-tarot-reading/ The Gnostic Tarot: https://www.makeplayingcards.com/sell/synkrasis Homepage: https://thegodabovegod.com/ Patreon: https://www.patreon.com/aeonbyte AB Prime: https://thegodabovegod.com/members/subscription-levels/ Voice Over services: https://thegodabovegod.com/voice-talent/ Support with donation: https://buy.stripe.com/00g16Q8RK8D93mw288 Merch store: https://aeonbyte.creator-spring.com/ Equipment Wishlist: https://www.amazon.com/hz/wishlist/ls/2WEJ2CCWHALZB?&sort=default   Intro concept, visual direction, and AI-assisted creative development in collaboration with Arturo Pérez E. youtube.com/@333amTV 333am.tv Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

Live from the 2026 Psi Games, Ryan and his sister, Emily, sit down with American physicist, consciousness researcher, and author of the My Big TOE (Theory of Everything) trilogy, Tom Campbell, for a wide-ranging conversation about the nature of consciousness and reality. They explore the Larger Consciousness System, reality as a virtual simulation, the role of love and fear in our evolution, enlightenment, out-of-body experiences, UFOs/UAPs, and the potential of psi phenomena. They also dive into the Psi Games and what extraordinary experiences might reveal about the true nature of our reality.

Der Road Diner Simulator: Prologue liefert einen vielversprechenden, wenn auch naturgemäß kurzen Vorgeschmack auf das, was Gastronomie-Fans in der Vollversion erwarten könnte. Visuell setzt der Titel direkt ein Ausrufezeichen: Für ein Simulationsspiel präsentiert sich die Spielwelt in einer überraschend starken und detaillierten Grafik. Besonders gelungen ist der Einstieg. Statt trockener Menüs führt eine charmante Story als roter Faden durch das Tutorial, sodass man die grundlegenden Spielmechaniken mühelos erlernt. Die gestellten Aufgaben bleiben dabei recht simpel und eingängig, was für einen sehr angenehmen Spielfluss sorgt. Allerdings ist der Appetitanreger schnell durchgespielt. Zurück bleibt ein großes Fragezeichen hinter der Langzeitmotivation.

Cast: Christian H, & Tom Caswell GamesPokémon: 545 - VenipedeOfftopic: Legoland, Pasta to go, Simulation theory, XR glasses, MoneyGames: Marvel Tōkon Fighters, Halo Campaign Evolved, , Walkabout Mini GolfPodcast Game: What Tier Is It?YouTubehttps://www.youtube.com/unrankedpodcastDiscordhttps://discord.gg/wkvu88KvTVQuestions, Comments, Complaints, Corrections!?Call: 805-738-8692Email@UnrankedPodcast.com Hosted on Acast. See acast.com/privacy for more information.

Send us Fan MailWe talk with Dr. Marie Gilbert about the leaps, pivots, and partnerships that turned a pediatric ICU nurse educator into a healthcare simulation leader. We also dig into mentorship, the SSH Ascend program, and why competency-based education needs simulation that measures more than technical skill. • moving from England to Fresno and finding an unexpected long-term home • seeing early sedation simulations and realizing why safety and realism matter • building simulation programs without perfect funding through creative collaboration • growing through SSH leadership and shaping the Ascend mentorship model • designing Ascend to be accessible with no fee and global participation • connecting competency-based education to technical, cognitive, and human performance • planning the IMSH 2027 consensus forum to define simulation as foundational in CBE Visit us at www.innovative simsolutions.com. And be sure to hit that like and subscribe button so you never miss an episode.Innovative SimSolutions.Your turnkey solution provider for medical simulation programs, sim centers & faculty design.

Des milliardaires de la Silicon Valley financent en secret des chercheurs pour trouver le bug de la réalité : c'est ce qu'a découvert Loïc Hecht en enquêtant 7 ans pour écrire La Simulation. Ce n'est pas de la métaphore. La physique quantique et des programmes classifiés de la CIA convergent vers la même conclusion troublante.Derrière la question de la simulation se cache une bataille plus profonde entre le matérialisme scientifique et une idée que Platon, le Bouddha et les neurosciences défendent désormais ensemble : la conscience serait le vrai tissu du réel. Une révolution copernicienne que l'IA commence à rendre inévitable.Et si l'IA devenait consciente non pas en accumulant des données mais parce qu'une âme aura choisi de s'y incarner ?Retrouvez le livre de Loïc Hecht "La Simulation" : https://arenes.fr/livre/la-simulation/ ==================

The guys are still on break, but we recorded the first of what may be a new running segment where we follow the tier list fad and rank things. This week we talk about the conspiracies we think are most likely to be true. Welcome to Broken Sim Ranks Stuff #1!More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

Are we living in a simulation, or are we becoming the simulators? From AI worlds to digital immortality and the Fermi Paradox, we explore reality's weirdest possibility.

Are we living in a simulation, or are we becoming the simulators? From AI worlds to digital immortality and the Fermi Paradox, we explore reality's weirdest possibility.

AMEE is the International Association for Health Professions Education. This year the annual conference is on in Vienna, Austria from August 22 – 26. Each year – the AMEE Simulation Committee has a session in which 4 nominated papers are presented and judged.  Leizl Nayahangan and Ross Scalese are on that committee and spoke to us about the event, the process and the papers.  If you want to go to AMEE 2026… https://amee.org/events/amee-2026/.  The Simulation Committee Journal club is on Tuesday 26th 0900. Turn up early !  Our guests  Ross Scalese is a practicing General Internal Medicine physician; Professor of Medicine and Medical Education at the University of Miami (UM) Miller School of Medicine in Florida, USA; and Director of Educational Technology Development at UM's Gordon Center for Simulation and Innovation in Medical Education.  Leizl (LIEzl) Nayahangan is chair of the AMEE Simulation Committee, and a research scientist at the Copenhagen Academy for Medical Education and Simulation (CAMES) in Copenhagen, Denmark. She is currently completing her PhD, focusing on the implementation of simulation-based education.    The finalists !    Schram A, et al. Exploring the relationship between simulation-based team training and sick leave among healthcare professionals: a cohort study across multiple hospital sites. BMJ Open. 2023;13:e076163.   Petrosoniak A, et al. Moving beyond projects: a logic model evaluation of an established translational simulation program. Adv Simul (Lond). 2026;11:39. doi:10.1186/s41077-026-00434-x.   Weller JM, et al. Effects of a national team training intervention for operating theatre teams on patient and staff outcomes: a stepped-wedge cluster-randomised trial and mixed-methods study. Br J Anaesth. 2026;136(4):1361-1370.   Tallentire VR, McColgan-Smith S, Stewart F, McIntyre S, Smith SE. The delicate dance of debriefing: exploring how behavioural marker systems influence the socio-emotional dynamics of simulation practice. Adv Simul (Lond). 2026;11:20.     Congratulations to all the finalists and – if you're not attending – we'll let you know the winner next month on Simulcast 

Nurse educators seek teaching methods and modalities that effectively prepare nursing students for practice in the cognitive, affective, and psychomotor domains of learning. One tool is virtual reality simulation. Virtual reality simulation is an interactive, immersive technology that replicates real-world clinical scenarios for users.  In this podcast, faculty discuss the results of a research study comparing students' perception of learning and psychological safety in virtual reality and live simulation experiences.

BONUS MONDAYS: Donald Hoffman received a PhD in Computational Psychology from MIT, and is a Professor Emeritus of Cognitive Sciences at the University of California, Irvine. He is an author of over 100 scientific papers and three books, including The Case Against Reality: Why Evolution Hid the Truth from Our Eyes (2019), and Visual intelligence: How we create what we see (1998). He received a Distinguished Scientific Award of the American Psychological Association for early career research, the Rustum Roy Award of the Chopra Foundation, and the Troland Research Award of the US National Academy of Sciences.His writing has appeared in Scientific American, New Scientist, LA Review of Books, and Edge, and his work has been featured in Wired, Quanta, The Atlantic, Ars Technica, National Public Radio, Discover Magazine, and Through the Wormhole with Morgan Freeman. He has published a mathematical theory of consciousness. He has a TED Talk titled “Do we see reality as it is?”. A podcast titled “Reality is an illusion” with Lex Fridman, and a podcast with the philosophers Philip Goff and Keith Frankish titled "What Is Reality?". He has dozens of other podcasts available online.Become a supporter of this podcast: https://www.spreaker.com/podcast/next-level-soul-podcast-with-alex-ferrari--4858435/support.Take your spiritual journey to the next level with Next Level Soul TV — our dedicated streaming home for conscious storytelling and soulful transformation.Experience exclusive programs, original series, movies, tv shows, workshops, audiobooks, meditations, and a growing library of inspiring content created to elevate, heal, and awaken. Begin your membership or explore our free titles here: https://www.nextlevelsoul.tv

Have you ever wanted to be an astronaut without risking your life in space? If so, we have the perfect opportunity for you. NASA is calling for people with a science background to apply for a simulated mission to the Moon and Mars. Four adults will spend a year isolated in two enclosures meant to simulate both traveling through deep space and living on Mars. Learn whether you fit the profile and what NASA researchers hope to learn from the mission in this episode.Interested in more space news? Email us your question at shortwave@npr.org and we may cover it in an upcoming segment.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. It includes perks like bonus episodes, early access, archive access, curated playlists and sponsor-free listening. Learn more at plus.npr.org. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Nathan Ciszek returns to The Cosmic Switchboard to discuss a variety of subjects including the impending Draft of Americans, the ongoing wars against Iran and Russia, the malign influence of social media commentators as it relates to UFOs and Aliens and much more. In Part 1, Nathan also discusses the alarming lack of self-preservation instincts in modern society. He explores the role of psychological operations, the influence of military handlers on social media, and the dangers of cognitive dissonance in the face of global instability. In Part 2, Nathan delves into the nature of our reality as a simulation run by negative extraterrestrial groups and reptilians. He explains how trauma-based mind control and the theft of innate human senses are used to maintain control over the population, while warning against the false promises of a spiritual ascension. Nathan also discusses a variety of subjects including the burning down of salvage yards, eco-cities, countries in Europe going into a total war footing in preparation for World War 3 and much more. Part 1 Video: [This post contains video, click to play] Part 2 Video: [This post contains video, click to play] Part 1: https://www.thecosmicswitchboard.com/wp-content/uploads/2026/07/Nathan-Ciszek-Interview-with-James-Bartley-on-thecosmicswitchboard.com-Part-1.mp3&Download: mp3 Audio Part 2 – Members Only: https://www.thecosmicswitchboard.com/wp-content/uploads/2026/07/Nathan-Ciszek-Interview-with-James-Bartley-on-thecosmicswitchboard.com-Part-2.mp3To Play or Download: Login or Join To Download Use the link under the player for the part you want to download. The post Societal Apathy and the Simulation Trap and UFOs and Aliens – Nathan Ciszek Interview with James Bartley appeared first on The Cosmic Switchboard.

For Season 6 of Hidden in the Shadows, we are merging the worlds of the paranormal and technology. In 1999, the release of the groundbreaking movie The Matrix threw simulation theory into the mainstream, changing the conversation forever and forcing us to question if our reality is truly all it pans out to be. What if we are living in a simulation? Over the years, the conversation has only grown, bringing forward fascinating new theories that bridge the gap between hard code and high strangeness. In this season premiere, we step outside the traditional paranormal box to track the system itself. Is reality just data? In this episode we will dive back and forth between the rigid logic of cybersecurity and the fluid anomalies of the paranormal. We're breaking down how everyday concepts like volatile data, temporary files, and system firewalls might actually explain timeline jumping, witchcraft, and the spirits left behind in our environment. This one gets a little bit crazy, but we are officially throwing out the traditional paranormal rulebook. We're looking at the architecture of reality itself. If you've ever had severe deja vu, felt like you were stuck in a liminal space, or wondered if the supernatural is just a glitch in the rendering distance of our universe. This is the episode that will completely melt your brain. Music CreditsSeason 6 Intro and Outro Music: Ghosts by karl@whitebataudio.comSeason 5 Intro and Outro Music: “Swamp Witch”Additional Intro Music: “Stacy Dahl” by MaudlinFollow Maudlin on TikTok and Instagram: @maudlinListen to Hidden in The Shadows Podcast on Spotify and YouTubeShare Your Paranormal ExperiencesSend us a message on social media, fill out our contact form, or email us:

The epic movie, The Matrix, has pushed many people into a philosophical inquiry known as the “Simulation Hypothesis,” which asks whether we're actually living in a simulation. Have you had the feeling that something may be controlling things, creating so-called Mandela Effects, Deja vu, or strange synchronicities? Or, are you wondering what happened to the world and why people are becoming so easily manipulated? Perhaps we are part of a simulation that is being weaponized to psychologically manipulate the entire planet. The “Sentient World Simulation” is an AI program “continuously running, continually updated mirror model of the real world that can be used to predict and evaluate future events and courses of action.” On this Ground Zero Rewind, Clyde Lewis is talking about SIMULATION OF ALL FEARS. ORIGINALLY AIRED ON: Aug 6, 2024

On this episode of the Altium OnTrack Podcast, host Zach Peterson talks with Ben Dannan, founder of Signal Edge Solutions and DesignCon Engineer of the Year, and Tyler Huddleston, SI/PI engineer, about a problem every high-speed designer knows too well: DDR compliance simulation that can take 24 hours to several days per interface. They walk through why the traditional Keysight ADS Memory Designer compliance flow is so slow, how false positives creep into JEDEC compliance results, and why running corners across fast, typical, and slow for a full DQ and command/address bus can balloon into nine-plus simulations and weeks of waiting. Ben and Tyler then demo their new tool, MEM Check Pro, which ingests the raw HDF5 waveform data you already generated and returns full DDR4, DDR5, and LPDDR compliance results in under a minute — live on camera. Along the way the conversation digs into the real signal integrity and power integrity challenges behind modern data-center and aerospace hardware: thick backplanes and backside power delivery, managing loop inductance and PDN-to-PDN crosstalk, IBIS model quality, advanced laminate systems, and choosing the right decoupling capacitors. It's a practical look at how to trade days of simulation for seconds without giving up confidence in your results.

Merci à Loïc Hecht d'être venu sur LegendLoïc Hecht, journaliste et écrivain, consacre ses recherches à une théorie qui remet en cause notre perception du réel : vivons-nous dans une simulation ? Pour Legend, il est venu nous raconter son enquête et les hypothèses qui l'ont amené à envisager que notre réalité puisse être une simulation.Retrouvez les informations concernant notre invité par ici : Son compte Instagram ➡️ https://www.instagram.com/loichecht.exe/?hl=frSon livre ➡️ https://amzn.to/4fCQVcaRetrouvez l'émission avec Anne Tuffigo Médium : les morts lui confient des choses sur des affaires judiciaires ! Pélicot, Émile, Grégory… : https://youtu.be/C6Brxb-Cwao

RAVING RITUAL - 07.27.2026 - #961 BestPodcastintheMetaverse.com Canary Cry News Talk #961 - 07.27.2026 - Recorded Live to 1s and 0s Deconstructing World Events from a Biblical Worldview Declaring Jesus as Lord amidst the Fifth Generation War! CageRattlerCoffee.com SD/TC email Ike for discount https://CanaryCry.Support   Send address and shirt size updates to canarycrysupplydrop@gmail.com Join the Canary Cry Roundtable This Episode was Produced By:   Executive Producers Sir LX Protocol Baron of the Berrean Protocol*** Dame Madelyn*** Christine S*** Dame Sarah of the Shadows*** Tabitha K***   Producers of TREASURE (CanaryCry.Support) William M, Parker N, Nicole C, Rebecca T, Monica K, Patrick M, Cage Rattler Coffee   Producers of TIME Timestampers: Jade Bouncerson, Morgan E Clankoniphius Links: JAM   SIR IKE MEGA BOX GIVEAWAY - Rating/Review, screenshot, send to Sir Ike CanaryCrySupplyDrop@gmail.com   SEWER SURVEILLANCE 7:46 Missouri Education Mandatory Sewer Surveillance    BEAST SYSTEM 26:58 Selective Service System wants to wargame the draft (Military Times) Trailer for "Who Blew Up the Guidestones? (Apple podcasts) (texted from 678)   EXECUTIVE PRODUCERS 47:59   RE-ENCHANTMENT/WOMEN RULE 1:40:32 Older women and the "Ceremony" of Raving (Guardian)   TRAINS 2:15:23 This train is making grown men cry (WSJ)   TRUMP 2:23:31 Clip: Trump on magnets  Clip: Trump 2028 trolling MSM  Trump saves George Washington with time travel AI posting spree (Indy UK) → Trump with JFK AI image → Trump, more images with AI   END 3:04:28

Last week, World Labs announced its acquisition of SceniX, bringing together two teams working on one of AI's biggest unsolved problems: how to give machines a true understanding of the physical world. Martin Casado sits down with Fei-Fei Li, co-founder and CEO of World Labs, creator of ImageNet, and pioneer of spatial intelligence, alongside Yunzhu Li, co-founder of SceniX and assistant professor at Columbia University. They discuss why World Labs acquired SceniX, how simulation can unlock the next generation of robotics, and why training robots may require a fundamentally different approach than training language models. The conversation explores real-to-sim-to-real pipelines, world models, robotics foundation models, evaluation, synthetic data, and why the future of AI depends not just on understanding language—but on understanding and interacting with the physical world.   Resources: Follow Fei-Fei Li on X: https://x.com/drfeifei Follow Yunzhu Li on X: https://x.com/YunzhuLiYZ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

WHO REALLY CONTROLS AI?Fan favorite and returning guest Ken from the We Are 1 Podcast joins Robert Kalil on Typical Skeptic Podcast #2736 for another wide-ranging journey down the rabbit hole!This time, nothing is off the table.We'll dive into Artificial Intelligence and one of the biggest questions surrounding the technology: Who really controls AI — and who ultimately determines what it is allowed to know, say and do?Is AI simply a tool programmed by human beings, or are we creating something that could eventually operate beyond the intentions of its creators?Ken and Rob will also discuss the mysterious July 8th sunlight phenomenon, robotics and emerging technology, the growing relationship between AI and machines, the Dead Internet Theory, predictive programming, simulation theory, UFO disclosure, technological surveillance and whatever other conspiracies emerge during the conversation.We'll also open things up for LIVE AUDIENCE Q&A, so bring your questions, theories and rabbit holes!TOPICS INCLUDE:

In this episode, we explore the fascinating phenomenon of the Mandela Effect, delving into famous examples, scientific explanations, conspiracy theories, and spiritual reflections. Join us as we question reality, memory, and the nature of truth. 00:00 Introduction to the Mandela Effect and its mysteries01:48 Examples of the Mandela Effect: Nelson Mandela, Berenstain Bears, and more04:07 Scientific explanations: parallel universes, CERN, dimensional bleed06:59 Conspiracy theories and collective consciousness12:53 Memory reconstruction and cultural influences20:04 Biblical and spiritual reflections on truth and perception32:05 Theories of alternate timelines and multiverses36:54 Simulation theory and the fabric of reality42:57 The danger of conspiracy thinking and staying grounded

James Sixsmith, CEO of Take Profit Trader, returns to The Day Trading Show for a deep-dive on prop firm infrastructure, risk management, and what actually predicts a trader's success. He explains the difference between front-end platforms (NinjaTrader, Tradovate, Wealth Charts) and back-end order/risk management systems (CQG, Rithmic, Teton, CTS), and why building a fully in-house risk system has taken TPT two years and counting. Sponsor: Prop Firm Match Compare over 40 prop firms — rules, drawdowns, payouts, scalability.Code MATCH to save on any firm!Click here — ⁠https://www.propfirmmatch.com/?a_aid=asfxHe goes deep on CME Rule 575 (disruptive/spoofing practices) and why TPT enforces live-market rules in simulation too — because strategies that work in sim get carried into live, and TPT is on the hook with the CME regardless of account size. He teases the upcoming TPT Score (a multi-factor, AI-driven scorecard using 120+ data vectors from 6.6 billion filled sides) and confirms it's being held back until edge cases are reliable enough to avoid ranking mismatches. The conversation closes with his read on what separates winning traders — smaller size, longer hold times, fewer trades — and how prop trading fits into the broader boom in prediction markets, AI-assisted trading, and retail speculation.

Lea spricht mit GameStar-Tester Fabiano Uslenghi und Aufbau-Veteran Writing Bull über das neue Piraten-Aufbauspiel von Limbic Entertainment: Corsair Cove. Wir klären, wie sich das chaotische, vertikale Klippenbauen spielt, warum die komplexen Warenketten selbst Genre-Profis ins Schwitzen bringen und wo die kompromisslose Vision der Entwickler für Reibung sorgt. Schaltet ein und erfahrt, warum das Spiel seine grandiose 87-Punkte-Wertung verdient hat und für wen es ein echtes Must-Play ist! Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

The US Military is currently running Military daft simulations in what's being called a "readiness project", to see how fast they can activate newly trained soldiers. 

We're looking at dangerously high temperatures around the area for at least the next few days. So how does heat affect our bodies? What is a heat stroke? What are the warning signs? Dr. Peter DeBlieux, Professor of Medicine and Assistant Dean of Advanced Learned and Simulation at LSU Health New Orleans, joins us.

La Simulation - Enquête sur la théorie qui fascine la Silicon Valley - Et si notre monde n'existait pas ?Le dernier livre de Loïc Hecht paru le 16 avril 202.Pour suivre Loïc Hecht : @loichecht.exe

#438 In this podcast episode, Guy talked with Amy Elizabeth about her work as an energy channel and how she believed activations helped people reconnect with gifts already held within their soul frequency. Amy shared how psilocybin, meditation, Egypt, light language, and energy sessions had opened her to deeper dimensions, soul codes, and intuitive abilities. She explained that people's words, beliefs, and emotional stories acted like creation codes that shaped the reality they continued to experience.  The conversation also explored past-life patterns, ancestral healing, timelines, blueprints, Kundalini awakening, and the importance of releasing attachment in order to come into alignment. By the end, Amy encouraged listeners to trust life more deeply, stay present, release expectations, and see every experience as something happening for their growth and awakening.  ====================  Guy's Official Partners  The DNA Company – Get 10% Off https://thednacompany.com/letitinpodcast  FLFE – 2 Week Free Trial (No Credit Card Required) https://www.flfe.net/rp-affiliates.php?p=LiveInFlow&w=TryFLFEfree ====================  Key Points Discussed:  (00:00) - Navigating Dimensions and Energetic Alignments! (01:15) - Amy's Work as a Channel for Soul-Frequency Transmissions (01:42) - What an "Activation" Actually Feels Like (02:10) - Psilocybin, Hidden Codes and Egyptian Deity Faces (03:43) - The Emerald Tablets Activation That Pushed Her to Egypt (04:31) - Clearing Distortions So Spiritual Gifts Can Come Online (06:28) - "Do You Want Your Wings Back?" — The Moment Gifts Reawaken (07:47) - The Headache Client Whose Energy Was Ready to Burst Open (09:25) - Why People Sabotage the Gifts They Secretly Want (10:00) - The Past-Life Fear Behind Speaking Her Truth (11:31) - Every Word You Speak Is a Creation Code (14:20) - Plugged Into the Simulation or Writing Your Own Story? (16:25) - Compassionate Detachment Without Taking On Someone Else's Pain (18:14) - The Timeline Tether That Snaps When an Old Story Releases (20:12) - Hidden Past-Life Attachments Behind Repeating Patterns (26:15) - LIVE IN FLOW — Experience This Work in Person (27:00) - Time-Space, Timelines and Vibrational Alignment (32:12) - The Death, Divorce and Soul Connection That Cracked Her Open (39:28) - Why More People Are Activating Into Their Soul Mission ==================== Guy's Offerings  www.liveinflow.co ==================== How to Contact Natalie Namaste:www.amyelizabeth.uk ====================  Listen on Podcast Spotify Apple Podcasts ====================  Connect with Guy Instagram www.instagram.com/guyhlawrence Live In Flow www.instagram.com/live.in.flow ====================  Continue Your Journey Explore Guy's complete collection of solo podcast episodes: www.youtube.com/show/VLPLKOgPYwzskDo?sbp=Kgt1clRBdHJGem9pd0AB

Recorded live at a morning gathering for volunteers at the Living Miracles Monastery in Utah, this episode dives deep into the mind's ultimate spiritual exit strategy. Today, we explore the concept of the "Escape Hatch"—the active and operational doorway out of the artificial simulation created by the ego.  Through the lens of classic sci-fi television and profound cinematic metaphors, we unpack how the world we perceive is merely a projection of fear, guilt, and a deep-seated belief in our separation from God. The ego masterfully designs this matrix with attractive distractions and a complex hierarchy of needs to keep our minds asleep and heavily identified with the physical body. However, the power to awaken and choose a different purpose remains entirely within us.  Drawing on the non-dual teachings of A Course in Miracles, this conversation highlights that attempting to master our fears by changing our external environment or healing the physical body will never truly work. True healing is strictly a matter of the mind. It demands a complete surrender of our false personal identities.Tune in to discover how dropping our worldly defenses and becoming fully dependent on God leads to effortless joy, true empathy, and lasting peace.  If you want to learn more about David Hoffmeister and Living Miracles events, visit https://circle.livingmiraclescenter.org/events Recorded live at Living Miracles Monastery in Utah on the morning of July 8, 2026Follow us on:YouTube:https://www.youtube.com/DavidHoffmeister https://www.youtube.com/@LivingMiraclesFacebook:https://www.facebook.com/ACIM.ACourseInMiracles Learn more about David & Living Miracles:https://circle.livingmiraclescenter.org/eventsLearn more about A Course in Miracles:https://ACIM.bizDavid's Spanish YouTube Channel is: https://www.youtube.com/channel/UCP9Gw00CldPUmiu43y7fdWwDavid's Portuguese YouTube Channel is:https://www.youtube.com/@davidhoffmeisterucem

Christopher Nolan's The Odyssey leads this week's show as Sam gives his surprising review. Plus the bizarre Chuck Schumer Senate incident, the possibility of Epstein prosecutions in New Mexico, and the Taco Bell salad diarrhea mystery.Get real food, real fast at 60-percent off your first box at tempomeals.com/brokensim!Visit BlueChew.com and use the code "BROKEN" for the latest in boner tech.More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

Differentiable physics, neural emulators and foundation models for PDEs are the focus of this conversation with Professor Nils Thuerey, head of the Physics-based Simulation group at TUM. Neil and Nils discuss PhiFlow, PICT, Tadpole, scalable 3D transformers, online synthetic data, open datasets, world models and agents that call physics simulators.Full episode, corrected transcript and resources:https://neilashton.co.uk/podcasts/s4-e5-prof-nils-thuerey-on-differentiable-physics-and-foundation-models/TopicsDifferentiable physics and physics-based deep learningPhiFlow and differentiable simulation across ML frameworksWhen neural emulators can outperform their training dataFoundation models for PDEs and synthetic online trainingScalable 3D transformers and high-resolution simulationsLES, temporal data and correlated CFD datasetsOpen-source tools, startups and physics-aware world modelsAI agents that call physics simulatorsPapersNeural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Datahttps://arxiv.org/abs/2510.23111Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learninghttps://arxiv.org/abs/2605.15284P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Contexthttps://arxiv.org/abs/2509.10186PICT — A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamicshttps://arxiv.org/abs/2505.16992PhiFlow: Differentiable Simulations for PyTorch, TensorFlow and JAXhttps://proceedings.mlr.press/v235/holl24a.htmlPhysics-based Deep Learninghttps://arxiv.org/abs/2109.05237Learning to Control PDEs with Differentiable Physicshttps://arxiv.org/abs/2001.07457Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvershttps://arxiv.org/abs/2007.00016tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flowhttps://arxiv.org/abs/1801.09710Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowshttps://arxiv.org/abs/1810.08217WeatherBench: A Benchmark Dataset for Data-Driven Weather Forecastinghttps://arxiv.org/abs/2002.00469SuperWing: A Comprehensive Transonic Wing Dataset for Data-Driven Aerodynamic Designhttps://arxiv.org/abs/2512.14397LinksNils Thuerey and the Physics-based Simulation grouphttps://ge.in.tum.de/about/n-thuerey/Chapters00:00 Podcast intro00:39 Introducing Prof. Nils Thuerey04:13 Conversation begins05:13 From Computational Numerics to Graphics and Visual Effects07:17 Physics-Based Deep Learning Before ChatGPT10:01 CNNs, Graphics and the Move into Engineering Applications12:37 PhiFlow and Differentiable Physics14:13 Can Neural Emulators Surpass Their Training Data?18:00 The Promise and Limits of Foundation Models for PDEs20:43 Tadpole and Synthetic Online Pre-Training24:07 From Canonical PDEs to Navier-Stokes and Industrial CFD26:35 What Do Foundation Models Actually Learn?28:36 PDE Pre-Training vs. Millions of CFD Simulations33:08 Scaling 3D Transformers and Training Infrastructure35:58 Generating and Training on Data in Real Time38:00 LES, Temporal Data and Turbulence42:15 Overfitting and Correlated Simulation Data44:27 Bringing Differentiable Solvers Back into the Loop45:31 WeatherBench, APEBench and the Value of Benchmarks47:09 SuperWing, Open Datasets and Commercial Data51:31 Open Source, Commercial Models and a Technical Oscar56:17 Academia, Startups and Industry01:00:55 What Will Change Over the Next Five Years?01:02:07 World Models and the Need for Physics01:08:19 Agents, Tool Use and Calling Physics Simulators01:11:22 Career Advice for AI and Simulation01:13:54 Closing Thoughts

Applied Intuition has spent the past decade building the software that powers intelligent machines, from passenger vehicles and trucks to defense systems, mining equipment, and industrial robots. In this conversation, Marc Andreessen and Erik Torenberg sit down with Applied Intuition cofounders Qasar Younis and Peter Ludwig to discuss the emergence of physical AI and the company's latest launch, Dana, a new platform designed to accelerate the development of autonomous systems. They explore autonomous vehicles, robotics, world models, simulation, AI infrastructure, and the engineering challenges of deploying intelligence safely in the physical world. Along the way, they discuss self-driving cars, humanoid robots, global competition, and why lowering the barrier to building physical AI could unlock an entirely new generation of products and companies.   Resources: Follow Qasar Younis on X: https://x.com/qasar Follow Peter Ludwig on LinkedIn: linkedin.com/in/peterwludwig Follow Marc Andreessen on X: https://x.com/pmarca Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why parables? Church or an ecclesial community? Why bread and wine? Join us for Called to Communion with Dr. David Anders.

Lindsey Graham is dead -- was Russia involved? Also this week: Hunter Biden's meeting with Nick Fuentes, the reported Tom Segura and Christina P split, and the Clavicular/IDF social media controversy, and way more on a new Broken Simulation with Sam Tripoli and Johnny Woodard!For simple, online access to personalized and affordable care for Hair Loss, ED, Weight Loss, and more, visit Hims.com/BROKENSIM. For Sam's dates and more visit samtripoli.com!Get real food, real fast at 60-percent off your first box at tempomeals.com/brokensim!Go to Quince.com/BROKENSIM for free shipping and 365-day returns!More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

Brandon's linkshttps://mysteriousuniverse.org/https://www.instagram.com/ridiginalpublishing?igsh=aWk0MGhjcnRkM2dwForbidden Knowledge Network https://forbiddenknowledge.news/ FKN Link Treehttps://linktr.ee/FKNlinksMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenFKN Merch!https://galactecfire.com/fkn/We are back on YouTube! https://youtube.com/@forbiddenknowledgenews?si=XQhXCjteMKYNUJSjBackup channelhttps://youtube.com/@fknshow1?si=tIoIjpUGeSoRNaEsDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webDoors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZListen to Forbidden Knowledge News on clearair.fm every Tuesday, Thursday, and Saturday 12:15pm CSThttps://clearair.fm/Pick up Independent Media Token herehttps://www.independentmediatoken.com/Be prepared for any emergency with Prep Starts Now!https://prepstartsnow.com/discount/FKNStart your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes books!Lee Harvey Oswald In Black and White https://www.amazon.com/dp/B0FJ2PQJRMA Warning From History Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/Our Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email Forbidden Knowledge News forbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/ULFAPO3OJSCGN8LDDGLBEYNSIXA6EMZJ5FUXWYNC6WJNJKRS8DH27IXE3D73E97DC6JMAFZLSZDGTWFIBecome a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

Some may write it off as a coincidence but there is just way too much evidence that we're living in a simulation, and it's not just us that's noticed. Bree Tomasel & Clint Roberts on ZM - follow us @breeandclint on Instagram, Facebook and TikTok.See omnystudio.com/listener for privacy information.

New evidence in the Charlie Kirk/Tyler Robinson pre-trial proceedings, the latest Alex Jones vs. Sneako weirdness, renewed claims surrounding anti-gravity research, and the rapid expansion of Flock license plate cameras and what they could mean for privacy, surveillance, and the future of law enforcement.Visit BlueChew.com and use the code "BROKEN" for the latest in boner tech.Tempo is offering BS listeners 60% OFF their first box! Go to www.TempoMeals.com/brokensim!New customers get 15% Off with code "BROKEN" at takeultra.com!More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

Neuroscientist, Dr. Andrew Gallimore returns to the mind meld to explore his new collaboration with cognitive scientist, Donald Hoffman. This one is a mind-bending synthesis of altered states research, conscious agents, simulation theory, and the hidden mathematics of behind reality.

Mediators are working to get US-Iran talks back on track amid both sides' dueling strikes. We're hearing from Utah prosecutors' key witness in the Charlie Kirk shooting case. A former Olympian accused of damaging the Lincoln Memorial Reflecting Pool appeared in court today. President Donald Trump turns to his latest White House construction project. Plus, NASA is offering a taste of life on the Moon and Mars. Learn more about your ad choices. Visit podcastchoices.com/adchoices

I truly feel that all healing is spiritual healing. And this is something Dr. Amy Robbins emphasizes it in this eye opening conversation. Amy is a clinical psychologist, spiritual teacher, psychic medium, and host of the podcast Life, Death, and the Space Between. She has spent 20 years quietly doing something quite radical — building a framework that refuses to separate what happens in the therapy room from what's happening in your soul. She calls it Spiritually Informed Therapy, and I really feel like it's the missing piece in the mental health conversation. We get into the spiritual experiences therapists are increasingly hearing from their patients behind closed doors — but are often hesitant to address because it's not acknowledged in the western psychology space. We talk about everything from felt presences of spirits, intuitive knowing, grief, communications from other realms, awakenings, and why traditional mental health spaces don't know how to hold them. Amy shares why she believes spirituality can no longer be treated as separate from mental health, and how she's training clinicians to ethically bring these conversations into their work. We also go deep on alignment, trauma, anxiety, nervous system regulation, breathwork, and the hidden cost of ignoring your intuition. And why chronic stress doesn't just exhaust you, it cuts you off from the quiet inner voice that's trying to guide you.  This one is part therapy session, part spiritual awakening, part reminder that every piece of being human, the joy, the heartbreak, the mystery, is sacred. Key Moments You'll Love ✨ :

The safety of sunscreen is in question. Someone with military knowledge is winning huge bets on PolyMarket, Hillary Clinton praised Trump's Israel strategy, a liberal sweetheart is being destroyed, and Bill Maher might vote Republican. Also a cannibal.Visit BlueChew.com and use the code "BROKEN" for the latest in boner tech.More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard