Podcasts about transactions

Louise Phillips Forbes started her real estate career making just $8,400 in her first year. More than three decades later, she has closed over $6 billion in real estate, and she credits much of that success to a philosophy that has very little to do with chasing transactions.   James Dwiggins and Keith Robinson sit down with Louise, founder of the Louise Phillips Forbes Team at Brown Harris Stevens, to discuss why relationships, generosity, service, and authenticity have remained at the center of her business. Louise shares how she built her sphere organically, why other agents should be viewed as colleagues instead of competitors, and why treating every relationship like an opportunity for a commission can actually work against you.   They also explore how New York real estate has changed throughout Louise's 35-year career, the growing battle over private listings, balancing service with being a strong negotiator, and why even a $6 billion producer is still willing to rethink how she runs her business.   Connect with Louise on LinkedIn - Instagram (team) - Instagram (self) - Facebook and online at louisephillipsforbes.com. The industry is evolving. The question is: will your business evolve with it? Join us at Zillow Unlock 2026 and be part of the conversation shaping what comes next. Register today at unlockconference.com and use code REIU20 for 20% off your ticket*.   *Code can be used on all full priced passes leading up to the event and cannot be combined with any other discounts.   Subscribe to Real Estate Insiders Unfiltered on YouTube! https://www.youtube.com/@RealEstateInsidersUnfiltered?sub_confirmation=1   To learn more about becoming a sponsor of the show, send us an email: jessica@inman.com   You asked for it. We delivered. Check out our new merch! https://merch.realestateinsidersunfiltered.com/   Follow Real Estate Insiders Unfiltered Podcast on Instagram - YouTube, Facebook - TikTok. Visit us online at realestateinsidersunfiltered.com.   Link to Facebook Page: https://www.facebook.com/RealEstateInsidersUnfiltered Link to Instagram Page: https://www.instagram.com/realestateinsiderspod/ Link to YouTube Page: https://www.youtube.com/@RealEstateInsidersUnfiltered Link to TikTok Page: https://www.tiktok.com/@realestateinsiderspod Link to website: https://realestateinsidersunfiltered.com This podcast is produced by Two Brothers Creative. https://twobrotherscreative.com/contact/   The views and opinions expressed on Real Estate Insiders Unfiltered are those of the hosts and guests in their personal capacities and do not necessarily reflect the views or positions of AGNT, Inc., eXp Realty, LLC, NextHome, Inc., or any of their respective affiliates, subsidiaries, officers, or directors.  

Walmart collected about $2.9 billion in tariff refunds and spent it on roughly 11,000 rollbacks in Walmart US. Transactions grew and operating income rose 28.8%. The comp still slowed to 2.6% excluding fuel, the weakest quarter since 2020, with the softness concentrated in lower-income households. Walmart raised full-year guidance on the assumption that the second half improves on the back of that price investment, which puts a refund that will not repeat into the base of next year's math.Lowe's earned $4.27 a share on $2 billion more revenue than last year, when it also earned $4.27. Comparable sales rose two tenths of one percent. Almost all of the revenue growth was acquired, from a building products distributor and an interior finishes installer that sell into new residential construction, and Lowe's removed the top of its full-year outlook four separate times on the call. Online grew 15.7%. The release blames persistent do-it-yourself macro pressure for the rest, which is a long way of saying the Saturday deck lumber customer has not come back.Target's traffic did come back. Comps grew 3.8% with 3.6 points from traffic, and apparel and accessories grew $4 million on a $4 billion base.Ipsy is launching a marketing services arm and will no longer say what its revenue is. Six years ago it published 4.3 million subscribers and a billion dollars.Plus the Investor Minute: Ferrero buys Purely Elizabeth, Amazon buys DuckDB Labs but not DuckDB, Mubadala takes majority control of Arrive Logistics, Blank Street raises $105 million, Lavanta raises $22 million.The Watson Weekly is sponsored by Avalara. Tax compliance gets harder with every new channel, state, product and market. See what Avalara Agentic Tax and Compliance does about it at avalara.watsonweekly.com#watsonweekly #walmart #lowes #target #ipsy

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss Astros and Marlins org shakeups, Walker Jenkins' callup, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Patrick Copen, Ryan Cesarini, Zach Ehrhard, and Landon Harmon. Topics Discussed:Latest at Prospects Live - 1:41News, Injuries and Transactions - 4:46Callups and Promotions - 16:58Patrick Copen - 28:47Ryan Cesarini - 32:12Zach Ehrhard - 39:02Landon Harmon - 48:14Recommendation Rankings - 54:50*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

In this episode, Steve Luther shares insights into his boutique real estate business in Nashville and his international ventures, emphasizing relationship-building, technology integration, and strategic growth.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss the Dodgers fraud scandal, Kade Anderson's debut, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Lonnie White Jr., Eliazar Dishmey, Joniel Hernandez, and Coy James. Topics Discussed:Dodgers Fraud Scandal - 0:20Latest at Prospects Live - 3:08News, Injuries and Transactions - 5:50Callups and Promotions - 13:14Lonnie White Jr. - 25:11Eliazar Dishmey - 33:10Joniel Hernandez - 39:46Coy James - 46:04Recommendation Rankings - 53:41*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

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

Bongani Bingwa speaks to Carol Paton, News24 Politics Editor on the Democratic Alliance changing course in regards to their support for Mark Burke as the Reserve Bank investigation continues. They also touch on what the investigation is looking at. 702 Breakfast with Bongani Bingwa is broadcast on 702, a Johannesburg based talk radio station. Bongani makes sense of the news, interviews the key newsmakers of the day, and holds those in power to account on your behalf. The team bring you all you need to know to start your day Thank you for listening to a podcast from 702 Breakfast with Bongani Bingwa Listen live on Primedia+ weekdays from 06:00 and 09:00 (SA Time) to Breakfast with Bongani Bingwa broadcast on 702: https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/36edSLV or find all the catch-up podcasts here https://buff.ly/zEcM35T Subscribe to the 702 Daily and Weekly Newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio7See omnystudio.com/listener for privacy information.

What if the biggest advantage in sales isn't your product, your pricing, or your tech stack... but simply being yourself?    In this episode of Sales Lead Dog, Christopher Smith sits down with Pete Auerbach, Sr. Vice President of National Sales at Ad.net, to discuss why authentic relationships, not transactions, are what actually close deals and build lasting business.    Drawing on nearly a decade leading the national sales team at Ad.net and a career built in digital advertising, Pete shares hard-won lessons on leading as a challenger brand, staying competitive at every stage of your career, balancing the player-coach role, and using AI and CRM to help sales teams work smarter.    If you're a founder, sales leader, revenue executive, or seller looking to build stronger customer relationships and a more resilient sales team, this conversation is packed with practical insight you can put to work today.    What You'll Learn  Why authenticity beats the "sales persona" every time • How to build relationships that turn into multi-year deals • The challenger-brand mindset: doing everything at an A level   • Why the best sellers work harder during a slump, not less   • How to escape "whack-a-mole" and actually execute your plan   • What servant leadership and empathy do for team culture   • Why CRM is a "necessary evil" and how AI is about to redefine it   • How to use AI as an enabler for your team, not a way to cut headcount    About Pete Auerbach  Pete Auerbach is a sales leader and digital advertising strategist with nearly a decade of experience at Ad.net, where he leads the national sales team while continuing to work directly with agencies and brands. He helps marketers uncover new opportunities for growth through innovative search, social, and creator media solutions, with a focus on driving measurable results and building lasting partnerships.    Pete got his start in advertising through internships on Madison Avenue before moving into the agency world and, eventually, sales leadership on the West Coast. A former two-sport college athlete, he brings a competitive, team-first mindset to how he builds and coaches his teams. Based in Los Angeles, Pete lives with his wife and two dogs, is the proud father of two adult children, and is an avid golfer and cyclist who carries a competitive edge into everything he does.    Connect with Pete Auerbach  LinkedIn https://www.linkedin.com/in/peteauerbach/  Learn More About Ad.net https://ad.net/    About Sales Lead Dog  Sales Lead Dog is hosted by Christopher Smith, CRM technology and sales process expert, and founder of Empellor CRM. Each episode features sales leaders who have separated themselves from the rest of the pack, sharing how they achieve success with their teams and their CRM strategy.    Unless you are the lead dog, the view never changes.  Connect and Learn More  All episodes and show notes: https://empellorcrm.com/salesleaddog/    If this episode brought you value: 

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss Joshua Baez's historic debut, a listener email about Ronny Cruz's contact gains, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Cale Wetwiska, Carlos Tejera, Dylan Dreiling, and Aaron Walton. Topics Discussed:Latest at Prospects Live - 4:14News, Injuries and Transactions - 6:23Callups and Promotions - 18:54Cale Wetwiska - 39:27Carlos Tejera - 43:57Dylan Dreiling - 53:00Aaron Walton - 1:01:04Recommendation Rankings - 1:06:57Email Question - 1:08:56*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Indians make around 24 billion UPI (Unified Payments Interface) transactions a month. The combined value of these payments is nearly Rs30 trillion. UPI is at the heart of India's digital public infrastructure. The best thing about UPI payments: they are free. But now this could change. The government recently passed the Taxation and Other Laws (Amendment) Bill, 2026, in the Lok Sabha. This Bill, among other things, amends the Payment and Settlement Systems Act, 2007, which had ensured that no charge can be levied on UPI transactions. But now, this is no longer the case. The government has claimed that this charge, known as the Merchant Discount Rate (MDR), may only be applied on transactions above a certain threshold, that too only on merchants, and not on consumers. But analysts believe the costs will be passed on to the consumers. Why is MDR needed for UPI payments? Does it have anything to do with US pressure in the context of trade deal negotiations, as alleged by Opposition leaders? Is UPI really ‘free of charge' even as it stands today? Guest: L Srikanth from Cashless Consumer, a consumer collective Host: G Sampath, Social Affairs Editor, The Hindu Producer: Jude Weston Learn more about your ad choices. Visit megaphone.fm/adchoices

August 9, 2026 - Season 17, Episode 6 of The Terrible Podcast is now in the can. In this Monday morning show that was recorded on Sunday night, Alex Kozora and I start by talking about the recent transactions that the Pittsburgh Steelers have made over the course of the last several days. Alex and I then recap the overall health of the Steelers going into and coming out of the team's Saturday night practice at Latrobe Memorial High School. We discuss how and why we will need to monitor the status of WR Michael Pittman Jr. on Monday afternoon. With the last camp practice being on Saturday night, Alex and I recap that session during this Monday morning show. We go through each and every position group and discuss several different players that stood out during the team's night practice at the high school. We spend a little bit of extra time on the quarterbacks in this show with the team's first preseason game of 2026 now closing in. After recapping the Saturday night training camp practice, Alex and I finally get around to recapping what the Steelers' three coordinators had to say when they talked to the media on Friday. Brian Angelichio, Patrick Graham, and Danny Crossman all had a few interesting things to say on Friday that were worth recapping during this episode. Several Steelers' interviews surfaced on Sirius XM NFL Radio during the team's Saturday night practice, so Alex and I throw in a few quotes from a few of those throughout this show as well. This 99-minute episode also discusses several other minor topics not noted in the recap above and we end things by answering a few email questions we received from listeners. steelersdepot.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Carve outs and spinoffs are only becoming more common in the corporate transaction space, forcing companies to reckon with the challenge of shared technology. The main pitfalls lie in how rights to data and other software intellectual property services should be divided between a parent company and newly spun-off subsidiary, leaving open the potential for knowledge and scale gaps.As Partner Stephen Gillespie explains to co-host Jekkie Kim on this episode of In the Public Interest, mitigating risk when facing these shared technology problems is crucial for avoiding complicated and costly divesture negotiations. He shares the importance of approaching these transactions with a cross-functional team that has the knowledge to identify all shared technology and software intellectual property, and why he expects the market for carve outs to continue heating up in the coming years.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dr. José P. Zagal is a game scholar and Director of the School of Interactive Games and Media at the Rochester Institute of Technology and avid game player as well. He teaches courses on game design, ethics in video games, and experimental games. Professor Zagal taught his first university-level class in 2000, has since supervised multiple award-winning student projects, and many of his former students work at leading game studios worldwide. Dr. Zagal has edited and authored numerous books and articles on game ethics, games education, game design, role-playing games, and more. He most recently co-authored Seeing Red: Nintendo's Virtual Boy (MIT Press 2024) and co-edited Fifty Years of Dungeons & Dragons (MIT Press 2024) and The Routledge Handbook of Role-Playing Game Studies (Routledge 2024). He was honored as a Distinguished Scholar by the Digital Games Research Association (DiGRA) and named a Fellow of the Higher-Education Videogame Alliance (HEVGA) for his contributions to games research. He also serves as the Editor-In-Chief of DiGRA's flagship journal Transactions of the Digital Games Research Association (ToDiGRA). Please check out these relevant links: RIT School of Interactive Games and Media The Quiet Year Prof. Zagal's Google Scholar Profile Tabletop Role-Playing Games in Chile: Early History, Context, and Adoption Cyberpunk 2020 Ghost Busters (West End Games) Phoenix Dawn Command Welcome to Dice in Mind, a podcast hosted by Bradley Browne and Jason Kaufman to explore the intersection of life, games, science, music, philosophy, creativity, and literature through interviews with leading creatives. All are welcome in this space. Royalty-free music "Night Jazz Beats" courtesy of flybirdaudio. Please follow us: Twitter/X: https://x.com/diceinmind Bluesky: https://bsky.app/profile/diceinmind.bsky.social

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Running an Australian eCommerce or Shopify store? Your customers have started shopping inside ChatGPT, and shoppers arriving from AI answer engines convert around 1.5x better than any other source.Melbourne eCommerce Aus Event - Tickets Here. Online shopping has become… boring? A recent Criteo study found 77% of Australians describe online shopping as unexciting, and that's just the starting point for one of our most forward-looking conversations yet.Ryan sits down with Matt Hurle from Criteo to unpack the findings of their latest report and what they mean for anyone running an eCommerce business in Australia. Matt brings 20 years in advertising, 15 in digital and 10 in martech, from cold-calling rural businesses out of the Yellow Pages to sitting at the front edge of AI-powered retail media.The big theme: the path to purchase is no longer linear. Consumers are bouncing between publishers, retailer sites, social platforms and AI answer engines like ChatGPT, and increasingly starting their entire shopping journey inside those answer engines, at the very first spark of an idea. Criteo was the first technology company integrated with OpenAI via API and has already run 2,000+ campaigns in that environment. Early signal? Shoppers arriving from ChatGPT convert about 1.5x more efficiently than any other source.We also get into the eternal marketer's tension Ryan sums up perfectly: "The thing I love about digital marketing is you can track every dollar. The thing I hate about digital marketing is you can track every dollar." Attribution, brand vs. performance, the resurgence of SEO (or GEO/AIO, whatever we're calling it this week), influencer strategy, and why customer support and honest reviews now beat polished influencer content for building real trust.Key TakeawaysThe funnel is dead — long live discovery. Consumers now begin shopping journeys inside AI answer engines, far earlier than the traditional mid-to-lower funnel signals marketers relied on. Brands need to show up higher up the discovery journey.Commerce data is the new gold standard. Moving away from demographics and guesswork toward real commerce signals — what people add to cart, actually buy, and browse before and after. Criteo observes 1T+ in annual eCommerce transactions across 730M+ daily active shoppers.Show up across surfaces, not just your own. Criteo's independence means it serves ads on whichever "surface" (open web, ChatGPT, Meta, TikTok, Pinterest, X) is most likely to convert — not a walled-garden's owned inventory.ChatGPT shoppers convert ~1.5x better. They've researched and validated inside the answer engine, so trust is high by the time they land.SEO/GEO/AIO matters more than ever. As search shifts from keywords to full-sentence, conversational queries, rich product data and verifiable messaging determine whether you appear in AI answers — or get left out of the consideration set entirely.Trust beats polish. Verified reviews and genuine customer support now outweigh paid influencer content. 200 five-star reviews can be less trusted than a 4.7 average. Reply to bad reviews honestly. (Ryan's Nero merino example and Matt's Artisan chopping-board recall story both make the point.)Match the influencer to the job to be done. Celebrities drive reach and exposure; micro-influencers drive engaged, community-level trust and conversion. Different jobs, different tiers.AI answer engines stay out of sensitive categories. OpenAI keeps advertising away from health-related matters, directing people back to their GPs.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss several deadline day trades, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Bryce Mayer, Kevyn Castillo, Samil Serrano, and Jacob Bresnahan.Topics Discussed:Latest at Prospects Live - 1:07Trades, News, Injuries and Transactions - 2:47Callups and Promotions - 38:09Bryce Mayer - 45:15Kevyn Castillo - 53:12Samil Serrano - 1:00:27Jacob Bresnahan - 1:06:41Recommendation Rankings - 1:13:56*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss several deadline day trades, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Bryce Mayer, Kevyn Castillo, Samil Serrano, and Jacob Bresnahan.Topics Discussed:Latest at Prospects Live - 1:07Trades, News, Injuries and Transactions - 2:47Callups and Promotions - 38:09Bryce Mayer - 45:15Kevyn Castillo - 53:12Samil Serrano - 1:00:27Jacob Bresnahan - 1:06:41Recommendation Rankings - 1:13:56*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*Consider subscribing to Prospects Live (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prospectslive.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠), starting at just $7 a month, to get access to amazing tools and content such as:PLive+ Peak ProjectionsTop 1300 Dynasty Rankings (with Auction Values and League Analyzer)Top 600 Prospect RankingsOpen Universe RanksTrade Analyzer and Trade MatchmakerFYPD ADPTop 20 team scouting reports with added fantasy contextDaily sheets (including for Spring Training and College)Private discord channels for tier 70 and up.Additional written and audio content, including more from us! Also check out the Fantasy Baseball Discord to interact with us and many other great fantasy/dynasty/prospect minds (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://discord.gg/fantasybaseball)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Finally please rate and review the podcast and follow us on X and Bluesky if you have not done so already as that would really help us out.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Après le premier semestre, le constat des professionnels est là : le marché est résilient, malgré la guerre en Iran et la remontée des taux d'intérêt. Un ralentissement et une transformation de l'activité certes, mais pas de crise majeure à l'horizon comme en 2022. Ecoutez L'angle éco avec Pierre Herbulot du 05 août 2026.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Send us Fan MailWhy are private equity, physician groups, and the Stark Law like insects heading to a bug zapper? In this episode, Captain Integrity Bob Wade explains why. Hear why ownership changes do not eliminate Stark Law risk, why compensation redesign deserves careful attention, why documentation is your best defense, the mother of all definitions, and the research behind insects being attracted to light bulbs. Learn more at WadeHealthLaw.com

Après le premier semestre, le constat des professionnels est là : le marché est résilient, malgré la guerre en Iran et la remontée des taux d'intérêt. Un ralentissement et une transformation de l'activité certes, mais pas de crise majeure à l'horizon comme en 2022. Ecoutez L'angle éco avec Pierre Herbulot du 05 août 2026.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

In the old days, every part of a supply chain was made up of trading partners who knew each other by name. Transactions were sealed with a handshake or a phone call. But with the global supply chains of the 21st Century, supply chain management has become more complex. You can learn more in this episode or read about it on our blog For more information about the MRPeasy software, visit our website: mrpeasy.com

In this episode, Valerie Jefferson shares her journey from a lifelong passion for real estate to becoming a successful agent and investor. She discusses her strategies for leveraging relationships, overcoming challenges, and planning for scalable growth in the real estate industry. In this episode, we explore real estate investing, scaling strategies, team building, and marketing insights with a seasoned investor. Discover practical tips on managing properties, leveraging finances, and growing your portfolio effectively.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true 'white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a "mini-mastermind" with Mike and his private clients on an upcoming "Retreat", either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas "Big H Ranch"? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Buck Reising Show Hr 1- Ramon Foster's Camp Thoughts, Titans Weekend Transactions & Will Levis SpeaksSee omnystudio.com/listener for privacy information.

In the last hour of #ReactionMonday's 3 Man Front we continued updating you on MLB transactions, had #PatPonders, and WHY Damian is still on his Kalen DeBoer rant! See omnystudio.com/listener for privacy information.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss a flurry of deadline trades, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Andreimi Antunez, Rory Fox, Miguel Hernandez, and Juan Parra.Topics Discussed:Latest at Prospects Live - 1:28Trades - 4:39News, Injuries and Transactions - 20:30Callups and Promotions - 37:52Andreimi Antunez. - 54:54Rory Fox - 1:01:09Miguel Hernandez - 1:07:48Juan Parra - 1:13:59Recommendation Rankings - 1:22:24*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss a flurry of deadline trades, as well as a number of injuries, promotions and debuts and this week's pickup recommendations including Andreimi Antunez, Rory Fox, Miguel Hernandez, and Juan Parra.Topics Discussed:Latest at Prospects Live - 1:28Trades - 4:39News, Injuries and Transactions - 20:30Callups and Promotions - 37:52Andreimi Antunez. - 54:54Rory Fox - 1:01:09Miguel Hernandez - 1:07:48Juan Parra - 1:13:59Recommendation Rankings - 1:22:24*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*Consider subscribing to Prospects Live (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.prospectslive.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠), starting at just $7 a month, to get access to amazing tools and content such as:PLive+ Peak ProjectionsTop 1300 Dynasty Rankings (with Auction Values and League Analyzer)Top 600 Prospect RankingsOpen Universe RanksTrade Analyzer and Trade MatchmakerFYPD ADPTop 20 team scouting reports with added fantasy contextDaily sheets (including for Spring Training and College)Private discord channels for tier 70 and up.Additional written and audio content, including more from us! Also check out the Fantasy Baseball Discord to interact with us and many other great fantasy/dynasty/prospect minds (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://discord.gg/fantasybaseball)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Finally please rate and review the podcast and follow us on X and Bluesky if you have not done so already as that would really help us out.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Recording credit card transactions in either QuickBooks Desktop or QuickBooks Online is pretty easy to do as long as you have the correct procedures in place to do it accurately.  Over the years, I have seen many different ways businesses have recorded their transactions and how quickly their financial reports can reflect inaccurate data.  Most times when I walk them through the correct way to record these transactions, they are amazed at how simple and accurate it can be.  In today's episode, I am going to walk you through one of the best processes for recording your credit card transactions and making payments in your QuickBooks file, all the way through saving your receipts and reconciling your credit card statement to ensure you have all your transactions accounted for.  I am also going to mention a few of the ways that I have seen businesses record their transactions so that you can see if you would benefit from this new process in your business.  Listen in today if you are tired of struggling to record your credit card transactions and you are looking for a solution to make it simple and accurate.  This episode is perfect for you if you are if you are getting ready to start your small business, you're a solopreneur, entrepreneur, small business owner, virtual online bookkeeper or virtual assistant and you are either looking into or already using QuickBooks Desktop or QuickBooks Online…  Join us in a community built specifically for accountants and high-stress professionals.  You'll receive support, accountability, and a community that understands what you're going through. We focus on stress reduction, increasing productivity, time management, goal achievement, health, happiness, and desired lifestyle:  https://www.financialadventure.com/community Schedule your Complimentary Stress Audit And Clarity Session, where we'll work together to create a clear and focused plan and overcome the obstacles that stand in your way so that you can move forward and immediately start enjoying your life with less stress, increased productivity, and more time to spend doing what you love with the people you care about: https://www.financialadventure.com/work-with-me Accountants, CPAs, Bookkeepers, Tax Preparers & Financial Professionals, sign up here to get updates on upcoming opportunities & grab the Audit Of Your Well-Being & Balance Guide here: https://www.financialadventure.com/accountant Ready to set up your business?  I have a program to help you get your business set up so that you can start making money.  Sign up for this program here: https://www.financialadventure.com/start Are you ready to try coaching?  Schedule an Introductory Coaching Session today.  You'll have the opportunity to see how you like coaching with an Introductory Coaching Session: https://www.financialadventure.com/intro Join us in the Mastering Your Small Business Finances PROFIT LAB if you are ready to take control of your business finances and create the profitable business you are striving for.  Are you ready to generate revenues and increase the profit in your business: https://www.financialadventure.com/profit If You Are Ready To Choose, Start Or Grow Your Side Hustle, Get Your Free Checklist And Assessment Here: https://www.financialadventure.com/sidehustle Grab Your FREE guide:  5 Essential Strategies For Stress-Free Bookkeeping: https://www.financialadventure.com/5essentials Your FREE Online Virtual Bookkeeping Business Starter Guide & Success Path Is Waiting For You: https://www.financialadventure.com/starterguide Join Our Facebook Community:  https://www.facebook.com/groups/womenbusinessownersultimatediybookkeepingboutique The Strategic Bookkeeping Academy, including Bookkeeping Basics, is open for registration!  You can learn more and sign up here: https://www.financialadventure.com/sba Looking for a payroll solution for your business?  You can get an exclusive 15% discount on your payroll services when you sign up here: https://www.financialadventure.com/adp QuickBooks Online - Save 30% Your First 6 Months: https://www.financialadventure.com/quickbooks Sign up for a virtual coffee chat to see if starting a Bookkeeping Business is right for you: https://www.financialadventure.com/discovery Show Notes:  https://www.financialadventure.com This podcast is sponsored by Financial Adventure, LLC ~ visit https://www.financialadventure.com for additional information and free resources.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

On this episode, Kyle (X:@Sonny_108/BS:@Sonny108) and Taylor (X/BS:@DynastyPickups) discuss a couple of recent trades, as well as a number of transactions, promotions and debuts and this week's pickup recommendations including Brooks Caple, Ryjeteri Merite, Wilder Dalis, and Luis Fragoza.Topics Discussed:Latest at Prospects Live - 0:59News, Injuries and Transactions - 5:07Callups and Promotions - 19:29Brooks Caple. - 30:03Ryjeteri Merit - 37:25Wilder Dalis - 43:20Luis Fragoza - 52:08Recommendation Rankings - 58:36*Send us an email to dynastybaseballpickups@gmail.com to have your question answered on a future episode of the podcast*

Warren Kornblum, CEO of Shadow Branding and former Global CMO of Toys "R" Us, joins John Golden to break down how brands earn lasting emotional loyalty. Kornblum explains how to win share of heart by defining a clear brand purpose, delivering it consistently from the CEO to the front line, and adding human empathy where AI and automation fall short. Learn more at https://shareofheart.com/

First Pitch Podcast Jake Crumpler (@jakecrumpler) and Carson Picard (@CarsonPicardPL) detail everything you need every morning to update your fantasy baseball team. Tune in daily to be updated on news, injuries, pickups to consider, and today's streamers. Join Our Discord & Support The Show: PL+ | PL Pro - Get 15% off Yearly with code PODCASTProud member of the Pitcher List Fantasy Baseball Podcast Network Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this Money Talks: Felix Salmon is joined by Nobel Prize winning-economist Alvin Roth to discuss his new book, Moral Economics, which uses controversial topics—like prostitution and organ sales—to explore how morality shapes a market … and how we can apply economics lessons to big ethical issues.Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.