Podcasts about SF

John Lund proposes the following qurstion: which era would you rather relive: the Joe Montana-led 49ers, the dominant Giants under Bruce Bochy, or the Curry-led Warriors. Is nostalgia a factor? We discuss, plus Andy Baggarly on how SF will attempt to rebuild pitching staff in off-season with a potential lockout looming.See omnystudio.com/listener for privacy information.

#920: Meta settles a major case that alleges its platforms can be addictively harmful for teens. Nvidia has become a banker to the AI boom. Bill Gates expresses his concerns about the pace of AI and society's ability to govern it in an essay. NYC passes SF as the country's biggest tech hub. Iceland has a ridiculously low murder rate. Japan's Prime Minister defends her habit of working from home.  Learn more at https://go.amex/morningbrew  Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

   If you ask anyone in KC, it's a stone cold lock that Patrick Mahomes in starting against the Broncos in Week 1 and that includes asking anyone in the media.  But media outside KC are pointing out some contradictions in what the Chiefs are saying, and doing right now, and interpret this situation as totally up in the air.    I'd never heard of Farm School until I had lunch with Tim and Britt Cross of www.crosskitchenskc.com Wednesday.  Their oldest attends Farm School "Lolly and Pops" in Raymore and were featured on a piece produced by KSHB-TV.  This is one of the greatest school ideas ever... you have to hear this.    Senator Josh Hawley is targeting Flock Cameras alongside.... Bernie Sanders?   JOCO property taxes are going up again next year, your democrat leaders couldn't hate you more.    In Sports, the Royals win streak ends at 9 as they faced another Mizzou starting pitcher in Toronto.  The Big 12 joins the Big Ten and SEC in not allowing pros to come back to college and I think they made a mistake at least in the short term.  TCU pulls credentials from the largest media outlet covering their football team as they head for Ireland to face Bill Belichick's UNC team.  And TCU is 1000% in the wrong.    A new documentary by the best sports talk show host in KC, Carrington Harrison,  debuts Thursday night at Screenland Armour Theater in NKC and it's called Armageddon at Arrowhead.  It's about that epic MU-KU football game at Arrowhead nearly 20 years ago.    ESPN dumps something for college football, Tom Brady's store in SF gets robbed and a girl is dominating the Little League World Series.

San Francisco rents are rising fast: According to Zumper, an apartment listing website, the median rent for a 1-bedroom apartment in the city has surpassed $4,000 a month. If you've hunted for an apartment in SF recently, we'd love to hear your story. Just tap this link and follow the instructions to leave us a voice note. You can also call at (415) 710-9223. Learn more about your ad choices. Visit megaphone.fm/adchoices

Well, we lost another 80-year-old. Tim Curry, the star of Rocky Horror Picture Show, has passed away. Even if Stella Langley surpasses Mariah Carey, Christmas is always right around the corner. Stella Lefty is coming to SF! Is the Meta settlement a good thing? Betty Crocker is selling cake handbags. Football season is upon us, so Vinnie is making us hungry with the best tailgate foods. In honor of Dolly Parton, let's play a new game!

Hour 1: Dolly Parton has passed away at 80 years old. There was no shortage to Dolly's bright light and career success. Taylor Swift, Jack White, Beyonce, Paul McCartney, and Jamie Lee Curtis have already shared tributes to Dolly, and there will be a special on CBS. Taylor Momsen is singing with Soundgarden for an NFL Halftime show. Blockbuster was making HOW MUCH on late fees? Don't worry, you might still be able to buy a glow-in-the-dark bunny. Hour 2: Follow the gang on Instagram! Check out @alice973 for links to our personal profiles. Sascha Baron Cohen's new movie is almost here. Heads up comedy fans - Netflix is dropping 4 documentaries you might be interested in. Donnie Darko is getting a sequel! For now it's just only a book. Spiderman is officially bigger than Avengers: Endgame. The popcorn bucket craze is growing. Another update on Pumpkin. Breaking news: Meta has settled for $18B with 48 states ushering in a legal responsibility for social media companies. To payout the full amount, Meta is demanding YouTube and TikTok also set time limits for underage users. It's National Dog Day! Enjoy your doodle - while you can. Jorts are BACK! But, this time they're long. Hour 3: Let's Bridge The Gap! Can Olivia pull out the first-ever listener 3-peat?! Or, will San Franciscan Tiffany crush her dreams. Sarah isn't fooling around with these questions today, so it's anybody's game. Let's play! Then, if you still say these words, you're old! Put on your slacks and head down to the beauty parlor. Plus, some apps and websites that refuse to be forgotten. What are Yahoo and MySpace up to these days? Hour 4: Well, we lost another 80-year-old. Tim Curry, the star of Rocky Horror Picture Show, has passed away. Even if Stella Langley surpasses Mariah Carey, Christmas is always right around the corner. Stella Lefty is coming to SF! Is the Meta settlement a good thing? Betty Crocker is selling cake handbags. Football season is upon us, so Vinnie is making us hungry with the best tailgate foods. In honor of Dolly Parton, let's play a new game!

On today's MJ Morning Show:Fester & Michelle discuss salonDon't expect privacy when filmed in public Morons in the newsMJ on Facebook MarketplaceNiece of Scientology leader speaks outClearwater business employee allegedly embezzles over $2 millionChris Hansen movie "Primetime" screening"Scromiting"Electronic shock glovesNot on a stranger's car to get videoMJ's new headphonesTPA changed mind about ditching TSA for private companyOwner of SF 49ers caught in prostitution stingBreakfast mistsake not to make over 40 years oldAmerica's vegetableMJ's mom has a chart for mail deliveryTeen attacked on Palm Harbor golf course over slow playBest and worst drivers by car brandShopping in a thrift store, finding a swimsuit with a little extraMichelle binge watched "Tires"Pilot error... with the microphoneCoffee drinking affects testosterone levelsMeghan and Harry newsSpider Man vs Titanic at the global box officeKanye West won't play Russia after allTaylor Sheridan accused ot stealing "Yellowstone" conceptPower out at MJ's homeOnlyFans model wants to donate to teachers' wishlistsOnlyfans model headed to prison over tax evasion (not the same model)See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Susan Slussewr of the San Francisco Chronicle recaps Rafael Devers' return to Boston and the Giants' injury concerns. We also touch on the Padres' new ownership and their potential impact on the team's future. An injury to Carson Whisenhunt has left the rotation uncertain after leaving the game with arm pain last night as SF is now in full evaluation mode for the rest of the season.See omnystudio.com/listener for privacy information.

Episode 413 of the John1911 Podcast is now live: Staccato stumbles. Wilson SFX9. Seekins Precision 6mm ARC. Our favorite gun lube.  CEO of SF 49's sacked in Ohio.    Kraken & Marky John1911.com "Shooting Guns & Having Fun"

Dr Eric Davis joins Andy for a major conversation about recovered UAP technology, NHI bodies, alleged government contact and decades of secret reverse-engineering efforts.Davis begins with his bottom line up front, explaining why he believes UAP are real, controlled by non-human intelligence and have interacted with humanity for thousands of years.He discusses the classified evidence he says he has personally examined, including original documents and photographs from a foreign Cold War crash retrieval; the Armed Forces Institute of Pathology and its alleged storage of preserved NHI bodies; a recovered craft apparently abandoned by its occupants; and evidence of contact involving a government office and defence-industry contractor.The conversation also covers Congress, AARO, the White House PURSUE programme, Trump and the SF-312, Holloman, Kingman, Robert Bigelow, James Lacatski, Jay Stratton, the credibility of different NHI morphologies and reverse-engineering programmes allegedly revived every ten years.This interview contains claims based partly on classified material that is not currently available for independent public examination.

On this week's Relic Radio Science Fiction, SF '68 brings us its story from May 24, 1968, titled, The Will. Listen to more from SF '68 https://traffic.libsyn.com/forcedn/e55e1c7a-e213-4a20-8701-21862bdf1f8a/SciFi947.mp3 Download SciFi947 | Subscribe | Spotify | Support Relic Radio Science Fiction

Another Boeing strike in the works // WA State among 10 worst for drunk driving crashes // Should you invest in the college funds or activities? // SF 49ers owner arrested

Send us Fan MailMy guest on the show today, Mary Howe, is an Air Force veteran and daughter of a military legacy family.  Mary's mom is a retired Army Major; her father, Master Sgt. Paul Howe was in Delta and was in the Battle of Mogadishu, which later became the source of the movie Blackhawk Down. Her grandfather was Lt. Col. Charles Beckwith, the founder of Delta Force. To say that her family has a history of service at the highest levels would be an understatement.A bit about Mary. Mary Howe is a former Air Force Special Operations AC-130U aerial gunner turned Family Nurse Practitioner, now working in veteran disability medicine. Her work sits at the intersection of service, identity, and personal responsibility—shaped by a life spent in high-performance environments and a refusal to let performance define her worth.She is the writer behind Unfinished Business, a body of work that documents growth in real time, without the polish or the illusion of having it all figured out. Her perspective challenges the habit of outsourcing direction, calling people back to internal leadership, self-trust, and the work that happens long before anything looks successful from the outside.Her background spans military service, medicine, and a lineage rooted in Special Operations, but her focus now is simple: helping people think better, take ownership, and build something internally that doesn't collapse when circumstances change.It was an honor and joy chatting with Mary. Please enjoy my conversation with someone who is truly inspirational.In today's episode, we discuss:·      Her mom, Connie, was a Major in the Army. Her dad, Master Sgt. Paul Howe was Delta and was in the Battle of Mogadishu, and Grandpa was Lt. Col. Charles Beckwith, the founder of Delta Force.·      The differences in Delta, SF, Ranger Regiment, Ranger Tab, SEALS, PJ's: Delta is now called Combat Applications Group (CAG).·      A group of PJs was on the coast of Florida that saved 11 people who had to ditch their plane in the ocean. They had about 5 minutes of fuel before they had to go back to base.·      Growing up in a military legacy family, what was the talk like around the dinner table?·      Her rebellious stage growing up. What did that look like and how did her parents deal with that? ·      Growing up in a strict home. Were her parents bouncing quarters off her bed?·      Not realizing the gravity of her father's service until she read the book Blackhawk Down. ·      Her father's thoughts regarding the book and movie Blackhawk Down after he lived it.·      SERE training.·      Being a gunner on an AC-130U and her being deployed in four separate combat missions.·      Post-military life. Becoming a Nurse Practitioner and continuing to be of service.All of this and more on today's episode of the Cops and Writers podcast.Check out Mary's website!Head on over to my website!What's the craziest thing you saw when you were a cop?My first week on the job, a guy running at me with a butcher knife. He'd just killed his brother over the last hot dog.That's chapter 1. There are 33 more.Police Stories: The Rookie Years just launched - available on Amazon. Search 'Police Stories Patrick O'Donnell' or click thSupport the show

Vă invităm la o nouă lecturare a rubricii de întrebări și răspunsuri pe teme duhovnicești, alcătuită pe baza întrebărilor trimise de urmăritorii site-ului O Chilie Athonită: Bucurii din Sfântul Munte.Răspândiți dragostea!Pentru Pomelnice și Donații accesați: https://www.chilieathonita.ro/pomelnice-si-donatii/Pentru mai multe articole (texte, traduceri, podcasturi) vedeți https://www.chilieathonita.ro/

Am stat de vorbă cu părintele Marius Soponaru, slujitor la Penitenciarul Aiud, despre o rană tot mai adâncă a societății noastre: dependența de droguri. Am vorbit fără ocolişuri despre cum consumul de droguri pătrunde în licee, despre vârsta tot mai fragedă a celor care încep cu substanțe psihoactive și despre legătura dintre adicția de ecrane, dramele din familie și consumul de substanțe. Mai presus de toate, am aşezat ieşirea: spovedanie sinceră, post serios, participare zilnică la Sfânta Liturghie și ascultare la un duhovnic.Vizionare plăcută!Pentru Pomelnice și Donații accesați: https://www.chilieathonita.ro/pomelnice-si-donatii/Pentru mai multe articole (texte, traduceri, podcasturi) vedeți https://www.chilieathonita.ro/

John Dickinson and John Lund recap a 41-17 preseason Game 2 win for SF. The 49ers' running back situation is looking a bit more promising after a strong showing from Kaelon Black and Sincere McCormick in the team's latest preseason game. The duo impressed with their performances, leaving some to wonder if they could be the back answer to Christian McCaffreys. Meanwhile, the 49ers' wide receiver situation is still up in the air, with Jacob Cowing and Jordan Watkins vying for a spot on the roster. The team's pass rush is also a concern, with a lack of depth on the outside beyond Nick Bosa.See omnystudio.com/listener for privacy information.

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

This week on Without A Country, Corinne Fisher discusses the Netflix documentary CHAOS: The Manson Murders directed by Errol Morris based on the 2019 nonfiction book CHAOS: Charles Manson, the CIA, and the Secret History of the Sixties, written by Tom O'Neill with Dan Piepenbring. LOS ANGELES! Get your tickets to GASH October 25: https://events.leapevents.com/event/gash-2026TIMESTAMPS:0:01:12 – Intro: welcome to the evergreen episode, show format explained0:02:14 – Introduces the doc Chaos & Tom O'Neill's book premise0:04:32 – Manson's backstory begins: musician ambitions, "the family" forms0:07:22 – Dr. Louis "Jolly" West introduced — MK Ultra hypnosis/mind control angle0:10:43 – MK Ultra's SF ties + Manson's parole officer working out of the free clinic0:14:08 – Dennis Wilson/Beach Boys collaboration, "Cease to Exist" song theft0:16:39 – Terry Melcher rejection theory as motive for the Tate murders0:37:39 – Reading the critical review of the documentary0:38:02 – Comparison to the Lindbergh baby kidnapping conspiracy theory0:41:10 – Critique of the doc's pacing / should've been a docuseries0:44:33 – O'Neill's original Premier magazine assignment that started it all0:46:24 – Haight-Ashbury Free Clinic as an alleged MK Ultra hub0:47:39 – The chilling 1968 FBI memo about "lining up celebrities against the wall"0:49:21 – Bobby Beausoleil's simpler theory: Manson wanted control, not politics0:50:23 – Bugliosi's Helter Skelter theory picked apart1:07:23 – Operation Chaos (CIA) explained, distinct from Cointelpro1:08:15 – Jolly West's full bio — the LSD elephant experiment1:11:07 – June 2026 congressional hearing on MK Ultra, Rep. Anna Paulina Luna1:15:35 – Deep dive: does brainwashing even work scientifically?1:20:42 – Closing thoughts and final takeaway on the documentarySUBSCRIBE TO THE PATREON:https://patreon.com/WithoutACountry?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLinkFOLLOW WITHOUT A COUNTRY ON IG: https://www.instagram.com/withoutacountrypodcast/FOLLOW CORINNE ON IG: https://www.instagram.com/philanthropygalFOLLOW MIKE ON IG: https://www.instagram.com/themharrington/FOLLOW ALONG:Rolling Stone piecehttps://www.rollingstone.com/tv-movies/tv-movie-features/chaos-manson-documentary-netflix-review-1235292868/Chicago Sun-Times piece (mentioned but not read on-air)https://chicago.suntimes.com/movies-and-tv/2025/03/06/chaos-review-netflix-charles-manson-murders-ciaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

@PermissionToStanPodcast on Instagram (DM us & Join Our Broadcast Channel!), TikTok & YouTube!NEW Podcast Episodes every THURSDAY! Please support us by Favoriting, Following, Subscribing, & Sharing for more KPOP talk!KCON MEOVV / IZNA Giveaway by JOCO!Comebacks:TOMORROW X TOGETHER (TXT), MASHIRO (MADEIN/KEP1ER), ZEROBASEONE (ZB1), BIG BANG, OURBIRTHDAY, GEONROUNG ft JOOHONEY (MONSTA X), ENHYPEN, TUIDE, NEXZ, ALPHA DRIVE ONE, NCT 127, SF9, RIIZE, TWICE, TAEMIN (SHINEE)MVs: (Next week!)XDINARY HEROES Leader GUN-IL kicked out of groupKCON LA Day 2 & 3 recap: IZNA, ILLIT, ALPHA DRIVE ONE, TXT, MEOVV are JOCO's highlightsCORTIS Tour LA & SF shows recapMEOVV LA Offline Fansign pt. 1: MAKESTAR recap (KpopNara next week!)BTS JIN thanks ARMY for respecting his privacy at Six Flags theme parkBTS V shares his right ear dropped to 30% hearing capacitySTRAY KIDS "THIS & THAT" afterparty fanmeetAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Tidligere folketingsmedlem for SF, Halime Oguz, forfatter Sara Omar og foreningsstifter Kefa Abu Ras har alle to ting til fælles. De har udtalt sig negativt om negativ social kontrol i muslimske miljøer, og det har betydet at de i flere år er blevet angrebet på deres troværdighed, levebrød og familiebånd på sociale medier af kendte debattører. En reel smædekampagne. Pilestræde folder i dag historien og det prominente persongalleri ud. Og så undersøger vi, hvornår en kritisk debat bliver til en smædekampagne. Gæster: Christina Hilstrøm, journalist på Berlingske og Signe Westermann Kühn, journalist på Berlingske Vært: Alexander Wils LorenzenSee omnystudio.com/listener for privacy information.

Are You the One? We have one spot left in both cohorts Designing Your New Life in Retirement small group program, beginning in September. Is now the time to start working on what you’ll be retiring to? Learn more here | Sign up here Prepare for Takeoff: Is your money better prepared for retirement than you are to live it? Take this free 5-minute assessment to highlight may need you attention to build the retirement you’ve earned. __________________________ We spend a great deal of time thinking about how much we exercise. Esther Gokhale asks another question: How are we using our bodies during all the hours when we aren’t exercising? Esther Gokhale, creator of the Gokhale Method, became interested in posture after experiencing a severe L5-S1 disc herniation while pregnant with her first child. Surgery was followed by another herniation, and she began searching more broadly for ways to change how she moved in everyday life. In this conversation, Esther explains why she considers posture a missing pillar of wellness and why she believes ordinary activities (walking, sitting, standing, bending and even lying down) can become what she calls “life exercises.” We explore why sitting itself may not (completely) deserve its terrible reputation, what she means by a “J-spine,” how walking mechanics affect the entire body, and what modern adults can learn from young children and populations whose traditional movement patterns have been preserved. Most encouragingly for people in the second half of life, Esther argues that we should distinguish between what becomes common with age and what is necessarily natural. Her message is clear that our capacity for improvement often lasts much longer than we assume. _____________________ Bio Esther Gokhale (GO-clay) has been involved in integrative therapies all her life. As a young girl growing up in India, she helped her mother, a nurse, treat abandoned babies waiting to be adopted. This early interest in healing led her to study biochemistry at Harvard and Princeton and, later, acupuncture at the San Francisco School of Oriental Medicine. After experiencing crippling back pain during her first pregnancy and unsuccessful back surgery, Esther Gokhale began her lifelong crusade to vanquish back pain. Her studies at the Aplomb® Institute in Paris and years of research in Brazil, India, Portugal and elsewhere led her to develop the Gokhale Method, a unique, systematic approach to help people find their bodies' way back to pain-free living. Gokhale’s book, 8 Steps to a Pain-Free Back, has been translated into ten languages. Esther Gokhale has taught at corporations such as Google & IDEO, presented at conferences including TEDx(Stanford), consulted for the trainers of the SF 49ers and several Stanford sports teams, and conducted workshops for physician groups at Stanford, Kaiser Permanente, Sutter Health, UCSF. The New York Times gave Esther the title “The Posture Guru of Silicon Valley. A Stanford clinical trial is currently studying the Gokhale Method. _____________________ For More on Esther Gokhale Website – Gokhale Method 2-minute Video Overview of the Gokhale Method  8 Steps to a Pain-Free Back by Esther Gokhale ____________________ Other Retirement Podcasts You May Like The Joy of Movement – Kelly McGonigal Eat Your Ice Cream – Ezekiel Emanuel, MD, PhD Make Your Next Years Your Best Years – Harry Agress, MD _____________________ About The Retirement Wisdom Podcast There are many podcasts on retirement, often hosted by financial advisors with their own financial motives, that cover the money side of the street. This podcast is different. You'll get smarter about the investment decisions you'll make about the most important asset you'll have in retirement: your time. About Retirement Wisdom I help people who are retiring, but aren't quite done yet, discover what's next and build their custom version of their next life. A meaningful retirement doesn't just happen by accident. Schedule a call today to discuss how the Designing Your Life process created by Bill Burnett & Dave Evans can help you make your life in retirement a great one — on your own terms. About Your Podcast Host Joe Casey is an executive coach who helps people design their next life after their primary career and create their version of The Multipurpose Retirement.™ He created his own next chapter after a 26-year career at Merrill Lynch, where he was Senior Vice President and Head of HR for Global Markets & Investment Banking. Joe has earned Master's degrees from the University of Southern California in Gerontology (at age 60), the University of Pennsylvania, and Middlesex University (UK), a BA in Psychology from the University of Massachusetts at Amherst, and his coaching certification from Columbia University. In addition to his work with clients, Joe hosts The Retirement Wisdom Podcast, ranked in the top 1% globally in popularity by Listen Notes, with over 2 million downloads. Business Insider recognized Joe as one of 23 innovative coaches who are making a difference. He's the author of Win the Retirement Game: How to Outsmart the 9 Forces Trying to Steal Your Joy. __________________________ Wise Quotes On Posture “Posture is very underrated as a pillar of wellness…Change the way you are living in your body….It’s sort of indispensable that we do life more skillfully because that’s where the big opportunities are.” On Misconceptions on Aging “Don’t…lower our standards and call things that are common natural.” On Why Now? “It’s never too late.” __________________________ The views and opinions expressed by guests on The Retirement Wisdom Podcast are those of the guests and do not necessarily reflect the policy or position of the host or Retirement Wisdom, LLC. The Retirement Wisdom Podcast covers the non-financial aspects of retirement. From time to time we may invite guests who discuss other aspects of retirement planning, solely for educational purposes. Listeners are advised to consult qualified financial and/or medical professionals on those matters.

Last week's news today! (Today's news is: Matthew is sunning himself on the beach/sheltering from the West Country rain)00.00.40: Introduction00.03.14: Thank you to our new patrons: Jonathan Brandenburg; Ink Chronicles; Steve Scott00.11.28: World of Gaming: GenCon second largest gaming convention after Spiel Essen,  74000, attendees; Cluster Truck is out in print for Traveller.00.22.15: #RPGaDay creating characters00.56.44: Next time and goodbye Effekt is brought to you by Effekt Publishing. Music is by Stars in a Black Sea, used with kind permission of Free League Publishing.Like what we do?But our game, Tales of the Old West via our website and download Tales of the Old West QuickDraw available for free on DriveThru. The core rules are now available on DriveThru too.Put our brand on your face! (and elsewhere)Buy pdfs via our DriveThru Affiliate linkLeave a review on iTunes or PodchaserFind our Actual Play recordings on effektap ★ Support this podcast on Patreon ★

This Week on the Toy Power Podcast; we have Damian (aka Truly_Truly_Truly_Outrageous); Rocking back into the Studio to assist us with a 'Musical Spin' on our Regular Segment: The Team! This round we are covering the JEM Universe; so we are switching out the topic from a 'Special Forces Team'; to a 'Truly Outrageous Rock Group!' The Band! So in this case, we are picking a: Vocalist, Guitarist, Keyboard or Drummer, as well as a Wild-Card addition. Then Damian with his incredible passion & knowledge, gets to select not only the Band Manager for this Universal Group; but also an ideal Venue / Arena for them to play at! This was genuinely a lot of fun to discuss!! Then we chat towards the Latest Marvel film that is: Spiderman - Brand New Day. It's killing the box-office right now; so we go into full spoiler discussion & try to decipher that end credit scene too?!?! Rounding out the ep; we reveal our upcoming "Best Cartoon Intro Tournament". Frank breaks down the initial ladder, (subject to adjustment); & potentially what to expect over future eps! All this & more! Enjoy!! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Andy Baggarly of The Athletic joins John Lund to discuss the challenges of building a sustainable team through the farm system and the importance of developing young players. He also touches on the team's current struggles, including their lack of fundamental baseball and the impact of one-run games, and his thoughts on the announcement that the All-Star game will return to SF in 2028.See omnystudio.com/listener for privacy information.

In Hour 3 John Lund recaps De'Zhaun Stribling's strong performance in the 49ers loss to the Titans, plus Andy Baggarly on the Giants' farm system development and how SF will take a look at youth during remainder of the season, plus we also touch on the upcoming All-Star Game in San Francisco and what it means for the city. See omnystudio.com/listener for privacy information.

In Hour 3 John Lund recaps De'Zhaun Stribling's strong performance in the 49ers loss to the Titans, plus Andy Baggarly on the Giants' farm system development and how SF will take a look at youth during remainder of the season, plus we also touch on the upcoming All-Star Game in San Francisco and what it means for the city. See omnystudio.com/listener for privacy information.

Andy Baggarly of The Athletic joins John Lund to discuss the challenges of building a sustainable team through the farm system and the importance of developing young players. He also touches on the team's current struggles, including their lack of fundamental baseball and the impact of one-run games, and his thoughts on the announcement that the All-Star game will return to SF in 2028.See omnystudio.com/listener for privacy information.

Vineri, August 14 - Inaintepraznuirea Adormirii Maicii Domnului; Sf. Prooroc Miheia;

140+ real business ideas database: https://clickhubspot.com/pkce Episode 850: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk about massive businesses hidden in plain sight.  — Show Notes: (0:00) The wild economics of lab testing on monkeys (11:01) Salad oil crisis (15:35) the art of noticing  (24:38) enthusiasm (27:39) know thy 8-year old self (32:32) be shameless (37:08) Sam goes to SF for 7 days — Links: • Qoves - https://www.qoves.com/  • Charles River - https://www.criver.com/  • How To Do Great Work - https://www.paulgraham.com/greatwork.html — Check Out Sam's Stuff: • Hampton (joinhampton.com): My community for founders. Average member does $25m/year. Many of the guests are members. Get after it...apply: http://joinhampton.com/mfm — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com  • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Shaan uses Mercury across all of his companies. you can too: http://mercury.com/  Mercury is a fintech company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., Members FDIC • I run all my newsletters on Beehiiv and you should too + we're giving away $10k to our favorite newsletter, check it out: beehiiv.com/mfm-challenge My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano /

Jon, Leigh, and Sue Phillips chat about Science Fiction Literature at Dragon Con. We discuss the books, ideas, and fan-favorite sci-fi that shape the track. We look at what fans can expect from the track: space opera, first contact, near-future fiction, cyberpunk, hard SF, and all the other books that keep readers coming back for more. Leave us a voicemail: (813) 321-0884Shop The Unique GeekSuggest a 50 Days Podcast Idea Be sure to check out our Facebook social media thingies. Have a question for the directors or maybe something you want us to try and get info on? Then leave a comment, email us, or call the comment line.Email: 50days[ at ]theuniquegeek.comFacebook: facebook.com/TheUniqueGeekYouTube: YouTube.com/TheUniqueGeek Read the transcript The post 50 Days of Dragon Con 2026 – Day 26 – Sci-Fi Lit first appeared on The Unique Geek.

State of the Bay explores the risks of extreme heat and how our city can prepare, delves into the debate over whether San Francisco should establish a public bank and hears about SF's boom and bust history.

在忙碌中迷失了節奏？讓心靈深呼吸。《城市使命》每日 7–10 分鐘短篇靈修，為你的日常靈性充電！我們透過經文與生命見證，把你的通勤與休息時間，轉化為與神對話的神聖時刻。不長篇大論，只給你最純粹的屬靈養分。現在就收聽，在城市的喧囂中找回你的屬靈方向！Overwhelmed by the hustle? Take a deep breath. We offer 7–10 minute short devotionals to recharge your spirit on the go. Through quick biblical insights and powerful testimonies, we turn your commute or coffee break into a divine dialogue. Simple, deep, and exactly what your soul needs today. Tune in now, quiet the noise, and realign your spiritual compass!

Jacob Lopez has looked really good lately (2:50). ... Cam Smith hit massive home run in SF (10:30). ... Skubal is throwing more sinkers with the Dodgers (16:21). ... What the heck is the fourth out rule (18:23)?? ... News (23:35): Nathan Eovaldi went on the IL. ... Chandler Simpson stayed red hot (38:47). ... Andrew Painter is doing interesting things (47:53). ... Let's play SCOTTY DOESN'T KNOW (55:07). ... We wrap up with leftovers, bullpen updates, streamers and Team Name Tuesday (1:03:38).Subscribe to our YouTube channel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠youtube.com/FantasyBaseballToday⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Sign up for the newsletter at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.cbssports.com/newsletters

Tune in at our new time - 1 p.m. for a conversation with two California born Singer/Songwriters backstage at Live Oak Music Festival. Emily Nenni found her way to Nashville and the honky-tonk sounds of the local bar scene that welcomed her in and encouraged her writing and performing style - which includes inviting everyone up to the dance floor to cut loose. Sam Chase of The Sam Chase and the Untraditional has blended his SF punk rock beginnings with rock 'n roll and some folk music to create a raucous live show led by great storytelling and moments of surprise!Listen Tuesdays from 1-2 p.m. on KCBX.

Will the Mariners make the playoffs? We have been just assuming the talent would play out all season, but there is nothing showing that will happen in 2026. Former Mariner broadcaster Dave Sims joins the show to talk about the upcoming series between the Yankees and Mariners, the Mariners struggles, and more. Robbie Ouzts is out for the season, what does this mean for the Seahawks? Why won't the Mariners fire Dan Wilson? Joe Sheehan joins the show to talk about the Mariners struggles, is there any sign of a turnaround? The deeper numbers behind Cal Raleigh's season-long slump. John Lund from KNBR joins the show to talk about his return to SF radio, the substation, and more shenanigans. Checking the Tacoma Dodge textline and talkbacks. Softy joins for cross talk.See omnystudio.com/listener for privacy information.

Astros Win Game 1 in SF... Texans Near Preseason Opener

Astros Win Game 1 in SF... Texans Near Preseason Opener

在忙碌中迷失了節奏？讓心靈深呼吸。《城市使命》每日 7–10 分鐘短篇靈修，為你的日常靈性充電！我們透過經文與生命見證，把你的通勤與休息時間，轉化為與神對話的神聖時刻。不長篇大論，只給你最純粹的屬靈養分。現在就收聽，在城市的喧囂中找回你的屬靈方向！Overwhelmed by the hustle? Take a deep breath. We offer 7–10 minute short devotionals to recharge your spirit on the go. Through quick biblical insights and powerful testimonies, we turn your commute or coffee break into a divine dialogue. Simple, deep, and exactly what your soul needs today. Tune in now, quiet the noise, and realign your spiritual compass!

Group Chat News is back with the biggest stories of the week including... Zach returns to break down what he's seeing inside San Francisco, why he thinks you have about 18 months to get really rich, and what happens to everyone else. Plus Zuckerberg spars a UFC fighter on a barge in the middle of Lake Tahoe, and Kai Cenat and Speed attempt one of the hardest challenges in gaming. This week's Group Chat covers: Zach's take from inside SF — and why his timeline keeps getting shorter every week "You have about 18 months to get really rich" — and then what Why less than 25 people on earth are using AI to its full potential The company running seven employees and three agents at 300 pull requests a day Agents that proactively fire contractors and stand up their own businesses Can an entrepreneur still spot the gap on a Target shelf when P&G has infinite data? Taste as the last human advantage — and whether that holds Purpose, meaning, and whether people need hard things given to them The optimistic case: UBI, Waymos, deflation, and everything just getting cheap Zuckerberg vs. Merab Dvalishvili on a barge sparring Kai and Speed's hardcore Minecraft marathon, and why the clips matter more than the game Parasocial fame, the chat as a Pavlovian loop, and how streaming ends And much more! Drop us a 5-star rating and a review if you're rocking with the show.

In this episode, I speak with Nigerian Science Fiction writer Wole Talabi about his first science fiction novel, The Fist of Memory.The Fist of Memory didn't disappoint; the best way I think to describe it might be John Woo meets Arthur C. Clarke in Africa. It is certainly one of my favorite books I have read this year. A bullet opera that manages to combine a first contact story, noir, African cyberpunk, and hints of African magic.We talk about growing up as a SF reader in Nigeria, getting into writing and the construction of this amazing novel.•You can find my books here:Amazon-https://www.amazon.com/David-Agranoff/e/B004FGT4ZW•And me here:Goodreads-http://www.goodreads.com/author/show/2988332.David_AgranoffTwitter-https://twitter.com/DAgranoffAuthorBlog-http://davidagranoff.blogspot.com/

This week on my podcast, I read Why businesses lie about AI, a recent essay from my Pluralistic newsletter that breaks down Nikhil Suresh’s essay describing the total absence of any proof that any business is benefiting from AI deployment. One person who’s had a lot of opportunity to observe the shear between the stated... more

This Week on the Toy Power Podcast; we are quite Excited & equally Privileged for reaching our Milestone 10th Year of Podcasting Fun! So to Celebrate; we have several things lined up to Celebrate this! First off we are sharing our Annual Daily Collection Pics of Your Collections via Social Media- on Instagram & Facebook! Thankyou; & please submit a pic if you haven't already. Big reveal too; we unveil a New TPP Logo! Huge thanks to Cody Hulks aka SADC_TheClown for bringing this to life for us! But that's not all; we FINALLY have an avenue for you to purchase our Merch via the RedBubble website (See link bellow). And if that all wasn't enough; then listen out; as we have Special Guest: Damian, aka: Truly_Truly_Truly_Outrageous in the studio; to bring us up to speed on all the Exciting News & Collaborations he has been involved in for the world & passion of the Jem Universe!! Plus, as any good Birthday's are known for; some nice Presents! Well, Master Colin Betts has it in spades; as we open some beautiful & very very thoughtful gifts to round out the episode. Thankyou good sir!! A genuinely fun episode & wider Celebration of our little Podcast & the AMAZING Community we have built around us. Thank-you everyone for your on-going support!Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Self-proclaimed burrito connoisseurs Philip Teresi and Producer Gabe Navarro react to an article that argues a debate over the price of a burrito became a proxy fight over a much larger issue: whether Republicans are taking voters' concerns about affordability seriously enough ahead of the 2026 midterm elections. We get some local mentions alongside some of the national ranking taquerias in SF, LA and SD. Please Like, Comment and Follow 'Philip Teresi on KMJ' on all platforms: --- Philip Teresi on KMJ is available on the KMJNOW app, Apple Podcasts, Spotify, YouTube or wherever else you listen to podcasts. -- Philip Teresi on KMJ Weekdays 2-6 PM Pacific on News/Talk 580 AM & 105.9 FM KMJ | Website | Facebook | Instagram | X | Podcast | Amazon | - Everything KMJ KMJNOW App | Podcasts | Facebook | X | Instagram See omnystudio.com/listener for privacy information.

Stefon Diggs is a Commander (02:32)! Are you buying or selling? What does Diggs mean for Jayden Daniels? The guys also provide important updates on the large, looming electrical substation within arm's reach of the 49ers' practice field (10:25) before getting to their XX rules to win your 2026 fantasy football league (19:41). Then Bill Simmons joins the show to pitch a new fantasy football idea (01:03:11). Intro (00:00) Stefon Diggs (02:32) SF substation (10:25) XX rules to win your league (19:41) Bill Simmons throws "grenades" at the guys (01:03:11) Another Millennial Banger (01:35:54) Reminders: 1. LIVE SHOW COMING THIS MONTH: We'll be in Los Angeles for a live show at The Fonda Theatre in Hollywood on Thursday, August 27. Details here: https://www.theringer.com/events. Get your tickets now! Go, go, go. 2. DK ALERT. DK ALERT. That's right. DK joined the Discord. Don't miss out: https://discord.gg/BrcmaHFdqd 3. RANKINGS LAST UPDATED AUGUST 3: https://theringer.com/fantasy-football/2026-preseason 4. Follow us on Instagram and TikTok @ringerfantasyfootball. Heifetz also posts his cat a bunch on @dannyheifetz. 5. Email us punishment ideas for year two of the Ringer Fantasy Football League: ringerfantasyfootball@gmail.com Verizon Simplicity Plan. One plan. One price. Learn more at verizon.com (https://www.verizon.com/) Hosts: Danny Heifetz, Danny Kelly, and Craig Horlbeck Guest: Bill Simmons Producers: Cameron Dinwiddie, Abou Kamara, Carlos Chiriboga, Nikhil Behal, and Austin Gayle Learn more about your ad choices. Visit https://podcastchoices.com/adchoices Learn more about your ad choices. Visit podcastchoices.com/adchoices

"Is your dick going to be synced to porn?"That's not hypothetical — it's already on the market. Here, intimacy expert Susan Bratton walks me through "weird" phenomena at the frontier of sex tech and AI, including synchronized strokers, tele-dildonics (I'll bet that's a word you've never come across before), role-player game (RPG) worlds where you can get down and dirty with an Aztec warrior woman, and adaptive AI that learns what gets you off and gives you more of it.But where does this genuinely help? And where can it quietly wreck your dopamine system and/or real-world relationships? We delve into all of it, including the exact device suite that Susan and her boyfriend use for long-distance sex. Imagine being thousands of miles apart, still having synchronized orgasms on video. (I did not expect the "hard-boiled egg" comparison, but you'll get it once you hear it.)Plus:How a woman can regnerate her vaginal tissue (YES, this helps if she's dry after/during perimenopause/menopause)The 96% statistic about what most porn is actually showing youThe ancient technique that separates orgasm from ejaculationWhy penis pumps are not the cheesy novelty you think they areAnd a fundamental truth that Susan and I agree on: The best sex tech in the world means nothing without the communication skills underneath it. Plus what I consider the quote of the episode: "I want to live forever and fuck till I die!"—Come to the retreat!As of this recording, we have 4 spots left for our Labor Day weekend retreat! Sept 3-7th in Northern California (about 2 hrs north of SF). We work hard to keep it financially accessible, and payment plans are available.As one man says, "If you're thinking about going, you're already there."https://evolutionary.men/retreat/---Work with usReady to go deeper than the podcast and take action? Jason and I will help you break old patterns and transform your sex & love life for good. To see if you're a fit for our flagship program, Pillars of Presence, book a call here. Start anytime. (https://evolutionary.men/apply/)—Mentioned on this episode:Please Her In Bed (Melanie's streaming course on how to talk about sex with women in a way that actually helps them to open up): www.pleaseherinbed.comDrive Desire (Susan's full toy/device library): www.drivedesire.com—Memorable quotes from this episode:"No one's ever given her these types of strokes before.""They don't want you addicted to the yoni. They want you addicted to the screens.""Your dick is a tongue, not a piston.""You don't owe me an ejaculation.""How's your hoo-ha doing?""When you are a turned-on woman or a turned-on man, you're turned on to life." "Your sexual vitality and your vitality are two sides of the same coin."

Chad and Jordan discuss extensions for Chris Olave and Zay Flowers, plus SF wide receiver injuries and signings, and did I get enough WR edition.Plus hours of premium content this month!  You can get all the DTT Patreon content for $10 a month at patreon.com/DynastyThinkTank.Follow Chad on Twitter: @chadparsonsNFLFollow Jordan on Twitter: @mcnamaradynasty

Hour 1: Unwritten by Natasha Bedingfield has become a classic. Bob is recommending a RomCom set in SF. Carrie Underwood is back for another year of Sunday Night Football. Pete Davidson has been spotted with another hot woman. Tom Cruise's daughter has dumped the Cruise name. Has she been hanging out with Brad Pitt's kids? Female cyclists are stuffing their bras. The new season of Ted Lasso has dropped. Marketing is a powerful thing. Today's gimmick: A magical t-shirt that can handle your messy lifestyle. Hour 2: Perez Hilton is having a mental health crisis. The Voice is getting a celebrity version. Keke Palmer to host! Joe Jonas, Queen Latifah, and Riley Green will be the judges. There's a football game on TOMORROW! These things have become socially acceptable in the last decade. Sarah has a major mom moment around tracking her son. Hour 3: It's time to Bridge The Gap! Sarah and Vinnie's weekly trivia battle of the generations. Nicole is back for GenX against Angie for the Zillennials! The air quality this weekend might be in question due to fires up north. Your feel good story of the day: The Guinness World Record for hopscotch. Nude dining - are you in? What's the ideal temperature for sleeping? Hour 4: Sarah pulls off one of the greatest teases of all time. Jelly Roll is stepping back to heal. The top 5 songs on the Billboard Charts are ALL COUNTRY. Ella Langley is part of an elite group of artists on the charts. What is National Underwear Day? Nearly 9 in 10 parents wish their kids had more of an old school childhood. What, kids don't play hide and seek anymore?

Unwritten by Natasha Bedingfield has become a classic. Bob is recommending a RomCom set in SF. Carrie Underwood is back for another year of Sunday Night Football. Pete Davidson has been spotted with another hot woman. Tom Cruise's daughter has dumped the Cruise name. Has she been hanging out with Brad Pitt's kids? Female cyclists are stuffing their bras. The new season of Ted Lasso has dropped. Marketing is a powerful thing. Today's gimmick: A magical t-shirt that can handle your messy lifestyle.

Silver and JD react to the breaking news that SF is shipping Robbie Ray to the Padres and we get the fellas' thoughts on SF's trade with Philly regarding Luis ArraezSee omnystudio.com/listener for privacy information.

Purchase the BDGE Fantasy membership here: https://bdge.co/membership0:00 - it's rude to skip introductions0:24 - josh downs, WR, IND (round 9)3:24 - jk dobbins, RB, DEN (round 9)6:09 - round 87:00 - quentin johnston, WR, LAC (round 8)8:19 - round 79:27 - sam laporta, TE, DET (round 7)10:39 - tucker kraft, TE, GB (round 7)12:45 - jordyn tyson, WR, NO (round 7)13:15 - jayden daniels, QB, WAS (round 6)15:41 - mike evans, WR, SF (round 5)18:05 - rladd mcconkey, WR, LAC (round 4)20:02 - cam skattebo, RB, NYG (round 4)22:45 - davante adams, WR, LAR (round 4)23:22 - devonta smith, WR, PHI (round 3)24:24 - javonte williams, RB, DAL (round 3)24:48 - josh jacobs, RB, GB (round 3)26:28 - chase brown, RB, CIN (round 2)27:50 - kenneth walker, RB, KC (round 2)28:59 - round 1subscribe to the bdge dynasty channel: https://ytube.io/3pZklisten to the bdge dynasty podcast: https://bityl.co/NzJ1bdge nfl trivia youtube channel: https://ytube.io/3jmJjoin the BDGE discord: https://discord.gg/77BxrqCF6Fsubscribe to the BDGE podcast | https://linktr.ee/bdgefollow me on the socials | https://linktr.ee/nickercolanoContact▪️ business inquiries | business@bdge.co▪️ customer support/help | help@bdge.co▪️ fantasy questions can go in our discord | https://discord.gg/AvpY3QJTAythis video is about (Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy