Podcasts about Rooting

Hey Winner, Why can pricing feel so uncomfortable when you know you're good at what you do? In this conversation with my mastermind group (think view-like without the politics), we're digging into some of the fears and beliefs that can make it difficult to confidently price our offers. We talk about the fear that people won't pay, the pressure we can feel once someone does pay, and why hearing “no” feels a whole lot more personal when you're selling something you created yourself. We also get into the deeper money beliefs that can show up as Christian entrepreneurs, including the idea that being helpful and getting paid somehow don't belong together. If you've ever wondered whether you're charging too much, questioned whether your work is really worth the price, or taken someone's “no” a little too personally, this conversation will help you look at pricing from a different perspective. Rooting for you ~ Gabe   New to the podcast? Start here: https://redhotmindset.com/podcast-start/   CONNECT WITH ME: ➡️ Website: https://redhotmindset.com/ ➡️ Join the Action-Driven Collective: https://redhotmindset.com/rha/ ➡️ 30-Day Sprint coaching package: https://redhotmindset.com/sprint/ ➡️ Resources: https://redhotmindset.com/#free ➡️ Shop: https://redhotmindset.com/products/   LISTEN TO HEAR: Why fear can play such a big role in how we price our offers The difference between someone rejecting your offer and rejecting you personally Why charging more can bring up fears about whether you can deliver the promised result How easily we can devalue our own expertise and experience Why faith, service, generosity, and getting paid for valuable work don't have to be at odds   LINKS MENTIONED IN EPISODE:

Check Out Our Merch: https://shop.jomboymedia.com/collections/the-mlb-collection Booking.com the easiest way from where you are to where you want to be. Go on, book that trip - it's easy. https://www.booking.com Stream MLB live on FOX One Use our code for 10% off your next set of MLB tickets on SeatGeek*: https://seatgeek.onelink.me/RrnK/YANKS2026. Sponsored by SeatGeek. *Restrictions apply. Max $20 discount Download the Fanatics Sports & Casino App now, the new all-in-one app for sports fans, bringing together ways to bet, trade, or play in one experience. Disclaimer: 21+ only. Availability differs by state. Terms apply. See app for details. Sportsbook: GAMBLING PROBLEM? Call 1-800-GAMBLER or 1-800-MY-RESET, CT call (888) 789-7777, MA call (800)-327-5050, NY call (877) 8-HOPENY, MD visit mdgamblinghelp.org. Markets: Event contract trading is offered by Morton St. Trading Investments, LLC, d/b/a Fanatics Markets, a CFTC-registered futures commission merchant and NFA member. Event contracts carry risk of total loss and changing prices. Not good for all investors. See app for important disclosures. +++++++++ Timestamps: 0:00 Yankees Lose But We're Rooting for Them 5:00 So Many Roster Moves and Injury Updates 10:00 Judge Return Plan 11:40 Game 1 Was the Worst Loss of the Year 20:40 Yankees Win Game 2 It Was Fun! 26:15 Yankees Lose Game 3 and the Series 32:45 Pride of the Yankees: Elmer Rodriguez 40:15 Pride of the Yankees: Luis Garcia Jr. 45:15 Yankee MFer 1:04:45 Ben Rice 1:08:45 Need Jazz to STEAL 1:14:25 Austin Wells Ugly Strikeouts This Series 1:22:25 Yankees Positioned Terribly for This Red Sox Series Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Uncertainty is hard, especially when your mind believes that thinking through every possible “what if” will keep you safe. You might replay conversations, prepare for things that haven't happened, or convince yourself that if you can just figure out every possible outcome, you'll finally feel at peace. But what if the constant problem-solving is actually keeping the anxiety going? In this episode, I'm talking about what happens when you stop trying to eliminate uncertainty and instead learn how to sit with “I don't know” without immediately spiraling, and how that space can become an opportunity to practice trust, dependence on God, and a different way of responding to your thoughts.Rooting for you,JessicaWant help taking the next step?If this episode helped you recognize some of the thoughts, emotions, or patterns keeping you stuck, you don't have to stop with simply understanding what's happening. Here are two ways I can help you take the next step:1. Untangle Your Thoughts is my self-paced course designed to help Christian women understand what's happening beneath their thoughts and emotions, identify the patterns keeping them stuck, and learn practical tools to begin responding differently all while keeping God at the center of the process.Inside, I walk you step-by-step through the process so you don't have to keep wondering, “Okay, but how do I actually change this?”→ Start Untangle Your Thoughts: Click here!2. Start 1:1 Christian Mental Health Coaching with Jessica. Sometimes you don't need more information you need someone to sit with you, help you slow everything down, and work through what is actually happening underneath your thoughts, emotions, and patterns.We'll look at what you're experiencing, uncover what's keeping you stuck, and work toward practical next steps that help you move forward with God.→ Book a free 20-minute consultation: Click here!Website: www.jessicahottle.comEmail: jessica@jessicahottle.comThe information shared in this podcast is for educational and informational purposes only and is not intended as medical or clinical advice. While we discuss mental health topics, this is not a substitute for professional care. Please consult with a qualified healthcare provider for advice specific to your situation.

The Drive played what Browns fans and radio hosts head to say about cheering against their own team, and how its dangerous it is to cheer against the team.

Hey Winner, I'm back!!! ...and I want to catch you up on why it's been quiet around here this summer, and what's changing starting this fall. If you've been feeling unsure about the season you're in, wondering if you're really called to what's next, or wondering if you've already missed your window, then this episode is for you. Rooting for you ~ Gabe   New to the podcast? Start here: https://redhotmindset.com/podcast-start/   CONNECT WITH ME: ➡️ Website: https://redhotmindset.com/ ➡️ Join the Action-Driven Collective: https://redhotmindset.com/rha/ ➡️ 30-Day Sprint coaching package: https://redhotmindset.com/sprint/ ➡️ Resources: https://redhotmindset.com/#free ➡️ Shop: https://redhotmindset.com/products/   LISTEN TO HEAR: Why I stepped away this summer, and why the podcast is moving to two seasons a year instead of a constant weekly cadence A story about a man I met that changed how I think about time, obedience, and what actually matters What obedience actually looks like when it doesn't make sense, feels hard, or doesn't make sense to anyone else How to tell if you're in a waiting season or a stuck season, and what to do either way   LINKS MENTIONED IN EPISODE: Join the 30-Day Sprint: https://redhotmindset.com/sprint  

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

At some point, many Christians find themselves asking a question they never expected to ask: “God, I've tried to follow You. I've prayed. I've made changes. So why does this still hurt so much?” When suffering doesn't match the life we thought obedience would produce, it can expose beliefs about God we didn't even realize we were carrying.If you've ever wondered whether you did something wrong, prayed enough, or somehow missed God because life still hurts, this episode will help you slow down and look at what may be underneath that question and what it means to trust God when doing everything “right” doesn't guarantee the outcome you hoped for.Rooting for you,JessicaWant help taking the next step?If this episode helped you recognize some of the thoughts, emotions, or patterns keeping you stuck, you don't have to stop with simply understanding what's happening. Here are two ways I can help you take the next step:1. Untangle Your Thoughts is my self-paced course designed to help Christian women understand what's happening beneath their thoughts and emotions, identify the patterns keeping them stuck, and learn practical tools to begin responding differently all while keeping God at the center of the process.Inside, I walk you step-by-step through the process so you don't have to keep wondering, “Okay, but how do I actually change this?”→ Start Untangle Your Thoughts: Click here!2. Start 1:1 Christian Mental Health Coaching with Jessica. Sometimes you don't need more information you need someone to sit with you, help you slow everything down, and work through what is actually happening underneath your thoughts, emotions, and patterns.We'll look at what you're experiencing, uncover what's keeping you stuck, and work toward practical next steps that help you move forward with God.→ Book a free 20-minute consultation: Click here!Website: www.jessicahottle.comEmail: jessica@jessicahottle.comThe information shared in this podcast is for educational and informational purposes only and is not intended as medical or clinical advice. While we discuss mental health topics, this is not a substitute for professional care. Please consult with a qualified healthcare provider for advice specific to your situation.

Catching up with the Chat/Discord! College basketball league pass rankings! Our college basketball rankings based on rooting interests! The Sleepers Podcast is now available daily with new episodes every Monday-Friday! The College Basketball stock market is LIVE on Stakeholder! Buy low or sell high on teams as they lose and add players in the portal! Join using our link for an instant $15 bonus: https://stak3holder.com/join/sleepersmedia Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Big O is Rooting for Kanter in the WNBA 8-10-2026

Hour 3 opens up with Isaac and Suke discussing preseason football and Suke warns people to not give in and think that everything is believable from the preseason. Then the guys wonder if Cal Raleigh will turn it around. Then Joey Harrington joins the program for his weekly appearance. Joey talks about Jayden Daniels wanting LSU to not let anyone where his #5. Later, Joey and the guys discuss Drew Allar in the NFL as Joey worked with him at the NFL combine.

Crowder is disappointed to see several Dolphins fans rooting against Tua Tagovailoa in Atlanta this season.

Jason explains why he's rooting for Deshaun Watson over Shedeur Sanders after Browns coach Todd Monken named Watson the starter for this Saturday's exhibition opener. Watson's fall was real — the suspension, the injuries, the shame — and so is the climb back. Sanders hasn't fallen yet, and that's exactly the danger. Grace is free for the sinner who repents. It is not a coronation. Watch, then decide which quarterback you're actually rooting for. ➢ Today's Sponsors PreBorn has helped rescue more than 400,000 babies, and every single day, they continue that work by offering mothers something powerful and life-changing: an ultrasound. Will you help us? Just dial #250 and say the keyword “BABY” or donate securely at https://Preborn.com/FEARLESS ➢ Show Outline 00:00 Deshaun Watson Named Starter, and the Worship of Athletes 03:52 Watson's Fall from Grace and Personal Agency 08:13 The Browns' Coronation and the Next Black QB Brand 09:54 Rooting for Redemption Over Hype 15:19 Panel: Watson vs. Shedeur Sanders 22:16 Watson's Preseason Start and Injury Concerns 26:31 Russell Westbrook: Empty Calories Debate 42:17 Jayden Daniels and the LSU Jersey Dispute 49:58 The WNBA Is MTV's "Real World" Now ➢ Follow Our GUESTS https://www.youtube.com/@skap_attack https://x.com/SteveKim323 https://www.youtube.com/@TheShemekaMichelle  https://x.com/VirgilWalker ➢ Subscribe to Jason's other channel https://www.youtube.com/JasonWhitlock?sub_confirmation=1 https://www.youtube.com/@JasonWhitlockHarmony?sub_confirmation=1 https://www.youtube.com/@JasonWhitlockBYOG?sub_confirmation=1 https://www.youtube.com/@JasonWhitlockClips?sub_confirmation=1 ➢ Connect with Jason on Social Media:  https://x.com/JasonWhitlock  https://www.instagram.com/realjasonwhitlock/ https://www.facebook.com/jasonwhitlock ➢ Send Jason an Email FearlessBlazeShow@gmail.com ➢ Support The Blaze Visit https://TheBlaze.com. Explore the all-new ad-free experience and see for yourself how we're standing up against suppression and prioritizing independent journalism. Support Conservative Voices! Subscribe to BlazeTV at https://www.fearlessmission.com and get $20 off your yearly subscription. Learn more about your ad choices. Visit megaphone.fm/adchoices

Karoline Leavitt is leaving the White House press secretary job at the end of August, just weeks after coming back from maternity leave with her second child. President Trump called her one of his most trusted aides and one of the best press secretaries in the history of the office. She's 28, the youngest ever to hold the job, and she made it clear she cannot be the mom her two young kids deserve while carrying the nonstop demands of the briefing room. Trump is moving her into a top outside advisory role focused on the midterms and the Republican Party. Names already floating as possible replacements include White House communications director Steven Cheung, a California native and longtime Trump fighter who filled in while she was out. The job has chewed through press secretaries for years. Leavitt lasted through a second pregnancy, a combative media strategy, and constant heat — then chose family. Pat also covered: Does Jenna Ortega have the same “Ariana Grande disease” everyone is talking about? Wisconsin voting officials flag missing memory sticks and election problems Congrats to the NEW Powerball billionaire in Illinois Do we actually control the Strait of Hormuz — or is that just the claim? Final Pat Gray Bingo until next season … thanks for playing Is stepping away the right call when the country needs strong voices? Have you ever had to walk away from a big opportunity because of your kids? If you want unfiltered truth and commonsense analysis that cuts through the noise, hit subscribe and turn on notifications. 00:00 Pat Gray UNLEASHED! 00:23 Issues with Wisconsin Votes 06:46 NBC News Responds to Wisconsin Voting Issues 08:05 Hasan Piker Upset with Voting Results 09:35 Hysteria over Trump Taking a Different Plane 18:06 Karoline Leavitt Leaving the Trump Administration 30:00 Mitch McConnell Song 31:55 Footage of Karoline Leavitt & President Trump 32:37 Fat Five 46:08 Open Plea for Luigi Mangione? 51:03 Cam Higby & James O'Keefe in Minnesota 52:22 Dr. Abdulrahman Mohamed El-Sayed "Blue's Clues" Ad 55:15 100+ YouTube Videos Deleted by Abdul El-Sayed 56:08 Abdul El-Sayed on Bernie Sanders & Trans Health Care 59:37 Tim McBride on Trans Rights 1:04:34 Veronica Ivy Trophy 1:08:09 James Talarico's Past Radical Statements 1:12:16 Home Remodeling Woes 1:15:33 President Trump is Out of Touch 1:24:38 Pat is Rooting for Trump & America 1:28:24 Toaster Strudel Wins! 1:30:30 Sergey Brin VS. California's Billionaire Tax 1:33:06 Hollywood Stars Learn to Shut Up about Politics 1:34:17 FINAL Pat Gray BINGO! Winner for the Year Learn more about your ad choices. Visit megaphone.fm/adchoices

Can I be honest about something? I spent a long time thinking that giving something to God meant I wasn't supposed to feel anything about it anymore. Like caring and surrendering were opposites. They're not. In this episode, I'm digging into what 1 Peter 5:7 is actually inviting us into... because casting your cares isn't the same as becoming indifferent to them. It's the difference between this matters to me, and this depends on me. I'm also talking about the either/or thinking that keeps so many of us stuck, and what it looks like to finally live in the both/and - where you can have feelings and have faith at the same time. Rooting for you,JessicaI'd love to hear from you where you're at, what you're walking through, and what would actually help you most right now.If you have a minute, click this link and fill out a short survey. Your answers will help me create episodes, tools, and resources that truly meet you in this season. Your voice matters to me.If you want support that's specific to you and your story, learn more about working with me 1:1 as your mental health coach: https://www.jessicahottle.com/mental-health-coachingOr book your free 20-minute consultation here!Email me at >> jessica@jessicahottle.com

Ken Carman openly admits he's straddling the fence on the Browns' quarterback competition, caught between wanting long-term stability and sympathizing with keeping a likable fourth quarterback like Tyler Huntley Green around, while Anthony Lima counters that roster spots need to go to players who can actually help win games. The two dig into Chris Mueller's point that raw stats like completions and interceptions don't capture context, using Shedeur Sanders' touchdown throw to Harold Fannin and his performance in the Tennessee loss as examples of how emotion and results can distort honest evaluation. Ken concedes he was too easy on Sanders in the moment because he was celebrating in the stadium with beer in hand, and both agree Todd Monken deserves a fair shake this year since last year's QB competition was legitimately mishandled while this year's hasn't been. They close by noting Monken's first show as head coach airs tonight and teasing a Guardians segment built around the movie First Blood.

Tune in as Rolo Tony (@PoorOldRoloTony and These Guys Got Juice) jumps back onto the podcast to examine The Voices, the 2014 movie that uses black comedy, thriller, and horror to tell the tale of a plumbing fixtures factory worker whose hallucinations direct him down a path of chatting with his talking pets and wrestling with his murderous side. Rooting for Ryan Reynolds to pick bold and interesting roles, putting a dark twist on the Talking Animals trope, the psychology of serial killers, and the misogynistic violence that lurks within the outwardly pleasant Nice Guy archetype stand out as some of the topics for this episode.Directed by Marjane Satrapi, The Voices stars Ryan Reynolds, Anna Kendrick, Gemma Arterton, Ella Smith, Jacki Weaver, Paul Chahidi, Stanley Townsend, Adi Shankar, Sam Spruell, Valerie Koch, and Paul Brightwell.Spoilers start at 29:45Create your podcast today! #madeonzencastrHere's how you can learn more about Palestine and IsraelHere's how you can keep up-to-date on this genocideHere's how you can send eSIM cards to Palestinians in order to help them stay connected onlineGood Word:• Rolo Tony: Mississippi Grind• Arthur: Ruby SparksReach out at email2centscritic@yahoo.com if you want to recommend things to watch and read, share anecdotes, or just say hello!Be sure to subscribe, rate, and review on iTunes or any of your preferred podcasting platforms!Follow Arthur on Twitter, Goodpods, StoryGraph, Letterboxd, and TikTok: @arthur_ant18Follow Arthur on Bluesky: @arthur-ant18Follow the podcast on Twitter: @two_centscriticFollow the podcast on Instagram: @twocentscriticpodFollow Arthur on GoodreadsFollow Arthur's Bookstagram: @arthurs.book.nookCheck out 2 Cents Critic Linktree

Carl and Mike get back into some Falcons talk as they continue to discuss why they are ready for the Falcons to say "who the guy is" in regards to the starting QB position and agree that "we're all rooting for Michael Penix Jr." however believe the chances of him winning the job are slim due to there being no timetable on when he will be cleared.

What if you could go back in time and kill baby Hitler? Michael Tomasky's new satirical novel Killing Baby Hitler explores nature vs. nurture, time travel gone wrong, and whether one monster really explains the world's evil. In this conversation with Matt and Tomasky (editor of The New Republic) discuss:• Why democratic socialism is rising (and what popular policies like child care and data-center limits have to do with it)• Rooting for a young Adolf Hitler in fiction• How tragedy can create monsters• The hilarious botched kidnapping scene • Alternate history where there's no Holocaust… but human nature still produces plenty of darkness• Literary influences (Milan Kundera, Kurt Vonnegut) and writing a political novel in today's publishing world Get the book: Killing Baby Hitler by Michael Tomasky https://www.amazon.com/dp/1682194752?lv=shuf&channelId=500&plpRedirect=mhFallback Subscribe to Matt Lewis on Substack: https://mattklewis.substack.com/Support Matt Lewis at Patreon: https://www.patreon.com/mattlewisFacebook: https://www.facebook.com/MattLewisDCTwitter: https://twitter.com/mattklewisInstagram: https://www.instagram.com/mattlewisreels/YouTube: https://www.youtube.com/channel/UCVhSMpjOzydlnxm5TDcYn0A– Who is Matt Lewis? –Matt K. Lewis is a political commentator and the author of Filthy Rich Politicians.Buy Matt's books: FILTHY RICH POLITICIANS: https://www.amazon.com/Filthy-Rich-Politicians-Creatures-Ruling-Class/dp/1546004416TOO DUMB TO FAIL: https://www.amazon.com/Too-Dumb-Fail-Revolution-Conservative/dp/0316383937Copyright © 2026, BBL & BWL, LLC

Today we'd like to share with you a wonderful episode of one of our favorite podcasts, Hyperfixed. Host Alex Goldman chats with an anonymous hacker who took down a white supremacist dating site. LINKS:Martha Root's Social MediaMartha Root's Chaos Communication Congress PresentationMartha's Website, OK StupidGet tickets for the Hyperfixed live show! Hosted on Acast. See acast.com/privacy for more information.

 Last week I talked about shame - that deep, quiet belief that something is fundamentally wrong with you - and how it gives birth to the survival patterns so many of us have been living inside of. This week, I'm taking it one step further. Because when shame goes unnamed and those survival patterns stay in place, they don't just stay quiet. They fuel anxiety. In this episode, I'm looking at how your inner critic is keeping your nervous system in a constant state of threat, why perfectionism and overthinking are fruit and not the root, and what it looks like to finally stop pulling at the fruit and start tending to what's underneath it. Rooting for you,JessicaI'd love to hear from you where you're at, what you're walking through, and what would actually help you most right now.If you have a minute, click this link and fill out a short survey. Your answers will help me create episodes, tools, and resources that truly meet you in this season. Your voice matters to me.If you want support that's specific to you and your story, learn more about working with me 1:1 as your mental health coach: https://www.jessicahottle.com/mental-health-coachingOr book your free 20-minute consultation here!Email me at >> jessica@jessicahottle.comThe information shared in this podcast is for educational and informational purposes only and is not intended as medical or clinical advice. While we discuss mental health topics, this is not a substitute for professional care. Please consult with a qualified healthcare provider for advice specific to your situation.

"The very moment you need to imprint the truth about who you are in Jesus, is the very moment the lie is vying for position." Dr. Rob Reimer said this on a past show and Susie has often quoted it. Here they're talking about not only knowing your identity in Christ, but deeply rooting it in your mind and soul.  Dr. Rob Reimer's book "Soul Care: 7 Transformational Principles for a Healthy Soul." Dr. Rob Reimer's latest book is "Renewal Culture: Welcoming and Sustaining Revival." Find out more about Soul Care Ecourses here (For 20% off ecourses, use this case-sensitive code: SUSIE) Check out Susie's podcast God Impressions on Apple, Spotify, or wherever you listen to podcasts! Faith Radio podcasts are made possible by your support. Give now: click here

Ash kicks off this episode with an announcement about where she's been, why she's been quiet, why this episode is weeks late and why she's approaching the future of TSFU a little differently now. The gals also finish their stroll through 2000s memory lane as they discuss the second and third episodes of Netflix docuseries Reality Check: Inside America's Next Top Model! Make sure to listen to Parts 1 and 2 if you haven't yet!TSFU Ep. 190: BINGE OR BUST? - Reality Check: Inside America's Next Top Model (Part I)TSFU Ep. 190: BINGE OR BUST? - Reality Check: Inside America's Next Top Model (Part II)"Models, judges and "ANTM" insiders — including Tyra Banks — look back at the reality show's complicated legacy in this eye-opening documentary series." - NetflixSeason 1, Episode 2 - "Still in the Running": After a distressing episode in Milan, "ANTM" explodes and chases a bigger spectacle. Infamous moments, court controversy, and audience clamor for more.Season 1, Episode 3 - "Rooting for You": As ratings slide, the show makes drastic changes. Tyra, the judges, and the models all confront what comes next when the "ANTM" era comes to a close.Join our Patreon for early releases, ad-free episodes, and tons of bonus content, including all 25 episodes of Ash Learns the Bible!Follow us on Instagram, where Ash is actually starting to post again!Audio editing by Malissa Coulson.Miss J's GoFundMe!

This show has been flagged as Clean by the host. IDEAS: Using a cheap phone plan with SMS for authentication. Mighty Text as a free SMS solution. Google VoIP number for shared accounts. Issues with SMS verification blocking VoIP numbers. Avoiding carrier-specific number blocks. Multiple SIM cards for cost-effective SMS. Rooting a phone to manage apps. Security concerns with third-party apps. Limited data usage for minimal phone plans. Challenges with app compatibility on rooted devices. Short-term phone solutions for SMS needs. Shared Google accounts for streamlined access. Avoiding premium SMS services like $15/month plans. Using Wi-Fi for data instead of cellular plans. Importance of SMS for MFA (multi-factor authentication). Transitioning from old phones to new setups. Balancing convenience and cost in phone plans. Reliance on SMS for banking and insurance access. Difficulty finding non-blocked SMS verification options. Preference for minimal, low-cost phone solutions. RECOMMENDATIONS: Use a shared Google account for SMS access. Opt for a cheap phone plan with unlimited texting. Try Mighty Text as a free SMS alternative. Avoid premium SMS services with high fees. Use Wi-Fi instead of cellular data for minimal plans. Choose carrier numbers over VoIP for critical services. Root a device to manage app settings. Test SMS compatibility with banks and providers. Consider multiple SIM cards for redundancy. Prioritize SMS for MFA over other verification methods. Monitor app updates for compatibility with rooted devices. Select phones with flexible data plans. Use downloaded content instead of streaming. Check for SMS blockages with new services. Explore low-cost phone options for minimal use. Maintain backup SMS methods for emergencies. Simplify phone setups to reduce costs. Verify SMS support before switching providers. Combine Wi-Fi and SMS for reliable connectivity. Share accounts to streamline digital access. Provide feedback on this episode.

We have updates on the case of Nolan Wells, the 18-year-old who disappeared over fourth of July while boating with his white friends. New audio has been uncovered from the day of his disappearance that raises questions about the official narrative–or does it? Join host Angela Rye along with special guest-hosts, Joy Reid and Garrison Hayes, for episode 142 of Native Land Pod. FYSA HEADLINES –Democrats have chosen South Carolina as their first-in-the-nation primary, giving Black voters there a larger impact on the presidential race. –A Protestor takes over the mic during a Madison, WI police press conference, following the police shooting death of Corey Ruiz. –Republican Rep. Nancy Mace proposes a bill to eliminate the Congressional Black Caucus. –The Senate confirms election-denier Jay Clayton as Director of National Intelligence. –Coppin State University President Anthony Jenkins has implemented a program offering in-state tuition to students from states with no HBCUs. LINKS AND RESOURCES Study on Poverty & Brain Health: https://www.washingtonpost.com/health/2026/07/27/stress-poverty-can-result-early-brain-decline-study-says/ Nolan Wells New Audio: https://www.threads.com/@datgurlnetta/post/DbUbes4ju3d?xmt=AQG07pC7utnyxcbXqQ39KrAZ6MU_8nD1fHk_mDj77uY9VaYMOJ0ywbO0FVhRFI9nc-psDEk&slof=1 SUBMIT A QUESTION Have a question for our hosts? Send a 60-second video to @nativelandpod and they may answer it on the show! Tutorial video for submitting questions: http://www.instagram.com/reel/C5j_oBXLIg0/ We are 96 days away from the midterm elections. Welcome home y’all! —--------- We want to hear from you! Send us a video @nativelandpod and we may feature you on the podcast. Instagram X/Twitter Facebook NativeLandPod.com Watch full episodes of Native Land Pod here on YouTube. Native Land Pod is brought to you by Reasoned Choice Media. Thank you to the Native Land Pod team: Angela Rye as host, executive producer, and cofounder of Reasoned Choice Media; Andrew Gillum as host and producer, Bakari Sellers as host and producer, and Lauren Hansen as executive producer; LoLo Smith is our research producer, and Nikolas Harter is our editor and producer. Special thanks to Chris Morrow and Lenard McKelvey, co-founders of Reasoned Choice Media. Theme music created by Daniel Laurent.See omnystudio.com/listener for privacy information.

Bickley and Marotta talk Cardinals, go through Social Studies, and play the Generation Gap.

"The very moment you need to imprint the truth about who you are in Jesus, is the very moment the lie is vying for position." Dr. Rob Reimer said this on a past show and Susie has often quoted it. Here they're talking about not only knowing your identity in Christ, but deeply rooting it in your mind and soul.  Dr. Rob Reimer's book "Soul Care: 7 Transformational Principles for a Healthy Soul." Dr. Rob Reimer's latest book is "Renewal Culture: Welcoming and Sustaining Revival." Find out more about Soul Care Ecourses here (For 20% off ecourses, use this case-sensitive code: SUSIE) Check out Susie's podcast God Impressions on Apple, Spotify, or wherever you listen to podcasts! Faith Radio podcasts are made possible by your support. Give now: click here

Can I tell you something that took me a long time to see in my own life? That voice telling you to push harder, do better, keep going - the one that sounds like it's helping you? It might not be encouragement at all. In this episode, I'm starting a two-part conversation about shame, self-criticism, and why being so hard on yourself is actually keeping you stuck rather than moving you forward. I'm going to talk about the difference between guilt and shame, how shame quietly fuels the patterns you can't seem to break, and what Galatians 5 showed me about the internal war so many of us feel while we're healing.  Also, don't forget!

Oh, hey, look at that...still talking about Sundiata this week! Suggested talking points: Rooting for Captain Dirk, Adopted by Foxes, Missed Thirst-Trap Opportunity, Oral Tradition Ad-Break, Throbbing for War, Acute Benjamin Button Syndrome Check out Gordie's TTRPG, Mythomorphosis If you'd like to support Carman's artistic endeavors, visit: https://www.patreon.com/carmandaartsthings If you like our show, find us online to help spread the word! Follow us on Twitter, Facebook, and Youtube. Support us on Patreon to help the show grow at www.patreon.com/wtfolklore. You can find merchandise and information about the show at www.wtfolklorepodcast.com.

Ravis discusses the Buckeyes cashing in, why (for right now) he's rooting for Dallas this season, and should a man get a pedicure? Follow Matt on X @mattravis and WWLS @sportsanimal, thesportsanimal.com, and The Sports Animal app!See omnystudio.com/listener for privacy information.

Mike, Ali, and Beau explain why they think Falcons fans should be rooting for Falcons quarterback Michael Penix Jr. to win the Falcons quarterback competition.

Adam Crowley and Dorin Dickerson think there might be a strong reason why Penn State fans will actually hope that Pitt Football beats Virginia Tech this season. Do Penn State fans hate their former head football coach James Franklin more than the blue and gold?

If God is good and God is powerful, why doesn't He take our pain away? It's one of the oldest questions we carry, and one of the most honest. In this episode, I'm not going to give you a tidy answer, because there isn't one. Instead, I'm looking at this question from four angles - clinical, logical, emotional, and spiritual - to explore what pain is actually doing in us, why God's love isn't measured by whether He removes every hard thing, and what shifts when we stop asking why He hasn't taken this away and start asking how He is meeting us in the middle of it.Also, don't forget!

You don't wake up one day and realize you've been surviving... you just keep going, keep functioning, keep pushing through. And that's exactly what makes survival mode so hard to spot. In this episode, I'm talking about what it actually looks like to live in a state of chronic stress and high alert, why so many high-functioning people have no idea they're there, and what your patterns might be trying to tell you. Because the goal was never just to exist - Jesus said He came so that we could have life abundantly. And there's a real difference between the two. Also, don't forget!

Did you know carpenter bees can get drunk while visiting passionflowers? In this episode of the Wild Herbs Podcast, Dr. Shawn Krosnick, a professor of biology at Tennessee Technological University, shares insights from her years of research on the genus Passiflora. She uncovers the floral anatomy of passionflower, its pollination biology, primary fruit dispersers, evolutionary relationships with other plant families, and why it holds the record for the greatest leaf shape diversity in the plant kingdom. Click here to watch this episode on YouTube! Resources:

Jeremy and Joe challenge each other with Spain and France trivia before diving into the high-stakes World Cup semifinals. They weigh the merits of Lionel Messi's historic run against England's quest for a long-awaited championship, exploring how a breakthrough for a long-suffering international team could serve as a positive omen for the Buffalo Bills. 06:19 - Spain France Trivia Challenge 11:51 - World Cup Semifinal Update 12:37 - Rooting For Team Messi 14:06 - Global Sports Omen Discussion

Hugh Douglas and Joe Giglio debate Zack Wheeler's choice to decline a late MLB All-Star Game invitation on the Midday Show. They also preview the Home Run Derby at Citizens Bank Park, weighing in on whether Kyle Schwarber or Bryce Harper has the better chance to win. 01:18 - Home Run Derby Preview 02:13 - Wheeler Declines All-Star Invite 03:04 - Predicting The Derby Winner 06:33 - Debating Wheeler's Disrespect Narrative 17:59 - Callers Discuss Wheeler Snub

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Chris Mack talks about the other rooting interests for American sports fans in the World Cup now that the US is eliminated.

Here's something I wish someone had told me sooner: forgiving someone and your body no longer reacting to them are two completely different things. You can sincerely release someone from what they owe you and still tense up when you hear their name. That's not bitterness. That's memory. In this episode, I'm getting honest about how scripture on forgiveness is often used in ways that create more shame than freedom, what forgiveness actually is and isn't, and why your nervous system responding to old pain doesn't mean your heart is in the wrong place. Also, don't forget!

Ken and Anthony rip Americans who publicly rooted for Belgium over Team USA, tying the backlash to political comments and arguing sports shouldn't get caught up in polarization, with John chiming in about his own path from conservative talk radio into sports for that exact reason. The conversation pivots to Brad Stevens defending the Celtics' decision to trade Jaylen Brown to the Sixers, with Ken taking a victory lap for correctly predicting it as a win for Boston given the punishing new NBA cap rules around paying two max players. John, a Philly native, and Ken spar over whether the Sixers' front office can be trusted to make the deal work. The segment closes with Ken questioning whether the new CBA effectively fleeced the Cavs on their Donovan Mitchell trade and whether a Mitchell trade return would even be worth pursuing now.

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"I'm a Christian... why am I still struggling with this?" It's one of the most common questions, and one of the loneliest to carry. Somewhere along the way, many of us picked up the idea that faith means not feeling deeply. But when you actually read Scripture, you don't find people hiding their emotions from God; you find them bringing their emotions to God. In this episode, I'm looking at what it means to process what you feel instead of suppressing it or spiraling in it, why ignored emotions don't just disappear, and what it looks like to be completely honest with God without being afraid that your honesty will push Him away. Also, don't forget!

Justin and Shaun invite Evan Roberts from WFAN's Evan and Tiki onto the show to talk Giants football, Jaxson Darts ceiling, If the Giants are ready to compete for the playoffs, and more. Follow Evan Roberts here:https://x.com/EvanRobertsWFAN?s=20https://www.youtube.com/@WFANNewYork Download the Fanatics Sportsbook app , use code JOMBOY https://fanatics.onelink.me/5kut/JOMBOY New customers who sign up and Bet $5, Get $100 in FanCash*. Use FanCash on bonus bets, profit boosts, team gear and more on Fanatics.com.  Use our code for 10% off your next SeatGeek order*: https://seatgeek.onelink.me/RrnK/JOMBOY10. Sponsored by SeatGeek. *Restrictions apply. Max $20 discount Use code GIANTS for 20% off Cozy Earth's Bamboo Sheet Set and Beach Towels → https://cozyearth.com/pages/giants   00:00 WFANs Evan Roberts Talks Giants Football 05:20 The Mets stink 08:03 WFANs Evan Roberts joins the show 08:50 The Giants are contenders NOW with John Harbaugh 11:30 Coaching matters in the NFL 12:50 Evan isn't bought in on Jaxson Dart 16:42 Dart has the quiet assassin element about him| 19:50 What is the ceiling of Dart 22:50 The pass rush is what's going to push the Giants 25:30 Bold Predictions 27:50 Darius Slayton is inevitable  32:30 Rooting for the Giants at all as a Jets fan 36:00 Which NY teams winning is the best for the city 37:50 Do the Jets have any hope 42:30 The Jets should run the ball more 43:30 Horrible coaching with Aaron Glenn's staff 47:20 Tiki Barber behind the scenes at WFAN 49:00 How to trigger Shaun 51:20 Biggest Giants Story in your career Check out our Merch: https://shop.jomboymedia.com/collections/talkin-giants Subscribe to JM Football for our NFL coverage: https://www.youtube.com/@JMFootball Follow all of our content on https://jomboymedia.com #giants #nygiants  *New customers in AZ, CO, CT, DC, IA, IL, IN, KS, KY, LA, MA, MD, MI, MO, NC, NJ, NY, OH, PA, TN, VA, VT, WV, or WY. Must toggle on this promotion in your bet slip and wager $5+ cash on any market (min. odds -500) within 7 days of account opening to receive $100 in FanCash. Promotional FanCash expires 7 days from issuance (at 11:59pm ET). Terms, including FanCash terms apply-see Fanatics Sportsbook app. Use FanCash on bonus bets, profit boosts, team gear on Fanatics.com and so much more. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Peyton Manning Rooting Against Vols? - The Playbook 6/29 HR 1 by Fanrun Radio

Will Peyton root for the Vols against Texas? Chris Johnson announces ALS diagnosis More on Vols Football and Baseball

We talked about the baseball lockout that is coming next season. Al is rooting for a lockout and Boomer wonders why.

If you keep second-guessing yourself, overthinking every decision, asking everyone else what you should do, or feeling like you don't know what you want anymore, this episode is going to hit. In this episode, Allie breaks down why you may feel disconnected from yourself, why decision-making feels so heavy, and why you keep talking yourself out of what you want. This isn't because you're weak, indecisive, or broken. It's often because you've spent years overriding your own instincts, people-pleasing, reading the room, choosing what makes sense over what feels true, and outsourcing your inner authority. You'll learn why overthinking, second-guessing, self-doubt, and constantly needing reassurance can be signs that you've lost connection with your own inner knowing, and how that disconnection impacts your follow-through, confidence, self-trust, routines, and ability to become the woman you actually want to be. If you struggle with self-trust, people-pleasing, indecision, overthinking, self-sabotage, not knowing what you want, or making plans you don't follow through on, this episode will help you understand what's really happening underneath the pattern. And if you're ready to stop starting over and become the woman who actually follows through, come to Allie's free live class Tuesday at 11am PT: Stop Starting Over: The 5 Steps to Becoming the Woman Who Follows Through Grab the link in the show notes or comment SENDLINK on Instagram and Allie will send it to you. Rooting for you! xo Allie

Plus, we discuss the best chick fight ever, (thanks Knicks parade), World Cup tourists falling in love with America gives us hope, and why a little baseball team named the York Revolution is about to sell a lot of jerseys.

(SPOILER) Your Daily Roundup covers thoughts on the last 3 episodes of Love Island, why both Aniya and KC had a horrible episode last night, who is anyone even rooting for, where they need to change the challenges, West not coming back to Summer House, & Mr Roll filed for divorce. Music written by Jimmer Podrasky (B'Jingo Songs/Machia Music/Bug Music BMI)Ads:Ollie - Get 70% off when you subscribe to your Welcome Kit. Go to https://ollie.com/RealitySteve use Promo Code: RealitySteve for 70% off.Zenni - Online eyewear shop. Now is the time for that long overdue purchase of eyeglasses or sunglasses. Go to https://zenni.com/podcast Promo Code: Podcast15 for 15% off your first order.Ro - https://ro.co/RealitySteve to see if you're eligible for the new GLP-1 pill on Ro.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you keep waiting until you "feel like it" to work out, post the content, clean the house, send the email, go to bed on time, or follow through on the thing you said mattered, this episode is for you. In this episode, Allie breaks down why your mood cannot be the authority over your identity, your goals, or your follow-through. Because "I don't feel like it" may feel true in the moment, but that doesn't always mean it gets to make the decision. This is not about forcing yourself, ignoring your body, or pushing through burnout. It's about learning the difference between rest and avoidance, self-leadership and mood-led living, and why your dreams, body, business, home, and self-trust deserve more stability than your current emotional state. You'll learn why consistency doesn't mean you always feel motivated, why boring consistency creates identity evidence, why small imperfect action still counts, and how to stop handing your future to your most tired, avoidant, overstimulated moment. If you struggle with consistency, follow-through, procrastination, all-or-nothing thinking, self-sabotage, waiting to feel ready, or letting your mood run your life, this episode will help you understand the deeper pattern underneath it. And if you're ready to stop starting over and become the woman who actually follows through, come to Allie's free live class Tuesday at 11am PT: Stop Starting Over: The 5 Steps to Becoming the Woman Who Follows Through alliecasazza.com/stopstartingover Rooting for you! xo Allie