Podcasts about recreating

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

In this episode of The CEP Mindset Show, Cassidy, Nick, and Adam dive into one of the most common traps athletes fall into before a new season — loading up on expectations before a new season begins. The episode covers why the weight of preseason expectations derails more athletes than any slump ever could, the difference between replicating and recreating after a great season, and the future self exercise that flips the entire relationship with the season ahead.

03:14 Lehmo 07:25 Yay Or Nay 11:39 Taken: Secret Mics 16:21 Pete's Country Music Game 20:14 Alana Watched A Thing 23:25 Jake Waterman 32:40 Taken Monologue 39:43 What's Trending See omnystudio.com/listener for privacy information.

Chef and Iron Chef America winner Mark Tarbell opens up about nearly drowning, spending the next three decades sleeping only two to four hours a night, and eventually learning to use breath, meditation, service, and the pause to make better decisions. He also reveals the obsessive preparation behind his Iron Chef victory, why he refuses to live in past glory, and how saying no can become a gift. Show Partners: Get your MENTAL FITNESS BLUEPRINT here! A special thanks to our mental fitness + sweat partner Sip Saunas Personal Socrates: Better Question, Better Life   Connect with Marc: https://konect.to/marcchampagne   Timestamps: 00:00 — The question that opens every interview: “Who are you?” 00:14 — Mark Tarbell on having the heart of a servant 00:45 — Where Mark's generosity and humility began 01:33 — The grandmother whose home was always open 02:03 — Nearly drowning and realizing we are one breath away 02:46 — When a high achiever discovers he is not in control 04:30 — Sleeping two to four hours a night for 30 years 05:29 — Why the breath became deeply personal 06:45 — Using breathwork to regulate a driven mind 08:56 — Teaching restaurant teams the power of the pause 09:23 — The three-option method for making decisions under pressure 12:27 — The Jacques Pépin book that changed Mark's life 14:16 — Fighting for an apprenticeship in Amsterdam 15:41 — Calling a Paris culinary school until they finally said yes 17:22 — Why chaos makes Mark feel calm 18:09 — The real role of a chef, server, or restaurateur 20:56 — How to stop other people's emotions from sticking 22:59 — Cooking for the Dalai Lama and struggling with meditation 24:28 — Finding stillness inside chaotic environments 25:10 — How preparing food can become a meditation 27:22 — The unexpected call from Iron Chef America 28:48 — Preparing for a 97% chance of losing 30:51 — Recreating the competition before ever entering it 32:28 — Why Mark has never watched his Iron Chef victory 32:58 — Refusing to live in the glory days 34:23 — Creating filters for new opportunities 34:49 — Recovering from “yes-itis” 35:39 — What happens during Mark's three-day decision pause 36:09 — Why no is the most underused word 36:29 — The gift of goodbye 37:11 — The advice Mark would give his 14-year-old self 37:30 — Living from the inside out 37:49 — Why love anchors everything 38:09 — Discernment versus judgment 39:40 — What Mark hopes his children see in him 41:18 — The ripple effect of following an authentic calling * Special props

If you start every Monday feeling like you are rebuilding your entire life from scratch, I made this episode for you. Not because you are doing it wrong. Because nobody ever showed you what it looks like to build a week that actually repeats itself so you do not have to keep recreating it over and over again. Every Sunday you are making hundreds of new decisions. Meals, laundry, kids, appointments, cleaning, work. And by the time Monday starts you are already mentally exhausted before you have even opened your laptop. In this episode I am walking you through exactly how to stop that cycle and build a repeatable week that protects your work block, removes the decision fatigue, and finally gives your business the consistent focused time it needs to grow. xoxo, Chelsi Jo . . . . . Free Workflow Workshop — The Four Workflows every business mom needs to consistently grow her business

Today's episode dives into a neuroscience lesson that explains why it's so difficult to truly break old patterns and limiting beliefs. The stories we tell ourselves ultimately become part of our identity that then lives in our body and reflects in our outside world (our jobs, money, friends, all of it).What limiting stories have you been telling yourself?What's ready to be released and moved out of your "old" identity?Understand exactly what's happening in your brain behind the scenes. This is step 2 to then truly shifting it.This is a small clip from our full workshop, The Glow Foundation, that's over 3 hours. For our advanced besties who want the full downlow now... access the full training here: https://www.alexandraninfo.com/theglowfoundationFOLLOW MEInstagram - https://www.instagram.com/alexandraninfo TikTok - https://www.tiktok.com/@alexandraninfo You Can Also Listen to Unf*ck Yourself Podcast HereSite - https://www.alexandraninfo.com/podcast Apple Podcast - https://podcasts.apple.com/us/podcast/unf-ck-yourself/id1647393740Spotify - https://open.spotify.com/show/4OfhtVIbV73xuSrZ2MnXKZ?si=f3fabaa47ca4482eYouTube - https://www.youtube.com/@AlexandraNinfo

The "man-moulders of the new age" vs. Imago Dei. __________ Partner with thousands of others in supporting The Colson Center by visiting colsoncenter.org/cornerstone.

Christopher Nolan's newest blockbuster film The Odyssey, with a star-studded line up including Matt Damon, Robert Pattinson, Zendaya, Anne Hathaway and Tom Holland, hits the cinemas next week, and it's already causing a stir. But what is the film really about? On this episode of the Fourcast, Krishnan Guru-Murthy sat down with Christopher Nolan and Tom Holland and explored the themes of revenge, the hero's journey, homecoming, and asked how the Greek concept of Zeus's law could be linked to the debate around immigration today. Later Krishnan spoke to classicist Edith Hall to delve deeper into the epic tale.

In late May at the WonderFest convention in Kentucky, a group of friends and model makers blew attendees minds with a one-to-one scale immersive replica of the original ILM model shop as it existed in Van Nuys, California, in 1976. This week we are thrilled to be talking to the creators of this Van Nuys 76 in 26 project Bryan Babich, Quincy Cutshaw and Jason Eaton. Listen as they explain where the idea to do this came from, who helped make it possible, all the little details and so much more. So turn on the stereo, celebrate the love and listen today! Join the Van Nuys 76 Facebook group here : https://www.facebook.com/share/g/18vdP5vcYr/?mibextid=wwXIfr See all the Tested videos here : https://youtu.be/xCtcDll-fNE?is=pxSLPAHvGSf8r6ml https://youtu.be/NgKCgopedJA?is=ylzM66ndn73fqIP3 https://youtu.be/-gELkfuYUbc?is=7SrzKb0oCt4SjFGI JOIN THE BLAST POINTS ARMY and SUPPORT BLAST POINTS ON PATREON! MANDALORIAN SEASON 3 BOBA FETT BEACH PARTY COMMENTARY! NEW ANDOR SEASON 2 EPISODE COMMENTARIES! HEAR EPISODES EARLY! Theme Music! downloadable tunes from episodes! Extra goodies! and so much MORE! www.patreon.com/blastpoints If you dug the show, please leave BLAST POINTS a review on iTunes, Spotify and share the show with friends! If you leave an iTunes review, we will read it on a future episode! Honestly! Talk to Blast Points on twitter at @blast_points "Like" Blast Points on Facebook Join the Blast Points Super Star Wars Chill Group here www.facebook.com/groups/BlastPointsGroup/ we are also on Instagram! Wow! www.instagram.com/blastpoints Your hosts are Jason Gibner & Gabe Bott! contact BLAST POINTS at : contact@blastpointspodcast.com May the Force be with you, always! This podcast is not affiliated in any way with Lucasfilm Ltd. LLC, The Walt Disney Company, or any of their affiliates or subsidiaries.

Join this channel to get access to early episodes!https://www.youtube.com/channel/UCzqhQ4tMBPu5c6F2S6uv0eg/joinAnythingCast Episode 235========================================• LISTEN ON ALL OTHER PLATFORMS!

Dr. Pierre Elias, a cardiologist and medical director of AI at NewYork-Presbyterian and Columbia, explains the impact of artificial intelligence advancements in the medical field, including EchoNext that received FDA approval through Pathway Labs, and how we can use AI without leaning on it too much.All three congressional candidates who were endorsed by New York City Mayor Zohran Mamdani won their primaries on Tuesday. Ed O'Keefe explains what the results could mean for the Democratic Party moving forward.Survivors and their families are accusing the Pentagon of downplaying the injuries service members suffered during the deadly Iranian drone strike in Kuwait on March 1. In an exclusive interview with CBS News' Jonah Kaplan, a wounded soldier said he "absolutely" believes the Army and the Pentagon have tried to downplay the incident.Portugal's Cristiano Ronaldo scored twice on Tuesday, making him the first player ever to score goals in six World Cups. The final round of the tournament's group stage begins on Wednesday. Cristian Benavides reports.Parents are feeling nostalgic for summers they grew up with and are jumping on the trend to give their kids a '90s summer. It's a push to swap screens for activities. Emily Oster, the founder and CEO of Parent Data, speaks with "CBS Mornings" about the trend and what parents can incorporate.UFC champion Conor McGregor speaks to "CBS Mornings" co-host Nate Burleson about the highs and lows of his career as he prepares to return five years after retiring.Since Kidz Bop started, it has racked up 45 No. 1s on the Billboard kid albums chart. "CBS Mornings" goes behind the scenes as Kidz Bop celebrates 25 years and meets a Broadway performer who credits the company for his start in the industry.

A perfumer in France has been recreating the smells in Joyce's Ulysees. The project was recently showcased at the Irish Embassy in Paris. Meabh McCurtin, an Irish fine fragrance perfumer based in Paris joined Sean this morning to discuss.

Text us your questions or topics for the show! We got you!Cass Morrow, Author of Disrupting Divorce: The NEW Man. Saving Struggling, Sexless, and Toxic Marriages.Kathryn Morrow, Author of Behind The White Picket Fence.Recreating The Spark!Lost the spark? Feel like roommates? Want to “get back to how it used to be”?Cass and Kathryn's reframe: chasing the spark is usually the wrong goal—because it's often just a mask for coasting, quitting, and emotional decision-making.In Ep447 of The ‘NEW' Marriage, they break down what “recreating the spark” actually means, why “settling” is downstream of coasting, and how to rebuild passion through growth—not nostalgia.

Part Two of this week's episode: our chat with the brilliant Paul Tonkinson is finally here!

Recorded live from Contact in the Desert 2026, we welcome filmmaker, researcher, and former MUFON Canada national director Luigi Vendittelli for an in-depth conversation about one of the most iconic and controversial stories in ufology.Luigi takes us behind the scenes of his groundbreaking documentary S4: The Bob Lazar Story (2026), detailing the years he spent collaborating directly with Bob Lazar. Together, they meticulously reconstructed the secretive S-4 facility at Papoose Lake—complete with its hidden hangars inside the mountain—and the legendary “Sport Model” craft, using cutting-edge digital tools like Unreal Engine 5 and Blender for forensic accuracy. From the technical challenges of bringing Lazar's descriptions to vivid, to newly uncovered evidence (including a rare 1942 map confirming the site's location), Luigi shares what it was like to verify and visualize details that have fueled decades of debate. We explore the reverse-engineering claims, the physics-defying propulsion systems, the personal toll on Lazar, and why this reconstruction feels more real than ever. Whether you're a longtime skeptic, a dedicated believer, or somewhere in between, this episode dives deep into the painstaking effort to honor and illuminate one of ufology's most enduring narratives.

Harav Yussie Zakutinsky Shlita

Dive into a fascinating discussion with hosts and brewers exploring the rich history of American beer. From historic recipes to modern collaborations, this episode highlights the enduring spirit of brewing in the United States and the power of beer to connect communities and preserve tradition.Key Topics:The revival of 18th-century brewing recipes, including Martha Washington's rules for brewing and hand-measured ingredientsCollaboration between modern breweries like Dynasty, Mount Vernon, and Chilly Hollow to recreate historical beersThe significance of local ingredients and sourcing authentic regional barley varieties like six-row maltThe impact of historic hops such as Liberty and Cluster in recreating authentic colonial-style brewsHow breweries are making historic styles accessible today with modern techniques and styles like table beerThe importance of community events, beer shares, and collaborations that celebrate American beer historyThe effort to release historic recipes online, encouraging breweries nationwide to participate in a movement honoring our brewing pastThe role of beer in cultural heritage, including its connection to the American Revolution and local historyTimestamps:00:00 - Introduction and social media shout-outs 00:13 - Brandy's favorite DC IPAs and local beer shout-out 01:00 - Mike shares about Dovetail Pills, brewed with German malt and hops 01:21 - Announcement of Declaration from DC Brau, a 5% sunny pale ale 01:45 - Favio Garcia introduces Eckhardt's Dark Czech Lager from Mike's recommendation 02:11 - Pete Jones discusses Music Remembered, a peach sour from Miesa Blenderie 02:26 - Brandy raves about Miesa's smoked peach beer and its unique qualities 03:16 - Transition to brewing at Chili Hollow in Berryville 03:29 - Favio explains the brewing location and historic context of Chili Hollow 04:13 - The collaboration with Chris Jakes and the history of Dynasty Brewing 05:06 - Pete and Mike find and adapt historic recipes from 18th-century texts 05:51 - Using Martha Washington's cookbooks and historical measures in brewing 07:14 - Converting old measurements and sourcing ingredients locally 08:55 - Challenges of translating 18th-century brewing instructions and methods 10:40 - Sourcing historical yeast strains and malt varieties (like six-row barley) 13:15 - The history of collaborations between Lost Lodgers and breweries like Rocket Frog and Quattro Goombas 14:44 - The inspiration behind brewing historic pale ales and bitters 15:35 - Partnership with Mount Vernon and the importance of local ingredients 16:02 - How historical recipes are adapted to modern brewing and local ingredients 17:02 - Celebrating five years of collaboration with Mount Vernon and local breweries 18:01 - The significance of recipes like Virginia Porter and the influence of colonial brewing laws 19:08 - Innovating with ingredients like rosemary and making historic styles accessible 20:01 - Pete's longstanding partnership with Right Proper and their historic brewing projects 21:15 - The enduring timelessness of brewing heritage and the importance of community support 23:48 - Celebrating new breweries and the future of American beer including the first woman and Black-owned brewery in DC 26:49 - The cultural and historical importance of styles like table beer and the diversity of brewing traditions 33:46 - The ongoing project of releasing historic recipes and engaging breweries nationwide 34:33 - The significance of hops like Liberty and the collaboration with regional hop growers 36:11 - Closing remarks and encouragement to support local breweries and historical brewing initiativesResources & Links:Dynasty BrewingChilly Hollow BrewingLost LagersRight Proper BrewingWheatland SpringAdditional:Stay tuned for the online release of historic recipes from Martha Washington and others—encouraging a broader movement to brew and celebrate America's brewing pastJoin the upcoming beer share event at Franklin Hall on June 18th and participate in the Land Beer Fest tripCheers to celebrating history through beer and supporting local, heritage-driven brewing initiatives!Thank you for tuning in! Follow what's happening in the DC scene at DCBeer.com and @dcbeer on social media. Support us at Patreon.com/DCBeer Thanks to our monthly supporters Brian Dauernheim Quinten Patterson C Sandoval Gilbert Glickstein Ethan Sapperstein Sean Whipkey Randy Mills Ryan Llalan Fowler Michael Losi Adam Heisenberg Brian Jeff Lucas Micaela Carrazco Lauren Sean Moffitt Anthony Scipione johnna infanti Catherine Ramirez Kristin Adam Frank Tyler Lynch Jared Prager Jeff Michael O'Connor Favio Garcia Josh Ellen Daniels Juan Deliz Mike Lastort James Wisnieski Chris Frome Sam Chip Tory Roberts Chris DeLoose Lauren Cary Clifton B Scott Pavlica Greg Antrim jeffrey garrison Alexis Smith Dan Goldbeck Anthony Budny Greg Parnas Frank Chang Kim Klyberg Chris Girardot Alyssa jeffrey katz Andrew MacWilliams Jamie Jackson Meegan Mike Rucki Nick Gardner Amber Farris Sarah Ray Peter Jones Blue2026 Brad Stengel Bill and Karen Butcher Jordan Harvey Stephen Claeys Julie Verratti

In this episode of Space Cafe Radio, host Torsten Kriening, Publisher of SpaceWatch.Global, sits down with Marshall Smith, CEO of Starlab Space, at the 41st Space Symposium in Colorado Springs. The conversation reconnects a story that began in Bremen back in 2018, when Marshall was wiring together SLS, Orion, and Gateway at NASA, and now finds him on the other side, building the commercial future he once championed from inside the agency.From NASA Insider to Commercial BuilderMarshall reflects on his transition from NASA, where he was always "commercial at heart" - pushing the system to go faster, do things differently, and question whether all those requirements were truly necessary. Now leading Starlab, he gets to put those convictions into action.The Time is Now for Commercial LEO"Now's the time to turn over Low Earth Orbit to commercial enterprise."After 54 years of space stations and more than two decades of permanent crewed presence on the ISS, the industry has learned enough. The technology readiness is there. The standards are there. The time has come for commercial enterprise to take over LEO so NASA can focus on the harder things- Moon, Mars, and beyond.Where Starlab Stands TodayMarshall reveals concrete progress: Starlab is past Critical Design Review with NASA (completed in December), in manufacturing, building structures, with long-lead items in process. They're roughly five to six years into the typical six-to-ten-year development cycle for a complex space vehicle. Some say they haven't hit the hard part yet, but Marshall responds: "We have the capability, we have the experience. Eyes wide open."The Real Gap RiskThe ISS retires around 2030. China's space station is operational today. Marshall is candid about the leaks, the aging equipment, the obsolete components, and the uncertainty about whether extension to 2030 or 2032 will be possible. Starlab's launch target is 2029 - and they intend to be there before the gap opens.Recreating the ISS Partnership - CommerciallyStarlab is a joint venture with Voyager as majority shareholder, joined by Airbus, Mitsubishi, MDA, Palantir, and Hilton -a multinational structure that recreates the ISS partnership at a commercial and business level. The same companies that built parts of the ISS are now building the commercial successor.Starship as the Launch PlanMarshall explains why he's not worried: Starship has already been to orbit, and Starlab only needs to reach orbit and deploy - no Moon landing required. By the time Starlab launches, Starship will likely be on its seventh version.The Manufacturing VisionMarshall hints at semiconductor manufacturing, biopharma, pill production, fiber optics, and a proprietary concept that could revolutionize the pace of in-orbit manufacturing. He predicts the demand will be so great that companies might want their own dedicated Starlab modules - and that copies could be built in roughly one to two years.The iPhone Moment for Space Stations"In 2007, somebody built a platform called an iPhone. It had a few games, didn't even do FaceTime. Now you can't walk around without your phone. CLDs are platforms. Ten years after operations begin, you're going to see things you would've never imagined - maybe ordering a replacement heart tuned to your DNA, printed in space."On Artemis 2Having been involved in Artemis 1 and 2 at NASA, Marshall shares his personal joy at the mission's success. For him, it's a signal to the world that humanity is going back to deep space, to the Moon's surface, building Moon bases, going to Mars.The Bigger Mission"It's about becoming a multi-planet species. Maybe one day becoming a multi-stellar species. I know that sounds crazy to some people. That's why I do this. Because I don't want to see us being here locked on this planet a thousand years from now."Marshall draws the parallel to the 1400s - when explorers asked "what if we cross this big body of water?" - and now humans are asking the same question about the void of vacuum. The exploration accelerates. We were built for this.For Listeners Who Think BigThis is a conversation about commercial space stations, the urgency of LEO transition, the iPhone-platform future of orbital manufacturing, and what it means to become a multi-planet species.Space Café Radio brings you talks, interviews, and reports from the team of SpaceWatchers while out on the road. Each episode has a specific topic, unique content, and a personal touch. Enjoy the show, and let us know your thoughts at radio@spacewatch.globalWe love to hear from you. Send us your thought, comments, suggestions, love lettersSupport the showYou can find us on: Spotify and Apple Podcast!Please visit us at SpaceWatch.Global, subscribe to our newsletters. Follow us on LinkedIn and X!

Steve Misamore has been drumming since he was 11 years old. He has been the drummer in Dierks Bentley's band since 2002. Steve moved to Nashville in 1993 from Houston TX. Before he met Dierks, he did all sorts of gigs in town from bars to studio work, to private gigs and everything in between. Steve met Dierks playing the bars in Nashville in 1999 and helped him open some doors at Sony/ATV. Steve has been working with Dierks ever since. Steve played on some of the early stuff like "Free and Easy" "Feel That Fire" as well as a few others. In this episode, Steve talks about: Creative ways to stay engaged with drumming when not touring The evolution of the Dierks Bentley gig Working with Dierks from the very beginning Recreating the drum parts from Dierks' records Translating the studio performance into a live performance Getting his pilots license The parody group Hot Country Knights The early influence of Herman Matthews ⁠⁠⁠Here's our Patreon⁠⁠⁠ ⁠⁠⁠Here's our Youtube⁠⁠⁠ ⁠⁠⁠Here's our Homepage Learn more about your ad choices. Visit megaphone.fm/adchoices

In today episode at You Can Overcome Anything Podcast Show, Cesar R. Espino brings to you a special guest by the name of Stacie Shifflett.At first glance, Stacie might look like any other accomplished woman well over 60. But behind those glasses and soft smile is a powerhouse who's acquired a $50 million software company with no cash upfront, became a sought-after federal government procurement expert with no formal training, owned a construction company, worked in hospitality, and raised llamas for 12 years. Stacie is the queen of reinvention. After her 28-year marriage ended, she created Modern Consciousness® and her Amazon bestselling book, Treasure Map to Joy™. She guides people through structured self-discovery — not by telling them what to do, but by helping them find the answers within. Her best advice: calm your emotional triggers. It's life-changing.Stacie Shifflett' message to you is:Joy is as unique to each of us as a fingerprintTo connect with Stacie Shifflett go to:https://modernconsciousness.com/https://www.facebook.com/ModernConsciousness/https://www.instagram.com/modernconsciousness/https://www.linkedin.com/in/stacie-shifflett-7b5a8922/empower@aware.lifeAnother amazing Episode of You Can Overcome Anything! Podcast Show.  If you are not subscribed yet, make sure you hit the Subscribe bottom and join us today.  To Connect with CesarRespino go to:

Experts hope a new floating wetland project will bring back natural “edge habitat” that disappeared when developers reshaped Boston's perimeter with landfill and seawalls. Recreating those destroyed coastal environments can help protect cities as climate change brings rising sea levels, increases stormwater runoff and disrupts ecosystems.

Step inside a 1980s Kentucky department store as Kayla Rae Whitaker shares the family secrets and ambition behind her novel Returns & Exchanges. Book Gang welcomes acclaimed author Kayla Rae Whitaker to discuss her much-anticipated new novel, Returns & Exchanges. Whitaker's immersive storytelling and meticulous research bring the 1980s era and its consumer culture to vibrant life. Set in Kentucky during the 1980s, this sweeping family drama follows Fred and Fran, a couple whose rags-to-riches ascent as department store owners brings both fortune and unexpected turmoil. As their business thrives, the family's personal lives become increasingly complicated in this messy family saga. Through multiple perspectives and intricate subplots, the novel explores themes of identity, desire, mental health, and the complexities of the American dream in this page-turning story. In this warm and insightful conversation, we discuss:

On this episode of Proof-of-PR, Kelley Weaver is joined by ​​Evan Drake, the founder of Soulcraft, an agentic marketing agency helping businesses of all sizes speak from their soul in the AI era. Evan has spent 20 years shaping brand narratives at companies like Apple and Chainlink. Soulcraft specializes in the new discipline of AI search optimization, and at the core of everything is something Evan calls a soul.md, a structured identity file that teaches AI agents to sound like you. He is also the creator of Opensoul as an 'agency of agents,' built entirely around intelligent automation. To stay up-to-date on upcoming guests and news by following us on Twitter at @ProofOfPR. #PRtips #TheBitcoinConference #ProofofPR #MediaRelations ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬● ⏰ Timestamps: 0:00 | Intro 1:32 | Who is Evan Drake? 4:07 | Recreating the Marketing stack 6:15 | Soul markdown standard 8:15 | Autonomous Agency of AI Agents 13:15 | Opensoul replaces cost of marketing hires 16:44 | Downtime of Claude AI & problem for businesses 19:20 | AI search optimization 22:14 | BITWIRE AD 26:15 | Placing brands into "high intent" AI conversation 28:05 | AI advertising with LLMs 32:01 | AI conversations on Twitter 33:24 | Will companies begin to pivot their marketing strategies? 41:04 | How should crypto brands think about AI and marketing? 42:54 | How to keep up with Evan Drake 44:50 | Outro ●▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬●

In this episode, Ben explores how confidence is often linked to the conditions surrounding our work rather than being a fixed personal trait. When people lose confidence, it can feel as though something internal has changed. However, Ben suggests that confidence is frequently influenced by external factors such as preparation, clarity, experience and support. One practical approach is to reflect on situations where you previously felt confident and performed well. By identifying the conditions that were present in those situations, you can begin to recreate them and give yourself a better foundation for confidence again. This perspective helps leaders focus on shaping the environment and conditions that support confidence rather than trying to force confidence through willpower alone. Resources mentioned in this episode: Work with Ben.

No obvious lede today. The news on Ahmad Hardy seemed to be more encouraging as the day went on yesterday. The cameras are fixed today. Call me insatiable again, see what happens. Cool For The Summer's wikipedia page is rich with text. Corbin got em. Expanding the Boi Empire. Pigeon Pie. Lousy with catchers. ATMA is an audio bake sale. McGreevy is gonna "try" to call in a 9. Old Busch Stadium urinals. Centuries of historic brine.Is McGreevy upset we didn't have him on yesterday after six shutty in San Diego? Jets 'R Us. You seen the cost of jet fuel lately, Dog? Is McGreevy a time traveler? Sharon's takin' heat. Trying to get ahold of The Colonel.Still efforting a busy Gabe DeArmond. Audio of Chaim Bloom talking about his approach if the Cardinals are in the hunt approaching the trade deadline. The current number set for Cardinal win total. Don't say landscape, you pretentious ass. Work harder, or marry better. You belong to the public.Is this Fergie or BEP? Doug, do you want Larry Nickel or Dan Janson? Larry hasn't seen the Hulk Hogan documentary on Netflix yet. Debating the legitimacy of WWE officiating. Larry explains Danhausen to Doug. I'm still talking. Top 5 Country rankings.Joined by The Colonel Gabe DeArmond of Power Mizzou talking about what we know so far on the Ahmad Hardy situation. We may not even know about this if Mizzou hadn't put out a statement. Hardy in stable condition. The original statement had a much more serious tone to it. On the football side of things, Jamal Roberts slides to RB1 if Hardy can't play. Obviously the most important thing is that it sounds like Hardy is going to be ok. What does the Mizzou team look like without him?A beautiful return song about Festus. Doug doesn't get himself down to Festus enough. Nick Wright has takes on players bringing their children to press conferences after losses. You ever have cereal with water? Out on hot cereal, especially porridge. Maybe bring your dog up after the end of the season.Doug guessing MLB power rankings. Friend of the show Michael McGreevy checking in from Sacramento. What was the scene like pitching in front of family and friends in San Diego? Finding different ways to get people out when the velocity is down. Michael didn't seem to want to answer Martin's question on being familiar with opposing players' stats. A busy wedding weekend that golf didn't fit into. The team's thoughts on exceeding expectations. Questions and observations from the audience. Trying to coax McGreevy to The Dotem. Soft yes.Look Doug, it's Brody Hermann. Brody breaking down what he's seen out of the Cardinals team so far this year. Brody doesn't want the Blues trading any draft picks. Dylan Holloway's health. Brody's covering a lot of ground here. Mizzou football and the new offensive coordinator and quarterback.Design Aire Heating & Cooling EMOTDStumbles into music theme guy. Iggy Azalea. The new show: After Onboarding. Recreating the LSU mom GIF at The White House. Take care of the melanoma before you pop off. Dan Janson is on the phone lines and wants to talk about the major golf event going on this week. Not the PGA, not The Dotem, but the APGA. Congrats, Dan, for volunteering. We're gonna try again with Mike on the phone lines. Mike just wants to give some praise and reminisce on Producer Joe fighting at strip clubs.You just can't teach this. You either got it or you don't. We're gonna build on this money. Doug's already losing interest in fantasy baseball. Southside Seamen don't accept mediocrity. The secret to good sleep is nearly being dead.And the winner of the Design Aire Heating & Cooling EMOTD is...See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(00:00-18:47). Stumbles into music theme guy. Iggy Azalea. The new show: After Onboarding. Recreating the LSU mom GIF at The White House. Take care of the melanoma before you pop off. Dan Janson is on the phone lines and wants to talk about the major golf event going on this week. Not the PGA, not The Dotem, but the APGA. Congrats, Dan, for volunteering. We're gonna try again with Mike on the phone lines. Mike just wants to give some praise and reminisce on Producer Joe fighting at strip clubs.(18:55-31:14). You just can't teach this. You either got it or you don't. We're gonna build on this money. Doug's already losing interest in fantasy baseball. Southside Seamen don't accept mediocrity. The secret to good sleep is nearly being dead.(31:24-33:23) And the winner of the Design Aire Heating & Cooling EMOTD is...See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most insurance producers spend their entire careers trying to fit in. They use the same scripts, wear the same suits, and pitch the same spreadsheets. But here is the hard truth: if you look like everybody else, you will be overlooked. In a crowded market, people pay a premium for scarcity.In this solo episode, I share my personal journey from being a "fat kid" in Size Husky jeans to a pro baseball player and, ultimately, a founder. I break down the mental trap of how the fear of judgment, comparison, and failure kills more insurance books than the competition ever could. We discuss the 20-60-20 Rule of prospect psychology, the four pillars of the Brand Authority Blueprint, and why elite producers measure themselves against the best version of themselves rather than their peers. If you're ready to leave the masses behind and recreate the future of your career, this episode is your wake-up call.▶▶ Sign Up For Your Free Discovery Callcompletegameu.com/agaKEY MOMENTS(00:00) Growing Up as the "Chunky Kid" in the 80s(01:14) The Enemy: The Annual Presidential Fitness Test and the Fear of Judgment(02:20) Standing Out: How Baseball Turned Me into a One-of-One Athlete(03:08) The Mental Trap: How the "Fat Kid" Came Back During Pro Ball(03:47) The Clear Message: If You Look Like Everyone Else, You Will Be Overlooked(04:22) Scarcity Wins: Why People Pay a Premium for the Unique Advisor(05:49) Why We Are Afraid: Overcoming the Fear of Standing Out(06:16) The 20-60-20 Rule: Winning the Undecided 60% of Your Market(08:01) Stop People-Pleasing the 20%: Why You Shouldn't Fear Sharing Your "Good Stuff"(09:34) The Brand Authority Blueprint: Four Pillars to Becoming a One-of-One(11:09) Traits of the Elite: Why One-of-Ones Invest in Themselves and Crave Feedback(12:34) Lions Don't Compare Themselves To Donkeys(13:27) The Usain Bolt Standard: Chasing Personal Records, Not World Records(14:35) Recreating the Future: Following the Footsteps of Gates, Jobs, and Huang(15:50) The Challenge: Four Questions to Rewrite Your Professional FutureCONNECT WITH ANDY NEARY

Demonstrating the difference between Schottky diodes and PN junction silicon diodes, and how your multimeter can influence your measurement when troubleshooting. Answering an interesting Twitter question. https://x.com/blind_via/status/2052223991839170919 https://www.tti.com/content/ttiinc/en/resources/blog/infographics/schottky-diodes.html https://engineering.purdue.edu/~ee606/downloads/ECE606_f12_Lecture17.pdf Forum: https://www.eevblog.com/forum/blog/eevblog-1746-schottky-vs-pn-diodes-measurement-traps/ 00:00 – Twitter question 01:21 – Recreating the problem 03:08 – Why doesn't it read zero volts? Let's try a PN junction diode 04:21 …

After Donald Trump announced a pause to the US operation in the Strait of Hormuz, Iran's online propaganda machine was quick to declare victory. Explosive Media, one of the groups behind Lego-style videos mocking Trump, proclaimed it "TACO Tuesday", ie that the US president had "chickened out". Meanwhile, Minecraft, the Minions and Simpsons-style characters are joining the legions of copycats. FRANCE 24's Technology Correspondent Peter O'Brien looks at how these videos are actually made.

In this episode, Cornelius Edison shares his inspiring journey of navigating life's shifts—from running a half marathon untrained to transitioning out of professional football—highlighting the importance of resilience, faith, and systemic impact beyond the game. Guest Links Cornelius Edison - LinkedIn Mission 22 - Official Website The Art of Hustle Podcast Instagram   Main Topics:   The mental and physical lessons learned from running a half marathon with minimal training How humility and community shape the coaching journey and personal growth Transitioning from NFL athlete to community builder and systemic thinker The role of faith, discipline, and purpose in overcoming life's setbacks Building long-lasting systems that outlive individual effort and foster legacy The importance of a beginner's mindset, vulnerability, and humility in reinvention Balancing intensity and grace within leadership, parenting, and community service Practical strategies for NFL players and athletes to retool their identity and emotional intelligence The significance of systems, routines, and continuous improvement over fleeting motivation Timestamps:   00:00 - Introduction: Navigating life's major transitions with resilience 00:28 - Cornelius shares his experience running the Bend Half Marathon 00:55 - Coaching others through mental barriers and humility 01:23 - The diverse community within his gym and the power of shared effort 02:22 - Patterns and similarities among gym members and why community is vital 03:21 - The process of self-discovery through hard challenges and seasons of life 04:43 - From childhood to NFL, the sacrifices, and joyful suffering 05:11 - Rebuilding identity post-football and the importance of faith 06:23 - Motivation versus discipline, and the reconfiguration of purpose over time 06:52 - The necessity of systems and long-term planning for sustainability 07:50 - Lessons from endurance challenges and pushing through discomfort 08:18 - The importance of testing your reservoirs to discover inner strength 08:48 - Post-career struggles, resilience, and turning adversity into impact 09:18 - Managing personal and professional setbacks: pivoting and re-strategizing 10:16 - Building systems that outlast individual effort and foster legacy 11:06 - The role of creative thinking and adaptability in professional reinvention 11:34 - Recreating oneself at any stage, especially after sports or military service 12:03 - Advocating for purpose-driven, intentional living and connecting with authentic self 12:33 - Learning from failures and engineering success through mentorship and humility 13:02 - The importance of a beginner's mind to stay humble and open to growth 13:29 - The value of slowing down, reflective practice, and intentionality 14:56 - Developing emotional intelligence post-career and managing new challenges 15:25 - The power of tracking emotions and seeking mentorship for continued growth 16:07 - The challenge of taking the first step into new arenas and retooling identity 17:03 - The significance of connecting with community and the role of vulnerability 17:32 - Lessons from sports about handling wins and losses, and building team cohesion 19:00 - The paradox of winning and losing: learning and growth through both 19:27 - Understanding the balance of positive and negative interactions in relationships 20:26 - Reflection on a pivotal loss and its role in deepening bonds and growth 22:52 - Comparing sports seasons to life lessons about belief, perseverance, and grace 24:14 - The importance of purpose, belief, and managing expectations in life and sports 24:55 - How discipline outperforms motivation and creates sustainable habits 25:32 - The evolution of motivation into obsession, driven by purpose and discipline 26:28 - Turning motivation into ingrained habits and the role of “action” in results 28:22 - The significance of systems over emotions for consistent performance 28:50 - The power of action, intensity, and execution in achieving success 29:19 - Building supportive, flexible systems that nourish community and sustain effort 29:49 - The balance of intensity and grace in leadership and parenting 31:26 - The integration of tough love and tenderness—lessons from notable figures like Gordon Ramsay 32:19 - Differentiating between tough love and bullying in leadership styles 33:18 - The importance of grace, boundaries, and adaptive parenting 34:14 - Nurturing conversations and real community connection in a digital age 36:12 - Teaching kids the value of sitting with themselves and embracing imperfection 37:39 - The power of intentionality in cultivating purpose and legacy 38:07 - The critical role of community, mentorship, and ongoing influence in youth development 39:39 - The importance of timeless principles and the dangers of distraction 40:07 - Recognizing one's unique purpose and the influence of internal calling 41:01 - Learning to filter information and focus on impactful work 42:12 - The need to reduce noise, verity, and over-focus on problems to create effective solutions 43:13 - Developing systemic solutions to community issues and small-scale problems 44:41 - Education, delay of gratification, and the importance of incremental development 45:41 - Transitioning from grand visions to small, meaningful actions with big impact 46:40 - Moving at the right pace: understanding the different speeds of public and private sectors 47:39 - Creating sustainable systems that withstand change and adversity 48:21 - Building legacy through systemic design and team empowerment 49:28 - The joy of seeing systems and people grow and thrive beyond individual effort 51:22 - The importance of resilient systems, day-tight compartments, and managing life's punctures 52:19 - Connecting with the community and sharing ongoing journey   To contribute to the the Post-Traumatic Growth of Veterans click here. To learn more about Mission 22's impact and programs, visit www.mission22.org or find us on social media. IG: @mission_22. Tiktok: @_mission22

Lark Voorhies on Saved by the Bell, Lisa Turtle, Fashion, and Hollywood Lessons In this special episode of Reza Rifts, host Keith Reza sits down with Lark Voorhies, beloved by fans around the world as Lisa Turtle from Saved by the Bell. Lark shares memories from the hit series, reflects on the energy of performing in front of a live audience, talks fashion and character style, and opens up about filming in Hawaii with the cast. Lark also discusses her experience with the upcoming docuseries, what made certain episodes unforgettable, and how the chemistry of the cast helped make Saved by the Bell such a cultural phenomenon. Along the way, Keith and Lark trade laughs, nostalgia, and heartfelt moments about Hollywood, celebrity, and staying true to yourself. The conversation wraps with Lark's advice for aspiring actors: stay focused, be ready for opportunity, invest in yourself, and use your platform to make a difference. It is a short but memorable interview packed with behind-the-scenes stories, humor, and inspiration. Guest Info Lark Voorhies is an actress best known for playing Lisa Turtle on the iconic teen series Saved by the Bell. In this episode, she reflects on her television legacy, her love of fashion, memorable filming experiences, and the mindset that helped shape her career.  Social Links Instagram: @reallalrkvoorhies Chapters 00:00 Intro and Keith's disclaimer about the short episode  02:37 Recreating the magic of Saved by the Bell  03:50 From Good Morning, Miss Bliss to Saved by the Bell  04:02 Favorite memories and the mall episode  05:02 On-set crew, comfort, and production experience  05:27 Hawaii filming stories and cast chemistry  07:15 Acting technique, scripts, and the pencil trick  09:03 Scene takes, polish, and playing Lisa Turtle  09:45 Future work, celebrity, and what comes next  10:18 Lark's advice for aspiring actors  11:53 Producing and being in charge  12:15 Comic cons, autograph moments, and final thoughts    #LarkVoorhies #SavedByTheBell #RezaRifts #KeithReza #CelebrityInterview  

Lords: Matt https://mtrop.net/ Cort Topics: Doom Modding and You The Boston Marathon Stampede This 1989 Shigeru Miyamoto quote. "When pornography escalates, it eventually crosses into the grotesque. I think the world of 'hidden secrets' in games has almost reached that same grotesque level. It's reached a point where it isn't measured by common sense anymore. It's just people getting bored and looking for stronger and stronger stimulation. We've hit a wall. When you're at the point where you have no choice but to go 'grotesque,' you have to start thinking of new ways to use the medium." https://shmuplations.com/itoimiyamoto/ Dammit I'm Mad by Demetri Martin https://www.reddit.com/r/Poetry/comments/zc6iic/poem_dammit_im_mad_by_demetri_martin/ Microtopics: Plugging in the middle of someone else's topic. Successfully comparing apples and oranges. Waking extremely early to meet with someone in the wrong time zone. Doing anything for 30 years. What monsters do. Making Doom mods before they figured out how to change the walls. The Hacker's Guide to Doom, by Hank Leukart. Anthropology of Doom modding. Roots: the Evolution of Doom Level Design. Demon of the Well. People heralding the end of Doom modding before you even get started. Megawads. Where's All the Data? Non-commercial Doom Source License. Jokes that take thirty years to get. The Vanilla Limitations of Doom. Recreating your high school in Doom, which was okay at the time but stopped being okay several years later. Dividing eras at a pinching point. People who are opposed to doors. Relaxed-Limit Source Ports. The Stand-Alone Project Era. DeHackEd patching the binary. Various sources of value you might get from modding Doom. Mark Z. Danielewski commenting on MyHouse.wad. Sonic Robo Blast 2. Falling Away Floors. Taking certain features and mushing them together into a new thing. Back when Doom rules the LAN parties. What you played on your Mac instead of Doom. A hardware mod that added looking up and down to Doom via a cardboard window that you can slide around the monitor. Making a Beeline for the Bungie Booth. A crowd of shifty eyed gamers waiting to sprint across the show floor full of unsuspecting exhibitors setting up booths. An okay one of those if you're into that sort of thing. Drinkable Cheeseburgers. Finishing the tutorial and quitting Marathon (2026) forever so that you can brag about your100% extraction rate. Doing non-euclidean architecture in your Marathon maps. Adding portals to the Doom engine. The three stages of development that every medium goes through. Paying the shareware fee to get your registered copy of WinBolo and/or LinBolo. The point when you have no choice but to Go Grotesque. Miyamoto quotes that modern-day Nintendo would never allow to escape containment. A completely constructed, untrue memory of how you played The Legend of Zelda. Miyamoto foretelling in 1989 that grotesque secrets in video games will lead directly to the cat hair mustache puzzle and kill adventure games. Slaughter-mappy. Hell Keep and the Fortress of Mystery. The one place in the Doom episode they can force a constrained-ammo puzzle. I rise; my bed on a sun. Rats peed on hope. Deified as a sign in ruby ash. Making a computer watch every episode of Seinfeld. Guided Markov. Writing a poem from both ends at the same time. Saying the moon is anything and squeezing a poem out of it. Doom mods where you can pull down the console. Porting ZZT to the Doom engine and vice versa.

In this podcast, Addison Heimann talks about Hypochondriac, recreating the famous Ghost scene, OCD exposure therapy, and much more. About Addison Heimann Addison Heimann is known for Hypochondriac (2022), Touch Me (2025) and Jeff Drives You (2019). Timestamps Thanks for Listening! Help out the show: Support This Is Horror on Patreon Listen to This Is … Continue reading

Step 7 in our 10-part series: open a Roth IRA at Vanguard, Fidelity, or Schwab and max out your contribution into a total stock market index fund. Spencer and Jamie break down why the LADS method (Low-cost, Automated, Diversified, Simple) beats stock picking and why Roth almost always wins for military pay. Topics covered: Why to open a Roth IRA at one of the big three: Vanguard, Fidelity, or Schwab Total stock market index fund options: VTI, SCHB, FZROX, VTSAX, VT Spencer's LADS method: Low-cost, Automated, Diversified, Simple How low fees compound — 3 cents per $100 vs. high-load funds from military-targeted advisors Buying the haystack instead of hunting for the needle (you already owned Nvidia a decade ago) Mutual funds vs. ETFs — why the difference doesn't matter for most investors Recreating a total US stock market in the TSP with 80% C Fund / 20% S Fund How the Roth IRA is a separate bucket from the Roth TSP — both have their own contribution limits Why Roth (pay taxes now) beats Traditional for most military families with low effective tax rates The narrow edge cases where Traditional might make sense (O-5+ doctors, some dual-military couples) Spencer's effective tax rate as a pilot and major was under 10% — often under 5% Resources mentioned: Vanguard, Fidelity, Schwab (Roth IRA providers) VTI — Vanguard Total Stock Market Index Fund SCHB — Schwab Total Stock Market Index Fund FZROX — Fidelity Total Stock Market Index Fund VTSAX — Vanguard Total Stock Market mutual fund tsp.gov (for current contribution limits) Bogleheads forum (for the mutual fund vs. ETF deep dive) Spencer and Jamie offer one-on-one Military Money Mentor sessions. Get your personal military money and personal finance questions answered in a confidential coaching call at militarymoneymanual.com/mentor. Over 22,000 military servicemembers and military spouses have graduated from the 100% free Ultimate Military Credit Cards Course, available at militarymoneymanual.com/umc3. In the course, you can learn how to apply for the most premium credit cards and get special military protections, such as waived annual fees, on elite cards like The Platinum Card® from American Express and the Chase Sapphire Reserve® Card. https://militarymoneymanual.com/amex-platinum-military/ https://militarymoneymanual.com/chase-sapphire-reserve-military/ Learn how active duty military, military spouses, and Guard and Reserves on 30+ day active orders can get annual fees waived on premium credit cards in the Ultimate Military Credit Cards Course at militarymoneymanual.com/umc3. If you want to maximize your military paycheck, check out Spencer's 5-star rated book The Military Money Manual: A Practical Guide to Financial Freedom on Amazon or at shop.militarymoneymanual.com. If you have a question you would like us to answer on the podcast, please reach out on instagram.com/militarymoneymanual. Military Money Manual may receive compensation from JPMC. Opinions expressed here are author's alone, not those of any bank, credit card issuer, airlines or hotel chain.

Gill Paul discusses Scandalous Women, the 1960s publishing world, and the iconic female authors who redefined storytelling in this backlist feature. Let's get some literary hinges to our reading lives in this backlist feature. This week's Book Gang conversation brings us together with international bestselling author Gill Paul to talk about Scandalous Women. Paul transports readers into the electric, high-stakes world of 1960s publishing, where two women didn't just write bestselling books—they changed what women were allowed to write about at all. If you've ever wondered what really happens behind the scenes of the books we love (the deals, the risks, the moments that quietly reshape an entire industry), this conversation is such a treat with a true insider. In this fascinating conversation, we discuss:

In this episode of Case Studies, Casey sits back down with Ty Williams, senior executive at Sunrun and one of the most respected leaders in the direct sales industry, for a deeper, more evolved conversation on leadership, reinvention, and staying relevant in a rapidly changing world.Ty shares how his thinking has sharpened through new challenges at scale, from navigating executive leadership to preparing for massive shifts in technology, energy, and the workforce. He introduces the idea of “sales craft” as something far greater than performance, a framework for life built on adaptability, judgment, and continuous growth.Together, he and Casey explore why comfort is often the biggest threat to long term success, how great leaders consistently recreate themselves, and what it means to operate with urgency in moments that truly matter. Ty also opens up about his obsession with optionality, his fear of stagnation, and the internal standard of always “impressing yourself.”This episode targets entrepreneurs and leaders who want to evolve with intention, embrace change, and refuse to settle at any stage of the journey. Hosted on Acast. See acast.com/privacy for more information.

Jeremy Weber is an avid outdoorsman, hunter, and hobby bowyer. He and his partner, Gwen, manage The Lodge at Water's Edge in Portersville, Pennsylvania - a mid century 80-acre micro-venue nestled in the Slippery Rock Gorge bordered by McConnells Mill State Park. Jeremy created Selfbows at Water's Edge where he offers bow carving experiences with accommodations on the Waters Edge property. Please enjoy this episode of Project Quiver on Salish Wolf with Jeremy Weber.          Episode Links: https://www.instagram.com/selfbows_at_waters_edge/https://www.facebook.com/people/Selfbows-at-Waters-Edge/61569938627847/https://www.instagram.com/the_lodge_at_waters_edge/https://www.thelodgeatwatersedge.com/Project Quiver at Anchor Point ExpeditionsShow Notes:Jeremy's story of the old estate and its transformation into a workshop for bow making and retreatsTechniques for harvesting and seasoning various tree species for bow wood, including American hornbeam, hickory, and OsageThe process of splitting and preparing wood using wedges, draw knives, and shellac preservation methodsExploring different bow styles and experimenting with materials like flowering dogwood, cherry, and elmThe significance of shooting both sides in archery for balance and brain healthThe historical perspective on bows from different eras, including a fascinating account of reconstructing a 17th-century Sudbury Native American bow using hand toolsJeremy's upcoming workshops at Waters Edge in September and how to participateChapters:00:00 - Introduction and overview of Waters Edge lodge and Jeremy's background02:24 - Description of the estate and property features including the main lodge, cabins, and creekside pool plans04:12 - The power of nature: ice chunks from river flood and weather impacts on the land05:13 - Bow carving workshops: schedule, group size, and what participants will learn06:20 - Harvesting trees on the property: identifying species and sustainable practices08:09 - Favorite woods for bow making and the unique characteristics of American hornbeam (muscle wood)09:45 - The process of bark removal and how seasonality affects harvesting11:10 - Comparing American hornbeam and hop hornbeam trees and their suitability for bows13:11 - Jeremy's journey into bow making, family history, and early archery experiences 15:08 - Building bows from different woods and personal experimentation with designs 16:48 - The workshop setup, tools used, and the importance of continual learning in bow craftsmanship 18:33 - On-site accommodations and the structure of a typical bow-making retreat 19:14 - Recommended bow styles for beginners and the forgiving nature of hickory 20:47 - The sequential drying process from felled tree to ready-to-carve stave 22:43 - Techniques for splitting wood with wedges and draw knives, and preserving with shellac 25:41 - Sourcing and working with Osage orange trees outside the property 28:16 - The exciting experience of harvesting Osage from local farms and the snowbound effort 33:16 - Experimentation with different woods like flowering dogwood, cherry, and elm 34:16 - Transition from modern to primitive hunting bows and the spiritual connection in archery 36:11 - Shooting from both sides to develop balance and challenge for the brain 40:23 - Recreating historic bows like the Sudbury bow using traditional tools and techniques 45:43 - Favorite tools for carving and current projects in Jeremy's workshop 47:02 - Jeremy's current bow projects, draw weights, and upcoming builds 48:27 - Challenges with snaky grain and the art of following grain patterns for optimal bows 50:02 - Inspiration from historic bows and making your own based on archeological exemplars 55:50 - How to connect with Jeremy and sign up for the September workshop

Blue has been an important colour in art and decoration since ancient times. The semi-precious stone lapis lazuli was used in ancient Egypt for jewellery and ornament and later, in the Renaissance, to make the pigment ultramarine, the most expensive of all pigments. For our guest this week Josh Blue from @bluestarseedco it represents the pinnacle of cannabis expression. The highly anticipated offering from his latest drop a blueberry blast from the past the infamous “Blue Moonshine” the indica leaning plant from DJ Shorts blue family Josh has put his sights on recreating this classic and this drop brings him and you one step closer to that coveted blueberry zone. We had a great show last time with Josh and look forward to another deep dive into growing and breeding, and of course some wild dead stories. Thanks to James Bean “Man on the Scene” @seedsherenow for putting this Show takeover together.So get that @dabx GO rig charged your @jerome_baker bong Clean with some ice

In 1980, the FIA issued a new set of competition regulations for GT and Rally car classes known as Group B. These rules allowed manufacturers wide latitude on  design and materials- and most importantly, no restriction on forced induction. Carmakers were also required to build fewer road car versions (just 200) to meet homologation. The result was an astounding array of racing brutes like the Audi Quattro, Ford RS200, Peugeot 205 T16 and of course the Lancia 037. But the speed and danger of Group B Rally brought things to an abrupt end and guaranteed the rarity of these cars... and that's where Brandon Hegedus of Hegedus Automobili comes in. He's a master craftsman who recreates halo cars (with some modern tweaks). Call them replicas, tributes, whatever- they are meticulously built and very impressive. VISIT HEGEDUS AUTOMOBILI: https://hegedusautomobili.comSUPPORT THE PODCAST:https://www.buymeacoffee.com/hpheritageSUBSCRIBE to Horsepower Heritage on YouTube:https://www.youtube.com/@horsepowerheritageFIND US ON THE WEB:https://www.horsepowerheritage.comINSTAGRAM: @horsepowerheritageHORSEPOWER HERITAGE is created, produced and hosted by Maurice Merrick.Get in touch with Maurice:https://horsepowerheritage.com/contactSupport the showHELP us grow the audience! SHARE the Podcast with your friends!

In this episode, we sit down player development coach Justin Cooper to unpack what player development really means, beyond drills and workouts. Justin shares his holistic philosophy of meeting each athlete where they are, empowering them to take ownership of their growth, and blending live, constraint-based training with intentional technical work. From youth athletes to pros, he walks us through how he builds individualized plans rooted in reality, context, and honest conversation.We also dive deep into the contrast between in-season and off-season training, the emotional side of development, and how to align with head coaches while working in the private sector. Justin explains why “living in reality” is the foundation of in-season success, why micro-workouts are underrated, and why going live is essential for real growth. Whether you're a trainer, team coach, or serious player, this episode is packed with actionable insight on how to structure workouts, build buy-in, and create environments that truly translate to game performance.Timestamps00:00 Weather talk, travel stories, and the infamous “free throw game” 08:40 Introduction to Justin Cooper and his player development background 09:29 What player development really means: meeting the athlete where they are 11:47 Blending training styles: live play, constraints, and skill work 13:47 In-season philosophy: living in reality and maximizing current role 15:25 Aligning with head coaches and speaking the same language 16:20 The importance of micro-workouts during the season 18:57 Player ownership and identity shifts 22:06 Managing emotions and controlling what you can control 24:15 Off-season approach: vision, planning, and building buy-in 26:40 Measuring progress without waiting for game results 29:12 Ideal off-season ratios: live vs. on-air work 31:39 Creating live environments with pros and managing risk 33:25 Coaching the defense and structuring live reps 35:15 The evolution of live training and player buy-in 36:54 Recreating game environments with limited resources 39:00 Teaching reads through guided defense and feel-based coachingCoaching Resources: https://byanymeanscoaches.com/BAM Blueprint Book: https://byanymeanscoaches.com/blueprint-bookIf you enjoyed this episode, share it with another coach or trainer who's serious about evolving their approach. Make sure to subscribe, leave a review, and tag us with your biggest takeaway from the conversation. We appreciate you being part of the BAM Coaches community.

In this episode, Martin Atkins (Public Image Ltd., Killing Joke, Pigface) joins the show from his Post-Punk and Industrial Music Museum to discuss the upcoming 45th-anniversary recreation of Public Image Ltd.'s third album, The Flowers of Romance. On Saturday, April 11, Martin returns to Reggie's in Chicago with a "murderers' row" of talent, including Chris Connelly, Robert Byrne, Leyla Royale, Orville Kline, Alicia Gaines, and Alan Lake, to bring the stark, experimental, and percussive record back to the stage. Martin reflects on the "Music Concrete" nature of the original recording sessions, which were defined by improvisation and disregard for traditional rock structures. He shares fascinating stories behind the album's signature sounds, such as the ticking of a Mickey Mouse watch used for the track "Four Enclosed Walls" and the CO2 fire extinguisher that opens "Under the House". He also sheds light on the band's internal dynamics during that era, including the departure of bassist Jah Wobble and Keith Levene's intense focus on video game. The conversation also covers Martin's recent performance of Killing Joke's "Extremities" and his complex, perhaps a bit strained, relationship with John Lydon. Martin discusses how his museum has become a surprising hub for younger generations who are just now discovering the textures and stories of the post-punk movement. I adore Martin, respect his career and business mind, and always love talking with him. Hope you enjoy the chat, and to see you at Reggie's!   ###   This episode is brought to you by Exploding House Printing. Based in Hermosa, they specialize in screen printing, embroidery, and custom merch for bands and brands. Visit explodinghouseprinting.com for a quote.  

In this episode, Martin Atkins (Public Image Ltd., Killing Joke, Pigface) joins the show from his Post-Punk and Industrial Music Museum to discuss the upcoming 45th-anniversary recreation of Public Image Ltd.’s third album, The Flowers of Romance. On Saturday, April 11, Martin returns to Reggie’s in Chicago with a "murderers' row" of talent, including Chris Connelly, Robert Byrne, Leyla Royale, Orville Kline, Alicia Gaines, and Alan Lake, to bring the stark, experimental, and percussive record back to the stage. Martin reflects on the "Music Concrete" nature of the original recording sessions, which were defined by improvisation and disregard for traditional rock structures. He shares fascinating stories behind the album's signature sounds, such as the ticking of a Mickey Mouse watch used for the track "Four Enclosed Walls" and the CO2 fire extinguisher that opens "Under the House". He also sheds light on the band’s internal dynamics during that era, including the departure of bassist Jah Wobble and Keith Levene’s intense focus on video game. The conversation also covers Martin’s recent performance of Killing Joke’s “Extremities” and his complex, perhaps a bit strained, relationship with John Lydon. Martin discusses how his museum has become a surprising hub for younger generations who are just now discovering the textures and stories of the post-punk movement. I adore Martin, respect his career and business mind, and always love talking with him. Hope you enjoy the chat, and to see you at Reggie’s! ### This episode is brought to you by Exploding House Printing. Based in Hermosa, they specialize in screen printing, embroidery, and custom merch for bands and brands. Visit explodinghouseprinting.com for a quote.See omnystudio.com/listener for privacy information.

In this inspiring conversation, Darin sits down with Javant Benton, the creator behind the popular platform Healthy Vegan Eating, whose personal health crisis led him to completely transform his life through food. After facing pre-diabetes, hypertension, and even the possibility of lymphoma, Benton realized he had to radically change the trajectory of his health. What followed was a deep exploration into nutrition, whole foods, and the power of a plant-forward lifestyle. Through trial, error, and relentless curiosity, Benton lost 85 pounds and began developing creative plant-based recipes that replicate the comfort foods people love—without the refined sugars, oils, and processed ingredients that damage long-term health. In this episode, Benton shares how diet became the foundation of his healing, why most people struggle to adopt healthier eating habits, and how creating "bridge foods" can help people transition toward better health without feeling deprived.     What You'll Learn How a looming health crisis forced Benton to completely rethink his diet and lifestyle Why many people don't take their health seriously until a major wake-up call The confusion people face when trying to navigate conflicting nutrition advice How Benton lost 85 pounds and reversed major health risks through diet Why plant-based eating doesn't have to feel restrictive or boring The concept of "bridge foods" that help people transition to healthier diets How recreating comfort foods can help people adopt healthier habits Why cooking and preparing whole foods can become an act of self-care The cultural conditioning around meat consumption and protein myths How improving your diet often triggers improvements in other areas of life     Chapters 00:00:00 – The deeper mission behind SuperLife and creating a roadmap to true health 00:02:11 – Javon's upbringing and how environment shapes lifelong habits 00:03:17 – Why epigenetics proves your health is not predetermined 00:04:00 – The slow buildup of disease: weight gain, pre-diabetes, and hypertension 00:04:47 – The moment everything changed: a potential lymphoma diagnosis 00:06:02 – Why fear is often the catalyst for real transformation 00:06:39 – The hidden dangers of modern food: chemicals, plastics, and processed diets 00:08:24 – Searching for answers: confusion in the world of nutrition advice 00:09:16 – The realization that food—not just exercise—determines your outcome 00:10:10 – The turning point: discovering evidence-based nutrition 00:11:21 – Overcoming peer pressure and committing fully to change 00:12:19 – The truth about fish, toxins, and environmental contamination 00:13:14 – The importance of nutrient awareness on a fully plant-based diet 00:14:01 – Why Javon ultimately chose a fully vegan lifestyle 00:14:27 – The bigger issue: industrial pollution and bioaccumulation in food 00:16:54 – The reality of factory farming and toxic meat consumption 00:18:03 – Why "clean food" matters more than diet labels 00:18:43 – Becoming your own health advocate and taking ownership 00:19:54 – Losing 85 pounds and inspiring others through visible transformation 00:20:46 – The real barrier: people don't want to give up the foods they love 00:21:04 – The breakthrough idea: making unhealthy foods healthy 00:21:28 – Recreating comfort foods without refined sugar, oil, or processed ingredients 00:22:12 – The trial-and-error process behind building healthy recipes 00:22:49 – Eliminating excuses: making healthy eating accessible for everyone 00:23:12 – "Don't change what you eat—change how you make it" 00:24:30 – Why plant-based eating is far more diverse than people think 00:25:23 – The power of "bridging" instead of forcing drastic change 00:25:53 – Shocking moments when people couldn't tell plant-based from meat 00:26:52 – The biggest mistake in the health space: no middle ground 00:28:14 – Why meeting people where they are is the key to lasting change 00:30:28 – The truth: you don't need to go all-in to benefit from plants 00:31:14 – Why personalization matters more than strict diet rules 00:32:35 – The "middleman" realization: going straight to plant nutrients 00:33:07 – The ethical and emotional shift around food choices 00:34:27 – Why eating plants becomes a form of self-respect and alignment 00:35:06 – How nutrition improves self-esteem and overall life choices 00:35:18 – "You don't know how good it feels to feel good" 00:36:13 – The raw truth: change often starts from selfish survival 00:36:49 – Cultural conditioning around food and breaking free from it 00:37:23 – Why leading by example is more powerful than preaching 00:38:09 – You can't force awareness—people must be ready 00:39:09 – Why you don't have to wait for a health crisis to change 00:40:09 – Debunking extreme diet trends and the importance of balance 00:41:05 – The two-year journey of creating the cookbook 00:42:12 – The real mission: helping people, not selling books 00:43:08 – Free access to hundreds of recipes and removing barriers 00:44:10 – Why passion makes even the hardest work enjoyable 00:44:59 – Final message: meet yourself where you are and start there     Thank You to Our Sponsors Therasage: Go to www.therasage.com and use code DARIN at checkout for 15% off Bite Toothpaste: Go to trybite.com/DARIN20 or use code DARIN20 for 20% off your first order. Manna Vitality: Go to mannavitality.com/ and use code DARIN12 for 12% off your order.     Join the SuperLife Community Get Darin's deeper wellness breakdowns — beyond social media restrictions: Weekly voice notes Ingredient deep dives Wellness challenges Energy + consciousness tools Community accountability Extended episodes Join for $7.49/month → https://patreon.com/darinolien     Find More from Javant Benton Website: hveating.com Instagram: @healthyveganeating Book: Make Your Own Cookbook     Find More from Darin Olien: Instagram: @darinolien Podcast: SuperLife Podcast Website: superlife.com Book: Fatal Conveniences      Key Takeaway The goal isn't to force people into some perfect diet overnight. It's to meet people where they are and help them improve step by step. If we can recreate the foods people already love using whole, nourishing ingredients, we remove the barrier of deprivation—and suddenly living a healthier life becomes something people actually want to do.  

In this episode of The Spiritual Investor Podcast, I share why certainty and expectation are not the same thing and what it actually costs you to confuse the two, and how the new version of you is the only place from which you can call in everything you want. This episode explores what it means to stop recreating from the past, why your external world shifts when you shift, and how breaking the structure doesn't require suffering — it requires commitment to living as the new version of you right now. In this conversation, I share: Why certainty carries only your power while expectations carry neediness How to let go of results without losing direction Why your to-do list is a function of your energy and not the other way around What it really means to break the structure without waiting for a crisis to wake you up How I called in a release so powerful it took down a 70-year-old barn on my property This episode is an invitation to stop waiting for circumstances to change before you step into who you already are, and to recognize that the new version of you is available right now — not after the evidence shows up. If you're feeling called to go deeper with this work, the next round of the Spiritual Investor Mastermind begins May 5th. It's a 12-week container where you go vertical — into the markets, into intelligent portfolio design, and into the new version of yourself. Learn more and apply at thespiritualinvestor.com/simastermind.

In this episode of Previously On, Jillian Bonanne is joined by her husband Tyler Branch to recap Episode 6 of Love Story: John F. Kennedy Jr. and Carolyn Bessette, “The Wedding.” With their own wedding still fresh in their minds, they dive into the pressure and expectations surrounding John F. Kennedy Jr. and Carolyn Bessette as their relationship faces growing scrutiny - not just from the public, but from the Kennedy family itself. Jillian also addresses the real-life controversy around the series, including Daryl Hannah's criticism of how the show portrays her.The episode picks up after John and Carolyn's public fight, leading to a tense but revealing conversation between Carolyn and Ethel Kennedy about what it truly means to marry into the Kennedy family. Jillian and Tyler discuss the theme of sacrifice throughout the episode and break down the "Top 5 Sacrifices Made in this Episode" from Carolyn leaving her job at Calvin Klein and moving into John's apartment to the emotional compromises her family makes.They also talk about the secretive wedding itself and how the show recreates the famously private ceremony and one of the most-talked about celebrity weddings of all time.Daryl Hannah op-ed in NY Times: https://www.nytimes.com/2026/03/06/opinion/daryl-hannah-love-story-jfk-jr.html"The Wedding" episode writer Juli Weiner on recreating that weekend: https://www.vanityfair.com/hollywood/story/love-story-jfk-jr-carolyn-bessette-wedding-episode00:00 Intro to pod02:40 Daryl Hannah NY Times op-ed reaction08:18 Episode 6 "The Wedding"13:11 Top 5 Sacrifices in Ep 613:34 Small wedding18:15 Carolyn's family25:18 Carolyn moves in with John27:48 Caroline is Maid of Honor31:53 Carolyn quits Calvin Klein39:22 Wedding guests41:39 Skinny-dipping42:32 Recreating the wedding44:26 Wedding hot takesThank you to Matt Buechele (@mattbooshell) for creating our new theme song. You can listen to "Sunscreen" on Spotify: https://open.spotify.com/artist/1gFHHF3QyQxjbbKXV3qLu9Buy our merch: ⁠https://www.etsy.com/shop/PreviouslyOnTeenTV⁠Follow Previously On Teen TV on Instagram: ⁠⁠https://www.instagram.com/previouslyon_teentv/Follow Previously On Teen TV on TikTok: ⁠⁠https://www.tiktok.com/@previouslyon_teentv⁠⁠Subscribe to our YouTube: ⁠⁠https://www.youtube.com/channel/UCe2lgvvZGKMrQ8v24FmDdWQ?sub_confirmation=1⁠

Nigel Baker: The Scrum Master Mistake of Copy-Pasting Success Instead of Recreating the Journey Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "I was trying to recreate the results of our team, not recreate the journey. And that is what killed me to begin with." - Nigel Baker   Nigel fell into Scrum Mastery almost by accident. Working at British Telecom in 2002—before most people had even heard of Scrum—his team adopted it not to speed up, but to add rigor to an already fast-moving tactical unit full of "pirates" who could get stuff done but needed guardrails. His first Scrum Master, Geoff Watts, got promoted and moved on, leaving a vacancy. Nigel was the third person asked—and the first to say yes. He loved the role, but his earliest mistake became his most enduring lesson.  On his very first daily Scrum, Nigel brought a big leather book and wrote down what every team member was doing, acting like a proto-project manager collecting status reports. The team already had all this information in their system—he was unconsciously positioning himself as the authority figure, having people report to him rather than to each other.  As Nigel evolved into an Agile Coach, the bigger failure emerged: trying to copy-paste the process that worked with his first team onto other teams, recreating the results rather than the journey that got them there. Each team needs to evolve its own process—there are no shortcuts to that growth.   In this episode, we refer to the importance of self-awareness and servant leadership in the Scrum Master role.   Self-reflection Question: Are you trying to replicate a successful process from a previous team, or are you investing in helping your current team discover their own path to effectiveness?   [The Scrum Master Toolbox Podcast Recommends]

Are your kids too clean for their own good? Amish children experience 90% less asthma than the national average—not despite getting dirty, but because of it. When scientists analyzed the dust in their farmhouses, they discovered an invisible ecosystem that was training immune systems to be resilient instead of reactive. In his episode, host Jason Wachob explores the groundbreaking research behind the "hygiene hypothesis" and its implications for modern parents raising kids in an increasingly sanitized world. This discovery is forcing us to rethink everything about dirt, bacteria, and health. And it's already led to treatments used by 100+ million people worldwide. Are we too clean for our own good? Study Link: https://pubmed.ncbi.nlm.nih.gov/37210851/ Chapters: [00:18] The Amish morning: A scene from another era [02:23] The asthma epidemic vs. farm kids' secret weapon [04:30] What's really in farm dust? (It's not what you think) [05:46] Bacterial lysates: Recreating farm protection in a lab [08:22] The hygiene hypothesis: Why clean might be too clean

If things start to improve but you still feel the urge to rush, overwork, or tighten control, this episode explains why — and how to stop repeating that cycle.  In part three of The Money Reset, Tiffany Carter explores why ease can feel uncomfortable after long periods of stress, why people unconsciously recreate urgency, and how to let money support your life without guilt, self-sabotage, or over-responsibility.  RESOURCES MENTIONED:  !!LAST CHANCE!! to apply this year: My Exclusive 2-Month Private Business Coaching Program APPLY HERE (*serious applicants only please)  **Holiday Abundance Sale** Make More Work Less: The Money Relationship Healing & Manifestation Program GET THIS LIMITED TIME OFFER HERE  Join the famous ProjectME Posse Business & Money Coaching Membership HERE  {FREE GIFT-LIMITED TIME} Walk into Your Wealthiest Season walking manifestation series + Guided Wealth Journal GET IT HERE  CONNECT WITH TIFF:  Tiffany on Instagram @projectme_with_tiffany   Tiffany on TikTok @projectme_with_tiffany  Tiffany on YouTube: ProjectME TV  Tiffany's FREE Abundance Email Community: JOIN HERE > The Secret Posse Digest   • Why calm and ease can feel unsafe to a stressed nervous system
  • How urgency becomes familiar even when it creates burnout
  • The psychology behind recreating pressure after success
  • Why worth often gets tied to struggle and responsibility
  • How to receive money, rest, and support without guilt
  • Letting success feel sustainable instead of loaded
  • Integrating ease, ambition, and safety into your identity  About The Money Reset:  The Money Reset is a three-part podcast conversation focused on healing the relationship between money, safety, and ease.  • Part One: Why money feels hard and where money stress actually comes from
  • Part Two: How to rebuild trust, safety, and ease with money without forcing positivity
  • Part Three: How to receive without guilt and stop recreating pressure once things improve  This series is designed for entrepreneurs, business owners, and ambitious people who want money to feel supportive again — not stressful, punishing, or overwhelming.