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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

Producer Felix Poon goes on a field trip to the headquarters of Consumer Reports, where they test snow blowers on wet sawdust, drop bicycle helmets on anvils, and cover space heaters in towels to see how long it takes them to catch fire.  How do they come up with this stuff? And, in a world of rampant consumerism, can Consumer Reports really be a force for good? Featuring Barrie Rosen, Paul Hope, Chris Regan, Scott Collum, Matt Schimmenti, Indu Sunkara, Paolo Fu, Sana Mujahid, Joan Muratore, and Phil Radford. Produced by Felix Poon. For full credits and transcript, visit outsideinradio.org. SUPPORT Outside/In is made possible with listener support. Click here to join our Patreon and get ad-free episodes of the podcast.  Follow Outside/In on Instagram, TikTok, or join our private discussion group on Facebook. ADDITIONAL MATERIALS In Consumer Reports' very first issue were investigations into the difference between Grade A versus Grade B milk (there was none) and the practice of “slack-fill,” when cereal companies put more air than cereal in their cereal boxes. Learn more about some of the topics referenced throughout the episode: Gas vs. Battery Lawn Mower: Which Is Better? Battery Platform Buying Guide Reducing the Risk of Arsenic in Rice Do you have the right to repair? Learn more about your ad choices. Visit megaphone.fm/adchoices

A new farming practice should earn a producer's confidence before it is trusted with an entire field. Oklahoma State University Extension specialists Brian Arnall, Ph.D., and Josh Lofton, Ph.D., explain how small-plot research becomes practical agronomic guidance for Oklahoma producers. They discuss replication, field variability, statistical confidence, and the steps researchers take before recommending changes involving nitrogen, plant population, varieties, or other crop-management decisions. They also explain why on-farm cooperators and producer-run trials are critical for testing whether research results hold up under commercial conditions. Key takeaways: Small plots help researchers isolate treatment effects by reducing differences in soil, rainfall, pests and field management. A large field demonstration may look convincing, but without replication it can be difficult to separate a treatment response from normal field variability. Results should usually be repeated across multiple years, locations, soil types and weather conditions before becoming a broad recommendation. Researchers may require greater confidence when a recommendation would significantly change established practices or put a producer's return on investment at risk. Producers can manage adoption risk by testing a new practice on a few strips or limited acres before applying it across the operation. Detailed Timestamped Rundown: 00:00–02:13 — Episode introduction and research question Dave Deken introduces the discussion of “big science on small acres” and previews how small-plot trials become real-world recommendations. Brian Arnall and Josh Lofton are introduced along with their OSU Extension roles.02:16–05:09 — Why researchers use small plots Arnall explains that an entire trial may fit within roughly a quarter acre. The smaller area allows researchers to keep treatments on similar soil and under comparable rainfall, pest pressure and environmental conditions. Small plots also make it possible to test many treatments with several replications.05:09–08:19 — Matching plot size to field variability Lofton explains that researchers must decide whether to minimize variability with smaller plots or include more variability within longer plots. Forage research may require longer plots, while detailed plant-physiology questions may be studied in one-foot-by-one-foot microplots.08:21–10:14 — Demonstrations versus replicated science Large demonstrations can show whether a practice appears workable across several acres, but they may not provide strong scientific evidence without replication. Researchers are cautious about using a producer's land for an idea that may reduce yield or profitability.10:14–13:26 — Testing across years and environments A practice that works repeatedly near Stillwater may respond differently in western Oklahoma's sandy soils or the wetter, heavier soils of northeastern Oklahoma. Researchers distinguish between an observation, a tentative practice to try and a formal recommendation.13:26–16:19 — Blocking, variety trials and experimental tradeoffs Lofton describes how treatments are grouped into blocks so each treatment experiences a comparable environment. Trials containing too many varieties can stretch across changing soils and conditions, so researchers may divide varieties into separate maturity groups.16:19–19:15 — Statistical error and recommendation risk The group discusses the possibility of concluding that a treatment works when it does not, or concluding that it does not work when it actually does. Arnall emphasizes the risk of recommending a product or practice that fails to produce a dependable return.19:15–22:10 — Why confidence standards can change Lofton and Arnall discuss the difference between 90% and 95% confidence. The appropriate threshold depends on the research question, the quality of the field conditions and the consequences of being wrong. Major changes to accepted practices demand stronger evidence.22:10–24:22 — Evidence behind major management changes The speakers compare agricultural risk with the much higher certainty required in medicine and engineering. Arnall says he had approximately six years of data before becoming highly vocal about delaying some nitrogen applications.24:22–26:54 — Challenging assumptions and explaining mechanisms Researchers do not rely on statistics alone. They ask whether a result makes biological and agronomic sense, discuss it with colleagues and collect additional plant or soil measurements to explain why it occurred.26:54–29:44 — Scientific disagreement strengthens recommendations Arnall and Lofton explain that members of the research team frequently disagree about mechanisms and interpretations. Those arguments continue until the data support a consistent OSU recommendation. Repeating work with different students or projects can provide additional confirmation.29:46–32:10 — Moving research into producer fields The discussion returns to the progression from controlled plots to practical use. Some practices move into formal demonstrations, while others are tested through a few producer-applied passes or strips. Starting with a limited area gives producers a way to evaluate an unfamiliar practice without risking the whole operation.32:10–34:31 — Why farmer-hosted small plots matter Arnall emphasizes that many small plots are located on commercial farms rather than research stations. These sites provide different soils, management histories and production environments, although the plots can create extra work for the cooperating producer.34:31–35:30 — Helping producers become experimenters Lofton describes producers who begin with one research project and then start conducting their own field demonstrations. County Extension educators can help producers establish useful comparisons and interpret the results.35:30–37:20 — Building a statewide cooperator network Arnall discusses working with different groups of cooperators over time and matching projects with farms that provide the appropriate crop, soil, management system and region. This network improves the relevance of OSU agronomy research.37:20–38:22 — Closing thoughts The episode concludes with appreciation for producer cooperators and an invitation for listeners to visit Red Dirt Agronomy, submit questions and learn more about the research discussed on the program. RedDirtAgronomy.com

The conversation explores the often-unseen world of product testing, field validation, software development, and farmer feedback that goes into launching new technology. Jared shares his unique role as a product tester, explaining how Ag Leader evaluates new products before they ever reach a customer's farm and how real-world farmer feedback helps shape final product decisions. The group discusses: What ZRow is and how it improves corn head guidance during harvest Why row guidance still matters in a world of RTK and precision GPS How Ag Leader integrates with existing row sensing systems The product testing process from concept to commercial launch Ag Leader's internal "hackathon" innovation program Common challenges encountered during product development How farmer cooperators help validate new technology The evolution of precision agriculture since Ag Leader's yield monitor origins Why user interface and simplicity matter just as much as technology The importance of data collection and data utilization How AgFiniti connectivity is changing support and decision-making The future of autonomy, machine guidance, and precision farming Why today's successful producers must embrace innovation and continuous improvement Beyond the technology discussion, the episode delivers a bigger message about modern agriculture: farms can no longer rely on "the way we've always done it." As margins tighten and operations become more complex, the producers who effectively leverage data, technology, and decision-making tools will be best positioned for long-term success. Whether you're a technology enthusiast, an early adopter, or simply curious about how new precision ag tools are developed, this episode offers a fascinating look behind the scenes at one of agriculture's most influential technology companies. Want Farm4Profit Merch? Custom order your favorite items today!https://farmfocused.com/farm-4profit/ Don't forget to like the podcast on all platforms and leave a review where ever you listen! Website: www.Farm4Profit.comShareable episode link: https://intro-to-farm4profit.simplecast.comEmail address: Farm4profitllc@gmail.comCall/Text: 515.207.9640Subscribe to YouTube: https://www.youtube.com/channel/UCSR8c1BrCjNDDI_Acku5XqwFollow us on TikTok: https://www.tiktok.com/@farm4profitllc Connect with us on Facebook: https://www.facebook.com/Farm4ProfitLLC/Farm4Profit Media is not a financial, legal, or tax advisor. Content is provided for informational purposes only, and we serve solely as a platform for third-party opinions. Any actions taken based on this content are at your own risk. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sean Reyes noticed that every shock absorber looks identical from the outside—and none of the automotive brands detail what's actually inside. So he built ShockSurplus, an education-first automotive parts company that turned that information gap into a bootstrapped, eight-figure business. For more on Shock Surplus and show notes click here Subscribe and watch Shopify Masters on YouTube!Sign up for your FREE Shopify Trial here.

Mark Sexton is the Director of Fish Science and Product Testing for Berkley. He joins the JMO Podcast to share behind the scenes information on how the new Lab Series soft baits were created. Besides being a decorated professional when it comes to designing great lures for anglers he is also a massive fish head himself. There's a lot to appreciate about Mark as well as the whole team at Berkley for what they do to make such legendary lures. Summit Fishing Equipment - https://summitfishingequipment.com PROMO CODE: “summit10” for 10% offOnX Fish - https://www.onxmaps.com/fish/app PROMO CODE: “JMO” for 20% offSouth Dakota Glacial Lakes - https://www.sdglaciallakes.comInstagram - https://www.instagram.com/the_jmopodcast/Facebook - https://www.facebook.com/JMOFishingPodcastWebsite - https://jmopodcast.com

Stay informed on current events, visit www.NaturalNews.com - Dioxin Testing and Product Safety (0:11) - Life After the Blockade and Oil Market Manipulation (1:56) - Interview with Mike Adams and Product Testing (3:10) - Impact of Dioxin Exposure and Testing Methodology (10:39) - Product Testing Results and Preparedness (35:28) - Life After the Blockade: Economic and Social Implications (39:16) - Oil Market Manipulation and Economic Impact (39:41) - Interview with Steve Quayle: Genetic Armageddon and AI (1:02:38) - Ancient Symbols and Their Connection to Modern Events (1:08:38) - Preparation for Future Challenges (1:19:18) - Peak Human Population and Mass Extermination (1:20:15) - Replacement of Human Beings by Hybrids (1:22:11) - Secret Labs and Human Incubatoriums (1:23:11) - Historical Figures and Demonic Entities (1:25:30) - Alien Disclosure and Stargates (1:27:24) - Hybridization and Genetic Engineering (1:37:02) - AI and Entity Intelligence (1:40:37) - Spiritual Warfare and End Days (1:52:34) - Yellowstone Park and Extremophiles (1:55:29) - Sponsorship and Product Promotion (1:59:08) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

In this new English episode of My Precious Data, cybersecurity expert Eddy Willems sits down with longtime colleague and friend Andreas Clementi, founder of AV-Comparatives, the independent organisation behind some of the most respected security product tests worldwide.For decades, AV-Comparatives has been the silent benchmark behind the cybersecurity industry. Vendors build products. Marketing teams make claims. But independent testing determines what truly works.Eddy and Andreas go back many years, to a time when antivirus testing was less structured and transparency in performance evaluation was still evolving. Together, they reflect on how independent testing became a cornerstone of trust in the security ecosystem.“Independent testing is not about attacking vendors, it's about improving protection for users,” says Andreas Clementi.“Without transparency and objective testing, trust in cybersecurity would simply collapse,” adds Eddy Willems.The conversation dives into:The origins of AV-ComparativesThe evolution of malware testing methodologiesFalse positives, performance testing and real-world scenariosThe pressure and responsibility of independent testingThe future of security validation in an AI-driven world“Testing must evolve as threats evolve. Otherwise we measure yesterday's problems, not tomorrow's risks.” – Andreas ClementiAn insightful and candid discussion about trust, integrity and the often unseen backbone of the cybersecurity industry.

When Lauren Gropper noticed the amount of disposable plastics used on film sets, she saw a design opportunity. That reframe built Repurpose into a 15-year-old brand selling compostable products while diverting 727 million pieces of plastic from landfills. For more on Repurpose and show notes click here Subscribe and watch Shopify Masters on YouTube!Sign up for your FREE Shopify Trial here.

Carrie Stevens  - Fed Up Foods On the Knowing What it Takes to be Successful: "When you're setting up your business, you want to make it convenient for the customer, but also it needs to be convenient for us because if we can't maintain it then we're going to get burnt out and we can't sustain it." Thousands of pounds of produce goes to waste every year.  This is due to many things, often having nothing to do with the actual taste or health of the produce.  Sometimes it just isn't pretty enough.  So what can be done with all of this good food that should be consumed? Carrie Stevens has a farm, butchers animals to sell and recently purchased the business named, Fed Up Foods.  This is a business that takes less than pretty food and turns it into beautiful sauces, relishes and pickled produce. Fed Up Foods got its start in the local farmers markets, thanks to Wisconsin's pickle law. Carrie Stevens is building on that foundation to bring locally sourced, shelf-stable products—ranging from pickle relish and maple ginger beets to cranberry applesauce—to more retail shelves and customers. Discover the surprising details behind what it takes to buy and run a canned goods business, from PH testing and food safety to sourcing "imperfect" produce and managing labels and inspections. Listen as Carrie explains her journey and what she has learned from building her sustainable food businesses. Enjoy! Visit Carrie at:https://www.fedupfoodswi.com/   Podcast Overview: 00:00 Woman-Owned Artisanal Canned Goods 03:41 Pickle Business Journey and Growth 09:04 Pasture Management and Livestock Rotation 10:44 Horseback Observation Resolves Calf Issues 13:23 Wisconsin Food Finance Support 17:00 "Work to Eat Philosophy" 21:21 Pickling Process and Variations 22:58 "Imperfect Produce Solutions" 27:59 "Pickled Beets Worth the Effort" 30:04 "Lard Pigs, Not Lean" 32:04 "Food Business Quality Challenges" 35:50 "Product Testing & Process Authority" 40:27 Scaling Production with Co-Packer 43:41 Cost-Effective Labeling Challenges 46:33 Frozen Meat Storage Advice 50:26 "Balancing Business and Convenience" 53:47 Cranberries: Creative Uses and Recipes 55:03 "Podcast, Support, Share Sauce" Podcast Transcription: Carrie Stevens [00:00:00]: And I said, hey, why don't you try the cranberry sauce in there? Because, you know, muddled cherries kind of look like cranberries in the cranberry sauce. And I picked them up just that day from the Mr. Ayan Rousch from Roush Century Farms in central Wisconsin. He gave me a nice little tour of his cranberry farm. Organic cranberries. Fantastic. James Kademan [00:00:20]: Sounds like another podcast guest. Yeah. Yes. Carrie Stevens [00:00:22]: So, yeah, just a little cranberry sauce in your old fashioned. James Kademan [00:00:27]: How about that? Carrie Stevens [00:00:27]: Make it the rest of the way however you like, your favorite way. James Kademan [00:00:30]: Foreign. Authentic Business Adventures, the business program that brings you the struggle stories and triumphant successes of business owners across the land. Downloadable audio episodes can be found in the podcast link fundedrawincustomers.com we are locally underwritten by the bank of Sun Prairie and today we're welcoming slash preparing to learn from Carrie Stevens of Fed Up Foods. Carrie, I'm so freaking excited. We're talking about food, which is always good. Carrie Stevens [00:01:00]: Always good. James Kademan [00:01:01]: We're talking about pickles, which is always good. Carrie Stevens [00:01:02]: Absolutely. James Kademan [00:01:03]: And we're talking business. So I feel like we got the trifecta here. Carrie Stevens [00:01:06]: Yeah, absolutely. James Kademan [00:01:07]: How's it going today? Carrie Stevens [00:01:08]: Good, good. James Kademan [00:01:09]: All right, tell us the story. What is Fed Up Foods? Carrie Stevens [00:01:12]: So Fed Up Foods is a woman owned Wisconsin based artisanal canned goods company. So I purchased the business this past August. So I'm fairly new to it. However, it has been around for about five years. So it was started by a woman in central Wisconsin and her, her background, she was a produce buyer at the food co op and, and kind of different roles like that, very involved in the farmer's market and she saw a lot of produce going to waste and that was bothersome to her. Well, you know, and if you, we also own a farm, I'll talk about that more. But for a while I was getting produce from the grocery store, feeding it to our animals when it's, you know, there's a lot of beautiful produce, but you know, what happens to that produce after they can't sell it anymore. James Kademan [00:02:03]: So you would get the stuff that was blem essentially or just didn't look pretty. Carrie Stevens [00:02:07]: Yeah, or it was too, you know, I had been there for a couple weeks and it was okay, it was going mushy or whatever. James Kademan [00:02:14]: Pigs like it, humans don't love it. Carrie Stevens [00:02:15]: Right, all right. Yeah. And humans go, so, so anyways, what do you do with that, that produce as it's going bad or almost going bad and it's not selling? So the previous owner had started with Doing some home canning, home pickling. And in Wisconsin there's a pickle bill. So you can pickle at home and sell at farmers markets up to a certain dollar limit. James Kademan [00:02:40]: That's fairly new, right? Carrie Stevens [00:02:42]: You know, I don't know the, the history of it. James Kademan [00:02:44]: Okay. I mean last 10 years or something like that, I feel maybe, maybe. Carrie Stevens [00:02:49]: And then there's like there's the cottage baker law too. So that's a different one. Bakers, they can just bake in their house and sell. James Kademan [00:02:56]: Is there a limit like you can't for bakers? Carrie Stevens [00:02:58]: No, I don't, I, I do not believe so. But don't quote me on that. James Kademan [00:03:01]: Okay? Carrie Stevens [00:03:02]: Contact your lawyer for that. All right, fair. But for picklers canners there is a dollar limit. So Once you hit $5,000 in sales for the year, then for the year you flip over to not being under the pickle law. So the previous owner had grown, the business, passed the pickle law. So that means I now produce out of a commercial kitchen. I have all sorts of licenses and fun inspections. But that also means the product I'm producing is PH tested and I temp test everything so it is safe to consume. Carrie Stevens [00:03:41]: But that, so that started, she started from that under the pickle law, making it in our house, selling it at farmers markets and grew a business to where it's in retail stores, food co ops, kind of boutique stores or stores that specialize in local products. So shelf stable product that is taking a consumable product that is going to go bad and preserving it. So, so you can put it in your pantry and eat it when you get to it. So I purchased the business and have, am continuing the same recipes, getting restocked in the same stores, selling through website. We also sell it through our farm. So we have a customer base that purchases from our farm, so we sell through there too and just kind of looking at different new avenues as well. But it's been quite the learning experience we've started. My husband and I have started a business before but purchasing a business is a little different. Carrie Stevens [00:04:46]: So a lot of interesting learning but you know, good, bad and otherwise. Right. Some good things, some things that I'll change but it all is a good learning process. So, so it's been, been interesting and you know, little bumps through the, in the road. But you know, my husband keeps reminding me that one thing at a time and just it's. And it's going to take time. So with any, with anything it is going to take time to figure it out. I burnt a whole batch of pear sauce. Carrie Stevens [00:05:18]: And you burnt a whole. James Kademan [00:05:20]: How big is the whole batch? Are we talking a cauldron? Carrie Stevens [00:05:22]: Like a hundred? Some jars. James Kademan [00:05:24]: Well, that's a fair amount. Carrie Stevens [00:05:25]: That's a fair amount. Yeah. I mean, but my kids still like it, so. Hey. James Kademan [00:05:28]: Oh, well, there you go. Maybe it's a new product. Right? Carrie Stevens [00:05:30]: Burn. So white elephants at Christmas. Going to be fun. James Kademan [00:05:34]: If people drink Zima, they'll eat burnt pear sauce. Right? Carrie Stevens [00:05:38]: I mean, it's not totally burnt. It's just a little burnt. James Kademan [00:05:40]: All right. A little t. It's charcoal, right? Like, what is that, tequila? Carrie Stevens [00:05:43]: Like a zest of charcoal. James Kademan [00:05:47]: Tell me. So you have a farm that you butcher stuff at, right? Carrie Stevens [00:05:51]: Yeah. So we raised beef, cattle, pigs, chickens, chickens for meat and chickens for eggs and sell all direct to consumers. So we purchased the farm seven years ago, moved onto the farm. It'll be six years ago this fall and pre pandemic. So fall of 2019, we took our first steers to the butcher, sold to friends and family. And then when the pandemic hit, I said to my husband, and maybe I should have taken these words back, but I said, hey, I think we can sell this. And now we. So that was fall of 2019, when we took two steers into the butcher. Carrie Stevens [00:06:33]: Now we take anywhere from three to five steers into the butcher every month. And we do about 50 pigs a year. I did 450 meat chickens last year. I'm gonna double it this year. James Kademan [00:06:50]: Wow. Carrie Stevens [00:06:51]: Because I sold out in about two weeks. James Kademan [00:06:53]: Holy cow. Carrie Stevens [00:06:55]: Yeah.

How founder Catherine Lockhart built Shelter Skin through deliberate growth, in-house manufacturing, and radical transparency.For more on Shelter Skin and show notes click here Subscribe and watch Shopify Masters on YouTube!Sign up for your FREE Shopify Trial here.

Animal Product Testing Debate by Ryan and Issac by 826 Valencia

AI firm Anthropic has raised a $13 billion Series F round that brings its post-money valuation up to $183 billion — funds the company says will be used to grow its enterprise adoption, deepen safety research, and support international expansion.  Also, OpenAI announced in a blog post on Tuesday that it agreed to acquire the product testing startup Statsig, and bring on its founder and CEO, Vijaye Raji, as the company's CTO of Applications. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, we talk about how to get real Amazon reviews without breaking the rules. Our guest is Adam Melenkivitz, founder of Test Squared, a platform that helps Amazon sellers grow with FTC-compliant reviews. He shares how sellers can earn trust, avoid fake reviews, and stay on the right side of the law—while also getting real feedback that helps improve their products. Topics discussed in this episode:  Why authentic Amazon reviews are more critical than ever for building consumer trust. What the new FTC rule means for Amazon sellers. How TestSquared's matching system works without breaking Amazon's rules. Why most reviews aren't five-star ratings and that's actually beneficial. What product categories work best for authentic review programs. How the 35-day review timeline varies by product type. Why 20 authentic reviews is the magic number for Amazon's algorithm. How proper vetting prevents fake testers from gaming the system. What the affiliate program offers for referral partners. Why being unconventional in marketing helps generate organic reviews. Links & Resources Website: https://www.testsquared.com/ Affiliate link: https://www.testsquared.com/affiliate LinkedIn: https://www.linkedin.com/in/adammelenkivitz/ Get access to more free resources by visiting the show notes athttps://tinyurl.com/4h9sdwtn SUPPORT OUR SPONSORThis episode is sponsored by Ahrefs — the all-in-one marketing intelligence platform trusted by SEO professionals, content creators, and digital marketers around the world. Whether you're doing keyword research, checking backlinks, or analyzing competitors, Ahrefs gives you the tools to make smarter marketing decisions.

In Episode 21 of Let's Set Your Table, host John D. and co-host Nick D. are joined by fellow HGI team members and Chicago representatives Kerry O'Connor and Jen Katta. Together, they share behind-the-scenes stories from the Hode Group's dynamic showroom in the heart of Fulton Market. From the importance of teamwork and relationship-building to the hands-on approach of product testing, the team reflects on how they create a warm, client-focused environment that truly sets them apart. Tune in to hear success stories, industry insights, and how the Hode Group delivers a one-stop-shop experience that brings lasting value to every customer interaction.   Takeaways The Hode Group showroom is a hidden gem in Chicago. Teamwork and collaboration are essential for success. Customer service is a top priority in the hospitality industry. Building relationships with clients leads to long-term partnerships. The HGI showroom offers a one-stop shop for all restaurant needs. The Hode Group represents over 30 different product lines. Product testing is crucial for customer satisfaction. The HGI team is passionate about what they do. Positive experiences lead to repeat business.   Chapters 00:00 – Introduction & Guest Welcome 01:45 – Inside the Fulton Market Showroom 03:30 – Representing Top Manufacturers 05:15 – Personal Journeys to the Hode Group 08:00 – Leadership, Culture & Family Ties 10:30 – Customer Service & Relationship Building 13:15 – What Sets the Hode Group Apart 15:00 – The One-Stop-Shop Experience 17:00 – Real Success Stories from the Showroom 19:00 – Product Testing & Turnkey Solutions 20:30 – Final Thoughts & How to Visit Links -  Explore our innovative, industry leading lineup of manufacturers we rep at: hodegroup.com -  Visit the Hode Group Chicago Showroom: hodegroupshowroom.com -  Listen to Let's Set Your Table on Apple Podcasts  

Amy Liu's journey to redefining clean beauty started in her own skin. As someone with eczema, she knew firsthand the struggle of finding beauty products that were both safe and effective. The industry was full of promises, but few products delivered results without irritation. After years of working behind the scenes at Smashbox, Kate Somerville, and Josie Maran Cosmetics, Amy found herself at a crossroads. She had built brands for others, but what about a brand built for people like her? People who wanted beauty without compromise? At 40, she took the leap and launched Tower 28, the first and only beauty brand 100% compliant with the National Eczema Association's ingredient guidelines. In this episode, Nancy and Amy unpack the highs and lows of entrepreneurship—overcoming the fear of failure, scaling a business while staying true to her mission, and bringing her husband on board as CFO. Recognized as Ernst & Young's Entrepreneur of the Year for Greater Los Angeles and one of Goldman Sachs' most exceptional entrepreneurs, Amy has proven that success doesn't have to come at the cost of integrity. This is a conversation about resilience, leadership, and the power of collaboration over competition. Above all, it's a story about turning a personal struggle into a movement that's changing the face of beauty.Timestamps:[00:00] Introduction[04:26] Struggling with eczema and the need for safe beauty products[05:52] Discovering the flaws in clean beauty[07:38] Why it took her so long to start her own business[08:54] Getting her first investment and launching Tower 28[10:42] Why launching at 40 was the right decision[11:58] The myth of overnight success and why experience matters[13:21] The role of a CEO and why she chose to stay in the position[14:47] Challenges of balancing leadership with business growth[16:32] Bringing her husband in as CFO and working with a spouse[18:15] Breaking through the crowded beauty market[19:44] The philosophy behind Tower 28's product development[21:18] Why sensitive skin testing sets Tower 28 apart[22:53] Expanding at Sephora and becoming a top-performing brand[24:36] When to raise money and how to choose the right investors[26:24] Private equity, beauty playbooks, and scaling successfully[28:10] The challenges of hiring and building the right team[30:28] Leadership lessons and learning from mistakes[32:42] Creating Clean Beauty Summer School and supporting founders[34:20] The importance of representation in the beauty industry[36:08] Why collaboration is more powerful than competition[38:35] Advice for struggling entrepreneurs in a competitive marketResources Mentioned:Tower 28 | WebsiteNational Eczema Association | WebsiteSephora Clean | WebsiteGoop Clean | WebsiteCredo Clean | WebsiteClean Beauty Summer School | WebsiteFollow Nancy Twine:Instagram: @nancytwinewww.nancytwine.comFollow Makers Mindset:Instagram: @makersmindsetspaceTikTok: @themakersmindset

Keypoint Intelligence's Riley McNulty and Pete Emory sit down with Carl Schell to explore where challenges lie in print today, from the office to production, and how robotics can turn those into a new revenue stream. But you need to first have the education necessary to understand not only the space but also the technology, and that's where the heads of Keypoint consulting and US lab testing, respectively, come in.

Discover a powerful, rarely used strategy to gain customer insights for product launches—no PPC, no need to look at reviews, and possibly more effective than AI tool insights. ► Instagram: instagram.com/serioussellerspodcast ► Free Amazon Seller Chrome Extension: https://h10.me/extension ► Sign Up For Helium 10: https://h10.me/signup  (Use SSP10 To Save 10% For Life) ► Learn How To Sell on Amazon: https://h10.me/ft ► Watch The Podcasts On YouTube: youtube.com/@Helium10/videos Join us for an enlightening episode where we sit down with the extraordinary Izabela Hamilton of Rank Bell. Izabela shares her unique approach to gaining valuable customer insights without relying on PPC or traditional reviews. She opens up about her personal journey over the past couple of years, highlighting the importance of taking time off to focus on personal growth and wellness. This candid conversation emphasizes the significance of listening to our bodies and the universe, recognizing when to step back to recharge and come back stronger.  Listen in as we explore the shifting landscape of Amazon's product ranking, focusing on authentic and customer-centric strategies. Izabela and Bradley discuss how brands are moving away from manipulated tactics and embracing the power of understanding shopper perspectives. With Helium 10 and RankBell, brands can now predict customer preferences more accurately and ensure their products earn their spot on page one. The episode highlights the importance of aligning products with customer needs and how a holistic approach to ranking can lead to significant revenue without heavy reliance on ads. Lastly, we dive into the power of customer feedback in product improvement, illustrated by a client's journey in overcoming design challenges with an anti-aging red light mask. By conducting thorough pre-launch testing and addressing issues early, brands can ensure product success and customer satisfaction. We also explore strategies for creating compelling Amazon listings and the unique opportunities for new brands to compete with established ones in the evolving online shopping landscape. Don't miss this episode packed with insights on adapting to changes, leveraging AI tools, and proactively meeting customer needs for long-term success. In episode 658 of the Serious Sellers Podcast, Bradley and Izabela discuss: 00:00 - Customer Insights Without Reviews or PPC 07:00 - Customer-Centric Ranking Strategies for Amazon 12:23 - Brand Optimization and Marketing Strategies 15:22 - Customer Feedback for Product Improvement 18:27 - Product Testing and Feedback Loop 22:30 - Customer-Centric Approach in Entrepreneurship 23:25 - RankBell Client's Amazon Success Story 25:14 - Amazon's Future in Search and Advertising 30:20 - Audit Your Listings for Longevity 34:46 - Customer Feedback and Competitor Insights

Dal Perio is a Senior Manager of Sensory & Product Insights at Starbucks, with 30 years of experience in Sensory Science, Consumer Research, and Marketing Research across seven Fortune 500 companies including Johnson & Johnson, Diageo, and Unilever.   His expertise spans Product Innovation, Consumer Research, Quality Assurance, and Product Testing. At Starbucks, he focuses on Sensory & Product Insights for various channels, ensuring optimal research solutions. He's actively involved in numerous professional sensory organizations and was mentored by Rose Marie Pangborn.   To be put in touch with Dal, please contact Aigora. To learn more about Aigora, please visit www.aigora.com

Episode 047 | Christine Preston is a compromised Skin Therapist (certificate in Skin Therapy from George Brown College) who has been certified since 2016. Since then, she's been helping women across North America achieve calm, clear skin by helping them identify their triggers and creating skin care routines that work. She opened the Skin Discovery Spa in 2021 where she helps her clients clear acne, calm rosacea and relieve eczema-prone skin. (Did someone say road trip?!

In this Convo of Flanigan's Eco-Logic, Ted speaks with Yin Chen, CEO and Chairman at Greenworks, a supplier of battery-powered outdoor power equipment (OPE) since 2003. With over 20 years at the forefront of home, yard, and do-it-yourself electric tools, Greenworks is redefining durability and eco-friendliness in the face of climate challenges, delivering high performance equipment focused on reducing carbon footprints.Ted and Yin discuss his background briefly, studying mechanical engineering at China's Donghua University, and business at Warwick University in the UK. He shares humble beginnings of Greenworks, recalling a meeting with one of their first clients placing a modified bike battery on the table, noting that it would be the centerpiece of all their applications. Fast forward, Greenworks now has more than 7,000 employees and manufacturing facilities in China, the U.S. and Vietnam, and offers everything from riding lawn mowers to snow removal tools and pressure washers – all powered by batteries. Yin shares his outlook on how Greenworks has shifted the narrative, and landscapers are now embracing battery power because it is in their best interest. Environmental benefits aside, Greenworks products improve workers' day-to-day well-being. As they engage with the cleaner battery-powered technology, landscape professionals can now focus more on their tasks without the negative side effects of inhaling toxic fumes.Yin also highlights Greenworks Optimus line of OPE, which was previously focused around the residential sector, but now focused on developing new products and comprehensive solutions for the commercial sector. The research and development, product testing, manufacturing, and customer service for the commercial equipment is being done at the Greenworks Commercial Center of Excellence in Morristown, Tennessee, ushering Greenworks Commercial into a cleaner, quieter, and more sustainable manufacturing future here in the US.

Send us a textAmazon sellers are facing problems with gated categories like toys, supplements, and child products. Failed product testing and safety certificates are blocking listings, especially for resellers. Learn how to handle compliance, invoices, and optimize product titles for better sales.Struggling with ads? Download My Amazon Guy's PPC guide to start saving money: https://bit.ly/4hHX72l#AmazonSelling #EcommerceTips #AmazonSellers #AmazonListings #AmazonFBAWatch these videos on YouTube:Amazon AI Rufus Is Useless https://www.youtube.com/watch?v=GVPdn2n8_o8&list=PLDkvNlz8yl_YEKE1B5o1uhbBm1QQcPzmYAmazon PPC Agency vs In House vs Auto Campaigns https://www.youtube.com/watch?v=m9s9vjtU5l4&list=PLDkvNlz8yl_YEKE1B5o1uhbBm1QQcPzmY&index=1-----------------------------------------------Listing issues costing you sales? Contact us now and fix your Amazon listings: http://bit.ly/3B1LvHtNeed expert advice? Book a coaching call and get personalized help: http://bit.ly/4eVgJxUTimestamps:00:00 – Failed Product Testing Blocks Amazon Sellers00:09 – What Are Gated Categories on Amazon?00:31 – Common Gated Products: Toys, Supplements & Health Items01:16 – Why Child Safety Certificates Matter02:10 – Resellers Facing New Gating Problems in Q403:05 – The Best Way to Handle Amazon Compliance Documents03:27 – Why Brand Name Should Go at the End of Titles04:12 – Changing Amazon Product URL for Google Search04:38 – Where Keywords Matter Most in Amazon Listings05:24 – Image Setup for Better Conversions on Amazon06:00 – Why Infographics Fail on Mobile Listings-----------------------------------------------Follow us:LinkedIn: https://www.linkedin.com/company/28605816/Instagram: https://www.instagram.com/stevenpopemag/Pinterest: https://www.pinterest.com/myamazonguys/Twitter: https://twitter.com/myamazonguySubscribe to the My Amazon Guy podcast: https://podcast.myamazonguy.comApple Podcast: https://podcasts.apple.com/us/podcast/my-amazon-guy/id1501974229Spotify: https://open.spotify.com/show/4A5ASHGGfr6s4wWNQIqyVwSupport the show

Shani Darden, the founder of Shani Darden Skincare, has built a name as Hollywood's go-to skincare expert. She is trusted by stars like Jessica Alba, Chrissy Teigen, and Kelly Rowland for her results-driven approach. After training under a top dermatologist, she developed her own formulations, launching her flagship studio in Beverly Hills in 2019 and securing a coveted spot at Sephora in 2020. In this episode, Shani talks about how she went from mixing masks in her childhood home to creating one of the most sought-after skincare brands. She opens up about navigating the beauty industry, building a brand without a roadmap, and staying hands-on with clients while scaling a business. You'll hear her insights on effective product development, the power of social media, and the strategies she learned over her career. Timestamps:[00:00] Introduction[04:08] Creating Retinol Reform and entering the skincare industry[05:14] Transitioning from service-based work to product development[06:27] Challenges in launching a skincare brand[07:35] The importance of trial and error in product creation[08:42] How Sephora became a major retail partner[09:56] The role of packaging and refining the brand[10:45] Building a team that aligns with brand values[11:33] Why hiring slow is crucial for long-term success[12:18] Social media's impact on personal branding[13:09] How client feedback shapes product development[14:21] The challenges of balancing retail deadlines with product quality[15:37] Tips for building an engaged online community[16:42] Overcoming self-consciousness in social media marketing[17:15] Experimenting with content strategies for better engagement[18:29] Finding balance between content creation and business operations[19:45] Adjusting to entrepreneurship and leadership roles[20:56] The importance of mentorship and learning from mistakes[21:33] Rapid-fire business and skincare advice[22:12] Common skincare mistakes and how to avoid themResources Mentioned:Retinol Reform | WebsiteSephora | WebsiteShani Darden Skincare | WebsiteFollow Nancy Twine:Instagram: @nancytwinewww.nancytwine.comFollow Makers Mindset:Instagram: @makersmindsetspaceTikTok: @themakersmindset

In this episode, get expert tips on ranking page one on Amazon, from keyword strategies and listing optimization to leveraging AI and actionable insights to boost your sales. Unlock the secrets to achieving the coveted page one ranking on Amazon as we sit down with Alina Vlaic from AZRank. You'll learn to master the nuances of constructing effective listings and targeting the right keywords to enhance your visibility and sales. Alina shares stories from her journey in the e-commerce world, highlighting the dynamic Romanian e-commerce community and how it's being driven forward by a strong IT and programming culture.   We trace the evolution of Amazon's ranking strategies, from the early days of product giveaways for reviews to today's focus on relevance and customer engagement. Discover how sellers have adapted to changes in Amazon's algorithm and hear about the tactics that continue to thrive. Alina and her team at AZ Rank, in collaboration with Helium 10, have pioneered methods like the CPR formula to help sellers navigate these challenges. Their insights shed light on crafting strategies that keep your products relevant in the ever-changing Amazon landscape.   Finally, we delve into the science of reverse engineering Amazon's AI, Rufus, to give your listings the competitive edge they need. Alina discusses the growing importance of rich media content like images, videos, and user-generated content, and how these elements can significantly amplify your product's appeal. Whether you're launching a new product or refining an existing one, learn how to harness tools like Helium 10 and Amazon's Product Opportunity Explorer to strategically position your brand for success. In episode 429 of the AM/PM Podcast, Kevin and Alina discuss: 00:00 - Strategies for Amazon Page One Ranking 04:40 - Entrepreneurial Journey in E-Commerce 15:47 - Evolving Strategies for Amazon Ranking 17:05 - Amazon Bans Activity, Goes Underground 19:41 - Developing Formula for Amazon Ranking 22:35 - Crucial Strategies for Amazon Ranking 27:57 - Optimizing Flat Files for Amazon Success 29:53 - Optimizing Amazon Launch Strategy and Ranking 31:22 - Product Testing and Data Collection Techniques 37:04 - Product Success Maintenance Planning Strategy 40:07 - Reversing Amazon AI for Ranking Success 44:42 - Amazon Listing Optimization Through UGCs 48:12 - Trends in Product Innovation 50:14 - Kevin King's Words of Wisdom

Stoked Oats' founder turned his morning oatmeal obsession into a multimillion-dollar CPG empire, while building a brand that fit his lifestyle—and not the other way around.For more on Stoked Oats and show notes click here. 

In the enterprise security news, a final few fundings before the year closes out Arctic Wolf buys Cylance from Blackberry for cheap, a sentence that feels very weird to say the quiet HTTPS revolution passkeys are REALLY catching on resilience keeps showing up in the titles of news items Apple Intelligence insults the BBC's intelligence MITRE ATT&CK evals drama Lastpass breach drama continues All that and more, on this episode of Enterprise Security Weekly Show Notes: https://securityweekly.com/esw-388

Kusha is an Inc 500 entrepreneur with a remarkable history of launching and exiting several 7- and 8-figure e-commerce businesses. With over a decade of experience in scaling brands on Amazon, Kusha has mastered the art of driving sales through advanced marketing strategies, data analysis, and impactful branding. His recent venture, Kazam, a revolutionary AI-Based SaaS tool, is transforming the way Amazon advertising campaigns are optimized, offering users significant profitability and organic ranking improvements.Highlight Bullets> Here's a glimpse of what you would learn…. Importance of product differentiation in e-commerceStrategies for effective product development and innovationInsights on launching and exiting seven and eight-figure e-commerce businessesThe role of PPC (pay-per-click) advertising in driving sales and visibilityUtilizing AI tools to enhance e-commerce operations and marketingSourcing strategies and the significance of building relationships with manufacturersThe impact of consumer trust in product sourcing, particularly regarding country of originThe necessity of a robust intellectual property strategy for protecting unique productsDiversifying sales channels beyond Amazon for greater control and customer relationshipsThe value of customer feedback and iterative improvement in product developmentIn this episode of the Ecomm Breakthrough Podcast, host Josh Hadley interviews Kusha, an Inc. 500 entrepreneur with over a decade of experience in e-commerce. Kush shares his journey of building and selling three Amazon-based brands, emphasizing the importance of product differentiation, effective PPC advertising, and leveraging AI. He discusses sourcing strategies, the value of design patents, and the significance of iterative product improvement. Kush also highlights the benefits of diversifying sales channels and building an email list. This episode offers actionable insights for scaling e-commerce businesses to eight figures and beyond.Here are the 3 action items that Josh identified from this episode:Create Differentiated Products: Stand out in a crowded market by offering unique products.Robust IP Strategy: Protect your innovations with utility or design patents.Leverage Digital Marketing: Use platforms like Meta and Google Ads to capture customer data and build a customer database.Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough PodcastEmail Josh Hadley: Josh@eCommBreakthrough.comShazamShopifyAmazon PPCCanvaGoogle AdsGlew.ioPickfuProduct PinionAlibabaUpworkRich Dad Poor DadSpecial Mention(s):Adam “Heist” Runquist on LinkedInKevin King on LinkedInMichael E. Gerber on LinkedInRelated Episode(s):“Cracking the Amazon Code: Learn From Adam Heist's Brand Scaling Secrets” on the eComm Breakthrough Podcast“Kevin King's Wicked-Smart Tips for Building an Audience of Raving Fans” on the eComm Breakthrough Podcast“Unlocking Entrepreneurial Greatness | Insider Secrets With E-myth Author Michael Gerber” on the eComm Breakthrough PodcastEpisode SponsorSponsor for this episode...This episode is brought to you by eComm Breakthrough Consulting where I help seven-figure e-commerce owners grow to eight figures. I started Hadley Designs in 2015 and grew it to an eight-figure brand in seven years.I made mistakes along the way that made the path to eight figures longer. At times I doubted whether our business could even survive and become a real brand. I wish I would have had a guide to help me grow faster and avoid the stumbling blocks.If you've hit a plateau and want to know the next steps to take your business to the next level, then go to www.EcommBreakthrough.com (that's Ecomm with two M's) to learn more.Transcript Area...

What does it mean to make a product inclusive?

How can crowd testing improve signup completion rates?

Aaaaaand we're back! Welcome to the Dialed In Podcast! In today's fully loaded episode, Matt talks about his favorite things from SEMA, some up-and-coming Mirka updates, expansion into Europe and Canada, Black Friday predictions, and a surprise GT4 RS allocation. 

What is localization... and what does it mean for testing?

After a behind-the-scenes tour of their shot shell manufacturing facility in East Alton, Illinois, Dr. Mike Brasher sits down with Nate Robinson, Ben Frank, and Grant Jeremiah from Winchester Ammunition to discuss how a long-standing passion for waterfowl hunting has helped Winchester become the most trusted name in waterfowl ammunition. From Dry-Lok to Blindside and new waterfowl loads such as Bismuth and Last Call TSS, the group discusses the innovation behind these products, their commitment to quality, and the design and testing that makes them the best in the business: Winchester Ammunition – the official ammunition of Ducks Unlimited and our proud partner in conservation.Listen now: www.ducks.org/DUPodcastSend feedback: DUPodcast@ducks.org

Eli Goodman: How A/B Testing Can Derail Product Development, A Product Leadership Story NOTE: We want to thank the folks at Tuple.app for being so generous with their stories, and supporting the podcast. Visit tuple.app/scrum and share them if you find the app useful! Remember, sharing is caring! Eli Goodman, Head of Product at Tuple, discusses a recurring anti-pattern in product development: the over-reliance on A/B testing. Reflecting on his experiences at two different companies, Eli illustrates how A/B testing, when misused, can slow down product progress and lead to a bloated team structure. He also shares strategies on how to avoid this trap and take responsibility for product decisions. How can product teams avoid hiding behind A/B testing and instead move forward with confidence? Listen in to find out! Featured Book of the Week: The Idea Factory by Jon Gertner In this episode, Eli Goodman, Head of Product at Tuple, shares the profound influence of The Idea Factory, a book about Bell Labs, on his career as a product manager. Eli delves into how the book's lessons on creativity, innovation, and the long-term impact of foundational ideas have shaped his thinking. What can today's product leaders learn from the story of Bell Labs? How does creativity fuel product success, even in today's fast-paced tech world? Listen in to find out.   About Eli Goodman Eli Goodman has been working on software teams for 17 years. He's been a full-stack developer and engineering manager at both large and small companies, including Etsy and Headspace. A few years ago, Eli transitioned to product management and is now the Head of Product at Tuple, a remote pair programming service used by companies such as Figma, Shopify, and many others in the software industry. You can link with Eli Goodman on LinkedIn, or email Eli at Eli@Tuple.app.

In this episode of Brains Byte Back, we explore the future of neurotechnology with a special guest, Israel Gasperin, founder and CEO of Zentrela. Israel's company is leading the charge in using artificial intelligence (AI) to analyze brainwaves, developing groundbreaking tools that measure cognitive impairment in ways traditional drug tests can't. In 2018, the historic legalization of cannabis in Canada is where Zentrela really took off. With government funding and support in his research, he was able to create cutting-edge tools for effectively measuring cannabis impairment. Today, Zentrela's AI-based EEG technology is being used globally to help a variety of industries, including cannabis, energy drinks, and wellness, conduct faster, more cost-effective product testing – giving consumers direct insight into the psychoactive and non-psychoactive effects of the products they are buying. They are essentially providing companies and consumers with a new kind of label—not the traditional listing of ingredients but rather the effects of the product. The future of brainwave analysis is shaping the way companies research, develop, and inform their customers like never before—Israel shares how.  Find out more about Israel Gasperin here (Linkedin) -  https://www.linkedin.com/in/israelgasperin/ Find out more about Zentrela (website) -  https://zentrela.com/ Brains Byte Back: Reach out to today's host, Erick Espinosa (Linkedin) -  linkedin.com/in/erick-espinosa Get the latest on tech news - https://sociable.co/  Leave an iTunes review  - https://rb.gy/ampk26 Follow us on your favourite podcast platform - https://link.chtbl.com/rN3x4ecY Find out more about our sponsor Publicize - https://publicize.co/startup-resources/

Lesley Hensell is co-founder of Riverbend Consulting, whose 85+ employees solve problems for e-commerce sellers. Lesley oversees Riverbend's service team, and she has personally helped hundreds of sellers get their suspended Amazon accounts and ASINs back up and running. She has been an Amazon seller for more than a decade. lifelong Longhorns fan, Lesley earned a bachelor's degree in journalism andan MBA from the University of Texas at Austin. She volunteers for A Wish with Wings, a wish-granting organization for little Texans with life-threatening conditions, and she serves on the Board of Directors for Hallie's Heroes, which funds bone marrow matches and medical research for kids with cancer and critical illnesses.Highlight Bullets> Here's a glimpse of what you would learn…. Importance of testing products sold on AmazonTypes of testing required for different product categoriesCommunicating with Amazon when disputing testing requirementsCosts associated with testing and using recommended labsReview manipulation issues and tactics leading to account suspensionsMarketing practices and review manipulationAppeal process for Amazon suspensions and dispute-only appealsChallenges and frustrations of dealing with Amazon account suspensionsEscalating issues to Amazon's executive seller relations teamRecommendations for influential books, productivity tools, and individuals in the e-commerce spaceIn this episode of the Ecomm Breakthrough podcast, host Josh Hadley interviews Lesley Hensell, co-founder of Riverbend Consulting, about Amazon account protection and suspension recovery. They discuss the importance of product testing, the risks of review manipulation, and the complexities of Amazon's enforcement process. Leslie advises on how to escalate issues to Amazon's executive team and the value of expert assistance. She also shares recommendations for books, tools, and influencers in the e-commerce space. The episode concludes with information on how to contact Riverbend Consulting for help with Amazon account issues.Here are the 3 action items that Josh identified from this episode:Action Item#1 Prioritize Product Testing Compliance: Ensure that all necessary testing documents, especially for products related to children, babies, ingestibles, and topicals, are readily available. Action Item#2 Exercise Caution with Review Practices: Understand the risks associated with review manipulation tactics, such as friends and family reviews, inserts, super URLs, and chatbots. Action Item#3 Seek Expert Assistance When Needed: Recognize the importance of seeking expert help, especially when facing account violations or dealing with Amazon's enforcement processes. Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough PodcastEmail Josh Hadley: Josh@eCommBreakthrough.comRiverbend ConsultingAmazon IncubatorWish with WingsHallie's Heroes on InstagramFTC lawsuit against AmazonEPA consent decree with AmazonCPSIA testingREACH testingAmazon's Appeal ProcessRescue Time

Our guest for this episode is Alessandra Zonari, an expert in the fields of skin regeneration and tissue engineering. During our conversation, we cover everything from how unhealthy skin can reflect and exacerbate systemic health issues to groundbreaking research on improving skin health to combat aging and chronic diseases.  In addition to her research, Zonari is the co-founder of OneSkin, a brand of skin longevity treatment products that leverage the company's proprietary OS-01 peptide (which OneSkin claims can extend skin health on a molecular level). I use OneSkin as part of my daily routine and have seen noticeable results, as shown in these before and after photos. This episode is packed with insights that can change the way you think about skincare and overall health. Join us to learn how you can enhance your health through innovative skincare strategies! In this episode: 00:00 - Introduction   01:50 - Welcoming Alessandra Zonari   02:04 - The Vital Role of Skin Health   04:25 - The Connection between Skin and Gut Health  05:09 - Clinical Insights on Skin and Systemic Inflammation   07:23 - Challenges With Ineffective Skincare Products   10:27 - Addressing Cellular Senescence in Skincare   19:10 - Innovations in Non-Invasive Skin Age Testing   21:46 - Using Ex Vivo Skin for Product Testing   25:05 - Sun Protection: Importance and Innovations   31:37 - Ensuring Safety and Efficacy in Skincare Formulation   36:12 - Closing Remarks and Additional Resources Don't forget to subscribe for more insightful conversations with experts in health, fitness, and beyond.  #PrimalShiftPodcast #AlessandraZonari #Skincare #SkinHealth  Learn more: Diet is the main driver of skin health, but a daily skincare regimen can also help. I use OneSkin (read my review) to help keep my skin looking and feeling great.  If your skin issues are related to problems in your digestive tract, supplements like Intelligence of Nature (ION+), bovine colostrum and freeze-dried beef organs can help repair the delicate lining inside your gut and ensure you're absorbing all the essential micronutrients your skin needs to heal. To learn more about how environmental toxins can negatively impact your skin health, check out How Xenoestrogens Make Us Sick, Fat and Infertile (with Dr. Anthony Jay) and watch my YouTube roundup of the non-toxic household and personal care products we use at home. Thank you to this episode's sponsor, MK Supplements!  Use code “primalshift” to save 15% on your MK Supplements order at https://shop.michaelkummer.com About Alessandra Zonari: Alessanda Zonari earned her master's degree in stem cell biology, as well as her Ph.D in skin regeneration and tissue engineering, at the Federal University of Minas Gerais in Brazil (in collaboration with the 3B's Research Group in Portugal). Her work in skin regeneration was awarded “Best Thesis” by UFMG, and she completed a second post-doctoral program at the University of Coimbra in Portugal. In 2017 she co-founded OneSkin, where she dedicated her time to understanding the mechanism underlying the aging process and developing science-based solutions to reverse the skin's biological age. She is a co-holder of five patents and has published over 20 peer-reviewed papers in scientific journals.  Email: alessandra@oneskin.co  Website: https://www.oneskin.co/  Instagram Link: https://www.instagram.com/oneskin.co Facebook Link: https://www.facebook.com/OneSkinTech/ Linkedin Link: https://www.linkedin.com/company/oneskin Tiktok Link: https://www.tiktok.com/@oneskin.co Amazon Link: https://www.amazon.com/stores/page/C1FD8C88-0F4A-479B-89EF-AF0493459ED8 More From Michael Kummer: Website: https://michaelkummer.com YouTube: https://youtube.com/@MichaelKummer Instagram: https://instagram.com/mkummer82 Facebook: https://www.facebook.com/realmichaelkummer   

In this episode of the Beginner Photography Podcast, I pull back the curtain on the deceptive world of influencer marketing within the photography industry. I'll uncover how financial incentives and unethical practices are designed to mislead you when buying new gear. Learn why developing your skills should always take precedence over accumulating more gear. Reflect on your genuine needs before making purchases, and arm yourself with the knowledge to distinguish honest reviews from biased ones. As you listen, I encourage you to question the marketing messages you encounter, prioritize honing your craft, and aim for transparency in your own creative endeavors. THE BIG IDEAS:Skeptical Consumption: Always question product reviews to determine if they're influenced by sponsorships. Trust your own instincts and needs.Skill Over Gear: Focus on improving your photography skills instead of constantly upgrading your equipment—it's your ability that makes the difference.Transparency Value: Understand the ethical implications of influencer marketing in shaping your buying decisions. Aim for transparency in your own practices.Personal Needs: Evaluate how, why, and if you will use a product before making a purchase—tailor your toolkit to your specific requirements.PHOTOGRAPHY ACTION PLAN:Evaluate Current Gear: List out all your current equipment and consider which items you use frequently. Identify gaps in your skills that you might improve with your existing gear before buying new items.Research Ethically: Check multiple sources for reviews to compare and contrast their opinions on new products. Look for content creators who transparently disclose their sponsorships and affiliations.Focus on Skill Development: Spend dedicated time each week practicing different photography techniques. Invest in courses or workshops that focus on building your core photography skills.Create a Test Environment: Set up controlled environments to experiment and learn more effectively, using the gear you already own. Document your findings and review how well your current gear meets your actual needs.Transform your Love for Photography into Profit for FREE with CloudSpot Studio.And get my Wedding and Portrait Contract and Questionnaires, at no cost!Sign up now at http://deliverphotos.com/ Grab your free 52 Lightroom Presets athttp://freephotographypresets.com/Connect with the Beginner Photography Podcast! Join the free Beginner Photography Podcast Community at https://beginnerphotopod.com/group Send in your Photo Questions to get answered on the show - https://beginnerphotopod.com/qa Grab your free camera setting cheatsheet - https://perfectcamerasettings.com/ Thanks for listening & keep shooting!

Send us a Text Message.Did you know that even if you're selling a toy product on Etsy, you are required to have it safety tested and certified? In this episode, dive deep into the crucial topic of toy safety and compliance. The first half of the episode focuses on the legal requirements set by the Consumer Product Safety Commission (CPSC) in the US. Gain an understanding of critical standards like ASTM F963 and ASTM D4236. Learn the potential financial consequences of non-compliance with real-life examples.  With 145,500 toy-related injuries reported in 2022, this episode underscores the importance of adhering to rigorous safety protocols and proper labeling to prevent accidents and ensure toys are safe for children.The second half of the episode introduces the Design Safety Toolkit from KID (Kids In Danger), a nonprofit organization. This free mini-course teaches toy creators how to integrate safety features directly into their designs, surpassing basic compliance to achieve real-world safety.  In this episode, you'll get an overview of a cornerstone lesson from this mini-course, The Design Safety Hierarchy, and how to apply it. By adopting a safety-centric mindset from the start, toy creators can significantly reduce risks and create safer products. If you've been questioning whether or not your toy needs safety testing, this is the episode for you. The practical tips will help you ensure your toys meet the highest safety standards. For additional resources and expert guidance, visit thetoycoach.com/220.Episode Cliff Notes:In 2022, there were 145,500 toy-related injuries reported in U.S. emergency departments for children ages 12 and under.Learn the essential action item to review and improve one aspect of your current designs from a safety perspectiveDiscover how the Design Safety Toolkit can help you develop safer products for freeLearn the hierarchy of design safety, including how to guard against injuries and affix required warning labelsFind out why embossing safety messages into products is a game-changer for complianceUncover the key toy safety requirements you must follow according to the Consumer Product Safety Commission (CPSC)Understand the financial and legal ramifications of non-compliance, including penalties up to $15 million USD.Hear a real-world case study that emphasizes the importance of anticipating how toys are used beyond initial safety testingSupport the Show.

Join us as Ben Acott uncovers intriguing tactics that make Feastables stand out in the crowded direct-to-consumer landscape. In our conversation, Ben expounded on the synergistic relationship between Feastables and the ultra-popular Mr. Beast, highlighting how leveraging the latter's extensive distribution network offers a unique advantage. This strategic alignment not only turbocharges product reach but also taps into an existing, engaged audience ripe for conversion. The way Ben's ventures harness such audience networks is a testament to the power of influencer marketing and shows the value of cultivating smart partnerships in building strong brands.Episode brought to you by More StaffingJoin 15k founders and marketers & get our pod highlights delivered directly to your inbox with the DTC Pod Newsletter!On this episode of DTC Pod, we cover:1. Branding with Mr. Beast2. Creator Partnerships in Brand Marketing3. Testing Talent and Products for Market Fit4. Scaling New and Existing DTC Brands5. Brand-Creator Collaborations6. Celebrity Partnerships7. Reinventing Boring Product Categories8. Building Brand Loyalty through ContentTimestamps04:05 Working with one of the most popular content creators in the world, Mr. Beast06:48 Lessons from building the brand MANSCAPED12:41 Analyzing product success, tracking metrics, and predicting customer acquisition cost (CAC)15:16 From launching Feastables to reformulation and rebranding22:53 How to make partnerships with content creators work24:41 Reinforcing confidence, shaping perception, and packaging success27:14 Factors that go into negotiation and compensation structures33:20 Choosing the right branding partner, validating partnerships through testing36:20 Producing content, testing, and promoting products online38:31 Making boring products cool through content marketingShow notes powered by CastmagicPast guests & brands on DTC Pod include Gilt, PopSugar, Glossier, MadeIN, Prose, Bala, P.volve, Ritual, Bite, Oura, Levels, General Mills, Mid Day Squares, Prose, Arrae, Olipop, Ghia, Rosaluna, Form, Uncle Studios & many more.  Additional episodes you might like:• #175 Ariel Vaisbort - How OLIPOP Runs Influencer, Community, & Affiliate Growth• #184 Jake Karls, Midday Squares - Turning Your Brand Into The Influencer With Content• #205 Kasey Stewart: Suckerz- - Powering Your Launch With 300 Million Organic Views• #219 JT Barnett: The TikTok Masterclass For Brands• #223 Lauren Kleinman: The PR & Affiliate Marketing Playbook• ​​​​#243 Kian Golzari - Source & Develop Products Like The World's Best Brands-----Have any questions about the show or topics you'd like us to explore further?Shoot us a DM; we'd love to hear from you.Want the weekly TL;DR of tips delivered to your mailbox?Check out our newsletter here.Projects the DTC Pod team is working on:DTCetc - all our favorite brands on the internetOlivea - the extra virgin olive oil & hydroxytyrosol supplementCastmagic - AI Workspace for ContentFollow us for content, clips, giveaways, & updates!DTCPod InstagramDTCPod TwitterDTCPod TikTok  -----Ben Acott - Chief Executive Officer at Magnetic LabsBlaine Bolus - Co-Founder of CastmagicRamon Berrios - Co-Founder of Castmagic

Thomas Neuberger has probably worn more pairs of shoes than anybody else over the last 15 years (with maybe the exception of Runner's World Director of Product Testing and Runner-in-Chief, Jeff Dengate). Thomas' deep experience in the running shoe industry gives him an unparalleled perspective on the evolution of shoes, why some features are no longer available, and the future of running shoes. In this episode, Thomas and I talk about: The 2009 origin story of Believe in the Run, a top shoe review company in the United States What it's like to test around 100 pairs of running shoes a year The evolution, trends, and fads of running shoes, and why personal runner preference matters How runners can look for and try running shoes that could work for their individual bodies, instead of focusing on a specific brand Tips for beginner runners on selecting a running shoe Stack height, arch support, material, heel-toe drop... What should you consider when looking for a pair of running shoes? Trends in running shoes: from minimalist and barefoot to maximum cushion How a certain model of running shoe can go for over $2,000 on the secondary market How running shoes can relate to foot health and injury (or injury prevention) Why you should be rotating your running shoes Super shoes: How often should runners wear them, whether they're worth it, and what to consider if you're looking for a pair The worst pair of running shoes Thomas ever tried, and some of the best pairs of running shoes from Thomas' experiences Running shoe wisdom: If your running shoes aren't fun, then you're running in the wrong shoes. Don't settle. I get many questions from runners about running shoes, so this episode will give you a ton of value to start making better shoe decisions. Links & Resources from the Show: Explore Believe in the Run Thomas Neuberger on Instagram Believe in the Run on YouTube Learn a great warm-up routine for runners Thank you DrinkLMNT! A big thanks to DrinkLMNT for their support of this episode! They make electrolyte drinks for athletes and low-carb folks with no sugar, artificial ingredients, or colors. They are offering a free gift with your purchase at DrinkLMNT. And this does NOT have to be your first purchase. You'll get a sample pack with every flavor so you can try them all before deciding what you like best.  DrinkLMNT's products have some of the highest sodium concentrations that you can find. Anybody who runs a lot knows that sodium, as well as other electrolytes like magnesium and potassium, are essential to our performance and how we feel throughout the day. If you're not familiar, LMNT is my favorite way to hydrate. They make electrolytes for athletes and low-carb folks with no Sugar, artificial ingredients, or colors. I'm now in the habit of giving away boxes of LMNT at group runs around Denver and Boulder and everyone loves this stuff. Boost your performance and your recovery with LMNT. They're the exclusive hydration partner to Team USA Weightlifting and quite a few professional baseball, hockey, and basketball teams are on regular subscriptions. So check out DrinkLMNT to get a free sampler pack and get your hydration optimized for the upcoming season. Thank you Previnex! After resisting most supplements for the better part of my life, I'm cautiously changing my tune. I'm now a Masters runner and in my personal life, I'm optimizing for longevity. I want to be my healthiest self for as long as possible and I'm excited to partner with Previnex to make that happen. Previnex uses the most bioavailable, clinically tested ingredients, the optimal form and dose of each ingredient, pharmaceutical grade manufacturing, testing of raw ingredients and finished products. For every purchase you make, they also donate vitamins to kids in need. Their new Muscle Health Plus is something I'm now taking. Turning 40 – and having a thin frame – has made me realize that I need to prioritize lean muscle mass to stay healthy and age well. Muscle Health Plus has creatine, essential and branched chain amino acids, and it's designed in a way to maximize protein synthesis and the absorption of amino acids. Muscle Health Plus will help you prevent muscle damage, which is particularly important for aging runners who want to protect themselves from muscle loss and recover faster after hard workouts. As is true for all of their products, Previnex adheres to the highest of standards: their ingredients are clinically proven to do what they say they're going to do. Previnex offers a 30-day money back guarantee. If you don't feel the benefits of their product, you get your money back no questions asked. With their focus on quality and customer satisfaction, I hope you'll try it! Use code jason15 for 15% off your first order at Previnex!

From Pepcom at CES in Las Vegas, we got a look at a new cooking “tool” from Meater. Keye Chen, who handles Content Creation, Logistics, Office Management, Product Testing and Sales talks about how the latest iteration of their meat thermometer works, the surprisingly long battery life, the unusual charging mechanism, and how their app interacts with it to give you a perfect meat result every time.  Show Notes: Support: Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect: Web:      http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices Mastodon:      https://mastodon.cloud/@chuckjoiner Facebook:      http://www.facebook.com/chuck.joiner MacVoices Page on Facebook:      http://www.facebook.com/macvoices/ MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice LinkedIn:      https://www.linkedin.com/in/chuckjoiner/ Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

WordPress contributors Pooja, Brian and Anand discuss how product testing is crucial and the learning process behind it. 

WordPress contributors Pooja, Brian and Anand discuss how product testing is crucial and the learning process behind it. 

A deep dive into claims with claims expert, Craig Weiss of Consumer Product Testing. What do the claims you see on labels actually mean? What's a strong claim and what's just puffery? And how can you as a consumer more effectively read labels? This podcast explored all this and more!    Interested in supporting the podcast? Find us on Patreon at www.patreon.com/theecowell

This podcast is about big ideas on how technology is making life better for people with vision loss. When we buy a product off the shelf, we rarely think about how much work went into getting it there. Between initial conception and going to market, life-changing technology requires a rigorous testing and development process. That is especially true when it comes to accessible technology for people who are blind or visually impaired. For this episode, Dr. Cal spoke to Jay Cormier, the President and CEO of Eyedaptic, a company that specializes in vision-enhancement technology. Their flagship product, the EYE5, provides immense benefits to people with Age-Related Macular Degeneration, Diabetic Retinopathy, and other low-vision diseases. But this product didn't arrive by magic. It took years of planning, testing, and internal development to bring this technology to market. This episode also features JR Rizzo, who is a professor and researcher of medicine and engineering at NYU — and a medical doctor. JR and his research team are developing a wearable “backpack” navigation system that uses sophisticated camera, computer, and sensor technology. JR discussed both the practical and technological challenges of creating such a sophisticated project, along with the importance of beta testing and feedback.   The Big Takeaways: The importance of testing: There's no straight line between the initial idea and the final product. It's more of a wheel, that rolls along with the power of testing and feedback. It's extremely important to have a wide range of beta testers engage with the product. Their experience with it can highlight unexpected blind spots and create opportunities to make something even greater than originally anticipated. Anticipating needs: When it comes to products like the EYE5, developers need to anticipate that its users will have evolving needs as their visual acuity deteriorates. So part of the development process involves anticipating what those needs will be and finding a way to deliver new features as users need them. Changing on the fly: Sometimes, we receive feedback we were never expecting. When JR Rizzo received some surprise reactions to his backpack device, he had to reconsider his approach and re-examine his fundamental design. Future-Casting: When Jay Cormier and his team at Eyedaptic first started designing the EYE5 device, they were already considering what the product would look like in the future, and how it would evolve. To that end, they submitted certain patents many years ahead of when they thought they'd need them — and now, they're finally being put to use.   Tweetables: “I'm no Steve Jobs and I don't know better than our users. So the best thing to do is give them a choice and see what happens.” — Jay Cormier, President & CEO of Eyedaptic “I started to think a little bit more about … assistive technologies. … And, I thought about trying to build in and integrate other sensory inputs that we may not have natively … to augment our existing capabilities.” — JR Rizzo, NYU Professor of Medicine and Engineering “I think the way we've always looked at it is the right way, which is you put the user, the end user, front and center, and they're really your guide, if you will. And we've always done that even in the beginning when we start development of a project.” – Jay Cormier “When we put a 10-pound backpack on some colleagues, they offered some fairly critical feedback that it was way too heavy and they would never wear it. … They were like … it's a non-starter.” — JR Rizzo   Contact Us: Contact us at podcasts@lighthouseguild.org with your innovative new technology ideas for people with vision loss.   Pertinent Links Lighthouse Guild Eyedaptic Rizzo Lab

Norrøna has been making gear since 1929, and this family-owned company aims to make the best outdoor recreation products on the market. But that's easier said than done, so Jonathan Ellsworth talks with Jørgen Jørgensen — the 4th-generation owner & CEO of Norrøna — about the brand's approach to product design & testing, and the origins, evolution, and future of the brand.TOPICS & TIMES:Origins & Evolution of Norrøna (4:32)Customer Satisfaction (31:39)Product Testing (34:03)Quality Assurance & Durability (41:14)Making the "Best Products" (47:46)Blister Labs & Current Standards (51:38)PFAS (56:20)Some of Jørgen's Favorite Norrøna Products (1:01:35)RELATED LINKS:Episode Sponsor: OpenSnowBecome a BLISTER+ MemberBlister Summit 2024CDC Factsheet on PFASCHECK OUT OUR OTHER PODCASTS:Off The CouchBikes & Big IdeasBlister PodcastCRAFTED Hosted on Acast. See acast.com/privacy for more information.

Here's the show rundown:Teamshares: Here's an interesting one. Teamshares has raised a lot of money and is buying a lot of SMBs. But that's just the start. It also plans to allow employees of those companies to buy most of their stock over time, while serving up centralized fintech services to all its sub-companies. Who doesn't love to chat about a new model?MoonPay's new venture arm: Crypto payment infra company MoonPay is getting into the venture game, with a focus on crypto, gaming, and fintech. The union of those three is crypto games, of course, but we have two eyes fixed on what MoonPay decides to invest in. New crypto-thematic, or crypto-adjacent funds are rarer these days, making the MoonPay news exciting.Rent Butter and Kiki: Now that the zero interest rate period is over, and the experiment in building new iBuying and mortgage service startups has partially concluded, renting is hot again. And thus, so too are rental-focused startups.The IPO drought has lasted longer than you anticipated: Working off a Crunchbase dataset, Alex has notes on just how long we have been waiting for real tech IPOs. The good news? They are (partially) back!What happens when you bring lean startup ideology into the AI world? A lot of experiments, it turns out.And that is us until next week. Due to an American holiday, Equity will kick off on Tuesday next week instead of Monday!For episode transcripts and more, head to Equity's Simplecast website.Equity drops at 7 a.m. PT every Monday, Wednesday and Friday, so subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. TechCrunch also has a great show on crypto, a show that interviews foundersand more!

Meet the founders of an Australian sustainable packaging brand using unique marketing strategies to bring home-compostable mailers to businesses across the globe.  For more on Hero Packaging & show notes: https://www.shopify.com/blog/hero-packaging-marketing-product-launches?utm_campaign=shopifymasters&utm_medium=youtube&utm_source=podcast 

In this episode (194) of Leupold's Hunt Talk Radio, Randy invites John Barklow, lead designer for Sitka Gear, and Tyler Johnerson, Randy's former camera guy and amazing hunter, for a discussion about how product testing works for high performance products.  We all benefit from these intensive testing processes, yet we seldom get to hear how it works, when it succeeds, and when it is time to go back to the drawing board. John and Tyler talk about the many products John has designed for Tyler to test and the process used to improve the Sitka Jetstream Jacket that launched this summer. Many hunting stories get told along the way, explaining the tough conditions that brought about a new idea.