Podcasts about picasso

In this episode Kent brings together Princess Diana, Dolly Parton, Night Train Lane, and Bob Knight in one sharp, funny, and off-the-cuff conversation about fame, legacy, sports, and the strange stories that stick with us. If you like your podcast with Texas flavor, old-school opinion, and a few surprises along the way, this one is for you. Kent opens with a memorable saying from Princess Diana, then moves through the public lives of iconic figures who stayed above the political fray and earned broad respect for it. From Diana's humanitarian work to Dolly Parton's country-music legacy and Michael Jordan's famous line about shoes, this episode keeps circling back to what makes public figures endure. You'll hear stories and commentary on: Why Princess Diana still resonates nearly three decades after her death How Dolly Parton became a beloved ambassador for Nashville, country music, and her own roots The wild origin story of Night Train Lane, including the moment that helped cement his legend Why Bob Knight inspired zero undecided opinions Stupid-criminal headlines, from a Chuck E. Cheese fight to a child with access to a gun College football chaos, including early-season surprises, quarterback pressure, and the latest rule fights Kent Hance also touches on university rankings tied to job outcomes, the hardest positions in sports, a stolen Picasso left behind like it was nothing, and the strange ways public figures, athletes, and even criminals end up making headlines. The result is part sports talk, part cultural commentary, part newspaper riff — and completely unpredictable. What makes this episode worth your time is the range: one minute you're hearing about Diana's legacy and the next you're in the middle of a classic Texas-style detour that lands on football, city naming debates, or the latest absurd news story. It's a reminder that the best storytellers can turn almost anything into something worth listening to. Perfect for listeners who enjoy candid opinions, Texas sports chatter, and stories with personality, attitude, and a little bit of bite.

Scott Wilson is a Denver-based conservation photographer and photo advocate for America's wild mustangs. Originally from Glasgow, Scotland, he spent over twenty years as a landscape photographer and corporate communications executive before a stage 4 colon cancer diagnosis changed the course of his life. Sunlight restrictions during chemotherapy pushed him to photograph wildlife from the shade of his car, and that habit led him to Colorado's Sand Wash Basin, where an encounter with a wild pinto stallion named Picasso set him on an entirely new path.What began as an artistic pursuit became an advocacy mission once Wilson witnessed the toll of a federal roundup on the herd he'd come to know. He is an independent conservation photographer and contributor to the We Animals photojournalism agency and Skydog Sanctuary. His black and white portrait "Anger Management" won Open Photographer of the Year at the 2022 Sony World Photography Awards, and his work has appeared in the BBC, the Daily Telegraph, and international galleries. In this conversation, Scott talks about the moment his lens turned from landscapes to advocacy, what he's learned photographing wild horses in their final free spaces, and why he believes the fight for their survival is really a fight for freedom itself.Notable Links:Scott Wilson WebsiteScott Wilson InstagramImage: AdonisImage: PicassoImage: Anger ManagementImage: Wild UnfreeSkydog Ranch and SanctuaryPhoto Advocacy*******MUENCH WORKSHOPS GIVEAWAY** Win a free workshop with me to Bosque del Apache, New Mexico in January, 2027. Application is free! CLICK HERE to learn more.*****This episode is brought to you by Kase Revolution Plus Filters. I travel the world with my camera, and I can use any photography filters I like, and I've tried all of them, but in recent years I've landed on Kase Filters.Kase filters are made with premium materials, HD optical glass, shockproof, Ultra-Low Reflectivity, zero color cast, round and square filter designs, magnetic systems, filter holders, adapters, step-up rings, and everything I need so I never miss a moment.And now, my listeners can get 10% off the Kase Filters Amazon page when they visit. beyondthelens.fm/kase and use coupon code BERNABE10Kase Filters, Capture with Confidence.Follow Richard Bernabe:Substack: https://richardbernabe.substack.comInstagram: https://www.instagram.com/bernabephoto/Twitter/X: https://x.com/bernabephotoFacebook: https://www.facebook.com/bernabephoto

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 a total stranger handed NeuroNoodle his brain. He pulled his own EEG, stripped out every identifying detail, and asked host Pete Jansons and Jay Gunkelman — the man who's read over half a million brain scans — to "read my brain" live on the show. What follows is a rare, unscripted master class: Jay decoding a real, de-identified brain map in real time, with Dr. Mari Swingle on the clinical side. Along the way: how to catch a brain map that's lying to you (Gauss's Law), why a 50Hz hum means the scan is European, how a sleep spindle and an SMR are the same wave, what very fast "glutamate beta" can suggest about a hyper-excitable cortex — and why Jay says the DSM has "no predictive validity" for choosing treatment. Then the reveal: a sharp, high-IQ 25-year-old who moved back home and burned out — and what Jay and Dr. Mari would do about it. Plus Joshua Moore on QEEG phenotypes and the "85%" finding.

Leon Black's relationship with Jeffrey Epstein exposed just how deeply the worlds of high finance, elite philanthropy, and blue-chip art could overlap without much meaningful scrutiny. Black, the billionaire cofounder of Apollo Global Management and one of the most powerful private collectors in the world, paid Epstein roughly $170 million over six years for financial and tax-related services, even though Epstein had already pleaded guilty in 2008 to offenses involving a minor and was a registered sex offender throughout much of their professional relationship. The newly released material showed that Epstein's role went well beyond giving Black occasional financial advice. Epstein became deeply involved in the machinery surrounding Black's enormous art collection, meticulously cataloguing works that were ultimately used as collateral for hundreds of millions of dollars in borrowing. Black's collection, once appraised by Christie's at roughly $2.7 billion, included extraordinary trophies such as Edvard Munch's The Scream, multiple works by Raphael, and a Picasso sculpture purchased for $125 million. Yet Black continued to portray his relationship with Epstein largely as a matter of financial expertise, insisting he had not understood the true extent of Epstein's criminality and describing himself as someone who had been misled. That explanation became much harder to swallow alongside Black's own acknowledgment that he knew about Epstein's 2008 conviction but did not regard it as sufficiently serious to stop doing business with him.The story was also an indictment of an art world that has repeatedly demonstrated an extraordinary capacity to overlook almost anything when enormous wealth, prestigious collections, and major donations are involved. Black did not merely purchase paintings; his money bought him extraordinary institutional standing, culminating in his chairmanship of the Museum of Modern Art, while his masterpieces circulated through museums that benefited from his patronage and prestige. Even after the Epstein relationship became impossible to ignore, Black remained on MoMA's board and continued appearing at major art fairs, museum dinners, galleries, sporting events, and elite cultural gatherings. That resilience illustrated one of the uglier realities of the contemporary art ecosystem: museums and cultural institutions frequently present themselves as moral authorities while remaining financially dependent upon billionaires whose money grants them astonishing insulation from ordinary reputational consequences. Black eventually surrendered leadership positions at Apollo and MoMA, but he was hardly exiled from the cultural establishment. The art world absorbed the scandal, issued the requisite expressions of concern, and largely moved forward with one of its most valuable collectors still inside the tent. In that sense, the Black-Epstein story was about much more than one billionaire's extraordinarily questionable judgment. It demonstrated how an industry built around opaque ownership, private transactions, tax strategy, asset-backed borrowing, billionaire philanthropy, and social exclusivity could provide the perfect environment for uncomfortable questions to remain unanswered as long as the person writing the checks remained important enough.to contact me:bobbycapucci@protonmail.comsource:The Strange Tale of Leon Black and Jeffrey Epstein | Vanity Fair

Leon Black's relationship with Jeffrey Epstein exposed just how deeply the worlds of high finance, elite philanthropy, and blue-chip art could overlap without much meaningful scrutiny. Black, the billionaire cofounder of Apollo Global Management and one of the most powerful private collectors in the world, paid Epstein roughly $170 million over six years for financial and tax-related services, even though Epstein had already pleaded guilty in 2008 to offenses involving a minor and was a registered sex offender throughout much of their professional relationship. The newly released material showed that Epstein's role went well beyond giving Black occasional financial advice. Epstein became deeply involved in the machinery surrounding Black's enormous art collection, meticulously cataloguing works that were ultimately used as collateral for hundreds of millions of dollars in borrowing. Black's collection, once appraised by Christie's at roughly $2.7 billion, included extraordinary trophies such as Edvard Munch's The Scream, multiple works by Raphael, and a Picasso sculpture purchased for $125 million. Yet Black continued to portray his relationship with Epstein largely as a matter of financial expertise, insisting he had not understood the true extent of Epstein's criminality and describing himself as someone who had been misled. That explanation became much harder to swallow alongside Black's own acknowledgment that he knew about Epstein's 2008 conviction but did not regard it as sufficiently serious to stop doing business with him.The story was also an indictment of an art world that has repeatedly demonstrated an extraordinary capacity to overlook almost anything when enormous wealth, prestigious collections, and major donations are involved. Black did not merely purchase paintings; his money bought him extraordinary institutional standing, culminating in his chairmanship of the Museum of Modern Art, while his masterpieces circulated through museums that benefited from his patronage and prestige. Even after the Epstein relationship became impossible to ignore, Black remained on MoMA's board and continued appearing at major art fairs, museum dinners, galleries, sporting events, and elite cultural gatherings. That resilience illustrated one of the uglier realities of the contemporary art ecosystem: museums and cultural institutions frequently present themselves as moral authorities while remaining financially dependent upon billionaires whose money grants them astonishing insulation from ordinary reputational consequences. Black eventually surrendered leadership positions at Apollo and MoMA, but he was hardly exiled from the cultural establishment. The art world absorbed the scandal, issued the requisite expressions of concern, and largely moved forward with one of its most valuable collectors still inside the tent. In that sense, the Black-Epstein story was about much more than one billionaire's extraordinarily questionable judgment. It demonstrated how an industry built around opaque ownership, private transactions, tax strategy, asset-backed borrowing, billionaire philanthropy, and social exclusivity could provide the perfect environment for uncomfortable questions to remain unanswered as long as the person writing the checks remained important enough.to contact me:bobbycapucci@protonmail.comsource:The Strange Tale of Leon Black and Jeffrey Epstein | Vanity Fair

En julio de 1991, tres meses antes de dejarnos, Miles Davis se presentó en el Festival de Jazz de Vienne. El 'Picasso del jazz', como le definió el Ministro de Cultura de Francia al condecorarle con la Legión de Honor, tocó en el anfiteatro romano de la ciudad a orillas del Ródano, con Kenny Garrett, Deron Johnson, Richard Patterson, Fowley y Ricky Wellman, temas como 'Hannibal' de Marcus Miller, 'Human nature' de Steve Porcaro y John Bettis éxito de Michael Jackson, dos composiciones de Prince ('Penetration' y 'Jailbait') y 'Time after time' de Cyndi Lauper.Escuchar audio

Friday, August 14. The seven stories you need to know today.Read today's briefing.

Aceptémoslo: todos hemos ido a un museo, hemos visto una obra extraña y hemos pensado "yo podría hacer eso". Pero, ¿realmente podríamos?

In this episode, Lyell K. Jones Jr, MD, FAAN, speaks with Karin G. Johnson, MD, FAAN, who served as the guest editor of the August 2026 Sleep Neurology issue. They provide a preview of the issue, which publishes on August 3, 2026. Dr. Jones is the editor-in-chief of Continuum: Lifelong Learning in Neurology® and is a professor of neurology at Mayo Clinic in Rochester, Minnesota. Dr. Johnson is a Professor in the Department of Neurology at the University of Massachusetts Chan School of Medicine–Baystate and the Sleep Medicine Division Chief at Baystate Medical Center in Springfield, Massachusetts Additional Resources Read the issue: continuum.aan.com Subscribe to Continuum®: shop.lww.com/Continuum Continuum® Aloud (verbatim audio-book style recordings of articles available only to Continuum® subscribers): continpub.com/Aloud More about the American Academy of Neurology: aan.com Social Media facebook.com/continuumcme @ContinuumAAN Host: @LyellJ Guest: @drsleepykarin  Full episode transcript available here Dr Jones: This is Dr. Lyell Jones, Editor-in-Chief of Continuum. Thank you for listening to Continuum Audio. Be sure to visit the links in the episode notes for information about subscribing to the journal, listening to verbatim recordings of the articles, and exclusive access to interviews not featured on the podcast.  Dr Albin: All right, welcome all. For the first time ever in the history of Continuum Audio, we are coming to you live from Chicago here at the AAN annual meeting. And now over to your host, the one and only editor-in-chief, Dr. Lyell Jones.  Dr Jones: Welcome, everybody. My name is Lyell Jones, editor-in-chief of Continuum, and I'm here today with Dr. Karin Johnson, and we're interviewing Dr. Johnson for the upcoming and recently published issue of Continuum on Sleep Neurology. We have been doing Continuum Audio for a while, but we're doing something different this time. As our listeners online can tell, we are recording this for the first time ever with a live studio audience at the American Academy of Neurology annual meeting in Chicago, Illinois. So, this is a fun experience for us. I hope it's been fun so far for you, Dr. Johnson.  Dr Johnson: Great to be here.  Dr Jones: It's great to have you. So, before we get into the interview, I do wanna introduce our team here for the live recording of the podcast. You've already heard Dr. Casey Albin's voice. Dr. Casey Albin is an associate professor of neurology at Emory University. Also serves as one of our associate editors at the journal and one of our Continuum Audio interviewers. So, she's going to be working the crowd today. Let's have a round of applause for Dr. Albin. And our guest of honor today is Dr. Karin Johnson. Dr. Johnson is a professor of neurology at UMass Chan Medical School and, Baystate Medical Center in Massachusetts. She is a world-renowned expert in sleep neurology and is the guest editor for the most recent issue of Continuum on Sleep Neurology. Dr. Johnson, welcome. Why don't you introduce yourself to our audience?  Dr Johnson: You did a great introduction, but I'm a clinical sleep medicine specialist. Spend my days seeing patients, taking care of people with narcolepsy, sleep apnea, restless legs, everything that comes my way. And then I have a side interest in doing sleep medicine advocacy, especially for permanent standard time.  Dr Jones: And we may get to that. I mean, that might be part of our conversation today. So, you've now read all of the articles in this issue, and it's a really great issue. There's a lot of new developments in sleep neurology. There are some updates for clinicians, people who see patients with sleep disorders that I think are, are timely and important updates. You have this unique view because you have just read all of these articles, really good articles by expert authors. When you read through these, Dr. Johnson, what was the biggest, what was the biggest thing that surprised you?  Dr Johnson: I think the biggest surprise for me is just so many changes in, in all of these articles. I realized how easy it was for us to make a journal that is so different from a few years ago. Whether it's Dr. Stahl's obstructive sleep apnea and new ways to think about endotyping sleep apnea that is gonna have treatment implications or the new treatments that are out there like tirzepatide, the changes that we're having with restless leg treatment. I particularly wanted to have a chapter on circadian neurology that Dr. Abbott did a great job really highlighting how if we think about the timing of when we give meds, the timing of when we eat, how that really can help neurological health, brain health, overall health, as well as mental health and cognition, especially as the AAN thinks about brain health as a whole, not just treating our patients, but how we can treat the population of people by improving sleep. I like how we hit on all these different areas in this issue.  Dr Jones: And I don't know how you managed to do it. They're just a small number of articles. We cover a lot of existing territory with well-characterized diseases, with new advances. But there's a lot of new stuff in sleep, and so somehow, it's all packed in there. It's really impressive. One of the things I was gonna ask you about was an evolution, and this has been a number of years now in how we manage restless leg syndrome. When I was training, it was all about dopamine agonists, and that was your first line. And over time, the evidence has supported moving away from that, and now we have more recent guidelines that have come out, and it's really the alpha-two delta-one calcium channel antagonists. How is that transition going? Do you still see people in practice who come in on dopamine agonists? How is that going? How's the field responding to that?  Dr Johnson: That's one of my most frequent restless leg consults. So even though it's been years since I have really initiated dopamine agonists in my patient, every day we get in people often on very high doses of dopamine agonists, and their doctors have just been escalating and escalating these meds over the years, and they come in with horrible augmentation. Their symptoms are much worse than they used to be, happening earlier in the day. And so, trying to get these patients off of these meds that are addictive, the way I like to teach about it is these dopamine agonists are the Fioricets of the sleep world. We know they work great, but in the long run, the patients are gonna be worse overall. And so, it's so hard to get people off these dopamine agonists, just like it's so hard to convince a headache patient that they don't need their Fioricet and that they're gonna be better off if we can get them off of it. What I think has really changed is we have more options to use. So, the alpha-delta-like agonists like gabapentin are now considered first line, but there's a lot of patients who they just don't work well enough with or they don't tolerate. And so, what do you do in that case? It's easy when that works, but and, when that doesn't work, we are being much more aggressive these days with iron replacement, potentially even trying to push ferritin levels in refractory patients up to three hundred, and using IV iron rather than just oral iron to get over the absorption issues to get the brain levels high enough. Motor stimulators, little cuffs that kind of go around the leg and stimulate the peroneal nerve in a certain way that not only can give people immediate relief, but also some data that suggests that over time it actually lessens their restless legs. We have agents like dipyridamole that work on the adenosine system in a sort of new novel pathway at addressing restless legs. And then the opiates, often meds like methadone or Suboxone can be used in some patients. But as we're getting more of these other options, often we don't need to go to those levels because we do have more to work with.  Dr Jones: So, the key point is lots of options. We're not starting with dopamine agonists anymore. And I think the fact that you're still seeing a lot of patients who have been initiated on that probably tells us there's an education gap field that we need to work on. So, another thing that I noticed reading through the issue was, and this feels like a change over the last few years, is the availability and the tendency to use in-home sleep apnea testing as opposed to formal, traditional in-lab. And that feels like a great new option, and maybe that increases and improves availability for patients who need access to the test. But how do you work through that?  Dr Johnson: So, I love in-home testing. We've been using it for over a decade. Other parts of the country where insurances didn't sort of mandate it are now being more mandated. I think the real change happened for a lot of places over the pandemic when labs closed down. But I think it's good because it brings a lot more patients to us. They get tested, they get tested quicker. People who would say, "I would never go into a lab. Oh, I'll do a home study." So, it just does bring more people in, and it gets them to treatment that they need that can really be life-changing. But it's not for everybody. The biggest people are people that have other bad pulmonary issues. If you're on oxygen therapy, you should not be getting a home study. That really should be a group of people that come in the lab. Similarly, if you have bad COPD, you probably should be getting a full in-lab study, so we can get more monitoring. Central sleep apnea is an interesting one. It can be very hard in some cases to differentiate the centrals and obstructive nature as well on a home study. Doesn't mean you can't do a home. So, if it's a person that just can't get an in-lab study easily, maybe you start with the home. If it looks purely obstructive, and you're all set, then you got an answer, and you can move on. But if you get back a home study that looks questionably central, they're gonna need to come into that lab. So, if you already know they're high risk because they're on narcotics, cause they have congestive heart failure, it's usually worth going straight to the lab. But again, you may consider a home study based on the patient. Patients that really cannot use the equipment can also be an issue. So, if they've had a debilitating stroke and have no one to help them put on that device, or cognitively they just can't handle the device, they're gonna be someone who's gonna benefit from coming into the lab and getting the help from the techs. So, those are the big populations that you might go starting for a home. And then the other thing that confuses a lot of people, the home is only for diagnostics. It really isn't for treatment. So, I have patients that say, "Oh, like, you can just titrate my CPAP with a home study." No. So if it's a treatment decision where they're not doing well on treatment, or I need to figure out do they need CPAP or BiPAP or IVAPS or one of these more complicated treatments, those are people that are gonna need to come into the lab to get that treatment portion of the evaluation.  Dr Jones: What a great summary. That's like everything I needed to know about who do I need to bring into the lab and who do I think maybe could do an at-home study. Really great. And speaking of devices, I think all of us who see patients in the room here and our listeners out there online have experienced patients, and this feels like a very recent phenomenon to me, are coming in with their commercial at-home wearable device. And they have printouts sometimes, and they show me their phone, and they give me some numbers that I don't really know how to interpret. Reading through this issue, I learned a couple of great new words. I learned about orthosomnia, right? So, people who become so preoccupied with their sleep, it keeps them awake at night, literally, right? I mean, it's a complete paradox. I learned about nearables, so things that aren't necessarily wearables that are just in the room while the patient is sleeping that monitor proxies for sleep quality, sleep stage, and other things. And I frankly, I'm not really sure what to tell patients. So, what do you tell patients who come in with all the data? Like, or how do you tell patients to use these?  Dr Johnson: I think these devices can go both ways. So, I do kind of say the pros and cons of these devices. I think for a lot of patients, they're empowering. It's getting them to think about sleep, to wanna know how good their sleep is. Are they getting enough sleep? So, if it's used in those ways, it's gonna be very helpful. I actually had a patient last week, and they noted that they're having big desats all night and could show me essentially an overnight oximetry data rather than me having to order it, and I had days of data, which sometimes can be too much. But in this case, it's like, oh, when he was on his side that night, he looked a lot better, so I can use that to give advice to the patient about particular treatments. He actually went down to Mexico, and a doctor friend gave him oxygen therapy while he was there randomly. And we could see on the nights that he had the oxygen therapy, it did really help his central sleep apnea pattern. And so that pushed us towards saying, "Let's qualify you for that up here in the States." So, I think in some cases it can give really important data. Now, I saw a posting on social media the other day of someone saying, "Can I get advice on how to improve my REM sleep? My tracker says I have no REM sleep, and I need to do something about it." There's really not data to support needing to do something about it. And so, I do think it can get some people on these wild goose chases, trying to get to a certain percentage of sleep. And these trackers, they're good in a lot of ways, but they're not perfect. He could be getting REM sleep that the tracker on him does not show. You want to relate it to what symptoms are they having. I think they can be very good for trying something out. So, let's say someone, has their tracker telling them they get five hours of sleep, and they try this intervention, and that helps them show that they got the seven hours of sleep, or they went from no REM to REM and it goes in the right direction. It can help give them that positive feedback that something they're trying, is working. But the absolutes for any given patient, it's hard to over-- What does it mean if it says you've got a 50% score versus a 70% score? That may or may not be meaningful in any given person, but again, they can compare themselves to themselves. If they were a lower score and now they're a higher sleep score because they did something that was meaningful, and that goes along with them feeling better, that can help give them that positive feedback to do something good.  Dr Jones: So, a little bit of a mixed picture.  Dr Johnson: Yeah.  Dr Jones: Sometimes they help. Sometimes they distract. Hopefully- Dr Johnson: And as a provider, sometimes it can be overwhelming because they're like, "Come look at my year's worth of data." And you're like, "No."  Dr Jones: Yeah.  Dr Johnson: You know, let me see one page or two pages of data and be like, "Yep, okay, I get it." Dr Jones: Just show of hands in the audience, who in the room wears a sleep device at night, like a ring or a, some kind of sleep monitoring app? That's about half the audience.  Dr Johnson: This is why they're here.  Dr Jones: So that's really helpful, and I think it is. You want to be supported by the data. You want to be supported by evidence and high-quality biometric evidence. Another big trend, and this has been a number of years in the making, is the understanding, Dr. Johnson, of the relationship between sleep physiology and neurodegenerative disease. One of the things I love about neurology is there's still so much left to learn about the normal physiologic functioning of the brain. So glymphatics and other aspects of sleep physiology that we didn't know about a decade or two ago. When you think about how that relationship has developed, sleep physiology, maybe sleep disorders and neurodegenerative disease, how has that changed your approach to talking to patients? Do you counsel patients differently now because of what we understand better about that?  Dr Johnson: Yeah, I mean, we are still limited with our data. We have so many studies that show the associations between whether it's not enough sleep, too much sleep, or having a sleep disorder like obstructive sleep apnea, and that being a risk factor for stroke or Alzheimer's or Parkinson's. But we still sort of lack the treatment trials that necessarily say, "If you treat obstructive sleep apnea, you're gonna have less dementia," or, "You're gonna be less likely to have that stroke." So, we have a lot of physiological studies, a lot of reasons why it makes sense, but we don't have that final, nail in the coffin to say, "If you do this, you'll definitely be better." So, we know certain groups are more at risk. If you have obstructive sleep apnea and you are symptomatic, you seem to have higher cardiovascular risk. If you have a person who's had a stroke and we find a milder case of sleep apnea, and they're someone that's totally asymptomatic. They say, "I sleep fine. I feel fine." There's not great data to say, "If you treat your sleep apnea, you're gonna be less likely to have a stroke." Now, if they come in and they're sleepy and their sleep apnea is really severe, and they have more hypoxic burden, which is also more connected with a lot of these risks, I'm going to say, "I think you are in the higher risk group of sleep apnea people who it's probably gonna be more likely to help your cardiovascular risk, your dementia risk." We can counsel them, and then it's really a personal decision. Some people are like, "No way. I'm never gonna use a CPAP machine, ever." And other people are like, "You know, my mom had a stroke. My dad had Alzheimer's. I want to do every possible thing I can to make it less likely that I have this outcome that I want to avoid." And so, you're going to take that in to, you know, do you want to try this treatment or not? It's a lot easier when you have outcomes that you can follow, like, "If I try CPAP, does my blood pressure get better? Do I stop having AFib attacks?" It's a lot harder when, will I or not get Alzheimer's ten years down the road or have that stroke?  Dr Jones: It's hard to get people to do things for kind of an abstract prevention down the road, but could be important. Are there trials going on that are going to assess this data?  Dr Johnson: Yeah. We currently have a big trial getting people right away, right after their stroke, on CPAP, and not only looking at prevention, but also looking at recovery outcome. It's been running for several years. Hopefully, we'll get enough data to close out the study coming up.  Dr Jones: We'll look forward to that.  Dr Johnson: Yeah.  Dr Jones: So, I'm really excited to get to our audience here, but before we do that, I do want to ask Dr. Johnson one more question. Dr. Johnson is famous for her advocacy for sleep in general, but specifically related to Standard Time. So, let's do a little experiment here. I didn't warn Dr. Johnson about this, so we'll see how she does. She does a ton of advocacy. She's a pro. So, pretend like we're in DC, and I'm a senator, and we just got in an elevator. You're going to give me your elevator pitch on what we should do.  Dr Johnson: So, you know, sleep is one of the few essential things in life. We need to eat, we need to drink, we need to have clean air, and we need to sleep and when we improve sleep, we can improve basically every outcome, whether it's academics, whether it's productivity, whether it's our physical health, our mental health. And the problem is we structure our lives in a way that really keep people, and especially our teenagers, from getting the sleep they need. And one of these structural things we do is permanent daylight savings time. Essentially, what you're doing is you're putting the sun out later, makes it harder to go to bed. I was just talking to someone, the sun's going down at 9:00, and you need to get your kid to sleep at 7:30, 8:00 so they can get the amount of sleep they need. That is almost an impossible task because their circadian rhythms are being pushed later, they can't fall asleep on time. Then you're setting their clocks an hour earlier, so when that alarm clock is going off at 6:00 AM in the morning, it's actually 5:00 AM in the morning. You're squeezing sleep from both sides, and it's basically impossible to get enough sleep. A lot of people think the only problem with daylight savings time is twice a year with the changes, and there are certainly harms related to that. So, a lot of people think if we went to permanent daylight savings time it would be better, and we got rid of those changes. What they don't realize is that permanent circadian misalignment by setting the sun more ahead, at 1:00 to 2:00 instead of at noon causes the sleep and circadian disruption all year round that leads to increased incidents of strokes, of heart attacks, of obesity, of cancer, of suicides, of depression, of worse academic grades. Again, pretty much every outcome you have there that relates to brain health, we have now data that shows that it's worse. And so, we can improve our lives if we can go to permanent Standard Time.  Dr Jones: You convinced me. How about that? If there were any skeptics in the room, I doubt there are any left. We only went to like the fifth floor there, and she... I'm like, "I'm voting for this. Whatever, whatever this bill is, I'm gonna vote for it." So, I'm excited to get to the audience here. Before we get to questions and answers, and we want you to get your questions ready for Dr. Johnson. I do have a couple of trivia questions. And we've been doing this for a little while now on the podcast. The first trivia question actually relates to arts and culture.  Dr Jones: What famous artist used transitions between sleep and wake states to inspire his art? Anybody know?  Guest Speaker 1: Is it Van Gogh?  Dr Jones: Not Van Gogh that I know of. There in the back.  Guest Speaker 2: Picasso.  Dr Jones: Picasso, not that I know of. Right here.  Guest Speaker 3: Salvador Dali.  Dr Jones: Salvador Dali. We have a winner. Thank you for your answer. So apparently, I read this. Salvador Dali would sit in a chair holding onto a metal key and wait until he fell asleep, and it would fall out of his hands and drop into a bowl, and it would wake him up. So, then he would pick it back up, and he would go in and out of sleep trying to generate hypnagogic hallucinations, basically, and he would use that to inspire his art. And you think about his art, maybe that kind of makes sense. All right, now I've got a neurology trivia question. Okay, so maybe we're a little more comfortable with the neurology trivia in here. What is the center in the brain that is responsible for REM sleep atonia?  Guest Speaker 4: The receptor is for erection in the lateral hypothalamus.  Dr Jones: That is not correct. REM sleep atonia. Right here.  Guest Speaker 4: Emilio Malgona, Hyannis, Massachusetts. Dorsal raphe nucleus.  Dr Jones: We'll give you credit for that. Very good. Excellent. So, the-  Dr Johnson: Well, no. That's actually the serotonin. He's talking about another one.  Dr Jones: Oh, I thought I heard, I thought I heard-  Dr Johnson: You heard dorsal  Dr Jones: ... I heard dorsolateral tegmental nucleus of the pod.  Dr Johnson: Not quite.  Dr Jones: You get a prize anyway, sir, just for, just for answering. Thank you very much. All right. So, we're all warmed up here. So, Dr. Albin, what do you think? Should we get some questions from the audience?  Dr Johnson: All right, we've got some questions.  Guest Speaker 5: I have a statement and a question.  Dr Jones: Please tell the podcast your name again, sir.  Guest Speaker 5: Steve Spar, New York City. The tyranny of the morning people. You don't want people, you don't want the sun to go down too late because it'll keep people up longer. I spent my whole life fighting people like you. I am a nighttime person. Why do I have to go to sleep earlier? I want to go to sleep later. I want to wake up later. I don't want to wake up at 7:00 in the morning. I want to wake up at 10:00. There's a certain tyranny that we must use circadian rhythms of the majority, and it persecutes people like me who are night people.  Dr Johnson: So that is a great question.  Guest Speaker 5: What say you?  Dr Johnson: What say me is actually the harms of daylight savings time are actually to the night owls, and don't really affect the morning people. I can still go to sleep on time and get up on time without that pressure of needing to go to work. The night owl people, they can't fall asleep until later. They want to sleep in earlier, but we're forcing them to get up an hour earlier for work and school. And because we're doing daylight savings time, you're not getting the morning light you need, you're getting too much light at night, and you are more sensitive to a delay in your circadian rhythm, which makes you even more of a night owl and increase the degree of social jet lag. So, we actually see that the harms and risks of things like depression, cardiovascular risks are much greater in night owls than they are in normal people or morning larks. And this is again why the risks are the highest for our teenagers, who are essentially all night owls. You're making it harder for them to fall asleep on time. You're making them more and more of a night owl that it becomes more out of line with our standard social schedule. So, what we can do for a night owl is say to our schools, say to life that we want to change our society norms of getting up early. But that has nothing to do with daylight savings time. That has to do with how we make our schedule Dr Jones: All right, next question. And introduce yourself to the audience.  Guest Speaker 6: Sure. I'm Sanjay Rathi from New Haven area, Neurology. Movement disorders, Parkinson's disease, sleep disruptions, sleep-regulating REM, RBD issues, what are your recommendations? And as things get worse, what additional intervention should we do?  Dr Johnson: Yeah, I think it's hard with a lot of our neurodegenerative disorders, it's a two-way sleep. The disorders themselves often worsen sleep quality, have decrease in their sort of circadian amplitudes, and so that can affect sleep ability. And so, trying to do the things that promote sleep, like getting lights down in the evening, keeping things dark and quiet, doing cognitive behavioral sort of therapies if that's needed can all be helpful. Very high incidence of obstructive sleep apnea or other sleep-disordered breathing, whether it's Parkinson's or other neurodegenerative disorders, so evaluating and treating that if need be. And some of these people, especially as they get later on, you may end up considering medication for insomnia because their underlying disorders was causing it and there's, and you're not going to CBTI your way out of it. We do have the new orexin antagonist sleep agents, which are more recommended for older people and probably safer agents than your Z drugs and some of the other sleep meds out there. So, some people should be on some of those meds if their sleep is so disrupted. I've seen some sleep studies where it's basically like wake, sleep, wake, sleep, wake, sleep all night long. And it's like, wow, you really cannot sustain sleep, and we think it's not just a behavioral thing. I think it is part of their underlying Parkinson's and underlying disorders that can really cause major sleep disruption.  Dr Jones: It's a great question. Before we get more from the audience here, Dr. Albin, I'm just curious, you know, you got some questions from online. Don't know if any of those stood out to you. And the other thing is, I think about your practice, Dr. Albin, as a neurointensivist, there's some great content in this issue on how to maintain an adequate sleep environment in the hospital and the importance of that for the acute episode, maybe for some long-term outcomes. When I was reading the article, I didn't really didn't think about the ICU setting. That must be-- what do you do in the ICU?  Dr Albin: Well, we happen to have a question about just that. Dr Jones: Well, there you go  Dr Albin: From Dr. Manners of Baltimore, Maryland. "What meds should I be giving patients in the ICU or the inpatient setting to preserve or recalibrate their sleep-wake cycles? Is there anything that we can do besides just getting them out of bed during the day?"  Dr Johnson: Meds are always hard cause as sleep doctors, we're usually the last one to recommend meds. But there are situations and scenarios where meds may be appropriate. I can't say what's one better than the other, and some of the meds we have probably aren't even available as options in the hospital. So, the, you know, again, the orexin antagonist may be a good class to try to use, but they may not be an option. There was a good study that looked at empowering the patient and whether or not the ICU patients are empowerable. But they give a card to the patients in the hospital and say, "Tell your nurse to turn off my TV and my lights. Do I need all the blood draws all throughout the night, or can it be put off to the morning?" And trying to empower the patient to ask for these things and do some of the behavioral things. And they found that doing that did improve the duration of sleep, did reduce some of the number of awakenings that people ended up having at night. So, I think the ICU is a very particular population where there's a lot of things you can't get rid of. But certainly, turning on the lights, turning off the lights, and trying to limit noises as much as you can, in those night hours, trying to give some sense of a 24-hour day. The other thing is feeding is really important to circadian rhythms. I had a patient that had a brain bleed and, after it, she just her circadian rhythms were just off, and part of it was she was getting tube feeds through the night. So, one of the very first interventions we did was to move her timing of her feeding so that it wasn't in sleep, and that really did help make a difference in getting her back on a pattern, along with light therapy and other behavioral techniques as well.  Dr Jones: It's a great question.  Dr Albin: Absolutely. I mean, I think that validates just that we spend a lot of time actually asking like, "Can we feed people during the day?" Or, "Can we, can we limit the amount of baths that are happening at 3:00 in the morning?" We also had another one from the audience that came from Dr. Lavina Singla of Mississippi, and I think a lot of our patients are asking this question. Is melatonin addictive?  Dr Johnson: Is melatonin safe? Is melatonin addictive? I think with any sleeping aid, people become addictive to what they perceive is the outcome. So, if they said, "This got me to sleep, and now I'm sleeping great, I don't want to come off of it." And so, you get this to meds that are truly addictive, but even meds that aren't felt to have that addiction, there is certainly a behavioral change. And that's a lot of what cognitive behavioral therapy is working with these patients on, is challenging that belief of maybe it isn't the med, maybe it's your internal belief and your worry about doing this. One thing about sleep is sleep happens when you are relaxed and calm and not worried. When you're worried about thinking that thing you're worried about is whether or not you're getting sleep, then you don't sleep. In terms of melatonin, if you don't need to use it, I wouldn't use it. If you are gonna use it, I'd try to use as low doses as possible. Do we know all the risks? We don't know. And especially I think there are potentially more risks in a growing child than, maybe someone who isn't having the same sort of hormonal, needs and growth needs. But then again, if you have, let's say, a kid with autism and melatonin helps him sleep, I'd much rather use melatonin than a lot of other agents, and if that really changes their functionality, that probably is very good for them and better than having them not get sleep. So, I think you have to weigh each individual situation and combine it, especially with the behavioral approaches so that hopefully this is not a long-term addictive thing you're on.  Dr Jones: So, it's complicated. Sounds like it.  Dr Albin: Not a straightforward answer.   Dr Jones: I thought that was gonna be just this hard no, but I guess it is something you have to think about. So, I want to really take a minute here to thank Dr. Karin Johnson, who has been our interviewee for this episode of the Continuum Audio Podcast sleep issue just came out. Really want to encourage our subscribers, our listeners, and our studio audience here to enjoy it. Thank you, Dr. Johnson, for joining us today. I want to give a big round of applause to Dr. Casey Albin for managing this crowd. Thank you to our listeners. Thank you to our subscribers. Thank you to you all for coming today.  Dr Monteith: This is Dr. Teshamae Monteith, Associate Editor of Continuum Audio. If you've enjoyed this episode, you'll love the journal, which is full of in-depth and clinically relevant information important for neurology practitioners. Use the link in the episode notes to learn more and subscribe. Thank you for listening to Continuum Audio.   

*Audio only here.  all video clips: nutshellsermons.com@nutshell_Sermons_Videos 

Why do women pick the bear? The answers might shock you, but they highlight deep-rooted truth about safety, trust, and male insecurity in modern dating.In this episode, we break down the viral "Man vs. Bear" debate, reveal the major red flags and signs of an insecure man, and dive into what women wish men actually understood about relationships today.Topics:- Why Women Pick the Bear (The Shocking Truth)- What the "Man vs Bear" Debate is ACTUALLY About- Top Signs of an Insecure Man in a Relationship- Controlling Behaviors & Trust Issues Disguised as Love- What Women Want Men to Know (Advice for Modern Men)- Final Thoughts & Community Discussion

We talk about "The Picasso" and public art.  Show Notes:The Art Newspaper: From the archive | the story behind the ‘controversial' Picasso sculpture that became a symbol of ChicagoChicago Public Library: 1967 August 15--Picasso Statue Unveiled In Civic Center PlazaSOMWTTW: A "Colossal Booboo": The Incredible Story of the Chicago PicassoArchitectural Digest: Pablo Picasso's Chicago Sculpture Celebrates 50th AnniversaryChicago Sun-Times: Human Bean? Mysterious group says there's a grown man living inside Cloud Gate, aka 'the Bean'Modern Dog Magazine: Picasso's Dogs Learn more about your ad choices. Visit megaphone.fm/adchoices

When you are proud of your work, you want the whole world to see and know it. That’s how one employee of the Pinakothek der Moderne felt, The Pinakothek der Moderne is an art museum built by Stephan Braunfels in Munich, Germany, with approximately 20,000 art pieces, It is one of the world’s leading institutions for painting, sculpture, photography, and new media, But one employee thought something was missing. Despite being surrounded by works from Picasso, Magritte, and Dalí, this 51-year-old freelance artist believed the museum needed something else-his own painting, Surely, as people stared with wonder at some of the magnificent 20,000 art pieces in this museum, they would become equally enamored with this guy’s work, Pride usually doesn’t end well. But for those who follow Jesus, humility is not just helpful-it’s essential.” Well, he certainly got noticed, and he was promptly fired, It is unclear if he was fired for his bad art or the fact that he drilled two holes in a wall to hang his two feet by four feet picture. But one thing is clear: humility could have saved him from embarrassment and losing his job. His story is a funny reminder that pride usually doesn’t end well. But for those who follow Jesus, humility is not just helpful-it’s essential. The post ESSENTIALS FOR CHRISTIAN LIVING – A Humble Attitude (YOU-Sum’26, Study 2, Session 4) appeared first on YOU.

2021年9月云南贵州卷听力第一节听下面5段对话。每段对话后有一个小题，从题中所给的 A、B、C 三个选项中选出最佳选项。听完每段对话后，你都有 10 秒钟的时间来回答有关小题和阅读下一小题。每段对话仅读一遍。1. Where is Alice now?A. In New York.B. In London.C. In Chicago.2.What does the woman think of the cake?A.It's not very fresh.B. It's not tasty.C. It's not expensive.3.What will the man do?A.Wait for a few minutes.B. Go to the room upstairs.C.Put off the meeting.4.How many times has the woman seen this movie?A. Twice.B.Three times.C.Four times.5.What is the woman going to do?A.Show photos.B. Goon a trip.C. Attend class.第二节听下面 5 段对话或独白。每段对话或独白后有2--4个小题，从题中所给的 A、B、C三个选项中选出最佳选项。听每段对话或独白前，你将有时间阅读各个小题，每小题5秒钟;听完后，各小题将给出5秒钟的作答时间。每段对话或独白读两遍。听第6段材料，回答第6--7小题。6. What is the danger of eating ice cream according to the man?A. Putting on weight.B. Catching a cold.C.Having a headache.7.What is the probable relationship between the speakers?A.Husband and wife.B.Teacher and student.C.Father and daughter.听第 7 段材料，回答第 8--10小题。7. Where was the boy this morning?A. In the classroom.B. In the art museum.C.At Ms. Green's house.9.How did Ms. Green feel about the boy's painting?A. It was boring.B. It was strange.C. It was pleasing.10.What is the topic of the conversation?A. An oil painting.B. A famous artist.C. An art lesson.听第 8 段材料，回答第 11--13小题。11. What does the woman say about raising a kid while working?A. It's a hard task.B. B.It requires skills.C. C.It keeps her busy.12. What does the woman's husband do at home?A. Do more housework.B.Care more about their kid.C.Spend more time reading.13.What does the woman often do with her kid?A. Do exercise.B.Take pictures.C. Go shopping.听第 9段材料，回答第14 至 17小题。13. What is Jennifer doing?A.Hosting a meeting.B.Serving a customer.C.Having an interview.15.What does Jennifer learn from her previous job?A.Sales techniques.B.Interpersonal skills.C.Language knowledge.16.What was Jennifer's worst experience at work?A.Receiving an unreasonable phone call.B.Arguing with the manager.C.Dealing with a complaint.17.What will Jennifer do next?A.Make a decision.B. Apply for the position.C. Wait for a call.听第 10段材料，回答第 18--20小题。18.What is Mary Glen?A.A lovely shop.B.A holiday place.C.A farmer's market.19.Where does the speaker suggest riding bikes?A.Along the River Dent.B. Along the Soli.C. Along the Evergreen Trail.20.Why does the speaker give the talk?A. To do promotion.B. To introduce the town of Soli.C. To discuss a sport.答案1-5BABAC 6-10 CABAC11-15BAACB 16-20 CCBCA录音材料 Text 1M:Alice! So nice to see you. I thought you went to New York. Why are you still here in London?W: It was my sister who went to New York. And she's in Chicago now. I've been here the whole time.Text 2M:Linda, have a taste of this cake and tell me what you think.W:OK. Hmm… I'm afraid it has gone bad. When did you buy it?Text 3M:Excuse me, isn't the staff meeting at 9:30? I've been waiting here for ten minutes, but nobody showed up.W:You're in the wrong room. It's one floor up.M:Oops! Thank you.Text 4M:Hey, you come to watch the movie again. Weren't you here yesterday?W:Oh, yes. I saw it last week and I saw it again yesterday. I always see my favorite movies four or five times.M:Here's your ticket. Have a nice time.Text 5W:Bob, your trip sounds wonderful, but I have to go now. My biology class starts in a few minutes.M:Oh, sorry, Caroline. I didn't mean to hold you up.Text 6W:It's hot today. I'd like to have some ice cream. Do you want some, dear?M:No, thank you. Do you know the danger you face when eating ice cream?W:Yes, gaining weight.M:No, not that. Have you heard about brain freeze?W:No, what's that?M:For many people, eating ice cream or drinking an icy drink too fast can produce a terrible headache. It usually hits in the front of the brain.People also call it “ice cream headache”.W:Okay. I see. You know what? After all these years, you sound more and more like my father.Text 7W:Mike, what lessons did you have at school this morning?M: We had art, but we didn't go to the classroom. Ms. Green took us to visit the museum in town.W:How come? What did you do there?M: We looked at paintings by famous artists.W: I wish I had been with you. Did anything attract you?M: A lot. But my favorite painting was by Picasso. It looked very strange.W:Did Ms. Green explain something about the paintings?M:Yes, and after that we made pictures for a competition. I drew a picture of Ms.Green. It looked like a Picasso painting.W:Did she like it?M:Ms. Green wasn't very pleased, and she thought it dull. So, I didn't win.Text 8M:Cindy, as a working mother, how do you balance work and family?W: It takes skills. It's an art, I should say. But raising a kid while working is not as hard as most people think.M:You mean it's not difficult?W:No, if you know how to share your responsibilities properly. My husband does more housework, and I take more care of our kid. I try to spend as much time with our kid as possible. She joins me in my daily activities.M:What do you mean?W:Working as a model, I do a lot of exercise every day to stay fit, and I encourage our kid to do it with me. It's like play to her. As a result,I'm fit, she's fit, and we spend time together.M: I see. What else do you do together?W:Things like reading picture books and going to the park.M: I really like your ideas.Text 9M:Nice to meet you, Jennifer. Can you tell us a few things about yourself?Perhaps your work experience.W:I'm a university student in my final year, and I major in English. I've had several part-time jobs, including office work and sales.M:What was your office work about?W: I worked at the reception desk and did some paperwork.M:What did you learn there?W:I received phone calls and met customers most of the time. And I supported staff from different departments at times. So, I learned to deal with various kinds of people.M:Did you apply any of the knowledge you got at university?W:Yes, I think I did use some of the language knowledge.M:What has been your worst experience at work?W:Well, once I met a customer who came to make a complaint. I tried my best,but he was just being unreasonable. I had to refer him to my manager. It was a hard day.M:Okay. Now, do you have any questions for us?W:No, not at the moment.M:Thank you, Jennifer. We have a number of people applying for this position,and we will call you with our decision within two weeks.W:Okay, thank you.Text 10W:Mary Glen, built on a working family farm, is a perfect place for holiday.Here, you'll find everything you need. We have five wooden houses with all modern facilities. Mary Glen is just three miles from the busy town of Soli, where you'll find quite a few lovely shops and great farmers' markets.The area around Mary Glen is great for riding bikes. So, do bring your bikes and try the Evergreen Trail, or you can go running along the River Dent. After a busy day, relax in our game room where there are lots of card games. Try a family holiday at Mary Glen. With prices as low as 425 pounds a week, you can't go wrong.

Giuseppe Scaraffia"Una strana coppia"Jean CocteauTraduzione di Simona MambriniEdizioni Adelphiwww.adelphi.itSole e Luna alle prese con l'educazione dei loro piccoli astri – una storia scritta e disegnata da Cocteau per i lettori più giovani.Per la strana coppia di questa storia l'educazione dei figli è una faccenda piuttosto complicata. Troppo presi dalle loro incombenze, il signor e la signora Sole (nata Luna) affidano i loro piccoli astri alle cure di un cane che si guadagna da vivere come venditore ambulante di palloncini. Ma le conseguenze di una scelta a dir poco avventata non tarderanno ad arrivare...Basato sull'edizione originale pubblicata in tiratura limitata nel 1948, questo libro invita i lettori più giovani ad avventurarsi nel mondo grafico di Jean Cocteau.Giuseppe Scaraffia (1948) è un giornalista, saggista e critico letterario italiano, tra le firme storiche de "Il Sole 24 Ore" e collaboratore de "La Stampa". Esperto di Ottocento e Novecento francese, dandismo e mondanità letteraria, ha dedicato numerosi saggi a figure come Baudelaire, i dandy parigini e la vita mondana europea tra Otto e Novecento. Tra le sue opere più note: "Dandies", "Il romanzo di Parigi" e vari volumi sulle grandi cortigiane e sui salotti letterari.Jean Cocteau (Maisons-Laffitte, 1889 – Milly-la-Forêt, 1963) è stato un poeta, scrittore, drammaturgo, regista e artista visivo francese, figura centrale dell'avanguardia del Novecento. Legato all'ambiente surrealista pur mantenendo una voce indipendente, ha attraversato quasi ogni forma espressiva: dal romanzo ("Les Enfants terribles") al teatro ("La Voix humaine"), dal cinema ("La Belle et la Bête", "Orphée") al disegno. Amico e collaboratore di artisti come Picasso, Stravinsky e Diaghilev, resta una delle personalità più poliedriche della cultura francese del XX secolo.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Tussen 2003 en 2020 woonde Noreen van Holstein in New Delhi en Goa, waar zij als sociaal-cultureel ondernemer uiteenlopende initiatieven ontwikkelde. Na zeventien jaar in India te hebben gewoond en gewerkt, keerde zij in 2020 met haar gezin terug naar Nederland. Vanuit Utrecht richtte zij de Lala Foundation op, die in 2022 de WasteBar lanceerde: een pop-upbar waar bezoekers een drankje kunnen betalen met opgeraapt zwerfafval. Teddy Tops gaat met Noreen van Holstein in gesprek. Noreen deelt deze cultuurtips met Teddy:  Tentoonstelling: Amrita Sher-Gil - ‘Europa is van Picasso, India is van mij' (https://drentsmuseum.nl/tentoonstellingen/amrita-sher-gil-europa-is-van-picasso-india-is-van-mij) Boek: De Eilanden van Goed en Kwaad - Adwin de Kluyver (https://www.adwindekluyver.nl/boek#eilandenvangoedenkwaad)

n Episode 293 of the Moonshots Podcast, Mike and Mark dive into Albert Read's inspiring book The Imagination Muscle to explore why imagination isn't an inborn gift—it's a muscle that grows stronger with deliberate practice.Together they unpack how the world's greatest creators develop original ideas, why boredom may be one of your greatest creative assets, and how adopting a beginner's mindset can unlock fresh thinking. They also discuss practical exercises to strengthen your imagination, overcome fear, and make creativity part of your everyday life.If you've ever felt stuck, uninspired, or convinced that you're “just not creative,” this episode offers a refreshing perspective—and a practical roadmap—to help you think differently and create with greater confidence.Primary Website: https://www.moonshots.io/Book: The Imagination Muscle by Albert ReadBuy on Amazon: https://geni.us/TsrReYWatch on YouTube: https://youtu.be/-bZ3cn7rCVw⸻Key Themes* Imagination is a skill that can be trained.* Great ideas come from combining diverse sources of inspiration.* Creativity flourishes when we deliberately make time for quiet thinking.* A beginner's mindset helps us escape routine and discover fresh perspectives.* Social media and constant stimulation can limit original thinking.* Fear is one of the biggest obstacles to creativity.* Small, consistent creative habits compound into lasting growth.⸻Concepts & BreakthroughsImagination Is a MuscleAlbert Read reframes creativity as a practice rather than a personality trait. Like physical fitness, imagination strengthens through regular use. Mike and Mark reflect on how this simple shift removes the pressure of waiting for inspiration and replaces it with consistent action.Collect Better InputsThe episode highlights that creative breakthroughs often result from unexpected combinations of ideas. Picasso, Aaron Sorkin and other creative masters all developed unique rituals for gathering inspiration before producing original work.Rather than searching for one brilliant idea, listeners are encouraged to become collectors—of books, conversations, observations and experiences—that can later combine into something entirely new.Protect Space for ThinkingModern life is filled with interruptions, notifications and endless streams of information. Albert argues that imagination needs space to breathe.Mike and Mark discuss creating intentional moments of quiet through walks, uninterrupted mornings, observation exercises and even scheduled boredom—allowing the mind to connect ideas naturally.The Power of the Beginner's MindExperts often become trapped by familiar thinking. Approaching problems with curiosity instead of certainty creates opportunities for innovation.Reading outside your normal interests, asking simple questions and deliberately seeking unfamiliar experiences all help develop fresh perspectives.Courage Before CreativityOne of the episode's strongest insights is that imagination grows alongside courage.Every sketch, poem, voice memo or imperfect idea strengthens resilience against fear of failure. Rather than waiting until ideas are perfect, creative confidence develops through repeated attempts and experimentation.⸻Habits, Tools & Mental Models* Treat imagination like a daily workout.* Collect ideas, quotes and observations in an idea bank.* Schedule periods without digital distractions.* Go for curiosity walks with the goal of noticing something new.* Read outside your usual genres and interests.* Combine two unrelated ideas to generate fresh thinking.* Use voice notes or journals to capture ideas immediately.* Practice one small creative act every day.* Embrace boredom instead of filling every quiet moment.* Ask beginner's questions, even when you think you know the answer.

In this episode of Exploring Art Podcast, we discuss Pablo Picasso's Les Demoiselles d'Avignon and why it became one of the most revolutionary paintings in modern art. We explore why the artwork shocked early audiences, the meaning behind its title, how it helped inspire Cubism, and whether Picasso's decision to keep it hidden was driven by fear, doubt, or defiance.

The fastest-growing dessert brand is Van Leeuwen ice cream… because a pint is not a solist, it's part of an orchestra.Reddit is considering removing itself from Google… because of Zero-click searches and Google Zero.Gen Z has saved 3x more than Gen X at the same age… so Jack shares his 401k balance.Plus, LeBron James took a huge paycut to bring his talents to Philadelphia…Submit your “Best Comeback Yet” for a chance to be featured in a special episode presented by our friends Liquid IV thebestcomebackyet.com. $RDDT $GOOG $ULGrab your Tickets to the IPO Tour: Our In-Person OfferingSan Francisco 9/23: https://www.ticketmaster.com/event/1C0064AFB5F688BDBoston 10/14: https://tickets.citywinery.com/event/tboy-the-ipo-tour-in-person-offering-8cdhupSeattle 11/4 (21+): https://www.axs.com/events/1446394/the-best-one-yet-ticketsNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.

Retrouvez l'épisode en version française ici : https://www.gdiy.fr/podcast/robert-gentz-vf/It's the largest e-commerce company in Europe, just behind Amazon.And it all started with flip flops.Robert Gentz co-founded Zalando in 2008, after his first startup left him too broke to buy a flight ticket home from Mexico.With his co-founder, they tested the market with a one-page flip flop shop in Berlin. It worked. So they built a full shoe destination on top of it.The growth that followed is hard to believe: 6 million euros the first entire year, 160 million in year two, 500 million in year three.Today Zalando does 20 billion euros in gross merchandise value across 29 countries.But Europe doesn't make it easy.Robert learned that the hard way when the TV ad that turned Zalando into a German phenomenon became "the worst ad in the Netherlands" a few months later.Same continent, completely different reaction.That's the real story of building at scale in Europe: 29 different markets, 29 different consumer psychologies, and no shortcuts around any of them.In this episode, Robert reveals:Why Europe's fragmentation is both Zalando's biggest obstacle and its strongest moatThe real reason free returns increase order value instead of killing marginsWhy Zalando builds its own tech stack instead of buying off the shelfHow AI is already rewriting the rules of e-commerceWhy he turned down a US listing and what that says about Europe's futureA rare look into a company that proved Europe can build at global scale in less than 20 years, and what it will take to do it again.You can follow Robert on LinkedIn.The GDIY15 code will get you 15% off on Zalando from €50 spent, valid until 31.07.2026 at 11:59pm.TIMELINE:00:00:00 - The flip flop test before the shoe empire 00:12:06 - Fundraising Needs a Reference Point 00:19:38 - Build vs Buy: When to Own Your Technology 00:30:24 - "Europe is the hardest market on earth" 00:40:58 - The biggest mistake Zalando ever made 00:53:11 - Why the US Out-Scales Europe 01:00:31 - Running a company is like co-parenting 01:10:12 - The Country Launch Playbook 01:19:03 - Democratizing fashion from Paris to northern Finland 01:28:54 - Why fashion is still one of the most inefficient businesses 01:36:43 - AI could turn us into a commodity 01:47:41 - Stock market : Why Chosing Frankfurt Over Nasdaq ? 02:01:52 - "Speed Is the Only Advantage That Matters in the AI Age"We referred to previous GDIY episodes : #496 - Sébastien Kopp - VEJA - Faire du business autrement#487 - VF - Anton Osika - Lovable - Internet, Business et IA : rien ne sera jamais plus comme avant#487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#311 - Pascal Meyer - QoQa - Vendre un Picasso à sa communauté : quand l'e-commerce n'a plus de limite#81 - Jacques Antoine Granjon - Cofondateur VEEPEE - l'aventure, l'hypercroissance, les marques et l'instinct#11 - Sébastien Kopp - VEJA 2/2 - Réussir dans la mode en préservant le monde#10 - Sébastien Kopp - VEJA 1/2 - concurrencer Nike et Adidas avec du Développement DurableA few recent episodes in English : #542 - VO - Yoni Assia - eToro - “AI Will Replace Most Traders in 18 Months”#513 - VO - Jesper Brodin - IKEA - 40 billion in revenue empire with no bank loan#500 - VO - Reid Hoffman - LinkedIn, Paypal - How to master humanity's most powerful invention#487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#475 - VO - Shane Parrish - Farnam Street - Clear Thinking: The Decision-Making Expert#473 - VO - Brian Chesky - Airbnb - « We're just getting started »#452 - VO - Reid Hoffman - LinkedIn, Paypal - L'humanité 2.0 : Homo technicus plus qu'Homo sapiens#437 - James Dyson - Dyson - “Failure is more exciting than success”#431 - Sean Rad - Tinder - How the swipe fever took over the worldWe spoke about :Fort KnoxSalesforce CRMAmazon Web ServicesN8N : AI agents and workflowsyou can see and controlLovableReading Recommendations :Delivering Happiness: A Path to Profits, Passion, and Purpose, by Tony Hsieh

Check out the episode in its original version here : https://www.gdiy.fr/podcast/robert-gentz-vo/C'est le plus gros e-commerçant d'Europe, juste derrière Amazon.Et tout a commencé avec des tongs.Robert Gentz fonde Zalando en 2008.Avec son associé, ils testent le marché en vendant des tongs en ligne.Le succès est immédiat, alors ils décident de lancer un vrai site e-commerce pour élargir leur offre à tous les styles de chaussures.La croissance qui suit est hallucinante : 6 millions d'euros de chiffre d'affaires la première année, 160 la deuxième et 500 la troisième.Aujourd'hui, Zalando réalise 20 milliards d'euros de volume d'affaires dans 29 pays.Mais l'Europe n'est pas un marché facile.Robert l'a appris à ses dépens quand une publicité télé aux retombées phénoménales en Allemagne est devenue à l'unanimité « la pire publicité » des Pays-Bas, quelques mois plus tard.Un seul continent, mais des réactions complètement différentes.Construire à grande échelle en Europe c'est s'attaquer à 29 marchés différents et chercher en permanence à comprendre 29 psychologies de consommateurs différentes.Dans cet épisode, Robert révèle :Pourquoi la fragmentation de l'Europe est le plus gros obstacle et la plus grosse force de ZalandoLa raison pour laquelle les retours gratuits augmentent la valeur des commandesPourquoi Zalando construit ses propres technologies plutôt que d'acheter des solutions existantesComment l'intelligence artificielle est déjà en train de réécrire les règles du e-commercePourquoi il a refusé une cotation aux États-Unis, et ce que ça dit de l'avenir de l'EuropeUn regard rare à l'intérieur d'une entreprise qui a prouvé que l'Europe peut construire à l'échelle mondiale, et ce qu'il faudra faire pour recommencer.Vous pouvez contacter Robert sur LinkedIn.Le code GDIY15 vous permettra de bénéficier de 15% de réduction sur Zalando dès 50€ d'achat jusqu'au 31.07.2026 à 23h59.TIMELINE:00:00:00 - Le test des tongs avant de construire un empire de la chaussure 00:12:06 - Le livre qui a façonné Zalando 00:19:38 - Construire ou acheter : quand posséder sa propre technologie ? 00:30:24 - "L'Europe est le marché le plus difficile au monde" 00:40:58 - La plus grosse erreur depuis la création de Zalando 00:53:11 - Pourquoi la puissance des États-Unis surpassent l'Europe 01:00:31 - Le plus grand retailer d'Europe après Amazon 01:10:12 - Le guide de lancement pour ouvrir un nouveau pays 01:19:03 - Démocratiser la mode à travers l'Europe 01:28:54 - Pourquoi la mode reste l'un des secteurs les moins efficaces 01:36:43 - "90 % de nos publicités sont générés automatiquement par IA" 01:47:41 - Pourquoi choisir la bourse de Francfort plutôt que le Nasdaq 02:01:52 - "Seule la vitesse compte à l'ère de l'IA"Les anciens épisodes de GDIY mentionnés : #496 - Sébastien Kopp - VEJA - Faire du business autrement#487 - VF - Anton Osika - Lovable - Internet, Business et IA : rien ne sera jamais plus comme avant#487 - VO - Anton Osika - Lovable - Internet, Business, and AI: Nothing Will Ever Be the Same Again#311 - Pascal Meyer - QoQa - Vendre un Picasso à sa communauté : quand l'e-commerce n'a plus de limite#81 - Jacques Antoine Granjon - Cofondateur VEEPEE - l'aventure, l'hypercroissance, les marques et l'instinct#11 - Sébastien Kopp - VEJA 2/2 - Réussir dans la mode en préservant le monde#10 - Sébastien Kopp - VEJA 1/2 - concurrencer Nike et Adidas avec du Développement DurableNous avons parlé de :Fort KnoxSalesforce CRMAmazon Web ServicesN8N : AI agents and workflowsyou can see and controlLovableLes recommandations de lecture :Delivering Happiness: A Path to Profits, Passion, and Purpose, by Tony Hsieh

* Apoie a Cultura: Chave Pix: 7296e2d1-e34e-4c2e-b4a0-9ac072720b88* Apoie a Cultura: Chave Pix: 7296e2d1-e34e-4c2e-b4a0-9ac072720b88O espanhol Pablo Picasso é considerado um dos mais importantes e populares pintores do século XX. Dentre as suas obras mais importantes estão "A Pomba da Paz", "Guernica", "Les Demoiselles d'Avignon". As obras de Picasso são classificada em períodos: Azul , Rosa, Cubismo Analítico e Cubismo Sintético.Essa é a nossa história de hoje. Se você gostou deixe seu like, faça seu comentário, compartilhe essa biografia com mais pessoas. Vamos incentivar a cultura em nosso pais. Encontro voces na próxima história. Até lá! (Tania Barros)- Contato: e-mail - taniabarros339@gmail.com

Doyen des ponts de la Seine, le Pont Neuf incarne à lui seul, lʹhistoire et les transformations de la capitale française. Incontournable des circuits touristiques, il a également inspiré de nombreux artistes, parmi lesquels Renoir, Picasso, ainsi que Christo et Jeanne-Claude qui lʹavaient emballé en 1985. Quarante et un ans après, cʹest lʹartiste français JR qui le rhabille. Son projet : transformer le pont en une immense caverne de 120 mètres de long. À cette occasion, Monumental revient sur lʹhistoire de ce monument emblématique avec Nicolas Lyon-Caen, chargé de recherche au CNRS.

In this episode we go through the case study of one of Picasso's most important masterpieces "Les Demoiselles d'Avignon"1907. Within this episode of the podcast we dive into a painting Picasso decided to hide from the world for 9 long years until its final unraveling to the world in Paris 9 years after its finish. This painting at first sight may not seem like it holds much meaning but through a deeper look in this episode we explore different themes that emerged from this artwork. From the start of cubism to the confinement of sexuality & the reasons for the african masks, we dive into every aspect and cover it all to revival Picasso's true meaning.

We dissect Picasso's debut painting and how it changed art forever by inspiring a new movement and style.

Pablo Picasso is known as one of the greatest artists of all time, and he made many works of art that are still talked about today. In this episode we will explore his work that is considered to be the most controversial of all time. ‘Les Demoiselles d'Avignon' is a painting that is made up of heavily distorted human forms, which are unlike any other traditional views of beauty that have been portrayed in art before. In this episode we will look at the genius behind this work of art and also explore the possible cause of such a work of art, and how Picasso used his paintings to express the deep rooted societal problems that were going on at the time. We will also explore the fine line between artistic innovation and pure ugliness, and ask the question, can a work of art be so visionary that it makes us feel uncomfortable? We will also look at how people view ‘Les Demoiselles d'Avignon' today, and compare it to how people viewed it after the trauma of history had developed. Join us as we delve into the many layers of this groundbreaking work of art, and have a discussion on whether or not it is beautiful, and what we really mean by the word beautiful. Is true beauty found in a work of art that is full of harmony, or in the cracks of a broken and fractured human form.

In this episode, we explore Pablo Picasso's Les Demoiselles d'Avignon and discuss its status as one of the most controversial paintings in modern art. We examine how concepts of beauty, ugliness, and cultural influence shaped public reactions and contributed to its recognition as a masterpiece. Join us as we connect philosophy, art history, and aesthetics to understand why certain works of art challenge audiences before inspiring them.

This episode explains the meaning behind Pablo Picasso's Les Demoiselles d'Avignon, considered one of the most significant paintings of modern times. This episode also talks about our first impressions of the painting, Picasso's reluctance to unveil it, and the changing nature of beauty over time. At intervals, we connect this case study to the topics from Chapter 2 (beauty, ugliness, aesthetic experience.) Is great art expected to be beautiful, or can anything that shocks initially be considered a masterpiece? Join us as we talk about how this piece continues to change how people talk about art today.

durée : 00:31:32 - Les Nuits de France Culture - par : Albane Penaranda - Le 26 avril 1937, la petite ville basque de Guernica est écrasée sous les bombes de l'aviation nazie. À Paris, Picasso saisit ses pinceaux. Naîtra une fresque monumentale, cri universel contre la barbarie et symbole d'une Espagne meurtrie. - équipe : Rafik Zénine, Hassane M'Béchour, INA Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Yetenek doğuştan mı gelir, yoksa sonradan mı gelişir? Deha gerçekten var mı, yoksa kültürel bir efsane mi? Spekülatif'in bu bölümünde Emre Dündar, yetenek kavramını felsefe, nörobilim, sanat tarihi ve eğitim perspektifinden ele alıyor. Antik Yunan'dan günümüze yetenek kavramının dönüşümünü anlatan Dündar; Mozart, Beethoven, Picasso, Leonardo da Vinci, Nietzsche, Schopenhauer, Aristoteles ve Einstein gibi isimler üzerinden deha mitini sorguluyor. Programda çocuklarda yetenek nasıl anlaşılır, üstün yetenekli çocuklar nasıl değerlendirilmeli, genetik mi çevre mi daha etkili, yaratıcılık nasıl gelişir, mutlak kulak (absolute pitch), sanat eğitimi, müzik yeteneği, resim yeteneği, çalışma disiplini ve iradenin başarıdaki rolü gibi birçok konu ayrıntılı biçimde tartışılıyor. Learn more about your ad choices. Visit megaphone.fm/adchoices

It's one of Australia's enduring unsolved art crimes: the theft of a Picasso from the National Gallery of Victoria. Now, 40 years later, author A.T. Prewitt has imagined who could have been behind the crime.Find more about the book at: https://www.panmacmillan.com.au/?authors=a-t-prewett Subscribe to Crime X+ to hear episodes early and ad free, unlock bonus content and access our slate of award-winning true crime podcasts Have a question for one of our Q+A shows? ask it at: lifeandcrimes@news.com.auLike the show? Get more at https://heraldsun.com.au/andrewruleAdvertising enquiries: newspodcastssold@news.com.au Crimestoppers: https://crimestoppers.com.au/ If you or anyone you know needs help Lifeline: 13 11 14Beyond Blue: 1300 22 4636Kids Helpline: 1800 55 1800See omnystudio.com/listener for privacy information.

A designer with 20+ years of experience sits in front of twelve competent, AI-generated screens... and can't evaluate a single one. Not because they're bad. Because something is missing. And the same thing happened to me.In this episode, I investigate this case like a detective story: What was taken from us? Who took it? The answer turned out to be the most hopeful thing I've learned about design in years, disguised as a crisis. Because the comps were never the design. The comps are the artifact. The design is the model in your head, and no tool can generate it.In this episode:The mystery: why the most trained design judgment goes offline in front of good AI output (and why "AI output is slop" is the wrong explanation)The three suspects: the AI, the speed, and the surprising real culpritWhy "the back-and-forth builds the model": the experiment that proves your value was never in the pixelsThe cold case behind it all: how we spent 20 years selling the first three rounds, and why that gap just closed foreverThe stakes ladder: what happens when a designer, a team, or a whole organization over-trusts AI outputTaste redefined: deciding, not decorating, and why having no opinion defaults to the averageThe open question: if artifacts are cheap, is the "living transcript" the design deliverable of the future?Your homework: five questions before pixelsResources mentioned:Christopher Noessel "Design Was Never the Comps: What I learned when Claude Design dumped a dozen screens on me"https://christophernoessel.medium.com/design-was-never-the-comps-what-i-learned-when-claude-design-dumped-a-dozen-screens-on-me-73893af464aeChristopher Noessel "RationaleBot: Why design's near-future deliverable is a living transcript"https://christophernoessel.medium.com/rationalebot-why-designs-near-future-deliverable-is-a-living-transcript-c70613215359Christopher Noessel — "Get Claude Design to present like a consultant" (follow-up with the practical prompt)https://christophernoessel.medium.com/get-claude-design-to-present-like-a-consultant-6bf22261717fRoger Wong "A Sunday Afternoon with Claude Design" (the squiggly line, Picasso's nine Guernica studies)https://rogerwong.me/2026/04/sunday-afternoon-claude-designAnthony Wood (House of gAi) "Claude Design Came for the First Three Rounds. Not the Designers."https://medium.com/@ant_95138/claude-design-for-designers-what-anthropics-new-ai-tool-means-for-the-craft-f1e5378fcb50Michal Malewicz — the Slopless manifestohttps://slopless.design/

The RPM Show Episode 26 Hosted by DJ DON PICASSO LIVE FROM ATLANTA, GA PLEASE DRIVE RESPONSIBLY LET US PUT YOU ON

Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It only saw the files. Never the live state. This episode, that wall comes down. marimo pair drops a coding agent right inside a running notebook, with full access to every variable Python is holding in memory. The notebook becomes a shared canvas. You point, it runs the code. You tell it to zoom in on the Picasso paintings, and the chart just updates. No MCP tools to wire up, no schema to describe. Just Python, and an agent that can finally see what you see. Trevor Manz is back to walk us through it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show marimo pair: marimo.io/pair Course transcripts announcement: talkpython.fm/blog anywidget: Jupyter Widgets made easy: talkpython.fm marimo: marimo.io blog: marimo.io GitHub: github.com given this: martinalderson.com llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm open issues: github.com Discord: marimo.io Marimo Pair: marimo.io OpenCode: opencode.ai AI Tooling for Software Engineers in 2026: newsletter.pragmaticengineer.com Watch this episode on YouTube: youtube.com Episode #555 deep-dive: talkpython.fm/555 Episode transcripts: talkpython.fm Theme Song: Developer Rap

This week on Mouse Chat, Sharpie takes us to Europe for a father-son high-school graduation trip through Spain and Portugal — a destination his oldest son picked as he heads off to study architecture. From Gaudí's Sagrada Família to a flamenco tablao, high-speed trains, beach towns, and Lisbon's tile shops, here's an honest trip report with real tips you can use to plan your own visit. What we cover this week: The route: flying Aer Lingus out of Pittsburgh with a 12-hour Dublin layover each way, then Barcelona (3 nights) → high-speed train to Madrid → train to Seville → local train to Cádiz → back to Seville → a flight to Lisbon, plus a day trip to Cascais. Barcelona: an 8-hour guided tour, an hour inside Sagrada Família (and why the light, the facades, and the windows are unlike any cathedral you've seen), the Gothic Quarter, a Picasso mural, Park Güell, the harbor cable car, neighborhood markets, and a breathtaking live flamenco show. Riding the rails: what first class on Spain's high-speed trains is really like (lounge, meal service, roomy seats) and the fields of sunflowers "as far as the eye can see" between Madrid and Seville. Madrid: a tuk-tuk tour on a 104°F record-heat day — including the AI-translation guide snafu — and how to roll with it when a tour doesn't go to plan. Cádiz: a relaxing beach-town break, a spa hotel with a full thermal suite and 75-minute massages for about $100 each, and one of the oldest continuously inhabited cities in Western Europe. Lisbon & Cascais: the cleanest city of the trip, the designer-lined Avenida da Liberdade, a rooftop-pool hotel, hunting down handmade Portuguese pottery and antique tiles, and cliff divers at a beautiful beach town 40 minutes away. Real-world tips: Spain's excellent public transit, the current euro exchange rate (about $1.15 to €1), walking 75 miles over nine days, and Sharpie's planning method — book the big-ticket sights, then fill the gaps with finds along the way. Thinking about a trip like this? Sharpie plans Europe travel for Mouse Chat's travel agency, Pixie Vacations — free planning, no booking fees. Get a free, no-fee quote at https://pixievacations.com/get-a-quick-vacation-quote/ or call 678-815-1584. Prefer to see Spain and Portugal by ship? You can browse and book Mediterranean and Atlantic cruises through our online booking engine at https://cruise.pixievacations.com. Listen and subscribe: Apple Podcasts https://podcasts.apple.com/us/podcast/mousechat-net-family-travel-podcast-disney-universal/id395503030 | Spotify https://open.spotify.com/show/1ujpZQKOnCuKcgArpnBQKQ. Questions or comments? Email comments@mousechat.net.

This week on JT's Mix Tape #88, the crew responds to the growing controversy surrounding the Book of Enoch after comments made by Wes Huff about a viral clip discussing Enoch, the Ethiopian canon, and the early Church Fathers.We dive into: • Why Jude quotes Enoch • What Tertullian actually wrote • The Ethiopian canon • Sola Scriptura • Fair use and content creation • Ancient giants • Dead Sea Scrolls • Modern art and the CIA • Hidden steamboats beneath Kansas • The strange Nuremberg sky battle • Conspiracy culture vs legitimate research • Why humility matters when studying history Whether you agree or disagree, our goal is to encourage thoughtful discussion while pointing people back toward Christ.

Join us this week as we sit with Uriel Landeros a.k.a CONQUISTA and talk about growing up, the punk scene, graffiti, playing rugby, conspiracies, stenciling over the Picasso and much more! Also, shoutout to Four Corners Brewing Co. for providing the World Cup Soccer Sample Pack of brews. Keep an eye out for them! Follow Conquista here: https://www.instagram.com/uriel_landeros_conquista/ https://www.conquistacompany.com/

Can the work outlive the man enough to forgive him? Frank Lloyd Wright designed the homes that quietly shaped how millions of Americans live — and he was, by nearly every account, a genuinely difficult person to love. David (Black Sheep Makery, Big Dudu, Dudetunes) joins me to trace Wright's life from a childhood box of wooden blocks to Fallingwater, the Guggenheim, and a tragedy at his own home that killed seven people. We use his story to ask a bigger question: when the art is this good, does the artist's life even matter — and where do we draw that line today? Drawings from Frank Lloyd Wright, the early period mentioned throughout the episode. (00:00) - Intro Essay: The Artist's Canvas Is How You Feel (05:53) - Show Open & Guest Welcome (09:32) - Who Was Frank Lloyd Wright? (10:50) - Born in Wisconsin, Died the Year Before Kennedy (14:03) - Fired From Adler & Sullivan for Moonlighting (16:32) - The German Book That Made Him Famous (18:29) - Organic Architecture, Prairie Style, and Designing Every Doorknob (22:53) - Japan, Zen, and the Tokyo Imperial Hotel (29:07) - Concrete, Steel, and Fallingwater (31:03) - The Affair, the Refused Divorce, and Taliesin (34:04) - The 1914 Taliesin Murders (41:45) - How Wright Invented the American Split-Level (48:16) - Beautiful but Leaky: Art vs. Function (53:50) - Picasso, Van Gogh, and the Myth of the Difficult Artist (58:17) - Where Is the Paris of Today?

A new week means new questions! Hope you have fun with these!Which bay forms most of the west coast of France and the northern coast of Spain?What are you doing if you're dining alfresco?Which color can the human eye distinguish more shades of than any other color?Europa was carried to Crete by Zeus in the form of a what?Which amendment guarantees the right to a speedy and public trial?Picasso at the Lapin Agile was the first published original play written by which Wild and Crazy Guy?What is the name of the AI character who helps the player, who often assumes the role of Master Chief in the Halo series?The sequence of numbers where each number is the sum of the two preceding is known as what?In "The Diary of a Young Girl" aka "The Diary of Anne Frank" what did Anne Frank call her diary as she wrote in it?Kate Pierson joined R.E.M. on the song "Shiny Happy People" and is known for being a singer of which band?Which titular fictional character coaches the also fictional team AFC Richmond?Along with Gondwana, what is the name of the ancient continental landmass that included North America, Europe, and most of Asia?Who was the first German model to become a Victoria's Secret Angel?During the revolutionary war, which leader had his statue in New York toppled and later melted into bullets?Kingston is the capital and largest city of what country?The Man Who Would Be King stars Michael Caine and which other actor in the leading roles?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

durée : 01:59:57 - Les Nuits de France Culture - par : Albane Penaranda - En quoi le voyage et la confrontation avec d'autres cultures ont-ils nourri la création artistique à l'aube du XXe siècle ? En 1967, Pierre Sipriot proposait un état des lieux des influences orientales et africaines dans les œuvres de Pierre Loti, Kipling, Picasso ou du compositeur Albert Roussel. - équipe : Mathias Le Gargasson, Antoine Dhulster, Rafik Zénine, Vincent Abouchar, Emily Vallat, Hassane M'Béchour, INA Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Life in the Peloton is proudly brought to you by MAAP   I don't know about you, but when I think about the Tour de France I hear one man's voice. A legend of commentary who's currently covering the Grande Boucle for the 53rd time, I am absolutely gee'd up to bring you my interview with the one and only Phil Liggett.   Phil really has seen it all from his early days as a wannabe racer through to finding the microphone, and a lifetime of calling the Tour de France and some of the biggest cycling moments in the last half century. He has spanned the careers of so many riders from Hinault and Lemond through the Armstrong years up to Pogačar and Vingegaard, and has got endless stories to tell.   This episode, which I recorded with Phil in person back at the Tour Down Under in January, was a real privilege. I've tried my hand and commentary, and it was amazing to hear Phil explain the art of calling a bike race. I learnt so much from him - it's like asking Picasso how to paint, Shane Warne how to spin bowl, or Pogi how to win a bike race; he is the absolute master of the craft. We even had a go at re-voicing the iconic finish to the 2016 Paris-Roubaix - you have GOT to hear that. An absolute once in a lifetime moment for me!   Guys, this episode is a really special one. We speak about so much - from Phil's perfect partnership with the late great Paul Sherwen, how commentary has changed throughout his career, and how his job is to - as he puts it - “stop the old lady from going to make a cup of tea”.   What better man to have on the pod during the Tour de France than this absolute icon. Get yourself comfortable and enjoy this chat with the master of the mic - Mr Phil Liggett.   Cheers, Mitch     This episode is bought to you by Saily and SHOKZ Get an exclusive 15% discount on Saily data plans! Use code PELOSURF at checkout. Download the Saily app or go to https://saily.com/pelosurf If you're after the best sports headphones - be it for cycling, running, or even swimming - get across to SHOKZ's website, and use the code LITP enjoy a cheeky discount for being a LITP listener - https://bit.ly/4skq7lK  

Part Two: Stephan Bajaio on Digital VisibilityAnika continues her conversation with Stephan Bajaio, diving deeper into what it really means to lead people in a way that outlasts you. Moving beyond business strategy, this episode explores the human side of leadership—passion, failure, servant leadership, mindfulness, and the balance between "vibe" and "logic" that defines sustainable team culture.In this conversation, Stephan shares his framework for leadership rooted in four core beliefs, his daily Tefillin practice and gratitude ritual, why Performance Improvement Plans are misused as firing mechanisms, and how parenting a daughter influences his approach to building confident, empowered teams.In This EpisodeThe four pillars of leadership: passion, failure, servant leadership, and empowermentWhy passion can't be taught—only discoveredCreating environments where vulnerability and failure are welcomed, not punishedFlipping the org chart: servant leadership as a behavior, not a positionHow to empower people in small doses and build their confidence through affirmationThe myth of PIPs as firing tools—and how they actually transform performanceWhy not every high performer should become a managerThe "human being" vs. "human doing" trap and how to escape itDaily mindfulness practices: Tefillin, gratitude, and intentionalityVibe + Logic: the duality that defines sustainable businessWhy your company culture is defined by how people leave, not how they arriveKey Timestamps02:28 – The four pillars of leadership begin04:01 – Failure is not only welcome, it's expected08:58 – Leadership is a behavior, not a position13:09 – Empower your people first—in small doses with follow-up affirmation18:51 – PIPs aren't meant to fire someone—that's corporate nonsense31:00 – The "human being" vs. "human doing" trap and daily mindfulness practice43:06 – Building confident, independent women and team members44:40 – Vibe + Logic: the duality that defines sustainable business50:00 – Stephan to participate in upcoming AI and workforce skills podcastsKey Insights & TakeawaysInsight 1: Passion Is Discovered, Not TaughtThe most valuable asset in leadership can't be developed through training programs. It has to exist within people. Stephan's approach to hiring is built on understanding what motivates people—what gets them up in the morning—because that's the fuel that carries them through failure and challenges.Insight 2: Leadership Is a Behavior, Not a PositionThe title doesn't make you a leader. Your actions do. Servant leadership means flipping the traditional org chart so your job becomes supporting those around you, not commanding from above. This shift in mindset changes everything about how teams respond to you.Insight 3: Empower in Small Doses, Then AffirmDon't just hand someone a big responsibility and walk away. Give them leadership opportunities in manageable pieces, then follow up with genuine affirmation. The power of a thank you is shocking—people often overlook how much courage it took someone to step outside their comfort zone.Insight 4: PIPs Should Improve, Not FirePerformance Improvement Plans are routinely misused as mechanisms to force people out. When done right, PIPs actually transform performance. Stephan has seen people on PIPs become star players because they understood the company wasn't trying to get rid of them—it was trying to help them succeed.Insight 5: Your Culture Is Defined by How People LeaveThe way employees exit your company says more about your culture than how you bring them in. If you're not celebrating the alumni who've gone on to bigger things, you're missing the point. People aren't assets to trap—they're people to empower.Insight 6: Being Requires Daily PracticeIt's too easy to go through life doing without ever being present. Stephan's two-year Tefillin practice—binding, prayer, gratitude—forces him to stop and recognize what he's grateful for. These rituals look different for everyone, but what matters is the intentionality.Insight 7: Vibe + Logic Is the Balance That WorksLeadership requires both creativity ("vibe") and technical rigor ("logic"). You need both—a Picasso without a frame is worthless, and a frame without art is empty. The key is recognizing where you're weak and building complementary teams that offset your blindspots.Resources & Links MentionedVibeLogic.com – Stephan's digital strategy firmConductor – The enterprise SEO platform he co-foundedPractical Pedagogy: The Art of Teaching for the Future of Work – New PodcastAbout Stephan BajaioStephan Bajaio is a leadership strategist and founder of VibeLogic, a digital strategy firm focused on making businesses visible to the customers who need them most. With 25 years in digital marketing, he co-founded Conductor (scaled to 65 people serving Fortune 500 brands), navigated a WeWork acquisition that took the company to $500M+ valuation, and stepped away when the timing was right. He's also a father to a five-year-old and a practitioner of daily mindfulness through Jewish rituals and gratitude practice.Connect with StephanLinkedInVibeLogic.comYouTube: VibeLogicSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Em 7 de março de 2004, o cineasta Jorge Furtado fez o que ele fazia quase todas as manhãs: pegou pra ler a edição da Folha de S.Paulo daquele dia. A maior foto da capa mostrava um quadro pendurado numa parede com a imagem de uma mulher sentada de braços cruzados. O Jorge reconheceu a obra logo de cara: era a “Mulher em Branco”, do Picasso. A matéria dizia que aquele “desenho de Pablo Picasso” tinha sido encontrado quase por acaso numa sala do INSS, em Brasília. O que era bem estranho… considerando que a “Mulher em Branco” pertence ao Metropolitan Museum de Nova York. Assim começou a saga do Jorge Furtado com aquele que ficou conhecido como “o Picasso do INSS”. Anos depois, a história inspirou a produção do documentário “O Mercado de Notícias”. E agora, em 2026, ela inspirou a repórter Bia Guimarães a embarcar numa espécie de caça ao tesouro: afinal, de onde veio e que fim teve o “Picasso do INSS”? Membros do Clube da Novelo podem ouvir os episódios do Rádio Novelo Apresenta antecipadamente, além de ter acesso a uma newsletter especial e a eventos com a nossa equipe. Quem assinar o plano anual ganha de brinde uma bolsa da Novelo. Assine em ⁠⁠⁠https://clube.radionovelo.com.br Inscreva-se no canal da Rádio Novelo no YouTube: https://www.youtube.com/@RádioNovelo Siga a Rádio Novelo no Instagram: https://www.instagram.com/radionovelo/ -- Cansado de rolar a tela e não encontrar nada para assistir? A Rádio Novelo agora é parceira da MUBI, um serviço de streaming com curadoria especializada em cinema. De 26 de junho a 6 de julho, você garante 70% de desconto por seis meses de assinatura. Acesse mubi.com/novelo e assista ao melhor cinema. -- Esqueça o cartão de crédito: com a Wise, você paga em mais de 40 moedas com a taxa de câmbio comercial — a mesma que você vê no Google. Converta e mantenha seu dinheiro na moeda de destino e pague sem se preocupar com cotação ou tarifas. Ah, e você ainda pode sacar em mais de 3 milhões de caixas eletrônicos. Quem sabe vai de Wise. ⁠Baixe o app hoje⁠.  -- Insider: tecnologia aplicada à rotina – peças que desamassam no corpo, facilitam a evaporação do suor e seguem confortáveis por horas. Utilize o cupom RADIONOVELO e tenha 15% OFF na 1ª compra e 10% OFF nas próximas – e ainda soma com os descontos do site. ⁠https://creators.insiderstore.com.br/RADIONOVELO Palavras-chave: artes plásticas, cinema, jornalismo, fake news

Jonny and Richard fear for the survival of a car icon. Also in this episode, the origin of the name Urus, strange trousers at a music festival, car problems in hot weather, Fast & Furious at 25, memories of Millbrook proving ground, choosing where to live based on wind, car colour and insurance costs, and another cracking car plucked from Car & Classic. For early, ad-free episodes and extra content go to patreon.com/smithandsniffTo buy merch and tickets to live shows go to smithandsniff.comThis episode is sponsored by Car & Classic https://candc.li/uc1yqz Hosted on Acast. See acast.com/privacy for more information.

Episode: 1595 In which Alfred Stieglitz and 291 anticipate Modern.  Today, Stieglitz and photography, art and reality.

A Note from James:Mark Pincus is one of the true OGs of the internet. You probably know him as the founder of Zynga, the company behind FarmVille, Zynga Poker, and Words With Friends. Zynga was eventually acquired by Take-Two in a transaction valued at approximately $12.7 billion. Before Zynga, Mark started Tribe, one of the first social networks—before MySpace and Facebook. He has spent more than 25 years building, failing, and studying what gets millions of people to click, play, share, and come back. His new book, Life at the Speed of Play, inspired me to start coming up with new business ideas while we were still recording.What I really love is how Mark teaches people to copy like a master without looking like a copycat. He has a framework called “Proven–Better–New.” Start with something that has already been proven. Make it obviously better. Then isolate the new idea you want to test. It's one of the best systems I've heard for creating products people actually want.We talk about the early days of Facebook and MySpace, the failure of Tribe, the gaming industry, consumer psychology, AI coding, and how agents could eventually network and work for us while we're doing something else.I loved talking with Mark. I was still thinking about this conversation afterward—and I'm literally building businesses based on what I learned. His new book is called Life at the Speed of Play. Listen to this episode, and then read the book.Episode Description:Most founders begin with an idea and then spend months—or years—trying to prove that people want it. Mark Pincus thinks that process is backward.At Zynga, Mark's teams built “failure machines”: simple systems that allowed them to test hundreds of concepts before writing the code. They put unfinished ideas in front of real users, watched what people clicked, and refused to build anything until the demand was obvious. The objective wasn't to avoid failure. It was to make failure fast, cheap, and useful.Mark explains the framework behind that process: Proven–Better–New. First, study an existing success down to every screen, click, and design decision. Then identify one improvement that current users would immediately recognize as better. Only after that should a team add the unproven idea—the part most likely to fail.James and Mark also examine the problems facing today's consumer entrepreneurs. AI has made software easier to build, but distribution has become harder. People aren't searching for new apps, established platforms restrict organic growth, and algorithmic reach isn't the same as users actively sharing something with friends.Mark uses the failure of his early social network, Tribe, to explain why virality is not enough. Tribe grew quickly but lacked retention and trust. He ignored the communities users loved because they didn't match the business model he had already chosen. That painful mistake became the foundation for much of his later product philosophy.The conversation ends with Mark's current experiments: personal AI agents modeled after members of his family, a proposed work network built specifically for agents, an enterprise AI company called Hivemind, and the difficult decision to end a four-year passion project without abandoning the instinct behind it.This is a practical conversation about testing ideas, separating instinct from ego, learning from the past, and killing the wrong product before it consumes the right opportunity.What You'll Learn:How to build a failure machine: Test headlines, offers, videos, and fake doors before investing in a finished product.How to apply Proven–Better–New: Begin with a proven behavior, make one unmistakable improvement, and isolate the risky innovation.Why distribution is now harder than development: AI can generate a prototype quickly, but it cannot guarantee attention, trust, or adoption.Why Tribe failed despite rapid growth: Virality without retention, safety, and alignment with user behavior does not create a lasting network.How to copy without becoming a copycat: Study successful products at the pixel level, preserve what works, and innovate only where it matters.When to abandon an idea: Preserve the underlying instinct, but stop funding the particular expression of it when the evidence turns against you.How AI agents may change networking: Agents could eventually search for opportunities, exchange work, build reputations, and bring useful leads back to their users.Timestamped Chapters: [02:00] Finding the “OMFG” Moment [02:58] A Note from James [05:00] Build a Failure Machine Before Building a Product [06:25] Testing Demand With Fake Doors and Broken Links [08:08] Writing Copy That People Actually Notice [10:52] Test More Ideas in a Week Than the Industry Tests in a Year [11:53] Why Neglected Products Become Innovation Labs [13:26] How Mobile Apps Slowed Product Experimentation [15:09] Can AI Bring Rapid Testing Back? [17:08] Why Consumer Technology Feels Uninvestable [18:38] The 90/10 Rule for Investable Platforms [20:08] Why Nobody Downloads New Apps Anymore [21:20] Franchises, “Spicy New,” and Healthy Platforms [23:21] The Internet's Lost Cocktail Party [27:58] Why Tribe Failed While Facebook Won [30:26] Virality Without Trust or Retention [31:31] Ignoring What Tribe's Users Actually Wanted [33:22] Facebook, Raya, and Designing for Trust [35:03] Social Networks as Lead-Generation Engines [37:12] Facebook, Instagram, and the App Nobody Knew It Wanted [37:51] Net Promoter Scores and the Feeling of Quitting a Drug [40:25] Algorithmic Virality vs. People Sharing With Friends [42:00] Building Products That Help People Create [43:47] What Entrepreneurs Should Build With AI [44:54] The Proven–Better–New Framework [47:12] What “Obviously Better” Actually Means [48:25] Why “All New Fails” [50:23] Zynga Poker and the Power of Removing One Click [52:00] What AI Does Well—and Where Humans Still Matter [54:25] Picasso, Slack, and Copying the Past [55:11] Adding Fun to Boring Enterprise Products [57:39] The Moral Arbitrage of Killing Your Ego [57:58] How to Copy Without Looking Like a Copy [59:10] Why Old Internet Mechanics Keep Returning [01:00:16] Anonymous Social Apps With an AI Twist [01:01:17] Don't Invent a New Business—Reinvent a Big One [01:02:00] Test 20 Variants Before Building One [01:02:58] Mark's Frustrating Experiments With AI Coding [01:05:29] Creating a Personal Team of AI Agents [01:07:57] Killing a Four-Year Passion Project [01:09:29] The “Social Membrane” of the Agentic Internet [01:09:57] Building a Work Network for AI Agents [01:12:16] Hivemind and the Human Side of Enterprise AI [01:13:52] Missing Twitch—and Knowing Your Zone [01:15:06] Why the Gaming Industry Still Isn't Social Enough [01:16:30] Chess Ratings, Competition, and Mark's Daughter [01:19:19] Writing Life at the Speed of Play [01:21:18] Don't Chase Every New Technology Race [01:22:05] Final ThoughtsAdditional Resources:Mark Pincus and the BookLife at the Speed of Play — official websiteLife at the Speed of Play — HarperCollins — published June 23, 2026. Mark Pincus on X — the account Mark recommends for updates on his agent-network experiments. Mark Pincus on LinkedIn Mark's interview about open-sourcing Stem Studio Zynga, Games, and Product ExamplesZynga's company history — covers its launch as a Facebook poker project and the development of FarmVille, CityVille, and Words With Friends. Words With Friends FarmVille Take-Two and Zynga acquisition announcement — the transaction carried an enterprise value of approximately $12.7 billion. Tribe.net history — the early social network Mark analyzes as a major product failure. Raya — the private community Mark discusses as an example of building trust through curation. Grow a Garden on Roblox See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The most influential poster in the history of art was an ad for a play. It was designed by a broke, unknown illustrator who only got the job because he was the one stuck working over the holidays. His name was Alphonse Mucha, and that single commission — a rush job nobody else wanted — turned him into the father of Art Nouveau. He didn't sit in a studio and find his direction. A customer handed it to him. Want to join Patrick for a live webinar? He hosts one every Monday, Wednesday, and Friday. Register here: asf.today/webinar That's the heart of this episode: a commission isn't a compromise. It's an idea-generation machine. A client drags you somewhere you'd never have chosen on your own — and every so often, that detour becomes your entire career. It happened to Mucha. It happened to a portrait painter named George Stubbs who took a few horse commissions and ended up the greatest equine painter who ever lived. It happened to a studio photographer named Dorothea Lange the day a government assignment sent her into the migrant camps. But before we get to the good news, we have to clear out the lies. The longer you spend in this business, the more you realize the "sacred truths" of the art world are mostly nonsense — and most of them are really just hobbyist rules wearing a business suit. (If you've heard me draw the hobbyist-vs-business line before, this is where it earns its keep — same line that runs under The Long Game.) In this episode: The Christmas shift that invented Art Nouveau — how Mucha got the job nobody wanted and never looked back Six "sacred truths" of the art business that are complete nonsense — and the one thing wrong with every single one of them "You need a niche before you can start" — why you don't pick your niche; the work reveals it "Good art sells itself" — the $128 of thrift-store junk that resold for $3,612 on stories alone, a $3.5M violin that earned $32 in a subway, and the painter who went from unsold to $2.5 million without changing a brushstroke "Never discount your work" — why that rule is real, why it isn't yours, and what the galleries who preach it actually do behind closed doors The line in the sand: hobby or business? Drucker said a business has exactly one purpose — to create a customer — and in that equation, you don't get the last word. The market does. "Nobody bought it, so I'm a failure" — the lie that makes good artists quit, and why Picasso died holding roughly 45,000 of his own unsold works Why constraints beat the blank canvas — Stravinsky, and the bet that produced Green Eggs and Ham in 50 words The honest catch: when a commission becomes a cage instead of a doorway, and how to tell the difference This week's homework: take the one commission you'd normally turn down — the weird request, the subject you'd never choose, the client who wants something slightly off from your usual. Say yes to it. Then watch where it drags you. Reply or DM me what you learned — I read every single one. Resources mentioned: Art Storefronts — the storefront engine for working artists The Mucha Foundation — the Gismonda poster and the birth of Art Nouveau Significant Objects — the experiment that turned $128 of junk into $3,612 with nothing but stories Pearls Before Breakfast — the Washington Post's Pulitzer-winning Joshua Bell subway story Freakonomics: The Hidden Side of the Art Market — how art is really priced (and why prices "only go up") Related episodes: The Gallery Test — Should Artists List Prices on Their Website? The Long Game — Why Your Website Will Still Be Working in 2055 POD and Samples — What Wyland and Gray Malin Actually Do 20 Ways to Grow Your Email List as an Artist — hobbyist or business, the honest cut So here's the takeaway. If you're a hobbyist, make whatever you want, forever, and be happy — there's no shame in it. But if you want a business, stop waiting for the market to reward your purity, because it never will. Go meet it. Say yes to the commission, the weird job, the thing you'd never have chosen — because that yes creates a customer, which is the only thing that makes you a business, and it just might drag you, like it dragged Mucha off that holiday shift, straight into the work you were put here to make. Stay Up To Date With The Latest https://linktr.ee/artmarketingpodcast

A Note from James:If I could tell my children to read one post of mine, it would be this post.Influence is how they will navigate a world of uncertainty.Robert Cialdini is the most influential person in the world. And by that I mean, he wrote the book Influence, which sold 3 million copies and defines the six critical aspects of all influence.Now he has a new book, Pre-Suasion, going 10x deeper into the concepts of persuasion. I got him on my podcast so I could ask the 1,000 questions I have.Small story from the book:If you name a restaurant “Studio 97” instead of “Studio 17,” people are more likely to tip higher.If you ask a girl for her phone number outside a flower store, triggering feelings of romance, she is more likely to give it to you than if you ask her outside a motorcycle store.And 500 other stories.The environment is just as important as what you say.Before the podcast began, I gave him a book as a gift: The Anxiety of Influence, a history of poetry.What would poetry have to do with influence and marketing?In all art, since the beginning of time, artists have built on the work of the artists of the generation before them.Beethoven depended on a Mozart to be a Beethoven. Picasso depended on a Cézanne. Without Michelson, there would be no Einstein.But poets, for some reason, would deny being influenced.“I never even read Ezra Pound,” shouted one poet at a critic.Poets want to be seen as original.Nobody is 100% original.This is the anxiety of influence.Almost all of our decisions, and even our creativity, are outsourced to the people around us who influence us: peers, teachers, religion, parents, bosses, etc.Our personality is our own particular mishmash of influences.How we deal with that anxiety, how we recognize the influences, learn from them, and build from them, is the birth of all of our creativity.Let me summarize the seven aspects of influence:Reciprocity: If you give someone a Christmas card, they will want to return the favor.Likability: Make yourself trustworthy. For instance, outline the negatives of dealing with you.Consistency: Ask someone for a favor. Now they will say to themselves, “I am the type of person who does James a favor.”Social Proof: If you are trying to get someone to do X, show them that “a lot of your peers do X.” For instance, if you are at a bar and you are a guy trying to meet women, bring your women friends and not your guy friends with you.Authority: “Four out of five dentists say…”Scarcity: “Only 100 iPhones left at this store!”Unity: You and I are the same because of location, values, religion, etc.I've used each of the above in business.They work.They will make you money.The entire purpose of language is to influence.We are not strong animals. We are weak.The language of influence saved us.Probably a word like “Run!” was the first word spoken.A word of influence.And it worked.I'm still running from the things I fear.So speak to influence.Don't speak to call a flower yellow.Speak to breathe spirit into an idea, to be enthusiastic, to convey emotion, to influence.This is the only way to have an impact with your unique creativity.I gave Robert the book as a gift — reciprocity — assuming we would have a great podcast.And we did.But then I thought later, I can't even remember how Robert got on my podcast.I highly recommend his book in the podcast and even in this post.As he got into his car after the podcast in order to go to his next interview, I started thinking:“Hmmm, who influenced who?”Episode Description:Robert Cialdini wrote the book on persuasion — literally. His classic Influence became one of the defining books on why people say yes, how decisions get shaped, and why the smallest cue in the room can change the outcome of a conversation.In this episode from the archive, James talks with Cialdini about Pre-Suasion, the idea that persuasion starts before the actual pitch. It begins with what people notice, what they feel, what is in the environment, and what frame has already been set before the first real ask is made.They talk about flower shops, restaurant names, voting booths, Warren Buffett's shareholder letters, Anwar Sadat's negotiation instincts, and the rabbi who helped save thousands of lives with one sentence. But the episode is not just about marketing. It is about how people make decisions under uncertainty — and how to use influence ethically, whether you are asking for a job, building a business, negotiating a deal, writing a sales letter, or trying to become more trusted.What You'll Learn:Why persuasion often begins before the message — and how small cues in the environment can make people more receptive.How Cialdini's original six principles of influence work: reciprocity, consistency, social proof, scarcity, authority, and liking.Why Cialdini added a seventh principle, unity — the feeling that “we are the same” — and why it can be even stronger than liking.When to use social proof versus authority, and how to decide which kind of evidence matters most in a given situation.Why admitting weakness first can build trust, and how Warren Buffett uses honesty as a persuasion tool instead of a liability.Timestamped Chapters:[00:00] Introduction and episode preview[01:25] Interview begins — James introduces Robert Cialdini and Pre-Suasion[03:12] The flower shop study: why context changes the answer before the question is asked[05:48] Valentine Street and the hidden power of unrelated cues[06:42] Wine stores, voting booths, and fluffy cloud mattresses[08:10] Are humans irrational, or are shortcuts necessary?[10:17] How the pictures on your wall can change what you write[11:36] The six — now seven — principles of influence[12:00] Reciprocity: the Hare Krishna flower example and the power of personalized gifts[16:40] Consistency: Anwar Sadat, Henry Kissinger, and giving people a reputation to live up to[19:30] Cialdini's undercover research with sales organizations[23:30] Social proof: medical no-shows, restaurant menus, and what happens when a message backfires[26:43] Social proof as feasibility: “people like me can do this”[29:07] Authority: when expert endorsement beats crowd validation[33:55] Why companies lose with better products when they fail to frame the decision properly[35:10] Building authority from zero by using honesty and scarcity[37:05] The Avis “We're number two” campaign and the trust value of admitting weakness[38:24] Warren Buffett's shareholder letters and the persuasive power of leading with mistakes[41:30] Unity: Cialdini's seventh principle of influence[44:24] The rabbi, the Japanese tribunal, and the sentence that saved a community[48:30] Applying unity in job interviews, dating, and negotiations[51:10] Loss aversion and how uncertainty changes persuasion[55:00] Why long sales letters can outperform short ones[55:30] Cialdini's practical framework: find what is true, direct attention to it, then make the case[59:00] Fake scarcity and why false urgency destroys trust[65:00] Closing thoughts on ethical influence and genuine specificityAdditional Resources:Robert Cialdini — Influence: The Psychology of Persuasion — Cialdini's classic book on the core principles of persuasion and compliance. Robert Cialdini — Pre-Suasion: A Revolutionary Way to Influence and Persuade — the follow-up book discussed throughout the episode, focused on what happens before the persuasive message itself. Berkshire Hathaway Shareholder Letters — referenced in the episode as a real-world example of trust-building through candor and weakness-first communication. Daniel Kahneman and Prospect Theory — Cialdini references the role of loss aversion and uncertainty in persuasion; Kahneman received the 2002 Nobel Memorial Prize in Economic Sciences for integrating psychological research into economic decision-making. Chiune Sugihara — the Japanese diplomat connected to the story Cialdini uses to explain unity and shared identity. The Avis “We're Number Two” Campaign — discussed as an example of turning a weakness into credibility by being honest before making the positive case.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In the summer of 1937, some of the 20th Century's most famous artists, writers and photographers were holidaying in the south of France. They included artist Pablo Picasso, photographer Lee Miller, poet Paul Éluard and the painter Man Ray.The group were part of the Surrealist movement – a style of art inspired by dreams and hidden thoughts that can look strange and bizarre - and one of their most recent converts was artist Eileen Agar. Through a 1985 BBC interview with Eileen, digital archivist Jonathan Charlton tells the story of that summer in an episode produced by Jane Wilkinson.Eye-witness accounts brought to life by archive. Witness History is for those fascinated by and curious about the past. We take you to the events that have shaped our world through the eyes of the people who were there. For nine minutes every day, we take you back in time and all over the world, to examine wars, coups, scientific discoveries, cultural moments and much more. Recent episodes explore everything from how the Excel spreadsheet was developed, the creation of cartoon rabbit Miffy and how the sound barrier was broken.We look at the lives of some of the most famous leaders, artists, scientists and personalities in history, including: the moment Reagan and Gorbachev met in Geneva, Haitian singer Emerante de Pradines' life and Omar Sharif's legendary movie entrance in Lawrence of Arabia.You can learn all about fascinating and surprising stories, like the invention of a stent which has saved lives around the world; the birth of the G7; and the meeting of Maldives' ministers underwater. We cover everything from World War Two and Cold War stories to Black History Month and our journeys into space.(Photo: Roland Penrose, Ady Fidelin, Picasso and Dora Maar, Cote d'Azur, France 1937. Credit: Lee Miller Archives)