Podcasts about Joon

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

Jason SweetTooth Williams is a singer, an actor and a writer. He's performed in cabaret, on TV, in films and on Broadway. He was in the Broadway musical “Be More Chill”, and regionally and Off Broadway in “Freaky Friday”, “Benny and Joon”, “Bloodsong of Love”, “The Untitled Unauthorized Hunter S Thompson Musical” and “Once Upon a Mattress”. And he's an original member of Joe Iconis and Family. On screen he appeared in “WeCrashed” and in episodes of “Only Murders in the Building”, “BULL”, “FBI”, “The Marvelous Mrs Masiel” and “American Horror Stories”.  My featured song is “The Buzz”, my latest single. Spotify link. —------------------------------------ The Follow Your Dream Podcast:Top 1% of all podcasts with Listeners in 200 countries! Click here for Start Here Click here for All Episodes  Click here for Guest List  Click here for Guest Testimonials Click here for Pillars Click here for Robert's Project Grand Slam Click here to Subscribe  Click here to receive our Email Updates Click here to Rate and Review the podcast —---------------------------------------- ROBERT'S NEWEST RELEASE:“THE BUZZ” - Ft. Darius de Haas (vocals) and Dave Eggar (Celo). Short, Sweet and Totally Different CLICK HERE FOR OFFICIAL VIDEO CLICK HERE FOR ALL LINKS —-------------------------------------- Audio production: Jimmy RavenscroftKymera FilmsConnect with the Follow Your Dream Podcast:Website - www.followyourdreampodcast.comFollow Robert's band, Project Grand Slam, and his music:Website - www.projectgrandslam.com    

A note about the work "Empty Summits" from An Chang Joon: "It was a strange confluence of events that made the essay possible, as the first version of this essay was much more of a revisionist history piece, with a focus on Shohei Imamura's film, as well as the folklore of ubasute, and how that warped into the myth of the goryeojang. As I was writing this for my MFA program, my grandfather in Korea passed away. Since I was in Baton Rouge, I was the only person in my family who couldn't make it to the funeral. When I rewatched the film to finish writing about it, I found myself stuck on scenes that weren't relevant to the topic I had in mind, until I realized why I couldn't stop watching (and crying at) certain points of the movie. I rewrote the essay, accepting that there was no way to write it without weaving my family into it. My relationship with my family is complicated and difficult. My family is convinced of an afterlife and believes—even finds comfort—in the idea that our grandfather is in a better place. I am unconvinced about this. But I dearly wish I could be—and am saddened to realize I am not."

Joon Sung Park is the Founder and CEO of Simile, the AI simulation company building foundation models of human behaviour; allowing companies to test how real people may think, decide and act before making a decision in the real world. Simile has now raised $300 million in total, including a $200 million Series B announced last week at a $2 billion valuation, led by Greenoaks and Index Ventures. AGENDA: 00:00 We Will Pay $100M for a Single Query on Some Models 10:00 Why Stock Markets May Not Exist in 5 Years Time 15:00 The Best Companies All Have Unique Data Acquisition Strategies 19:00 The Best AI Companies Have Clear and Fast Reward Functions 24:00 How We Sign Fortune 500 Companies for $10M Contracts in Weeks 32:00 Does Similie Kill Kalshi and Polymarket? Prediction vs Changing the Future 42:00 Inside Similie's $300M Raise; What Every Founder Needs to Know    

Episode No. 767 features artist Young Joon Kwak and curator Laura Igoe. Kwak is featured in the 2026 Whitney Biennial at the Whitney Museum of American Art, New York. Curated by Drew Sawyer and Marcela Guerrero with assistance from Beatriz Cifuentes and Carina Martinez, it's on view through August 23. Kwak uses sculpture, performance, and collective action to explore bodily transformation, intimacy, and the politics of visibility. Through visually lush, often highly detailed sculptures, Kwak challenges traditional and dominant modes of representation while manifesting ways that trans and queer bodies might be seen. Kwak is the co-founder of Mutant Salon and lead performer in the electronic-dance-noise band Xina Xurner with Marvin Astorga. They have had solo exhibitions at museums such as the Berkeley Art Museum & Pacific Film Archive, University of California, Berkeley; the Leslie-Lohman Museum of Art, New York; and ARKO Art Center, Seoul. Kwak's work is in the collections of BAMPFA, the Crocker Art Museum, Sacramento; the Dallas Museum of Art; the Speed Art Museum, Louisville; and the Los Angeles County Museum of Art. Igoe is the curator of "The Crossing: Picturing the American Revolution" at the Michener Art Museum, Doylestown, Penn. The exhibition looks at how artists have represented Continental Army commander-in-chief George Washington's crossing of the Delaware River on Christmas night, 1776. Upon reaching the New Jersey side of the river, Washington and his troops would attack carousing Hessian mercenary soldiers fighting for the British, earning a pivotal victory for the Patriots. "The Crossing" is particularly interested in how artists have built on and 'refuted' Emanuel Leutze's famed 1851 Washington Crossing the Delaware, which is in the collection of the Metropolitan Museum of Art, New York. The exhibition is on view through January 10, 2027. As discussed on the program: Kwak's Glitter Mani Festo. Igoe was a guest on Episode No. 732, when she discussed her election to the Jenkintown, Penn. school board. Instagram: Young Joon Kwak, Laura Igoe, Tyler Green. Air date: July 16, 2026.

Fluent Fiction - Korean: Tea and Tradition: An Artistic Encounter in Seoul Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-06-19-07-38-20-ko Story Transcript:Ko: 사람들이 북적이는 서울의 한 복잡한 거리, 전통차를 마실 수 있는 고요한 차 한 잔의 집이 있다.En: On a bustling street in Seoul, where people are swarming, there is a serene place called cha han jan-ui jib where you can enjoy traditional tea.Ko: 이곳은 여름의 더위를 피하고자 하는 사람들이 들어오는 아름다운 공간으로, 목조 건축과 섬세한 종이등이 주위의 소란을 잊게 해준다.En: This beautiful space attracts those seeking to escape the summer heat, with its wooden architecture and delicate paper lanterns helping people forget the surrounding noise.Ko: 향긋한 차 냄새가 여름 바람과 섞여 사람들을 집안으로 이끌고 있다.En: The fragrant scent of tea mingling with the summer breeze draws people inside.Ko: 오늘은 단오절.En: Today is Danojeol.Ko: 사람들은 대문짝만한 관심을 가지고 차 한 잔의 집에 모여 전통 차 시음을 즐기고 있었다.En: People, with great interest, gather at cha han jan-ui jib to enjoy tasting traditional tea.Ko: 이곳에 한 젊은 예술가인 준이 들어왔다.En: Among them, a young artist named Joon arrives.Ko: 그는 그림에 대한 영감을 찾고자 한다. 최근 그는 자신의 작품에 뭔가 부족함을 느끼고 있었다.En: He is looking for inspiration for his painting, as he has recently felt something lacking in his work.Ko: 한편 같은 공간에 하라는 이름의 열정적인 교사가 있었다.En: Meanwhile, there is a passionate teacher named Hara who is in the same space.Ko: 그녀는 한국 전통을 지키고자 하는 마음으로 여기에 왔다.En: She has come here with the intention of preserving Korean traditions.Ko: 매일매일의 교직 생활에서 훌쩍 떠나 그녀는 차 한 잔의 집에서 평안을 찾으려고 했다.En: Seeking to escape her everyday teaching life, she finds peace at cha han jan-ui jib.Ko: 차 시음회는 시작되었고, 사람들은 다들 그 차의 깊은 향과 맛에 매료되었다.En: The tea tasting session begins, and everyone is captivated by the deep aroma and taste of the tea.Ko: 준과 하라는 우연히 나란히 앉게 되었다.En: Joon and Hara happen to sit next to each other.Ko: 처음에는 서로 어색했지만, 이내 하라는 준에게 단오의 전통과 중요성에 대해 설명하기 시작했다.En: Initially awkward with each other, Hara soon begins to explain to Joon about the traditions and importance of Dano.Ko: 하라의 이야기 속에서 준은 그동안 보지 못했던 한국 전통의 상징성과 예술성을 발견했다.En: Through Hara's story, Joon discovers the symbolism and artistry of Korean traditions that he hadn't seen before.Ko: 그들의 대화는 자연스럽게 흘러갔고, 차 향기 속에서 두 사람의 마음은 더욱 가까워졌다.En: Their conversation flows naturally, and amid the scent of tea, their hearts grow closer.Ko: 시음회가 절정에 이르렀을 무렵, 두 사람은 각자의 열정과 이야기를 나누며 서로에게 새로운 영감을 주고받았다.En: As the tasting session reaches its peak, the two share their passions and stories, inspiring each other anew.Ko: 준은 하라의 설명을 들으며 그의 작품에 새로운 시각을 더하게 되었다.En: Listening to Hara's explanations, Joon gains a new perspective for his artwork.Ko: 한편, 하라는 자신의 문화적 지식을 공유하며 자신감을 얻었다.En: Meanwhile, Hara gains confidence by sharing her cultural knowledge.Ko: 이들이 차 한 잔의 집을 나올 때, 준은 전통을 그의 예술에 자연스럽게 녹여낼 수 있는 방법을 찾았고, 하라는 자신의 경험을 다양하게 나눌 수 있는 힘을 가지게 되었다.En: When they leave cha han jan-ui jib, Joon has found a way to seamlessly integrate tradition into his art, and Hara feels empowered to share her experiences more diversely.Ko: 서울의 북적거리는 거리로 다시 나가면서, 그들은 서로의 생각과 문화에 대한 새로운 감사함을 느끼며 마음속 깊이 미소 지었다.En: As they return to the bustling streets of Seoul, they smile deeply, feeling a newfound appreciation for each other's thoughts and cultures.Ko: 준의 예술은 풍성해지고 하라의 문화적 자부심은 더욱 단단해졌다.En: Joon's art becomes richer, and Hara's cultural pride grows stronger.Ko: 그리고 그들의 이야기는 이제 막 시작되었다.En: And thus, their story has just begun. Vocabulary Words:bustling: 북적이는serene: 고요한architecture: 건축delicate: 섬세한fragrant: 향긋한mingling: 섞여gather: 모여tasting: 시음inspiration: 영감lacking: 부족함passionate: 열정적인intention: 의도preserving: 지키고자captivated: 매료되었다aroma: 향awkward: 어색했지만symbolism: 상징성artistry: 예술성naturally: 자연스럽게perspective: 시각integrate: 녹여낼empowered: 힘을 가지게appreciation: 감사함richer: 풍성해지고diversely: 다양하게seeking: 찾고자 하는escape: 피하고자surrounding: 주위의intent: 마음으로cultural: 문화적

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

Anger isn't always the problem; what we do with it is. In this message, Pastor Joon shares what God's Word teaches about handling anger in a healthy, biblical way. Instead of suppressing our anger or letting it control us, we're called to surrender it to God, slow down before reacting, and examine whether our anger is producing healing or causing damage. Thank you for enjoying this full service with worship and a life changing message from Radiant Church. We pray this moves you closer to Christ and encourages you. For more life changing resources, visit us at www.weareradiant.com. Subscribe to our channel: https://youtube.com/weareradiantchurch To give online: https://weareradiant.com/give/ View the sermon notes for this message here: https://notes.subsplash.com/fill-in/view?page=SJyOz1DZMx Spanish translation messages are available on our Radiant Church Español YouTube channel. Visit https://weareradiant.com/espanol to watch and subscribe. Moving people towards Christ, Community and Calling. This is the vision of Radiant Church, led by Pastor Aaron Burke and based in Tampa Bay, FL. —— Stay Connected Website: https://weareradiant.com Radiant Church Facebook: https://www.facebook.com/weareradiant/ Radiant Church Instagram: http://instagram.com/weareradiant/

TUNE IN AND SUBSCRIBE FOR NOTIFICATIONS - help get past 1k via the YOUTUBE channel! This in-person liminalstream was a long time coming as Illustrator, Writer and WtH Seer Eric J. Millarhas been the better engine of the longtime WE THE HALLOWED magick media machine. Those of you might have caught the fun, raucous livestream dispatched DIMMING ROOM, but here it has been transfigured and hauntomantically ressurrected into a maximalist broadckast featuring analog travel footage and videomancy from Revelator Rosz, as well as a brand new "lore score" of improvised audiomancy ritual ribbon musick performed in the attic altar space to sinew the newfangled psychick entry into the PRAGMAGICK cast pantheon that perfectly resembles the transpositional utility of morphing mediums to exalt new entries. WE THE HALLOWED is the antidote to this apocalyptic slop eon of the multi-media mediums with #somatickmagick #independentpublishing and more, and no matter the financial squalor or downtrodden hardships of the longtime sacred antenna, it raises again like better wraith besting better angels! Check out the debut album from MARY JOON'S MOON DIVISION!https://moondivision.bandcamp.com ERIC J. MILLARhttps://wethehallowed.org/category/blog/eric-j-millar/ https://www.lulu.com/shop/eric-j-millar/wombat-2/paperback/product-dyd575j.html?q=&page=1&pageSize=4 somatick magick:https://wethehallowed.org/digital-death-detritus-hauntomancer/ ALL SIGHTS AND SOUNDS BY @revelrosz / REVELATOR ROSZ:https://keatsross.substack.com Each LIMINALSTREAM is devised using the DIVERGENT MAGICK GRIMOIRE:https://wethehallowed.org/divergent-magick-grimoire NO A.I. OR CLIP ART OR TEMPLATES OR ANYTHING MANUFACTURED AUDIO / VIDEO WISE WAS USED TO CONJURE AND CAST THIS (AND ALL) LIMINALSTREAMS. 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Joon Lee was a rising presence on ESPN and respected voice in sports journalism -- but all that changed when he lost his dream. But that wasn't the end. Joon relaunched his own YouTube and became the future of sports journalism. Ken and Joon discuss all that more an all-new Blathering ConversationGet Ken's Comedy Album ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠IN MY DAY⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Purchase Ken's book ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Why We Love Stars: The Great Moments That Built A Galaxy Far, Far Away.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Enjoy ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Moonagers⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠kennapzok.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

What does it really take to build a marriage that lasts? In this message, Pastor Joon unpacks what it means to live from this day forward with a love that commits, endures, and cherishes through every season. He reveals that mature love is not just feelings, but it is the key to a thriving, lasting relationship. Thank you for enjoying this life changing message from Radiant Church. We pray this moves you closer to Christ and encourages you. For more life changing resources, visit us at www.weareradiant.com. Subscribe to our channel: https://youtube.com/weareradiantchurch To give online: https://weareradiant.com/give/ View the sermon notes for this message here: https://notes.subsplash.com/fill-in/view?page=r1_rvaR6Zl Spanish translation messages are available on our Radiant Church Español YouTube channel. Visit https://weareradiant.com/espanol to watch and subscribe. Moving people towards Christ, Community and Calling. This is the vision of Radiant Church, led by Pastor Aaron Burke and based in Tampa Bay, FL. —— Stay Connected Website: https://weareradiant.com Radiant Church Facebook: https://www.facebook.com/weareradiant/ Radiant Church Instagram: http://instagram.com/weareradiant/

Ed and Justin are joined by Joon Lee to discuss his recent article Inside The Red Sox's Credibility Crisis. In a revealing discussion, Joon explains how the Mookie Betts trade was the turning point in the culture of a team that leading up to it had been the most successful baseball franchise of the new millennium, what led to this shift in priorities, the Alex Cora firing, and much, much more.Intrigued? Then it's time to listen to Pod by the River!

Fluent Fiction - Korean: Cherry Blossoms and Unforgettable Smiles: A Jeju Island Journey Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-04-27-07-38-19-ko Story Transcript:Ko: 봄의 따뜻한 햇살이 제주도의 아름다운 길을 감싸고 있었다.En: The warm spring sunlight enveloped the beautiful roads of Jeju Island.Ko: 붓다의 생일을 맞아, 제주의 거리는 생동감 넘치는 축제와 아름다운 연등으로 가득했다.En: In celebration of Buddha's birthday, the streets of Jeju were filled with vibrant festivals and beautiful lanterns.Ko: 벚꽃이 만개한 이 아름다운 섬에서 준과 민지는 특별한 여행을 계획했다.En: In this beautiful island where cherry blossoms were in full bloom, Joon and Minji planned a special trip.Ko: 준은 대학생이다.En: Joon is a college student.Ko: 그는 사진 찍는 것을 좋아하고, 이번 여행에서 완벽한 벚꽃 사진을 찍고 싶었다.En: He enjoys taking photos and wanted to capture the perfect shot of the cherry blossoms during this trip.Ko: 이번 대회에서 우승을 하면, 향후 진로에 대한 실마리를 찾을 수 있으리라 생각했다.En: He thought that winning this competition would provide a clue to his future career path.Ko: 민지는 준의 어린 시절 친구로, 항상 그의 내성적인 성격을 바꿔주려고 노력했다.En: Minji is Joon's childhood friend and always tried to change his introverted nature.Ko: 그녀는 활발하고 사교적인 성격을 가지고 있었다.En: She had a lively and sociable personality.Ko: 함께 떠난 이 여행은 둘 모두에게 기대됐었다. 특히 여름 방학도 앞두고 있어 더 특별했다.En: This trip together was anticipated by both of them, especially since the summer vacation was approaching, making it even more special.Ko: 하루 한낮, 둘은 지역 문화 축제에 참가했다.En: One afternoon, they participated in a local cultural festival.Ko: 다양한 전통 음식과 놀이가 그들을 반겼다.En: Various traditional foods and games greeted them.Ko: 그러나 갑자기 민지가 현지 별미를 먹고 나서 알레르기 반응을 일으켰다.En: However, suddenly Minji had an allergic reaction after eating a local delicacy.Ko: 그녀의 얼굴이 붉어지고 숨쉬기가 힘들어 보였다.En: Her face turned red, and she seemed to have difficulty breathing.Ko: 준은 당황했지만, 곧바로 마음을 가다듬고 민지를 근처에 있는 클리닉으로 데려가기로 결정했다.En: Joon was flustered but quickly pulled himself together and decided to take Minji to a nearby clinic.Ko: 벚꽃이 만개하는 시간은 이제 얼마 남지 않았지만, 준은 걱정에 사로잡혀 민지를 놓을 수 없었다.En: The time when the cherry blossoms were in full bloom was running out, but Joon was so worried he couldn't leave Minji alone.Ko: 그는 경쟁을 포기하고 그녀와 함께 있었다.En: He gave up the competition to stay with her.Ko: 클리닉의 대기실에서 민지는 치료를 받았다.En: In the clinic's waiting room, Minji received treatment.Ko: 그녀의 증상은 점차 나아졌다.En: Her symptoms gradually improved.Ko: 기다리며 준은 무심코 카메라를 들어 민지를 바라봤다.En: While waiting, Joon absentmindedly picked up his camera and looked at Minji.Ko: 민지는 그가 뭔가 말을 하자 조용히 웃었다.En: When he said something, she smiled quietly.Ko: 그 순간, 준은 셔터를 눌렀다.En: In that moment, Joon pressed the shutter.Ko: 벚꽃보다 더 아름다운 민지의 웃음이 그의 렌즈에 담겼다.En: Minji's smile, more beautiful than cherry blossoms, was captured in his lens.Ko: 그는 깨달았다. 완벽한 사진이란 특별한 순간을 담는 것이 아니라, 삶의 진정한 경험을 포착하는 것이었다.En: He realized that a perfect photograph is not about capturing a special moment but about capturing the true experiences of life.Ko: 그날 준은 다른 사람들과 연결되는 순간의 중요성을 다시 한 번 느꼈다.En: That day, Joon once again felt the importance of connecting with others.Ko: 민지는 완쾌되었고, 둘은 다시 여행을 즐길 수 있었다.En: Minji recovered, and the two were able to enjoy the trip again.Ko: 준의 마음은 사진의 새로운 의미로 가득 찼다.En: Joon's heart was filled with a new meaning of photography.Ko: 그는 목적지를 찾았다기보다, 그 길에서 진정한 만족감을 찾았다.En: Rather than finding a destination, he found true satisfaction on the journey.Ko: 벚꽃 앞에서 그날의 순간은 더없이 소중했다.En: In front of the cherry blossoms, the moments of that day were beyond precious.Ko: 준은 오래도록 그 순간을 기억하며, 민지의 웃음을 마음속에 새겼다.En: Joon cherished the memory for a long time and engraved Minji's smile in his heart. Vocabulary Words:enveloped: 감싸다vibrant: 생동감 넘치는lanterns: 연등introverted: 내성적인lively: 활발한sociable: 사교적인anticipated: 기대된approaching: 앞두다cultural: 문화various: 다양한delicacy: 별미allergic reaction: 알레르기 반응flustered: 당황하다nearby: 근처에 있는clinic: 클리닉absentmindedly: 무심코shutter: 셔터experiences: 경험true: 진정한symptoms: 증상gradually: 점차treatment: 치료destination: 목적지satisfaction: 만족감cherished: 소중한engraved: 새기다memory: 기억capture: 포착하다connection: 연결precious: 소중한

This week, Jacob sits down with the legendary Joon Maeng, a river whose reputation was built on grit, kindness, and a level of motivation that would inspire many to come.  https://www.instagram.com/joonmaeng/ Go Premium https://outerzonepod.com/ Discount Codes Save 15% at Nomatic https://nomatic.sjv.io/vPo3kj Discount applied at checkout! Save 20% off merch  https://shopfd.com/ Code -  PODCAST26 Save $5 on tickets Use code  OZLB1 OZATL OZORL OZCT OZIND OZSEA OZLV OZLB2 Produced by  Jacob Gettins https://linktr.ee/jako13 Formula DRIFT - https://www.formulad.com/  Edited by Kyle Mayhew - https://www.instagram.com/kaywhy_85/  Audio Engineering by J-One Audio Services -https://www.facebook.com/profile.php?id=100090486859184 Intro Song by Legna - https://www.tiktok.com/@originallegna  Get Your Hat - www.jako13.com Original Concept - Frank Maguire Instagram: https://www.instagram.com/the_outerzone/ TikTok: https://www.tiktok.com/@the.outerzone Facebook: https://www.facebook.com/people/The-Outerzone/61572435346956/ Shop FD: https://bit.ly/Shop-FD Discord: https://discord.gg/QWJmgqWWUr

Fluent Fiction - Korean: Blossoms of Courage: A Love Story Amidst Cherry Blooms Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-04-19-07-38-19-ko Story Transcript:Ko: 서울의 봄은 언제나 아름답다.En: Spring in Seoul is always beautiful.Ko: 나무에 꽃이 피고, 공기는 따뜻해진다.En: Flowers bloom on the trees, and the air warms up.Ko: 특히 남산공원은 벚꽃 축제로 가득 차 있다.En: Especially Namsan Park is filled with cherry blossom festivals.Ko: 사람들은 꽃 아래서 소풍을 즐기고, 사진을 찍으며 따뜻한 봄 햇살을 만끽한다.En: People enjoy picnics under the flowers, take photos, and bask in the warm spring sunshine.Ko: 이곳에서 준과 수진, 현이라는 친구 세 명이 있다.En: In this place, there are three friends: Joon, Sujin, and Hyun.Ko: 준은 대학교에서 환경문제를 공부하는 학생이다.En: Joon is a student studying environmental issues at the university.Ko: 그는 벚꽃 축제가 깨끗하게 유지되도록 자원봉사를 하고 있다.En: He is volunteering to keep the cherry blossom festival clean.Ko: 그의 친구 수진도 여기에 있다.En: His friend Sujin is also there.Ko: 준은 수진을 좋아하지만, 늘 망설이기만 한다.En: Joon likes Sujin, but he always hesitates.Ko: 오늘은 지구의 날이다.En: Today is Earth Day.Ko: 준은 깨끗한 지구를 만들기 위해 작은 일이라도 하고 싶다.En: Joon wants to do even small things to create a cleaner planet.Ko: 그는 수진에게 자신의 감정을 고백하고 싶다.En: He wants to confess his feelings to Sujin.Ko: 하지만 지금까지 용기가 나지 않았다.En: However, he hasn't found the courage yet.Ko: 남산공원은 사람들로 붐비고 있다.En: Namsan Park is crowded with people.Ko: 많은 꽃잎이 하늘에서 흩날린다.En: Many petals are scattered in the sky.Ko: 사람들은 피크닉을 하느라 바쁘다.En: People are busy having picnics.Ko: 그러나 사람들이 많아지면서 쓰레기도 많아졌다.En: But as more people gather, trash has also increased.Ko: 준은 사람들이 남긴 쓰레기를 치우기 위해 열심히 일한다.En: Joon works hard to pick up the trash left by people.Ko: "준, 힘들지 않아?" 수진이 물었다.En: "Joon, isn't it hard?" Sujin asked.Ko: "괜찮아. 깨끗한 공원을 만드는 것이 중요해," 준이 대답했다.En: "It's okay. Making a clean park is important," Joon answered.Ko: 수진과 준은 같이 쓰레기를 주우며 시간을 보냈다.En: Sujin and Joon spent time picking up trash together.Ko: 준은 수진과 함께 있는 시간이 참 좋다. 하지만 여전히 자신의 마음을 어떻게 전할지 고민된다.En: Joon really enjoys the time spent with Sujin, but he still worries about how to express his feelings.Ko: 현은 준의 마음을 알고 있다.En: Hyun knows Joon's feelings.Ko: 그는 준에게 말했다. "용기를 내. 수진도 네 마음을 알고 싶어 할 거야."En: He told Joon, "Gather your courage. Sujin will want to know how you feel too."Ko: 어느새 그날의 하이라이트, 벚꽃이 최대로 만개할 시간이 다가왔다.En: The highlight of the day, the time when the cherry blossoms would be in full bloom, approached.Ko: 준은 사람들이 떠난 후의 벚꽃길, 조용한 한 모퉁이로 수진을 데려갔다.En: Joon took Sujin to a quiet corner of the cherry blossom path after the people had left.Ko: "수진, 얘기할 게 있어," 준이 말문을 열었다.En: "Sujin, I have something to tell you," Joon started.Ko: 수진은 고개를 끄덕이며 기다렸다.En: Sujin nodded and waited.Ko: "오랫동안 좋아했어. 네가 정말 소중해," 준은 말했다.En: "I've liked you for a long time. You are really precious to me," Joon said.Ko: 수진은 순간 멈칫했다가 말했다. "나도 그래, 준. 너랑 있으면 기분이 좋아."En: Sujin hesitated for a moment and then said, "Me too, Joon. I feel good when I'm with you."Ko: 둘은 벚꽃비가 내리는 그 자리에서 웃음을 지었다.En: They both smiled in the place where the cherry blossom rain fell.Ko: 사랑과 우정이 좀 더 커진 순간이었다.En: It was a moment when love and friendship grew a little more.Ko: 시간이 흐르며 벚꽃은 떨어지지만, 준과 수진의 마음은 더욱 단단해졌다.En: As time passed, the cherry blossoms fell, but Joon and Sujin's hearts grew stronger.Ko: 그들은 함께 작지만 의미 있는 일들을 계속할 것을 약속했다.En: They promised to keep doing small but meaningful things together.Ko: 환경을 사랑하는 마음처럼, 서로에 대한 마음도 그렇게 커졌다.En: Just like their love for the environment, their feelings for each other grew.Ko: 준은 이제 말할 용기가 생겼다.En: Joon now had the courage to speak.Ko: 그는 자신의 감정도 중요한 가치라는 것을 배웠다.En: He learned that his feelings were also an important value.Ko: 두 친구는 앞으로도 같은 길을 걸을 것이다.En: The two friends would walk the same path in the future.Ko: 삶에서 소중한 것은 모두의 노력이 필요하다는 것을 깨달은 준이었다.En: Joon realized that everything precious in life requires everyone's efforts. Vocabulary Words:bloom: 피다cherry blossom: 벚꽃festival: 축제volunteer: 자원봉사hesitate: 망설이다confess: 고백하다courage: 용기crowded: 붐비다petal: 꽃잎scatter: 흩날리다trash: 쓰레기express: 전하다highlight: 하이라이트approach: 다가오다nod: 끄덕이다precious: 소중하다hesitate: 망설이다grow: 커지다meaningful: 의미 있는realize: 깨닫다environment: 환경quiet: 조용하다corner: 모퉁이path: 길fragrance: 향기effort: 노력scatter: 흩어지다enjoy: 만끽하다sunshine: 햇살gather: 모이다

Hosted by David and Nycci Nellis. On today's show: · Nevin Martell talks about Mess Hall NKOTB (that's New Kitchens on the Block); · 2026 marks the 80th anniversary of the opening of Brennan's in New Orleans, easily one of America's most famous restaurants. Among many iconic offerings – including the invention of Bananas Foster -, breakfast at Brennan's is a “thing” unlike any other. To celebrate 80 years, Brennan's owner, Ralph Brennan, is taking to the road, collaborating with some of America's other, time-honored restaurants. At the top of that list is DC's Occidental, where a special three-day collaboration -- Breakfast at Brennan's -- has been running all weekend. Ralph Brennan, joins us to talk about it; · Hive Hospitality – that is, Michelin-Star chef Ryan Ratino and his beverage director, Will Patton, and their team – has added to the offering that includes the one Michelin-Starred Bresca, two Michelin-starred JÔNT and a great cocktail bar, Press Club. It's Ox & Olive in Georgetown, a fresh take on the classic American steakhouse. Chef Ryan and Will join us today; · Chris Morgan is the chef and co-owner of the great Persian restaurant, Joon, in Tysons Corner. He's a two-time James Beard Award semifinalist, and that shows! Joon has ranked in Washingtonian's “100 Very Best Restaurants” every year since opening. So, Chris recently competed on “America's Culinary Cup,” where 16 of the country's most elite chefs compete; · Cody Yu, co-founder, Seven Teahouse in Leesburg, Virginia, which features a premium tea brand dedicated to sourcing and sharing authentic, high-quality teas from traditional tea-growing regions.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Hosted by David and Nycci Nellis. On today's show: · Nevin Martell talks about Mess Hall NKOTB (that's New Kitchens on the Block); · 2026 marks the 80th anniversary of the opening of Brennan's in New Orleans, easily one of America's most famous restaurants. Among many iconic offerings – including the invention of Bananas Foster -, breakfast at Brennan's is a “thing” unlike any other. To celebrate 80 years, Brennan's owner, Ralph Brennan, is taking to the road, collaborating with some of America's other, time-honored restaurants. At the top of that list is DC's Occidental, where a special three-day collaboration -- Breakfast at Brennan's -- has been running all weekend. Ralph Brennan, joins us to talk about it; · Hive Hospitality – that is, Michelin-Star chef Ryan Ratino and his beverage director, Will Patton, and their team – has added to the offering that includes the one Michelin-Starred Bresca, two Michelin-starred JÔNT and a great cocktail bar, Press Club. It's Ox & Olive in Georgetown, a fresh take on the classic American steakhouse. Chef Ryan and Will join us today; · Chris Morgan is the chef and co-owner of the great Persian restaurant, Joon, in Tysons Corner. He's a two-time James Beard Award semifinalist, and that shows! Joon has ranked in Washingtonian's “100 Very Best Restaurants” every year since opening. So, Chris recently competed on “America's Culinary Cup,” where 16 of the country's most elite chefs compete; · Cody Yu, co-founder, Seven Teahouse in Leesburg, Virginia, which features a premium tea brand dedicated to sourcing and sharing authentic, high-quality teas from traditional tea-growing regions.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Motoren-Wettrüsten, Szene-Logik und die Frage, warum so viele Menschen trotzdem nicht aufs Rad steigen. Wir haben Sissi Pärsch vom Podcast „Nimms Rad Industry Insights“ zu Gast – und sprechen einmal quer durch das, was in der Branche gerade schiefläuft. Sissi nennt es die „Inzucht der Ideen in der Fahrradbranche“ Wir tauchen tief ein und holen das an die Oberfläche, was oft untergeht: Alltag statt Szene, Zugang statt Credibility, Gefühl statt Datenblatt. Mit dabei: - JOON als Versuch, Bedürfnisse anders zu denken – und was davon übrig bleibt. - Lime als Gegenmodell mit Fahrzeugen für echte Nutzung statt Szene-Logik. - Werkstätten, die einladen – und solche, die abschrecken. - Die Fatbike-Debatte in den Niederlanden. Wenn ihr wissen wollt, warum 1500 Watt nicht die Lösung für alle Probleme sind: Diese Folge hören.

Our guest tonight went to a routine immigration check in last summer and his life changed forever! Sae Joon Park is a purple heart recipient and a combat veteran. You and I owe our freedom to men just like him. And yet, this evil regime swept him up in their dragnet and forced him to self deport over some harmless offenses from 20 years ago. Sae appeared in Kristi Noem's congressional hearings when Senator Magaziner represented Sae as an example that Trump was not just going after the bad element with his immigration policy. True patriots were getting targeted too. #trump #immigration #ice #kristinoem #purpleheart

Merriam-Webster's Word of the Day for March 18, 2026 is: jejune • jih-JOON • adjective Jejune is a formal word that means "uninteresting" or "boring." It is also used as a synonym of juvenile to describe things (such as behaviors, attitudes, etc.) that are immature, childish, or simplistic. // The movie adaptation employed surreal visual effects to tell the story, making the plot, jejune in the novel, archetypal rather than artless. // The professor made rude and jejune remarks about the students' artwork. See the entry > Examples: "While [author Helen] Garner has journaled most of her life, she burned her early diaries in a bonfire having deemed them too embarrassing or jejune." — The Irish Times, 29 Mar. 2025 Did you know? Starved for excitement? You won't get it from something jejune. The term comes to us from the Latin word jejunus, which means "empty of food," "hungry," or "meager." When English speakers first used jejune back in the 1600s, they applied it in ways that mirrored the meaning of its Latin parent, lamenting "jejune appetites" and "jejune morsels." Something that is meager rarely satisfies, and before long jejune was being used not only for meager meals or hunger, but also for things lacking in intellectual or emotional substance. It's possible that the word gained its now-popular "juvenile" or "childish" sense when people confused it with the look-alike French word jeune, which means "young."

Fluent Fiction - Korean: Jeju Island Adventure: Friendship Amidst Stormy Skies Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-03-12-07-38-19-ko Story Transcript:Ko: 제주의 봄은 마치 동화 속 한 장면 같았다.En: Spring in Jeju was like a scene from a fairy tale.Ko: 꽃들은 활짝 피었고, 하늘은 파란 물감으로 가득 채워져 있었다.En: The flowers were in full bloom, and the sky was filled with a blue hue.Ko: 마침내 미나, 준, 수빈은 제주도를 여행하기로 했다.En: Finally, Mina, Joon, and Subin decided to travel to Jeju Island.Ko: 그들은 친구였고, 이번 여행을 통해 더 많은 추억을 만들고자 했다.En: They were friends and wanted to create more memories with this trip.Ko: 미나는 늘 새로운 모험을 좋아하는 사람이었다.En: Mina was always someone who loved new adventures.Ko: 그녀는 제주의 오래된 등대에 가고 싶어 했다.En: She wanted to visit an old lighthouse in Jeju.Ko: 그곳에서 가장 아름다운 석양을 볼 수 있다고 소문이 나 있었다.En: Rumor had it that the most beautiful sunset could be seen from there.Ko: 반면, 준은 걱정을 많이 했다.En: On the other hand, Joon was quite worried.Ko: "우리가 그 높은 곳을 오를 수 있을까?" 준은 의아해했다.En: "Can we climb that high?" Joon wondered.Ko: 수빈은 그런 두 사람 사이에서 항상 균형을 잡아주는 역할을 했다.En: Subin always played the role of maintaining balance between the two.Ko: "미나야, 갈 수 있을까?" 수빈이 조심스럽게 물었다.En: "Mina, do you think we can go?" Subin asked cautiously.Ko: 미나는 확신에 차서 대답했다.En: Mina answered confidently.Ko: "당연하지, 이 기회를 놓칠 수 없어. 우린 해낼 수 있어!"En: "Of course, we can't miss this opportunity. We'll manage!"Ko: 수빈은 미나의 열정에 조금씩 설득되어 갔다.En: Subin gradually became convinced by Mina's enthusiasm.Ko: 결국, 미나와 수빈은 등대로 가기로 결정했다.En: Eventually, Mina and Subin decided to go to the lighthouse.Ko: 그들 옆에 있던 준은 깊은 숨을 내쉬며 함께하기로 결심했다.En: Joon, who was beside them, took a deep breath and decided to join them.Ko: 그는 두 친구가 있는 곳이 안전할 거라 믿었다.En: He believed that wherever his friends were, it would be safe.Ko: 그들은 등대로 가는 길에 높고 험난한 산길을 걸었다.En: They walked up a steep and rugged mountain path on their way to the lighthouse.Ko: 바람이 점점 세지고, 구름이 하늘에 가득 찼다.En: The wind grew stronger, and the sky filled with clouds.Ko: 그 순간, 갑자기 폭풍이 몰려왔다.En: At that moment, a storm suddenly came.Ko: 비는 쏟아지고, 바람은 더 강해졌다.En: Rain poured down, and the wind became even stronger.Ko: 친구들은 서로를 꼭 붙잡고 피할 곳을 찾기 시작했다.En: The friends clung to each other and began to look for shelter.Ko: 등대 근처에 있는 작은 오두막을 발견했다.En: They found a small hut near the lighthouse.Ko: 그곳에서 비바람을 피하며 서로에 대한 믿음을 되새겼다.En: Sheltering from the storm, they reaffirmed their trust in each other.Ko: "준, 고마워. 너가 없었다면 안전하지 않았을 거야," 미나는 진심으로 말했다.En: "Joon, thank you. If it weren't for you, we wouldn't have been safe," Mina said sincerely.Ko: 준은 고개를 끄덕이며 다시 말한다. "네 말이 맞는 거 같아. 가끔은 즉흥적으로 행동하는 것도 나쁘지 않네."En: Joon nodded and replied, "I think you're right. Sometimes acting on a whim isn't so bad."Ko: 수빈은 그들을 바라보며 미소지었다. "우리 다 괜찮을 거야."En: Subin looked at them and smiled. "We'll all be fine."Ko: 폭풍이 지나고 구름이 걷혀 하늘이 맑아졌다.En: After the storm passed and the clouds cleared, the sky brightened.Ko: 그들은 오두막 문을 열고 나왔다.En: They opened the hut's door and stepped outside.Ko: 하늘에는 장엄한 햇빛이 구름 사이로 비추고 있었다.En: Majestic sunlight was shining through the clouds.Ko: 세 친구는 그 광경을 보며 서로의 어깨를 감싸 안고 웃었다.En: The three friends looked at the sight, held each other's shoulders, and laughed.Ko: 이 순간, 그들은 깨달았다. 진정한 모험은 함께 하는 것이라는 것을.En: At that moment, they realized that true adventure is about being together.Ko: 제주도의 아름다운 풍경 속에서, 그들은 더욱 깊은 우정을 나누었다.En: Amidst the beautiful scenery of Jeju Island, they shared a deeper friendship.Ko: 그리고 그 기억은 언제까지나 그들의 마음 속에 남아 있을 것이었다.En: And that memory would remain in their hearts forever. Vocabulary Words:spring: 봄fairy tale: 동화bloom: 활짝 피다adventure: 모험lighthouse: 등대rumor: 소문sunset: 석양cautiously: 조심스럽게opportunity: 기회convinced: 설득되다enthusiasm: 열정eventually: 결국deep breath: 깊은 숨steep: 높다rugged: 험난한storm: 폭풍shelter: 피할 곳hut: 오두막sincerely: 진심으로whim: 즉흥적majestic: 장엄한realize: 깨닫다adventure: 모험amidst: 속에서scenery: 풍경deeper: 더욱 깊은friendship: 우정remain: 남다held: 감싸 안다clouds: 구름

Eight years after his first appearance on Taste Radio, Nick Green, co-founder and CEO of pioneering online grocer Thrive Market, returns with a clear message: the future of healthy living will be shaped less by trend-chasing and more by trust, technology and simplicity. In this episode, Nick explains how Thrive is navigating a rapidly evolving wellness landscape molded by AI, GLP-1 drugs and growing consumer skepticism, while staying grounded in its mission to make healthy living more accessible and affordable. He also shares how Thrive evaluates brands, why taste matters most, and why he increasingly sees the company – which generated over $900 million in revenue in 2025 – not just as an online grocery store, but as a personalized health platform for modern consumers. Show notes: 0:20: Nick Green, Co-Founder & CEO, Thrive Market  – Nick reflects on how both he and Thrive Market have evolved since his first appearance on the podcast in 2018. While acknowledging volatility across the natural products industry, he explains why Thrive remains focused on enduring trends around health, transparency and accessibility. Nick discusses how Thrive defines "healthy," how it uses data to personalize discovery for members, and why he believes AI will deepen the company's relationship with subscribers. He also explains how Thrive's membership model attracts a nationwide base of "wellness champions," and how its strict quality standards guide its curation strategy. He shares why authenticity and taste matter most when evaluating brands, and how Thrive serves as a launchpad for emerging companies. Nick also discusses Thrive's growing own-brand business, weighs in on trends like protein, functional mushrooms and non-alcoholic beverages, and describes how the company increasingly sees itself not just as an online grocer, but as a technology platform helping consumers navigate healthy living. Brands in this episode: JOON, SkinnyDipped, MudWtr, Four Sigmatic, Quantum Energy Squares

SEASON 4 EPISODE 61: COUNTDOWN WITH KEITH OLBERMANN BULLETIN: On his 66th birthday, British police arrest the former Prince Andrew (now Andrew Mountbatten-Windsor) on suspicion of supplying confidential government financial information to Jeffrey Epstein. They have 96 hours to decide whether to formally charge him for that or anything else. And we prosecute no one - least of all our parallel, Trump. HOURS EARLIER a South Korean court did not sentence insurrectionist former President Yoon Suk Yeol to death, as prosecutors had demanded. He tried to impose martial law on his nation in 2024 in a plot to use spurious charges of election fraud to justify ending democracy there. He gets life in prison. And we prosecute no one - least of all our parallel, Trump. AND JUST TO ROUND IT OUT: Overnight, President Zelensky of Ukraine snapped - to some degree, throwing an S-bomb at the Russians after the latest round of Trump-led stalling-tactic phony "peace talks" broke out with no result (or more correctly the result Putin wanted Trump to achieve: delay it all further). And we did nothing.See omnystudio.com/listener for privacy information.

Fluent Fiction - Korean: Laughter in the Halls: A Nurse's Seollal Lesson Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-02-19-23-34-02-ko Story Transcript:Ko: 병원은 눈과 추위로 가득한 겨울철 설날을 맞아 더 바빴다.En: The hospital was busier as it faced the Seollal of winter, full of snow and cold.Ko: 병원 복도는 따뜻한 옷을 입은 사람들로 분주했다.En: The hospital corridors were bustling with people dressed in warm clothes.Ko: 경쾌한 설날 장식이 저마다의 걱정을 잠시나마 잊게 했다.En: The cheerful lunar new year decorations made everyone forget their worries, if only for a moment.Ko: 민지는 막내 간호사였다.En: Minji was the youngest nurse.Ko: 그녀는 상사에게 좋은 인상을 남기고 싶어했다.En: She wanted to make a good impression on her superiors.Ko: 그런데, 바쁜 병원에서 정신을 집중하기가 쉽지 않았다.En: However, it wasn't easy to stay focused in the busy hospital.Ko: 민지의 친구 혜순은 그녀보다 경험이 더 많았다.En: Minji's friend Hyesun was more experienced than her.Ko: 그녀는 민지의 허둥거림을 가끔은 날카롭게 지적했다.En: She would sometimes sharply point out Minji's flustered mistakes.Ko: 어느 날, 민지는 복도를 급히 지나가다 무언가를 보고 멈췄다.En: One day, Minji was hurrying down the corridor when she stopped after seeing something.Ko: "조심해요!" 민지는 '환자'에게 말했다.En: "Watch out!" Minji said to a "patient."Ko: 그런데 사실 그것은 환자의 머리가 아닌 수박이었다.En: But in reality, it wasn't a patient's head, but a watermelon.Ko: 병원의 직원들은 웃음을 참을 수 없었다.En: The hospital staff couldn't hold back their laughter.Ko: 민지는 부끄러움에 얼굴이 빨개졌다.En: Minji's face turned red with embarrassment.Ko: 민지는 실수를 보완하기로 마음먹었다. 그래서 그녀는 환자 준에게 더 많은 관심과 추가적인 케어를 베풀었다.En: Determined to make up for her mistake, Minji decided to pay more attention and provide extra care to the patient Joon.Ko: 준은 민지의 진심 어린 노력과 실수를 너그러이 받아들였다.En: Joon graciously accepted Minji's sincere efforts and mistakes.Ko: 그는 민지가 얼마나 노력하는지 흥미롭고 재미있다고 생각했다.En: He found it interesting and amusing how hard she was trying.Ko: 준이 이렇게 말했다. "수박과 상담이라니, 나도 그럴 뻔했어!"En: Joon said, "Talking to a watermelon, I almost did the same!"Ko: 그의 말에 민지도 웃음을 터뜨렸다.En: His words made Minji burst into laughter too.Ko: 민지는 이제 자신도 웃을 수 있는 여유가 생겼다.En: Now she also found herself able to laugh.Ko: 이 경험 덕분에 민지는 병원에서 환자와 함께 그날의 소중한 기억을 나눴다.En: Thanks to this experience, Minji shared the cherished memory of that day with the patients in the hospital.Ko: 그녀는 자신의 실수에도 불구하고 웃음의 가치를 알게 되었다.En: She learned the value of laughter despite her mistakes.Ko: 병원의 다른 직원들도 한바탕 즐거운 웃음을 나누며 바쁜 하루에 여유를 찾았다.En: The other hospital staff also shared a hearty laugh, finding a moment of relaxation in their busy day.Ko: 그리고 민지는 더 편안하고 친근한 간호사로 성장했다.En: And Minji grew to become a more relaxed and approachable nurse.Ko: 병원은 여전히 바쁘지만, 마음만은 훈훈한 겨울이었다.En: The hospital remained busy, but their hearts stayed warm throughout the winter. Vocabulary Words:corridors: 복도bustling: 분주한decorations: 장식superiors: 상사focused: 정신을 집중하다flustered: 허둥거림mistakes: 실수embarrassment: 부끄러움graciously: 너그러이sincere: 진심 어린cherished: 소중한value: 가치relaxation: 여유approachable: 친근한determined: 마음먹다experience: 경험amusing: 재미있는cherished: 소중한memory: 기억despite: 에도 불구하고laughter: 웃음staff: 직원sharp: 날카롭게attention: 관심extra: 추가적인relaxed: 편안한approachable: 친근한relaxation: 여유cherished: 소중한hospital: 병원

Fluent Fiction - Korean: Finding Peace in Tradition: Joon's Gyeongbokgung Revelation Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-02-16-23-34-02-ko Story Transcript:Ko: 경복궁은 눈으로 덮여 있었습니다.En: Gyeongbokgung was covered in snow.Ko: 겨울의 경복궁은 고요하고 아름다웠습니다.En: The winter Gyeongbokgung was quiet and beautiful.Ko: 설날을 맞이하여 궁궐은 전통 장식으로 꾸며져 있었고, 많은 가족들이 모였습니다.En: In celebration of the Lunar New Year, the palace was decorated with traditional ornaments, and many families gathered.Ko: 준은 1년 동안 해외에서 공부를 하고 집으로 돌아왔습니다.En: Joon had been studying abroad for a year and returned home.Ko: 가족들과 함께 시간을 보내고 싶었습니다.En: He wanted to spend time with his family.Ko: 그러나 그는 자신의 미래에 대해 걱정이 많았습니다.En: However, he was very worried about his future.Ko: 그의 선택이 올바른 길인지 확신이 없었습니다.En: He wasn't sure if his choices were the right ones.Ko: 준의 가족은 전통적인 방식으로 설날을 경복궁에서 기념하고 싶어 했습니다.En: Joon's family wanted to celebrate the Lunar New Year at Gyeongbokgung in the traditional way.Ko: 준, 그의 누나 은지, 그리고 그의 사촌 민서는 궁궐을 돌아다니며 알록달록한 한복을 입고 즐거운 시간을 보냈습니다.En: Joon, his sister Eunji, and his cousin Minseo roamed the palace, wearing colorful hanbok and having a great time.Ko: 그러나 준의 마음은 복잡했습니다.En: However, Joon had a complicated mind.Ko: 가족의 기대에 부응하지 못하고 있다고 느끼고 있었기 때문입니다.En: He felt as though he wasn't living up to his family's expectations.Ko: 그는 은지에게 자신의 고민을 털어놓기로 결심했습니다.En: He decided to confide his worries to Eunji.Ko: "누나, 나는 내 미래에 대해 확신이 없어.En: "Sis, I'm not sure about my future.Ko: 가족들이 기대하는 만큼 내가 성공하지 못할까 봐 두려워.En: I'm afraid I won't succeed as much as the family expects."Ko: " 준은 조용히 말했습니다.En: Joon said quietly.Ko: 은지는 준의 눈을 보며 미소 지었습니다.En: Eunji looked into Joon's eyes and smiled.Ko: "준아, 우리는 네가 자랑스러워.En: "Joon, we are proud of you.Ko: 네가 어떤 선택을 하든 우리는 항상 네 편이야.En: Whatever choice you make, we are always on your side."Ko: "은지의 말은 준에게 큰 위안이 되었습니다.En: Eunji's words were a great comfort to Joon.Ko: 준은 가족의 사랑과 지지를 느낄 수 있었습니다.En: He could feel the love and support of his family.Ko: 궁궐 안에서 전통적인 제사가 열렸습니다.En: Inside the palace, a traditional ritual took place.Ko: 준은 그 의식에 참여했습니다.En: Joon participated in the ceremony.Ko: 신년의 새로운 시작을 기념하면서 그는 마음 깊이 평화를 느꼈습니다.En: Celebrating the new beginning of the year, he felt a deep sense of peace within.Ko: 눈 내린 궁궐에서, 준은 자신의 감정을 솔직하게 털어놓는 것의 중요성을 깨달았습니다.En: In the snow-covered palace, Joon realized the importance of expressing his feelings openly.Ko: 그는 자신의 길을 찾아가는 과정 자체가 의미 있다는 것을 이해했습니다.En: He understood that the process of finding his own path is meaningful in itself.Ko: 새로운 결심과 함께 과거의 두려움을 놓아주었습니다.En: With a new resolve, he let go of past fears.Ko: 설날의 끝자락에서, 가족과 함께 경복궁을 떠나는 준은 겸손함과 자신감이 섞인 미소를 지었습니다.En: As the end of the Lunar New Year approached, Joon left Gyeongbokgung with his family, wearing a smile mixed with humility and confidence.Ko: 그의 가족과의 강한 유대는 앞으로의 여정을 위한 큰 힘이 될 것입니다.En: The strong bond with his family would be a great strength for his journey ahead. Vocabulary Words:ornaments: 장식abroad: 해외에서confide: 털어놓다succeed: 성공하다expectations: 기대comfort: 위안ritual: 제사celebrate: 기념하다resolve: 결심humility: 겸손함confidence: 자신감bond: 유대complicated: 복잡하다ceremony: 의식participate: 참여하다process: 과정meaningful: 의미 있다expression: 표현approach: 접근하다gathered: 모였었다traditional: 전통적인minds: 마음quiet: 고요하다deep: 깊이path: 길snow-covered: 눈 내린realized: 깨달았다fears: 두려움decorated: 꾸며져 있었다abroad: 해외

Fluent Fiction - Korean: Chasing Clues and Friendship at Namsan Tower Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-02-01-23-34-02-ko Story Transcript:Ko: Namsan 서울 타워는 서늘한 겨울 하늘 아래 높이 서 있다.En: Namsan Seoul Tower stands tall under the cool winter sky.Ko: 눈이 덮인 길들이 이어지고 타워는 설날 장식들로 화려하게 꾸며져 있다.En: The snow-covered paths lead on, and the tower is splendidly decorated with Seollal decorations.Ko: 가족, 관광객, 그리고 모험을 찾는 사람들을 초대한다.En: It invites families, tourists, and those seeking adventure.Ko: Joon은 타워의 관람 데크에서 이상한 메모를 발견했다.En: Joon discovered a strange note on the observation deck of the tower.Ko: 메모는 보물에 대한 단서를 힌트로 남겼다.En: The note left a hint as a clue about treasure.Ko: Joon은 아마추어 탐정이다.En: Joon is an amateur detective.Ko: 퍼즐을 푸는 것을 좋아한다.En: He loves solving puzzles.Ko: Suhyeon은 그의 가장 친한 친구이다.En: Suhyeon is his best friend.Ko: 그녀는 조심스럽지만 언제나 Joon을 지지한다.En: She is cautious but always supports Joon.Ko: Minji는 같은 보물 사냥꾼인데, 신비스러우며 경쟁적이다.En: Minji is a fellow treasure hunter, mysterious and competitive.Ko: 그녀에게는 자신만의 비밀스러운 목표가 있다.En: She has her own secretive goal.Ko: 메모는 복잡하고 시간적으로 제한이 있다.En: The note is complex and time-sensitive.Ko: 기온은 매우 낮고, 곧 눈이 더 내릴 것 같다.En: The temperature is very low, and it seems more snow is soon to fall.Ko: Joon은 서두르지만, 혼자 행동하면 안 된다고 결심했다.En: Joon hurries but decides he shouldn't act alone.Ko: 그는 Suhyeon을 믿고 그녀에게 도움을 구하기로 했다.En: He trusts Suhyeon and decides to ask for her help.Ko: 세 사람은 함께 단서를 조사했다.En: The three of them investigate the clue together.Ko: 길은 얼음으로 미끄럽다.En: The path is slippery with ice.Ko: 바람이 매섭다.En: The wind is fierce.Ko: 하지만 Joon은 포기하지 않는다.En: But Joon doesn't give up.Ko: Suhyeon은 추운 날씨에서도 조언을 제공한다.En: Suhyeon provides advice even in the cold weather.Ko: Minji는 앞서려고 한다.En: Minji tries to get ahead.Ko: 경쟁은 점점 심해진다.En: The competition becomes more intense.Ko: 마지막 단서가 나왔다.En: The final clue emerges.Ko: Joon은 들뜨는 마음으로 미소 지었다.En: Joon smiles with an excited heart.Ko: "여기야!" 그의 목소리가 차가운 공기를 가르며 울린다.En: "It's here!" his voice rings through the cold air.Ko: Minji도 도착했다.En: Minji has also arrived.Ko: 긴장감이 맴돌았다.En: Tension lingers.Ko: 마침내, 보물은 발견되었다.En: Finally, the treasure is discovered.Ko: 그것은 작은 역사적인 유물이었다.En: It is a small historic relic.Ko: 진실과 우정을 상징한다.En: It symbolizes truth and friendship.Ko: Joon은 고민했다.En: Joon pondered.Ko: 하지만 그는 모두와 함께 나누기로 했다.En: But he decided to share it with everyone.Ko: Minji와 Suhyeon도 미소 지었다.En: Minji and Suhyeon also smiled.Ko: "함께이길 잘했어," Joon은 말했다.En: "I'm glad we were together," Joon said.Ko: 돌아가는 길에는, Joon은 깨달았다. "미스터리를 푸는 것은 친구들과 함께할 때 더 즐겁구나."En: On the way back, Joon realized, "Solving mysteries is more enjoyable when you're with friends."Ko: 끝이 행복하게 맺어졌다.En: It ended happily.Ko: Namsan은 눈 속에서 빛나고 있었고, 세 친구는 다시 시작할 준비가 되어 있었다.En: Namsan was shining in the snow, and the three friends were ready to start again.Ko: Seollal의 따뜻한 기운 속에서 그들은 새로운 해를 맞이했다.En: In the warm spirit of Seollal, they welcomed the new year. Vocabulary Words:stands: 서 있다decorated: 꾸며져 있다observation: 관람strange: 이상한hint: 힌트amateur: 아마추어detective: 탐정cautious: 조심스러운complex: 복잡한time-sensitive: 시간적으로 제한이 있는temperature: 기온slippery: 미끄러운fierce: 매서운competition: 경쟁intense: 심해지는emerges: 나왔다tension: 긴장감discovered: 발견되었다relic: 유물symbolizes: 상징한다pondered: 고민했다welcomed: 맞이했다mysterious: 신비스러운competitive: 경쟁적인secretive: 비밀스러운goal: 목표advice: 조언together: 함께resolve: 결심했다excited: 들뜨는

Welcome to the Writers Series on Voices on the Side! As part of celebrating my upcoming book - Mom, Unfiltered: Maternal Mental Health and Finding Freedom through Motherhood - I'll be focusing the podcast on conversations with fellow writers. From authors to essayists to journalists and professors, we are going to be talking about all things writing. In this first episode of the series, Joon Ae takes the position of host and interviewer and asks me about the book and how I became a writer. If you have a favorite writer you'd love to hear from or a specific question you're wondering about, leave it for me in the reviews (Apple) or comments (Spotify). Joon Ae websiteLeah website

In this episode of The Retina Channel Podcast, I sit down with Dr. Seong Joon Ahn from Hanyang University in South Korea to discuss a recent JAMA Ophthalmology study examining systemic drugs associated with underrecognized maculopathy. Using a novel two-step approach that integrates pharmacovigilance signal detection from the FDA Adverse Event Reporting System (FAERS) with nationwide health-claims data from South Korea, the study identifies several commonly used systemic medications— apixaban, paclitaxel, ibrutinib, fingolimod, and sildenafil—as being associated with an increased risk of maculopathy. We explore the study's methodology, key findings such as dose-response relationships, and the clinical implications for retina specialists and general ophthalmologists alike, particularly around patient counseling and the potential need for ocular monitoring in patients on these medications.   Kim J, Ahn SJ, Park J, Gower EW, Chung JE. Systemic drugs associated with maculopathy. JAMA Ophthalmol.2025;143(11):946-952. doi:10.1001/jamaophthalmol.2025.3612.

This episode is a discussion on the rising topic of iPad usage among kids with guests, Eric Liu and Nis Frome. The conversation explores the advantages of educational tools and tech literacy while discussing the downsides, like excessive screen time and dependency on digital devices. The discussion ranges from personal parenting experiences with technology to broader implications on children's development, focusing on how parents can strike a balance between tech exposure and healthy, active lifestyles. The guests also reflect on the importance of moderation and mindful screen use in today's tech-saturated world. About Our Guests: Nis Frome is a seasoned entrepreneur and angel investor renowned for his expertise in building and advising groundbreaking ventures. He co-founded Feedback Loop, acquired by DISQO, and has contributed to successful projects like Coderbyte, Session Rewind, and JOON. Nis has also invested in innovative startups such as DEN, Beam, Realm, and Reflex. Eric Liu is a dynamic entrepreneur, investor, and thought leader with deep insights into the evolving landscape of business and personal development. With a keen interest in the intersection of technology, innovation, and human behavior, Eric brings a unique and valuable perspective to every conversation. Thanks for watching! Takeaways: iPads can be educational tools but also serve as digital babysitters. Excessive screen time can negatively impact children's attention spans. Moderation is key when introducing technology to children. Exposure to technology is inevitable; teaching moderation is essential. Different activities on iPads can have varying impacts on children. Parents should evaluate the purpose of screen time for their kids. Creating a balanced environment with alternatives to screens is important. Tech literacy is important, but it can be developed without early exposure to iPads. The conversation around technology and children is nuanced and requires careful consideration. Ultimately, parenting decisions should be based on individual family dynamics and values.

Very few brands have reinvented themselves as successfully, or as culturally, as Coach. On this week's episode, Jim sits down with Joon Silverstein, Chief Marketing Officer of Coach, to unpack the bold transformation behind one of fashion's most compelling modern growth stories. Coach is part of Tapestry, Inc., the New York–based global house of iconic accessory and lifestyle brands that also includes Kate Spade. This past fiscal year, Tapestry achieved a record $7 billion in revenue, driven largely by double-digit growth at Coach — a powerful signal of the brand's renewed momentum and relevance.Joon's impact at Coach spans more than a decade. She joined the brand in 2014 as SVP of Global Customer Experience, went on to lead digital, creative, sustainability, and North America marketing, and ultimately founded Coachtopia: Coach's groundbreaking circular sub-brand built with and for Gen Z. As we close out the year and head into the holiday season, this conversation feels especially timely. It's about courage, confidence, creativity, and what it really means to build brands — and careers — that stand for something meaningful.---Learn more, request a free pass, and register at https://www.iab.com/Promo Code for $500 off ticket prices: ALMCMOPOD26---This week's episode is brought to you by Deloitte, TransUnion and the IAB.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

There's something powerful about the place God calls His house. In this message, Pastor Joon shares why the church is more than a tradition. The church is where God changes lives, where God builds His people together into a spiritual family, and where God reaches the world. If you've wondered whether church really matters, this message will remind you just how vital, beautiful, and life-transforming God's house truly is. Thank you for enjoying this life changing message from Radiant Church. We pray this moves you closer to Christ and encourages you. For more life changing resources, visit us at www.weareradiant.com.

In this episode, I sit down with Joon co-founders Cody Allen and Alexandre Bennet to talk about how they turned a 300-year-old family pistachio recipe into one of my favorite new snack brands. We dig into how they chose pistachios, grew into 1,000+ doors in just months, and built a brand that stands out in a crowded snack aisle. If you're a founder who loves a good origin story and wants the playbook for smart, fast growth, you'll want to hit play on this one.Startup to Scale is a podcast by Foodbevy, an online community to connect emerging food, beverage, and CPG founders to great resources and partners to grow their business. Visit us at Foodbevy.com to learn about becoming a member or an industry partner today.

Joon & Cody recap Tony Vitello's statement and (try) to put a bow on things.

Send us a textThis month we read and review The Girl Who Fell Beneath the Sea by Axie Oh. Like all of our reviews, the first part is spoiler free.Here's a little about The Girl Who Fell Beneath the Sea:Deadly storms have ravaged Mina's homeland for generations. Floods sweep away entire villages, while bloody wars are waged over the few remaining resources. Her people believe the Sea God, once their protector, now curses them with death and despair. In an attempt to appease him, each year a beautiful maiden is thrown into the sea to serve as the Sea God's bride, in the hopes that one day the “true bride” will be chosen and end the suffering.Many believe that Shim Cheong, the most beautiful girl in the village—and the beloved of Mina's older brother Joon—may be the legendary true bride. But on the night Cheong is to be sacrificed, Joon follows Cheong out to sea, even knowing that to interfere is a death sentence. To save her brother, Mina throws herself into the water in Cheong's stead.Swept away to the Spirit Realm, a magical city of lesser gods and mythical beasts, Mina seeks out the Sea God, only to find him caught in an enchanted sleep. With the help of a mysterious young man named Shin—as well as a motley crew of demons, gods and spirits—Mina sets out to wake the Sea God and bring an end to the killer storms once and for all.But she doesn't have much time: A human cannot live long in the land of the spirits. And there are those who would do anything to keep the Sea God from waking…Do you have a book you'd like us to review on this show? Send us an email at badassliteraturesociety@gmail.comIf you don't already, follow us on Instagram and FacebookArt by Justin Miller DesignCheck us out here!

Send us a textThe better than Van Helsing boys have spent their lives under the strict rule of their producer. Unaware of his dark past, they struggle to understand his increasingly erratic behavior. But when they begin to uncover the violent truths behind his mixing board, their world unravels, forcing them to confront having to produce the show without him. On Episode 683 of Trick or Treat Radio we discuss the film Abraham's Boys: A Dracula Story based on the short story from Joe Hill and directed by Natasha Kermani! We also talk about the upcoming Deathstalker film, isolationism and gaslighting from those you trust, and plenty of Dracula lore. So grab your monster hunting handbook, subvert any and all expectations, and strap on for the world's most dangerous podcast!Stuff we talk about: Steven Kostanski, The Void, Psycho Goreman, Frankie Freako, Deathstalker, Astron-6, practical FX, violence, sword and sorcery films, Jerry “The King” Lawler, f*ck WWE, The Incubus, Needful Things, the 13th Warrior, Eaters of the Dead, House of 1000 Corpses, Hunt for the Blood Orchid, Suspect Zero, Lets Scare Jessica to Death, The Last Exorcism, The Candyman, Watchmen, this day in horror history, Alexa Vega, bloody birthdays, The Tomorrow People, Machete Kills, Mothers Day, Psycho III, Zodiac, John Kassir, Rock and Shock, The Three Stooges, Benny and Joon, Will and Grace, Caveman, Todd Browning's Freaks, Rocket Ship XM, Invaders from Mars, Slash, Tim Seeley, Red Sonja, Rose McGowan, Deathwatch, The Dreadites, boomsword, Lucio Fulci, Conquest, covering the lens in vaseline, Sabrina Siana, Planet of the Gapes, Joe Hill, Abraham's Boys, Natasha Kermani, Titus Welliver, Jocelin Donahue, Frailty, Bill Paxton, PCU, Dogtooth, Yorgos Lanthimos, “the severed heads looked really good”, no style nor substance, Vanhelsing, movie of the week, “It's Better than Vanhelsing”, Batman, Monster: The Ed Gein Story, Wisconsin represents, Menendez Brothers, Brute 1976, Joe Knetter, Marcel Walz, Frute Brute, Countess Caramella, Brute 1976, Brut By Faberge, Mammoth, Robert Rodriguez, Greg Nicotero, Michael Jackson, Wolfgang Van Halen, Peter's Polar Bear Paradox, The Serial Killer Lookbook, Conquest and Divide, and The Ballad of Oswalt Patton.Support us on Patreon: https://www.patreon.com/trickortreatradioJoin our Discord Community: discord.trickortreatradio.comSend Email/Voicemail: mailto:podcast@trickortreatradio.comVisit our website: http://trickortreatradio.comStart your own podcast: https://www.buzzsprout.com/?referrer_id=386Use our Amazon link: http://amzn.to/2CTdZzKFB Group: http://www.facebook.com/groups/trickortreatradioTwitter: http://twitter.com/TrickTreatRadioFacebook: http://facebook.com/TrickOrTreatRadioYouTube: http://youtube.com/TrickOrTreatRadioInstagram: http://instagram.com/TrickorTreatRadioSupport the show

BGMania B-Sides #30 of BGMania: A Video Game Music Podcast. Today on the show, Bedroth explores the vibrant synth-pop soundtrack of Wheel World, the charming cycling adventure from Messhof and Annapurna Interactive. The neon soundtrack serves as the perfect backdrop for Kat's cycling adventures, featuring an incredible collection of tracks with dew-droppy bell melodies and electrifying synth propulsion. Created by artists from the Italians Do It Better label, including Johnny Jewel, JOON, and Orion, the music perfectly captures the mechanical and soulful combination at the heart of both cycling and electronic music. Whether you're a fan of retro synthwave, cycling games, or just great video game music, this B-Sides episode offers the perfect soundtrack for your own journey. Email the show at bgmaniapodcast@gmail.com with requests for upcoming episodes, questions, feedback, comments, concerns, or any other thoughts you'd like to share! Special thanks to our Executive Producers: Jexak, Xancu, Jeff & Mike. EPISODE PLAYLIST AND CREDITS Pulsar from Wheel World [Johnny Jewel, 2025] Cruise Control from Wheel World [Johnny Jewel, 2025] Here To Stay from Wheel World [Johnny Jewel feat. JOON, 2025] Never Surrender from Wheel World [Johnny Jewel, 2025] Darklands from Wheel World [Johnny Jewel, 2025] Villan Of Love from Wheel World [Johnny Jewel feat. Orion, 2025] I Want It All from Wheel World [Johnny Jewel feat. JOON, 2025] Cosmic Ghost from Wheel World [Johnny Jewel feat. Orion, 2025] Moon Rider from Wheel World [Johnny Jewel feat. Orion, 2025] Hold On from Wheel World [Johnny Jewel feat. JOON, 2025] LINKS Patreon: https://patreon.com/bgmania Website: https://bgmania.podbean.com/ Discord: https://discord.gg/cC73Heu Facebook: BGManiaPodcast X: BGManiaPodcast Instagram: BGManiaPodcast TikTok: BGManiaPodcast YouTube: BGManiaPodcast Twitch: BGManiaPodcast PODCAST NETWORK Very Good Music: A VGM Podcast Listening Religiously

Episode #382 of BGMania: A Video Game Music Podcast. Today on the show, Bryan closes out the month of July 2025 with another eclectic mix in Radio Hour, Volume 77! From the retro-chic to the otherworldly, the introspective to the high-octane, this episode features music from newly released indie gems, ambitious blockbusters, and some deeply nostalgic favorites. Tune in for 14 tracks, including recent submissions and requests from our amazing listeners across Discord, Instagram, and more. Whether you're here for bold guitar solos, synth-drenched atmosphere, or soul-stirring piano, this episode has something for every audiophile and game music lover alike. Settle in, throw on your headphones, and let the music take you on a journey. Email the show at bgmaniapodcast@gmail.com with requests for upcoming episodes, questions, feedback, comments, concerns, or any other thoughts you'd like to share! Special thanks to our Executive Producers: Jexak, Xancu, Jeff & Mike. EPISODE PLAYLIST AND CREDITS The Rail Forest from Sea of Stars: Throes of the Watchmaker [Eric W. Brown, 2025] Theme Song from Mars After Midnight [Lucas Pope, 2024] Moonlit Night Phantom from Shoujo Yoshitsuneden [Aki Hata, 2003] Autumn Days from SunnySide [JellyFox, 2024] Heartbeat Skipper from Paper Mario: The Origami King [Yoshito Sekigawa, Shoh Murakami, Yoshiaki Kimura, Hiroki Morishita & Fumihiro Isobe, 2020] Cliffs Of Dover from Guitar Hero III: Legends of Rock [Eric Johnson, 1990/2007] Rush Hour from SimCity 4 [Jerry Martin, 2003] Stardust & Danger from Wheel World [Johnny Jewel feat. Orion, 2025] Go from Wheel World [Johnny Jewel feat. JOON, 2025] Into The Maze from Wheel World [Johnny Jewel, 2025] A New Journey from Rune Factory: Guardians of Azuma [Noriyuki Asakura, 2025] Formidable Opponent from Shadow Labyrinth [Katsuro Tajima, 2025] Title Screen from Wuchang: Fallen Feathers [Anti-General, 2025] Reborn from REVEIL [Arina Tara, 2024] LINKS Patreon: https://patreon.com/bgmania Website: https://bgmania.podbean.com/ Discord: https://discord.gg/cC73Heu Facebook: BGManiaPodcast X: BGManiaPodcast Instagram: BGManiaPodcast TikTok: BGManiaPodcast YouTube: BGManiaPodcast Twitch: BGManiaPodcast PODCAST NETWORK Very Good Music: A VGM Podcast Listening Religiously

In this curated selection from the Happy Space Podcast, I'm bringing back voices that challenge, inspire, and expand our thinking about designing for inclusion and accessibility. These encore episodes highlight conversations that continue to resonate—on neurodiversity, workplace design, and the small shifts that can make a big difference. Whether you're tuning in for the first time or revisiting a favourite, I hope these episodes offer fresh insight into how thoughtful design can help everyone show up and perform at their best.If we want our world to be more inclusive, we need to pay close attention to accessibility - the ease with which individuals can participate - at work, at home, and in daily life. Disability consultant Marjorie Aunos shares her highly relevant personal and professional lived experience. We explore what compelled Marj to dedicate her life to supporting adults with intellectual disabilities at a tender 20 years old, what motivated her when she became a paraplegic as a single mom to her 16-month-old son, and how to be a better ally to those who have accessibility challenges.Marjorie Aunos, Ph.D. is a researcher, speaker, and consultant on accessibility and inclusion. She teaches organizations and educators to solution-find and build environments that are accessible, inclusive, and welcoming to families with disabilities. Marjorie is an internationally award-winning speaker, author of Mom on Wheels: The Power of Purpose as a Paraplegic Parent and contributing author to We Got This: Essays By Disabled Parents. Her TEDx talk “What we can learn from disabled parents” has over 150,000 views. CHAPTERS00:03:20 Marjorie's journey00:08:00 Building support networks00:14:40 An invitation for greater empathy00:16:47 Purpose from a young age00:21:00 What has shifted in recent years?00:24:30 Visible vs. invisible challenges00:27:55 How and when to help00:30:14 Do we treat those with disabilities differently?00:34:00 Acknowledge the disabled as experts LINKSFyre FestivalBenny & Joon (1993)UN Convention on the Rights of Persons with DisabilitiesHow Can a Watermark be a Human Rights Matter?Hidden DisabilitiesRick Hansen FoundationHold That Door…! Opportunities to Improve Accessibility are Closer Than You ThinkRemembering Air India Flight 182What we can learn from parents with disabilities | Marjorie Aunos | TEDxWesternU IMAGE CREDITS (see images on Youtube video)Marjorie and Thomas - credit Marjorie AunosAccessible space - Envato ElementsOld wheelchair symbol - Wiki CommonsSunflower lanyard - credit Hidden...

Johnny Depp played Sam, a tree-climbing, borderline-illiterate, aspiring mime. Shockingly, Sam's antics aren't even the quirkiest things taking place in 1993's Benny & Joon. Poker games with weird prizes, a snorkel-wearing sister, and a former horror movie actress are just a few of the ingredients making up this flaky crowd-pleaser. But now, decades later, is a quirky point of view appropriate for a love story involving a woman with mental illness? Is Aidan Quinn sexy or snoozy? And, why oh why, is Benny & Joon's house such an ungodly mess? The Old Roommates bust open a carton of raisins and give it all a revisit through their middle-aged lens. Crack open some peanut butter and join them.Old Roommates can be reached via email at oldroommatespod@gmail.com. Follow Old Roommates on social media @OldRoommates for bonus content and please give us a rating or review!#BennyandJoon #AidanQuinn #MaryStuartMasterson #JohnnyDepp #JulianneMoore 

Welcome to Watch. Review. Repeat. This is the podcast where two best friends discuss the latest in film and television and then do it all over again the following episode! Colton 16 and Andrew 17 sign up as expendables for South Korean filmmaker Bong Joon Ho's latest, 'Mickey 17'! 00:00:00 - Intro 00:05:37 - Colton and Andrew's Fun Facts About 'Mickey 17' 00:10:32 - Andrew's Totally Embarrassing Dad Joke of the Episode! 00:13:37 - 'Top Gun', 'Tombstone', and 'Batman: Forever' Actor Val Kilmer Dead at Age 65 00:17:55 - David Leitch in Talks to Direct 'Ocean's 14' 00:22:06 - Sarah Michelle Gellar to Return for 'Buffy the Vampire Slayer' Sequel Series Pilot 00:25:33 - Second and Final Season of 'The Sandman' Arriving on Netflix in July Following Sexual Misconduct Allegations Against Neil Gaiman 00:42:32 - Hasbro and Legendary Developing 'Magic: The Gathering' Film and Television Universe 00:46:40 - DC Studios Provides Update on Upcoming Film and Television Slate 00:58:16 - Neill Blomkamp to Write and Direct New 'Starship Troopers' Adaptation 01:02:36 - 'Coyote vs. Acme' Saved by Ketchup Entertainment, Expected to Release Theatrically in 2026 01:08:12 - Robert Pattinson Eyed for Villain Role in 'Dune: Messiah' 01:12:11 - 'Mickey 17' (Non-Spoilers and Recommendation) 01:39:21 - 'Mickey 17' (Spoilers) 01:53:21 - Catching Up With Andrew ('Daredevil: Born Again', 'Severance' Season 1, 'Toy Story' 1-4, 'Lightyear', 'Monster's Inc.', 'Monster University', 'Jurassic' Franchise, 'The Last of Us' Season 2, Boating Course ) 02:02:59 - Catching Up With Colton (Easter in New York, On Cinema at the Cinema, Midnight Suns, The Last of Us Part II, Yakuza 0, Nintendo Switch 2 Preorder, Underoath - The Place After This One, 'Abbott Elementary' Season 4) 02:16:59 - Catching Up With Andrew Pt. 2 ('American Idol') 02:20:34 - Conclusion/Outro Visit our website! Support us on Patreon! Thank you for listening, and please send any feedback to watchreviewrepeat@gmail.com! Intro/Outro Credit: Mechanolith Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License http://creativecommons.org/licenses/by/3.0/

This episode recorded live at the Becker's Hospital Review 15th Annual Meeting features Joon Lee, Chief Executive Officer of Emory Healthcare. Joon discusses Emory's transformation into an integrated health system, major workforce investments, and how data and analytics are powering cultural and operational change across the organization.

The latest episode of Elevator Talk features leaders from Joon, Scobucha, Hey!Hunger, Jozo and Fable Fish Co. Watch founders and CEOs introduce their brands and provide a recap of recent news and updates. This week's special co-host is Eleanor Hayden, the founder & CEO of Hayden Consultancy, who shared her thoughts, questions and feedback with the participants. She is joined by Ray Latif, the editor and producer of the Taste Radio podcast. Founders and CEOs of early stage food or beverage brands are invited to join future shows to pitch their products, discuss recent news and get feedback from industry experts. It is free to participate and interviews will be conducted remotely. Apply for a future episode of Elevator Talk.

When I tell you that this episode will not only change your life but may also change your dying, I mean it! Joon humbly shares his gentle wisdom with us as he shares about his experience as a hospital chaplain. He shares a bit about how a hard childhood led him to a compassionate career and where we can find and offer dignity at the end of a life. He has the ability to make a topic we all shy away from feel hopeful. Class is in session. Let's get curious! . . . . . Find Joon "J.S." Park on Instagram here: ⁠https://www.instagram.com/jspark3000/ ⁠ Order Joon's book here: ⁠https://www.thomasnelson.com/p/as-long-as-you-need/⁠ . . . . . Have a secretly extraordinary life? Apply to be a guest on my podcast in 2025 here: https://forms.gle/Z13WGj63oEfgmtjJ9 . . . . . Order your copy of my new book Reconnected HERE: ReconnectedBook.com  Let's keep in touch! Sign up for my newsletter to be the first to hear ALL my updates. https://app.e2ma.net/app2/audience/signup/1987227/1965424/ Interested in advertising with us? Reach out here. Book me to speak HERE: https://www.carloswhittaker.com/events  . . . . . FUNCTION: Go to functionhealth.com/CARLOSW to skip the waitlist and take ownership over your health today! Learn more about your ad choices. Visit megaphone.fm/adchoices

We're not exactly at odds, but there's definitely some healthy debate among the hosts around how to assess Expo West 2025. And it turns out, we're not alone. The biggest question on everyone's mind: how do we truly evaluate innovation, and what does it mean for the future of the food and beverage industry in the near term? Show notes: 0:25: Burner Apartment. ET x TR. Nom, Nom. Incremental Optimism. Snax & Bevs. Horny Goat Hummus. – Ray is in secret agent mode and shares a big announcement about Elevator Talk. Jacqui and Mike spill the goods on Nombase. John talks about why some folks misread innovation exhibited at Expo West, but Ray, of course, still has questions.  Jacqui highlights the possibility of a market correction, while Mike hails business fundamentals before getting excited about fruit bites and protein powders. John gets giddy about hummus and Jacqui shares a tingly product that makes some of the hosts blush.  Brands in this episode: Blue Hour, Cob, PWR-UP, Palmas, PKN, Jubilees, swinger, ISH, Pistakio, Joon, HYQ, Pulpito, Crushed Tonic, Honey Mama's, Onyx Coffee, Drywater, Ithaca Hummus, Graza, Cedar's, Cookie Chachi, Charmlee, Sturdy Sauce

  Matilda Joon      Dominique Stringfellow   In this cuntroversial episode we feature the dynamic poetry of Matilda Joon, and an in depth conversation between Lydia and Dominique Stringfellow concerning the need for female empowerment to replace the ever present imbalance in sexual relationships which have spiraled out of control.

In this powerful message, Pastor Joon explores the transformative power of decisions that are led by God, reminding us that every choice we make shapes our walk with Him. He challenges us to stay focused on His guidance, trusting that when we prioritize His will, our lives will glorify Him and reflect His purpose. Thank you for enjoying this life changing message from Radiant Church. We pray this moves you closer to Christ and encourages you. For more life changing resources, visit us at www.weareradiant.com.

Fashion is a huge part of the world's waste problem, but it doesn't have to be. Coachtopia founder Joon Silverstein shows how her company creates new designs from the waste products of another, a circular process that cuts the need for new raw materials — and rethinks what qualifies as "luxury." This talk was made in partnership with Coachtopia. Stay tuned afterward as Modupe shares her thoughts on one way companies could cut back on waste. Hosted on Acast. See acast.com/privacy for more information.

Fashion is a huge part of the world's waste problem, but it doesn't have to be. Coachtopia founder Joon Silverstein shows how her company creates new designs from the waste products of another, a circular process that cuts the need for new raw materials — and rethinks what qualifies as "luxury." (Made in partnership with Coachtopia)

Fashion is a huge part of the world's waste problem, but it doesn't have to be. Coachtopia founder Joon Silverstein shows how her company creates new designs from the waste products of another, a circular process that cuts the need for new raw materials — and rethinks what qualifies as "luxury." (Made in partnership with Coachtopia)