Podcasts about Reuse

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How did prompt engineering die so quickly? ☠️And what the heck does context engineering even mean? One of the trickiest things about LLMs is they're changing daily, yet they're the engines that drive business results. But if the engine is constantly changing, then you also have to change how you drive and the roads you take. That's why we're tackling context engineering in this installment of our Start Here Series, the essential beginners guide to understanding AI basics and growing your skills. Context Engineering: How to Get Expert-Level Outputs From AI Chatbots -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Evolution from Prompt to Context EngineeringWhy Prompt Engineering Is Now ObsoleteDefining Context Engineering in AI ChatbotsSix-Part Framework for Context EngineeringFour Layer System for Structuring AI ContextBuilding Reusable Context Vaults and SkillsConnecting Business Data to AI ModelsTechniques to Achieve Expert-Level AI OutputsImportance of Context Windows in Large Language ModelsContext Engineering Best Practices and ScalabilityTimestamps:00:00 "Access AI Community & Tools"03:08 "Mastering Context in AI"07:23 "Smart Models Require Less Precision"12:01 "Context Engineering Beats Prompt Engineering"15:49 "AI Context: Six Key Blocks"16:47 "Building Context for Better Results"19:53 "AI: Training, Not Easy Button"25:17 "Chain of Thought Prompting Decline"29:11 "Show, Don't Tell Techniques"32:13 "Context, Reuse, and Scalable Systems"33:19 "AI Chatbots: Memory and Skills"Keywords: context engineering, AI chatbots, expert level outputs, prompt engineering, large language models, business context, AI models, custom instructions, data access, context window, prime prompt polish, reusable context vaults, context vaults, skills file, memory enabled models, ChatGPT, Claude, Google Gemini, Microsoft Copilot, connectors, apps, searchable index, business data, personalized AI, context clues, reference material, examples, procedures, evaluation rubric, chain of thought prompting, generative AI, nondeterministic behavior, show don't tell technique, few shot examples, rubric first technique, grading criteria, output quality, scalable AI systems,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

I hope you had a wonderful week, my friend. If you didn’t – maybe this episode will cheer you up, eh? It’s time for the Homebrew Happy Hour podcast!… THE home brew #podcast where we answer all of your home brewing questions and discuss anything related to craft beer! A NOT SO SUBTLE REMINDER: If you appreciate the things we do here at Homebrew Happy Hour, consider joining our Trub Club! — https://www.patreon.com/bePatron?u=21132635 On Today’s Show: Recirculating Mashes, Yeast Re-Use & BIAB Bags  00:00:00 – 00:09:25 Patreon & Small Talk00:09:26 – 00:24:44 Random Australian Guy00:24:45 – 00:41:39 Recirculating Mash00:41:40 – 00:51:45 Reusing Yeast00:51:46 – 01:05:51 BIAB Bags Links for this episode:CellarScience Instant Water: https://morebeer.com/collections/cellarscience%C2%AE-instant-water%E2%84%A2?a_aid=HomebrewHappyHourCellarScience Premium Dry Yeast: https://morebeer.com/collections/cellarscience/index?a_aid=HomebrewHappyHourFLOTit 2.0: https://amzn.to/3NhMRnCOur Brewer’s Friend Page: https://www.brewersfriend.com/homebrew/brewer/220966/homebrew-happy-hour We want to hear from you! If you have a question that you'd like us to discuss on a future episode, please click on the “Submit a Question” link at the top of our website or you can now call in your questions via our questions hotline @ 325-305-6107 and leave your message after the beep. Let us know what you think and enjoy the show! cheers, joshua ———————– Thank you to our show's sponsor, Hops Direct! Family owned and operated, Hops Direct provides a wide variety of hop selection and ships directly to your door. Learn more by visiting https://hopsdirect.com/?utm_source=HHH&utm_medium=link&utm_campaign=HHH+link ————————– CellarScience offers premium dry yeast that delivers higher cell counts than typical liquid pitches, meaning you get a stronger, healthier fermentation without the hassle. The best part? You can Direct Pitch right into your wort—no starters, no waiting, just brewing. Whether you need their new ‘WEST COAST’ strain for a classic American IPA, or ‘JUNGLE’ for massive fruity esters, they've got your next batch covered. Join a recipe receiving tier of our Trub Club today because every kit that ships out now includes premium CellarScience Yeast, join at https://www.patreon.com/HomebrewHappyHour ————————– Real innovation in base malt doesn’t come around often. But as the world's largest producer of specialty malt, Viking is changing the game. Sourced strictly from local farmers in Northern Europe—where harsh winters naturally reduce the need for chemical pesticides—Viking delivers pristine, non-GMO barley that consistently wins gold medals at major pro and homebrew competitions. Because of direct importing, you get access to this exact same pro-level quality at a price that easily competes with standard, cheaper domestic malts.Join a recipe receiving tier of our Trub Club today because every kit that ships out now includes premium Viking Malt, join at https://www.patreon.com/HomebrewHappyHour————————– This episode is brought to you by Brewer’s Friend! Brewing beer at home isn't just about the ingredients, it's about precision. And that's where BrewersFriend.com comes in. Whether you're dialing in your very first recipe or perfecting your hundredth, Brewers Friend gives you the tools to brew with confidence. Their recipe builder, mash calculators, and water profile database helps take the guesswork out of the process so you can focus on what matters: making great beer! Plus, Brewers Friend isn't just software, it's a community of passionate homebrewers, sharing recipes, tips, and feedback. It's like having a brew club in your pocket! Head over to BrewersFriend.com today and take your homebrewing to the next level. Use promo code HAPPYHOUR to save 25% OFF premium memberships! That's BrewersFriend.com…because better brewing starts with better tools! Click here to use our link: https://bit.ly/3N7uQbm ————————– Become a Patron! Reminder that these episodes are ultimately made possible because of YOUR support. Consider becoming a member of our TRUB CLUB via our Patreon page and receive perks such as merch, exclusive group access and content, recipes, and some tiers even get monthly recipe kits mailed to you! https://www.patreon.com/HomebrewHappyHour #homebrewing #homebrewers #craftbeer #beer #brewing #craftbrew #kolsch #webcast #show

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

Il s'en passe des choses pour les adaptations de comics, au cinéma et à la télévision ! Nous poursuivons nos formats On Screen de l'été à un rythme plus que soutenu, et cette fois-ci nous allons revenir sur l'attendue saison 2 de X-Men '97, qui vient tout juste de s'achever sur Disney+. Il y avait bien des attentes après la première fournée d'épisodes réussie, il y a déjà... deux ans ! Autant dire qu'on avait hâte de se retrouver pour faire ce podcast !Le grand débat sur X-Men '97 saison 2Hé oui, nous avons (presque) réussi à retrouver toute notre dream team puisque Vesper et Daniel Andreyev sont revenus à nos micros pour cette émission, aux côtés de Spleenter (et promis, on ramènera aussi Frédérick Sigrist pour la saison 3 !). De quoi passer deux heures en bonne compagnie pour évoquer tout ce qui va, mais aussi ce qui ne va pas, dans X-Men '97 saison 2 qui s'est montrée bien fournie, peut-être même un peu trop. Et vous, qu'en avez-vous pensé ?Si vous appréciez notre travail et ces émissions, ne manquez pas de le faire savoir en partageant le podcast un peu partout, en en parlant autour de vous, en discutant sur notre Discord ou encore en nous soutenant sur notre page Tipeee. Merci de votre écoute et à bientôt pour le prochain podcast !Le programmeDiscussion sans spoilers - 09:30Partie avec spoilers - 53:54Soutenez First Print - Votre podcast comics (& BD) préféré sur TipeeeHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

پسرِ کاترین کبیر بود… اما قرار نبود سرنوشتش مثل بقیه‌ی خاندان سلطنتی باشه.پال، تزار آینده روسیه، در دل یکی از قدرتمندترین امپراتوری‌های دنیا بزرگ شد…اما پشت درهای بسته‌ی کاخ، اتفاق‌هایی می‌افتاد که هیچ‌وقت کامل روشن نشد.رفتارش کم‌کم تغییر کرد. تصمیم‌هاش عجیب‌تر شد. و فاصله‌اش با واقعیت بیشتر و بیشتر…چی باعث شد وارث تاج و تخت، تبدیل بشه به یکی از مرموزترین چهره‌های تاریخ روسیه؟این اپیزود، رفتن به دل زندگی کسیه که همه فکر می‌کردن آینده رو می‌سازه… اما خودش تبدیل به یک معما شد.-------------------------------------------

November is synonymous with Black Friday, great deals but what cost? Over consumption is endangering our already fragile planet. Is it time to change our ways? This week Do You Really Know is highlighting concepts and initiatives about reducing our consumption. All week long we'll be discussing anticonsumerist trends as an alternative to Black Friday. Zero waste is a good example of one that has become very popular in recent years. The practice helps reduce consumption of non-recyclables but it's also plain common sense. Let's look at some simple ways to recycle waste at home and also save some cash while you're at it. What about composting fruits and vegetables? What about food that has already gone bad? What about waste that isn't organic? In under 3 minutes, we answer your questions! To listen to the latest episodes, click here: ⁠⁠What is premium mediocre - the illusion of luxury?⁠⁠ ⁠⁠What is Gross National Happiness, a potential alternative to GDP?⁠⁠ ⁠⁠Why are my ears ringing?⁠⁠ A Bababam Originals podcast, written and produced by Joseph Chance. First Broadcast: 23/11/2022 Learn more about your ad choices. Visit megaphone.fm/adchoices

Go big, or… go home and create a local, enduring reuse economy?  With the 2026 FIFA World Cup spanning 39 days, 3 countries, and 16 host cities — and attracting almost seven million fans — it seemed to present a golden opportunity to showcase reuse at scale. And it did, just not at the World Cup games themselves. Because while FIFA didn't choose to reuse, several host cities came up with their own reuse initiatives at Fan Fests and watch parties, and planted seeds that will have lasting impact in their communities.  Seattle Public Utilities' Ashima Sukhdev and Toronto Environmental Alliance's Emily Alfred join us this episode to share their insights into how being a host city for the World Cup sparked conversation and enthusiasm for reuse systems in unprecedented ways. And while they still face many challenges, the reuse that did happen in Seattle and Toronto (and beyond) helped normalize the practice not just for soccer fans but for entire communities.  Resources: FIFA World Cup: A Missed Opportunity or a Catalyst for Enduring Reuse? Toronto Environmental Alliance: Kicking off with reusables: The opportunity of the FIFA World Cup 26™ for Canadian events Reuse Seattle Model RFP for reusable foodware at event venues Reuse Wins at Events LCA report Episode 178: Reuse Goalposts for Stadiums & Arenas Episode 159: A Reuse Playbook for Stadiums Get involved: Join the Reuse Solutions Network Support Upstream to make sure these stories continue to be heard and the reuse economy continues to grow — thank you!

Johan Odendaal – besturende direkteur, Southern Palladium Volg RSG Geldsake op Twitter

Government software development pipelines are increasingly integrating AI coding assistants, but Air Force Sustainment Center Software Directorate CTO Kurt Jarvis said success depends on improving how developers use existing code, not just generating new code faster. Speaking with GovCIO Media & Research at the Carahsoft DevSecOps Conference, Jarvis outlined an engineer-first approach that uses retrieval-augmented generation (RAG), vector databases and established DevSecOps pipelines to safely accelerate software development. Jarvis said one of the biggest opportunities for AI is helping developers discover and reuse decades of trusted software already maintained by the Air Force. The goal, he said, is to make AI a code‑reuse engine, not a code‑generation machine. He also emphasized that AI has not changed the Air Force's software engineering standards. Existing CI/CD pipelines are still the foundation for evaluating AI-generated code. AI can accelerate development, Jarvis said, but engineering discipline and human oversight remain essential to producing mission-ready software.

How can businesses sell circular propositions in a world that's rapidly changing? This episode of the Circular Economy Show tackles the marketing challenges and opportunities head-on. Pippa sits down with Jonathan Hall, Managing Partner at Kantar's Sustainable Transformation Practice, and Amanda Gandolpho, former Head of Brands at bike subscription service Swapfiets, to explore how to connect with today's consumers and drive demand for circular products and services. In this episode you'll discover: The surprising shift in societal values that's reshaping consumer buying habits     How to overcome marketing roadblocks like the value-action gap (where consumers say they want sustainability but don't always buy it) and the greenwashing problem     Practical strategies for marketing circularity effectively: Focus on consumer benefits, convenience, and solving real problems     Real-world examples: Learn how Swapfiets is using a circular business model (bike subscription) to disrupt transportation and prioritise customer experience This August on the Circular Economy Show, we're revisiting four conversations that help to navigate the marketing challenges and opportunities of switching to a circular economy. So listen in if you want to learn how to take something from a good idea to something that actually sells. Subscribe to The Ellen MacArthur Foundation for more insightful videos: https://www.youtube.com/channel/UCQAC2otE5_agzHZPnk3mE5w?sub_confirmation=1 Follow us online on these channels: Instagram: http://instagram.com/EllenMacArthurFoundation LinkedIn: https://www.linkedin.com/company/ellen-macarthur-foundation/ Website: http://www.ellenmacarthurfoundation.org

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In this final installment of our multi-part series, host Suzan Chin-Taylor welcomes back John "Grizz" Deal, CEO of IX Water, to discuss the future of industrial water reuse. As AI data centers and manufacturing place unprecedented demands on local water resources, facilities face a critical choice: adapt or risk running out.Grizz explains why traditional, over-engineered treatment models fail modern plants and highlights how targeted, fit-for-use treatment can cut capital expenditure while securing long-term operational resilience.Key Topics Covered:- The Water-Constrained Economy: How industrial growth and energy-intensive AI data centers are accelerating freshwater demand.- Fit-for-Use Treatment: Why treating recycled water strictly to its intended purpose (like cooling or dust suppression) yields massive savings.- Rethinking Wastewater: Shifting from generic multi-stage setups to targeted contaminant removal.- The Closed-Loop Business Case: Viewing industrial wastewater as a recoverable, cost-saving asset rather than trash.Connect with John "Grizz" Deal:CEO ~ IX WaterContact: grizz@ixpower.comLinkedIn: linkedin.com/in/coloradogrizzWebsite: ixwater.comI hope you find this episode as informative and as exciting as we have.Please let us know your thoughts about the episode!Connect with Suzan Chin-Taylor, host of The DooDoo Diva's Smells Like Money Podcast:Website: www.creativeraven.com | https://thetuitgroup.com/LinkedIn: https://www.linkedin.com/in/creativeraven/Email: raven@creativeraven.com Telephone: +1 760-217-8010Listen and subscribe here to your favorite platform:Apple Podcast - Google Podcast - Cast Box - Overcast - Pocket Casts - YouTube - Spotifyhttps://creativeraven.com/smells-like-money-podcast/ Subscribe to the Podcast:https://creativeraven.com/smells-like-money-podcast/Be a guest on our show:https://calendly.com/thetuitgroup/be-a-podcast-guestCheck Out my NEW Digital Marketing E-Course & Coaching Program just for Wastewater Pros:https://store.thetuitgroup.com/diy-digital-marketing-playbook-for-wastewater-pros#WaterReuse #WastewaterTreatment #IndustrialSustainability #WaterSecurity #FitForUse #CleanTech #CircularEconomy #DataCenterSustainability #WaterScarcity #SmellsLikeMoneyPodcast

AP's Lisa Dwyer reports on a growing trend to keep art supply costs down.

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Jake Snyder joins Keith Parsons to explain the mechanics of spatial reuse in Wi-Fi. They discuss practical implementation, the trade-offs involved in increasing signal detection thresholds, and why effective RF design remains the primary method for optimizing network performance. Episode Links: Jake Snyder's Website

Jake Snyder joins Keith Parsons to explain the mechanics of spatial reuse in Wi-Fi. They discuss practical implementation, the trade-offs involved in increasing signal detection thresholds, and why effective RF design remains the primary method for optimizing network performance. Episode Links: Jake Snyder's Website

Across Arizona, developers are transforming aging offices, obsolete retail centers, and underutilized commercial buildings into vibrant new destinations. But adaptive reuse projects often come with hidden challenges—from deed restrictions and zoning stipulations to parking deficiencies, building code upgrades, and neighborhood opposition. In this episode of Dirt to Development, we explore the legal, zoning, and entitlement strategies that can make or break an adaptive reuse project. Using real-world examples, we discuss how early due diligence, creative problem-solving, and proactive stakeholder engagement can turn seemingly impossible redevelopment opportunities into successful community assets. Whether you're a developer, investor, planner, architect, or land-use professional, this episode provides practical insights for navigating Arizona's growing adaptive reuse landscape.

Jake Snyder joins Keith Parsons to explain the mechanics of spatial reuse in Wi-Fi. They discuss practical implementation, the trade-offs involved in increasing signal detection thresholds, and why effective RF design remains the primary method for optimizing network performance. Episode Links: Jake Snyder's Website

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From the archive, in New England, a smart reuse (continued use?) of technology to keep folks connected.Read more about the payphones here ★ Support this podcast on Patreon ★

In Australia's suburbs, unwanted household items are often left on the kerb—ready to be discarded, donated or discovered. For Canberra resident Sunita Kotnala, those kerbside treasures became the foundation for a community venture. Founded in 2020, Women's Shed Canberra has since helped around thousands of migrant and culturally diverse women learn practical trade skills, gain confidence using power tools, and challenge traditional gender stereotypes. Today, the initiative is changing lives, one workshop at a time.

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Die groeiende impak van klimaatsverandering hervorm die versekeringsektor, met versekeraars wat groter onsekerheid in die gesig staar namate ekstreme weerpatrone meer gereeld voorkom. Old Mutual Namibia se onderskrywings- en herversekeringsbestuurder, Sesilia Nkoshi, sê die verskuiwing beïnvloed alles van onderskrywing en prysbepaling tot herversekeringskoste en polisstrukture. Nkoshi sê duideliker kommunikasie met kliënte is nou meer as ooit tevore nodig.

Individual pilots are great, but they don't scale themselves. So, how do you shift an entire system to move an unmovable market? This week on the Circular Economy Show, Lou and Fin explore the critical foundation of shared infrastructure. Whether it's launching scalable refill coalitions with Aldi and Ocado, aggregating future demand to bring 90% emission-reducing fuel to ocean transport, or rewriting retail habits at checkout counters across the US, to truly scale the circular economy, we have to stop building in silos and start building the shared infrastructure that allows everyone to win. Tune in to hear how three trailblazing women, Catherine Conway (GoUnpackaged), Ingrid Irigoyen (ZEMBA), and Kate Daly (Closed Loop Partners) are laying down the shared motorways that will allow everyone else to drive their own circular vehicles forward. If you enjoyed this episode, please leave a comment, give us a like or tell your work colleagues and friends, and don't forget to subscribe wherever you listen to or watch your podcasts. Subscribe to The Ellen MacArthur Foundation for more insightful videos: https://www.youtube.com/channel/UCQAC2otE5_agzHZPnk3mE5w?sub_confirmation=1 Find out more about our work here: www.ellenmacarthurfoundation.org Follow us online on these channels: Instagram: http://instagram.com/EllenMacArthurFoundation LinkedIn: https://www.linkedin.com/company/ellen-macarthur-foundation/  

Across the world, everyday products — from kitchen appliances to electronics — are often thrown away rather than repaired. The latest UN estimates suggest people generate around 2 billion tonnes of household and everyday waste each year. This week we travel to Argentina to meet people finding new ways to keep old things in use. We visit Club de Reparadores, where people are learning how to fix everything from toasters to microwaves, meet the women behind Lindor who transform old blankets into coats, and join the group Cybercirujas as they find new uses for discarded computers.People Fixing The World from the BBC is about brilliant solutions to the world's problems. We release a new edition every Tuesday. We'd love you to let us know what you think and to hear about your own solutions. You can contact us on WhatsApp by messaging +44 8000 321721 or email peoplefixingtheworld@bbc.co.uk. And please leave us a review on your chosen podcast provider.Presenter: Myra Anubi Reporter/producer: Jane Chambers Executive Producer: Richard Kenny Editor: Jon Bithrey Sound mix: Gareth Jones

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TikTok spent the week helping users spot AI while Meta spent it explaining why it didn't ask permission. In this Geek Freaks Headlines, Frank breaks down TikTok's new in-app AI literacy hub, built with NAMLE and deepfake expert Henry Ajder, plus the platform's milestone of labeling over 3 billion videos as AI-generated. He also digs into the feature everyone actually wants: a setting that lets you control how much AI content shows up in your feed.Then it's over to Meta, where the new Muse Image generator automatically opted every public account into having their photos used for AI creations, with the opt-out buried deep in the Sharing and Reuse settings. Frank covers the backlash from SAG-AFTRA and why unions are sounding the alarm on likeness rights, plus the real-world danger when AI slop goes from harmless puppy videos to fake celebrity crypto scams targeting your friends and family.Timestamps:00:00 TikTok's new AI literacy hub and detection push00:26 The feature we all want: metering AI in your feed00:34 Meta's Muse Image and the automatic opt-in problem01:01 SAG-AFTRA and the fight over likeness rights01:23 Why TikTok's transparency matters even if nobody's the good guy01:40 From harmless puppy slop to dangerous crypto scams02:02 What's next and following up on the falloutKey Takeaways:TikTok launched an in-app AI literacy hub built with NAMLE and deepfake expert Henry Ajder to help users identify AI-generated contentTikTok has labeled over 3 billion videos as AI-generated and is testing a feature that lets users control how much AI appears in their feedMeta's Muse Image generator opted all public accounts in automatically, with the opt-out buried under the Sharing and Reuse section in settingsSAG-AFTRA and other unions are pushing back hard because the tool puts actors' likenesses up for grabsThe stakes escalate fast: harmless AI puppy videos are one thing, but AI-generated celebrity endorsements pushing crypto scams are a real threatNeither company is the good guy here, but transparency and consent should be the baseline for AI on social platformsMemorable Quotes:"TikTok spent the week telling everybody how to spot AI. Meta spent the week saying screw your consent.""Consent is king, including in AI.""Meta using the likeness of, say, Natalie Portman to try to convince my friends that they need to invest in cryptocurrency, that is very bad."Call to Action:If you found this breakdown helpful, subscribe to Geek Freaks Headlines wherever you get your podcasts and leave us a review on Apple Podcasts or Spotify. Share this episode with someone who needs to check their Instagram settings right now. Join the conversation with #AI #TikTok #Meta #TechNewsStay Connected:For all the latest geek news, head to GeekFreaksPodcast.comFacebook: https://www.facebook.com/thegeekfreakspodcastThreads: https://www.threads.net/@geekfreakspodcastPatreon: https://www.patreon.com/GeekFreakspodcastListener Questions:Would you use a feature that lets you filter AI content out of your feed entirely? And have you checked your Instagram settings since the Muse Image rollout? Let us know!

Mike Amaral, QuietRock Product Manager for PABCO Gypsum, previews the upcoming webinar, "Managing Noise in Adaptive Reuse Projects." He discusses why acoustics are a critical consideration when converting commercial buildings into residential or mixed-use spaces, explaining the limitations of relying solely on STC ratings and the impact of flanking paths and structure-borne noise. Register for this free webinar

In this episode, Cory Connors welcomes Adam Brundage from Nike to discuss the company's wide-ranging sustainability efforts. Adam shares his unconventional path into sustainability beginning with a degree in meteorology and atmospheric science and how that led him to learning about life-cycle assessments, discovering greenhouse gas analysis, and pursuing a decade-long career at one of the world's most iconic brands.Key Topics Discussed:Adam's background in meteorology and atmospheric science and how it shaped his passion for climate impactLife cycle assessment (LCA) as a foundational tool for understanding Nike's environmental footprintNike's full value chain focus: raw materials, manufacturing, transportation, and packagingPackaging's role in Nike's overall carbon footprint (approximately 7%) and the Nike OneBox initiativeThe five major materials Nike focuses on: cotton, polyester, leather, foam, and rubberRecycled and organic material adoption: approximately 25% of Nike's current products contain at least one sustainable or recycled materialScaling renewable energy across Nike's global store and supplier networkThe evolution of Nike's shoe recycling program: from "Reuse a Shoe" (early 1990s) to the current "Recycle and Donate" modelNike's Refurbished program: cleaning and reselling lightly used returns to reduce wasteExtended Producer Responsibility (EPR) and its growing relevance to the apparel and textile industryTextile-to-textile recycling technology: converting old polyester garments back into raw polyester for new productsBio-based and biomass-balanced materials as the future of sustainable packaging and apparelThe challenge of translating complex climate data into actionable understanding for employees across all departmentsThe tension between cost and doing the right thing, and finding opportunities where sustainability and savings alignAI's promise and concern: its power consumption and the importance of powering data centers with renewable energyThe influence Nike's sustainability efforts can have on the broader apparel, footwear, and consumer goods sectorsResources Mentioned:Nike RecycleNike Refurbished programSway (seaweed-based bio material)Adam BrundageContact:Listeners can connect with Adam Brundage via LinkedIn or explore Nike's sustainability initiatives at Nike.com by searching "sustainability" or "recycle." More information on the Recycle and Donate program is also available through Nike's sustainability web pages.Support our Sponsors Learn more here:- 3M- Specright- Forest Thank you for tuning in to Sustainable Packaging with Cory Connors!https://anewearthproject.com/collections/new-earth-approvedConnect with CoryConnect with Cory on LinkedIn here: https://www.linkedin.com/in/cory-connors/I'm here to help you make your packaging more sustainable! Reach out today and I'll get back to you asap. This podcast is an independent production and the podcast production is an original work of the author. All rights of ownership and reproduction are retained—copyright 2022.

The Inis Cealtra Visitor Experience at the Rectory in Mountshannon has won a prestigious national award. The popular Clare visitor attraction, which was developed by Clare County Council, was successful in the Adaptation and Re-Use category at this years Royal Institute of Architects of Ireland Awards. Designed by McCullough Mulvin Architects, the award acknowledges the high-quality restoration and repurposing of the historic building into a flagship visitor attraction for the Lough Derg region. Acting Senior Executive Officer Tourism, Festivals & Events, Clare County Council, Theresa Hughes Lannon says it's a fitting recognition of what the centre offers.

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Sunscreen is essential when on holiday, and in the summer in general. But it doesn't come cheap, and often the contents aren't fully used by the time the holiday is over. It's tempting to reuse bottles from one year to the next, to save money and reduce waste. What many people don't know though is whether sunscreen remains effective once it's been opened, or whether using an out-of-date product is dangerous. How can I tell if my sunscreen has expired? What are the risks of using out-of-date sunscreen? What steps should I take to stay safe? In under 3 minutes, we answer your questions! In under 3 minutes, we answer your questions! To listen to the last episodes, you can click here: ⁠⁠Why do some people believe in ghosts?⁠⁠ ⁠⁠What is the placebo effect and how does it work?⁠⁠ ⁠⁠Could chronoworking make you work more efficiently?⁠⁠ A podcast written and realised by Joseph Chance. First broadcast: 16/08/2023 Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to WE LOVE TO HATE EVERYTHING and another trip to Brown Town—this week we're going full detective mode with WIVES OUT, our Sister Wives mystery deep dive.This week's lineup:

Aviation Week reported that a rocket startup pursuing tower-catch recovery validated a pre-cooling ground campaign, signaling progress toward integrated hot-fire testing. Tower-catch aims to capture a booster with launch tower arms to save mass and speed turnaround. SpaceX popularized the concept for Super Heavy, while Stoke Space has described similar plans and conducted a second-stage vertical test at Moses Lake in September 2023, along with securing access to Launch Complex 14 at Cape Canaveral. Pre-cooling supports reliable cryogenic operations for liquid oxygen and methane by stabilizing temperatures and reducing thermal shock. Competing recovery strategies include Rocket Lab's ocean splashdown and engine reuse and Relativity Space's legged landings for Terran R. Regulatory coordination with the FAA and range operators, plus infrastructure upgrades, will shape tower-catch timelines and economics.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Open Source draufschreiben und digitale Souveränität dazu sagen, fertig? Leider nicht. Gerade jetzt, wo Behörden, Unternehmen und Politik verstärkt über freie Software, Public Money, Public Code und europäische Unabhängigkeit sprechen, wird ein Problem immer sichtbarer: Open Washing. Produkte wirken offen, klingen nach Open Source und versprechen Kontrolle. Schaut man genauer hin, fehlen oft genau die Freiheiten, auf die es ankommt.In dieser Episode sprechen wir mit Johannes Näder von der Free Software Foundation Europe darüber, was freie Software wirklich bedeutet und warum die vier Freiheiten der entscheidende Maßstab sind. Wir klären, wie Open Washing funktioniert, warum Begriffe wie Source Available, Open Core oder souveräne Cloud schnell in die Irre führen können und weshalb digitale Souveränität ohne freie Software nicht funktioniert. Dazu schauen wir auf konkrete Beispiele aus der Praxis, von Bitwarden über ArangoDB bis hin zu öffentlichen IT-Projekten, und diskutieren, worauf Entwickler:innen, Verwaltungen und Beschaffungsstellen achten sollten.Wenn du wissen willst, wie du Open Washing erkennst, welche Red Flags es bei Lizenzen, Repositories und Enterprise-Modellen gibt und warum dieses Thema für Open Source, öffentliche Beschaffung und Europas Tech-Zukunft so relevant ist, bist du hier genau richtig.Bonus: Nach dieser Episode wirst du vermutlich nie wieder ganz entspannt auf eine Pricing-Page schauen.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Today we get to chat with Leah Watkins, the visionary behind FØLK Refillery & Supply, a sanctuary for zero-waste shopping in Kingston's historic Stockade District. Driven by a love for the planet and her community, Leah has transformed her passion into a hub for sustainable, local, and artisanal goods. Her commitment to eco-friendly living extends beyond the store, as she leads by example, encouraging others to embrace a more mindful and waste-free lifestyle. Our conversation begins with some background on what inspired Leah to a zero-waste lifestyle and the opening of her shop and community event space FØLK. She walks us through her own journey and some of the challenges she's overcome in striving towards zero-waste, educating us along the way about closed loop and sustainable business practices. Our conversation touches on mindful consumption, sustainable fashion and our own self care as we navigate the move to a simpler lifestyle.  She's focused on building community at FØLK and will continue to provide access to local and sustainable products as well as host makers and practitioners aligned with her values.  You can find FØLK online https://www.folkrefilleryandsupply.com/ and on Instagram for their events and community happenings https://www.instagram.com/folkrefilleryandsupply/ Like, Subscribe!  Watch us on Youtube https://www.youtube.com/@dancingwithwaterpodcast  Find us on Instagram https://www.instagram.com/dancingwithwaterpodcast/  Support us at Patreon https://www.patreon.com/dancingwithwater Find archive conversation from I want what SHE has https://iwantwhatshehas.org/ Learn more about Theresa and her offerings at https://www.anahatakingston.com/ Learn more about Jennifer and her offerings at https://www.cosmicmotherlove.com/

310 - From Airline Pilot to $1 Billion Self Storage Empire: Ryan Gibson's Blueprint for Passive Wealth What if the same checklist that keeps a commercial jet from crashing could also keep your real estate deals from blowing up your bank account? That is the mindset Ryan Gibson brought from the cockpit to the boardroom, and it is the difference between investors who build lasting wealth and those who get burned chasing the next shiny deal. In this episode, Ryan, president of Spartan Investment Group and co-host of Passive Income Pilots, breaks down the go or no-go framework he uses to evaluate every deal, the same discipline that keeps an airplane in the sky. He shares the 10/30/30/30 principle for structuring your net worth, explains why self-storage has outperformed every other asset class over the last 40 years at roughly 17 percent annually, and reveals how Spartan grew from a single house flip with his neighbor turned business partner into a billion-dollar portfolio spanning 15 states and over seven million square feet. He also gets into the strategy behind converting old retail boxes, including a former Kmart and Macy's, into thriving storage facilities, plus the surprising real-world hazards that come with owning thousands of storage units. This episode is essential listening for high-income professionals, especially airline pilots, who want true passive income without taking on a second job, as well as any real estate investor looking to diversify into a historically high-performing, recession-resistant asset class. If you have ever wondered how to evaluate a deal with the same clarity a pilot uses before takeoff, or whether self-storage deserves a spot in your portfolio, this conversation will change how you think about both. 5 Powerful Takeaways Learn the "extension clause" negotiating trick for 1031 exchanges that buys you extra time on the 45 and 180-day deadlines so you are never forced into a rushed, overpriced purchase. Discover the go/no go decision framework, borrowed directly from cockpit protocol, for knowing exactly when to walk away from a real estate deal and when you are too committed to turn back. Get the 10/30/30/30 net worth allocation strategy high-income earners use to balance liquidity, market growth, tax-advantaged real estate, and truly passive investments. Understand why self-storage has delivered roughly 17 percent average annual returns over 40 years, outperforming multifamily, data centers, and mobile home parks. Hear how a single house flip with a neighbor turned into a billion-dollar, 15-state self-storage portfolio, and what that growth story reveals about building investor trust early. 00:00 Show Intro 00:59 1031 Exchange Basics 03:22 Meet Ryan Gibson 04:51 1031 Exchange Pro Tip 07:00 From Pilot to Investor 11:32 Finding a Business Partner 13:55 Pilot Mindset for Deals 20:01 Leading Under Pressure 22:20 Passive Income Explained 24:12 10 30 30 30 Portfolio 28:33 1031 Legacy Planning 29:51 Why Self Storage Wins 33:00 Market Consolidation Play 36:14 Conversions and Reuse 37:32 Tax Foreclosure Surprises 40:34 Spartan Growth Plans 41:16 Wild Storage Stories 43:30 Badass Rapid Fire 48:34 Success Definition and Wrap About the Guest Ryan Gibson is a commercial airline pilot turned self-storage entrepreneur and president of Spartan Investment Group, now the 29th largest self-storage operator in the country with more than one billion dollars in capital organized across 15 states and over seven million square feet. He built Spartan alongside business partner and Army veteran Scott Lewis, growing the company from a single neighborhood flip into a major institutional-grade platform. Ryan also co-hosts the podcast Passive Income Pilots, where he teaches airline pilots and other high-income professionals how to build genuine passive income without adding a second job to their schedule. He applies the same disciplined, checklist-driven decision-making from his years in the cockpit to how he vets deals, operators, and markets today. More than 400 airline pilots have invested alongside him in Spartan's self-storage portfolio. Resources & Websites Mentioned https://spartan-investors.com ryan@spartan-investors.com Passive Income Pilots podcast (available on iTunes, Stitcher, and YouTube) Call to Action To learn more about Jen Josey, visit https://www.therealjenjosey.com/ To join REIGN, visit https://www.reignmastermind.com/ Stuff Jen Josey Loves: https://www.reignmastermind.com/resources Buy Jen Josey's Book: From Beginner to Badass: https://a.co/d/bstKlby New episodes drop every Monday Morning at 6am EST. See you next time.

Sweet John - See, You A-Tse, KIVA - Black Hole -- Hosting provided by SoundOn

Hello! This is Episode 412. This is Way #12 of the 44 Ways to Create Your Sustainable Home series. We’re continuing through Section Three: Sustainable Services and Infrastructure. In Episode 411, we looked at where household water goes, especially in indoor use, and how to reduce consumption through fixture selection and design decisions. In this episode, we’re looking at the other side of that equation: not just using less water, but capturing and reusing the water that falls on and around your home. Way #12 is: Store and Reuse Water for Greater Water Saving. [For all resources mentioned in this podcast and a free, downloadable PDF transcript, head to www.undercoverarchitect.com/412] In this episode, I share information on rainwater harvesting, greywater systems, and what it looks like to meaningfully reduce, or in some cases almost eliminate, your home’s reliance on mains water for outdoor use. I’ll also share a little of my own experience with whole-of-home rainwater supply, as we’ve been living entirely on rainwater for over a decade now. As always, if you'd like to access a full transcript of this episode and links to any resources I mention, head to www.undercoverarchitect.com/412. Now, let's dive in! RESOURCES MENTIONED IN THIS PODCAST: For links, images and resources mentioned in this podcast, head to >>> www.undercoverarchitect.com/412 Accessing my free '44 Ways' E-Book will simplify sustainability and help you create a healthy, low tox and sustainable home. You can download your free copy here >>> https://undercoverarchitect.com/ways Access the support and guidance you need to be confident and empowered when renovating and building your family home inside my signature online program >>> https://undercoverarchitect.com/courses/the-home-method/ Just a reminder: All content on this podcast is provided by Undercover Architect for reference purposes and as general guidance. It does not take into account specific circumstances and should not be relied on in that way. You should seek independent verification or advice before relying on this content in any circumstances, including but not limited to circumstances where loss or damage may result. The views and opinions of any guests on the podcast are solely their own. They may not reflect the views of Undercover Architect. Undercover Architect endeavours to publish content that is accurate at the time it is published, but does not accept responsibility for content that may or has become inaccurate over time.See omnystudio.com/listener for privacy information.

What you'll learn… (00:12) Why design reuse is energy conservation — and IP management is energy management (05:15) How the shift from physical tape-ups to CAD opened the door to reusable design data (08:09) The electric car analogy: charging cheap vs. designing under pressure (11:32) War stories from the field: reverse-engineering an F-14 Tomcat board and the square-pin-in-a-round-hole disaster (17:07) What happens when every designer controls their own library — and why that's a nightmare (21:33) Family trees, variants, and the art of tracking design history across decades (25:47) Why even great processes break down — and the role of the resident expert in keeping libraries honest (30:26) The designer as cross-pollinator: bridging mechanical, electrical, and manufacturing worlds (38:19) AI as a routing engine: from overnight Cadnetics autorouting to Tesla autopilot — and what both teach us about human oversight More about the episode… In this episode of the Printed Circuit Podcast, host Steph Chavez welcomes Paul Fleming, Senior Printed Circuit Board Consultant and IPC-certified design instructor — a 45-year industry veteran who built his own PCB service bureau in San Diego before spending two decades as an application engineer across Cadnetics, Cadence, Mentor Graphics, and Siemens, and becoming an educator through the IPC Designer Council. The conversation tackles why design reuse and IP management are the hidden levers behind every fast, cost-efficient hardware program — and why neglecting them is a tax companies keep paying long after the original engineer has left. Paul frames it simply: decisions are energy, and every design artifact is either stored potential or future debt. War stories from a reverse-engineered F-14 Tomcat board to a connector library defect that cascaded across hundreds of shipped units make the stakes concrete. Paul also addresses the human side: the self-preservation instinct that fragments shared libraries, the management–engineering gap that lets bad habits compound, and why the PCB designer — bridging mechanical, electrical, and manufacturing domains — holds more organizational leverage than almost any other role on the product team. The episode closes with a candid take on AI: powerful when an experienced engineer is watching the road, dangerous when they're not. Connect with Steph Chavez: LinkedIn Website Connect with Paul Fleming: LinkedIn PCEA Website

KIM A REUM - Beautiful City 陳小雲 - 舞女 -- Hosting provided by SoundOn

In this episode, Bard MBA student Eric Sargent interviews John Edel, founder and director of Bubbly Dynamics, about transforming derelict Chicago industrial buildings into closed-loop ecosystems for small businesses. John shares the story behind The Plant, a former meatpacking facility now home to 25 food producers, brewers, urban farmers, and manufacturers who share resources, waste streams, and community. He discusses industrial symbiosis, adaptive reuse, and slow money financing, and explains how Bubbly Dynamics proves that responsible, community-centered industrial redevelopment can be financially viable.

How does Ramesses II stack up to his predecessors? Why did ancient writers connect him with the Trojan War? In this episode we explore tales of Ramesses, told in antiquity, and consider his legacy in the modern world. Music: Keith Zizza and Luke Chaos. Bibliography Brand, P. (2010a). Reuse and Restoration. In W. Wendrich (Ed.), UCLA Encyclopedia of Egyptology. https://escholarship.org/uc/item/2vp6065d Brand, P. (2010b). Usurpation of Monuments. In W. Wendrich (Ed.), UCLA Encyclopedia of Egyptology. https://escholarship.org/uc/item/5gj996k5 Brand, P. J. (2023). Ramesses II: Egypt's Ultimate Pharaoh. Breasted, J. H. (1912). A History of Egypt. Bunsen, C. C. J. von. (1848). Egypt's place in universal history: An historical investigation in five books (C. H. Cottrell, Trans.; Vols. 1–5). https://hdl.handle.net/2027/mdp.39015050932519 Cooney, K. M. (2022). The New Kingdom of Egypt Under the Ramesside Dynasty. In D. T. Potts, N. Moeller, & K. Radner (Eds.), The Oxford History of the Ancient Near East, Volume III: From the Hyksos to the Late Second Millennium BC (pp. 251--366). https://doi.org/10.1093/oso/9780190687601.003.0027 Davies, B. G. (1997). Egyptian Historical Inscriptions of the Nineteenth Dynasty. Edwards, A. B. (1899). A Thousand Miles up the Nile (2nd edn). https://archive.org/details/thousandmilesupn0000edwa_e0y7/page/n9/mode/2up Kelly, B. (2010). Tacitus, Germanicus and the Kings of Egypt (tac. Ann. 2.59–61). The Classical Quarterly, 60(1), 221–237. https://www.jstor.org/stable/40984750 Kitchen, K. A. (1982). Pharaoh Triumphant: The Life and Times of Ramesses II, King of Egypt. Lietzelman, H. (2014). Pharaonism: Decolonizing Historical Identity. Prized Writing 2014-2015, 46–51. Neville, J. W. (1977). Herodotus on the Trojan War. Greece & Rome, 24(1), 3–12. https://www.jstor.org/stable/642683 Said, S. (2012). 2 Herodotus and the ‘Myth' of the Trojan War. In E. Baragwanath & M. de Bakker (Eds.), Myth, Truth, and Narrative in Herodotus (pp. 87--106). https://doi.org/10.1093/acprof:oso/9780199693979.003.0003 Sourouzian, H. (1988). Standing Royal Colossi of the Middle Kingdom Reused by Ramesses II. Mitteilungen Des Deutschen Archäologischen Instituts, Abteilung Kairo, 44, 229--254. Sourouzian, H. (2019a). Catalogue de la statuaire royale de la XIXe dynastie [Database]. https://www.ifao.egnet.net/bases/publications/bietud177/ Sourouzian, H. (2019b). Catalogue de la statuaire royale de la XIXe dynastie. https://www.ifao.egnet.net/publications/catalogue/9782724707571/ Tyldesley, J. (2001). Ramesses: Egypt's Greatest Pharaoh. Wilkinson, T. (2023). Ramesses the Great: Egypt's King of Kings. Learn more about your ad choices. Visit megaphone.fm/adchoices

Clint, Meg and Dan kick off their early-morning show chatting about cold temperatures around New Zealand, a studio clean-up that led Dan to dig antibiotics out of the bin, and a “What’s up?” battle that awards Alex $250 and a Scary Movie 6 double pass. They take a call from Linda, an infection and antimicrobial therapy nurse specialist, who explains antibiotic plans and the importance of finishing a course amid rising resistance. The hosts also debate supermarket and online grocery shopping, share extreme cheapskate money-saving hacks, and hear from callers who kept paying for unused gym memberships for years. Meg reveals she fractured her tailbone at a kids’ indoor playground, prompting listeners to share their own dumb adult injuries. 01:54 Cold Snap Texts 02:19 Studio Cleanup Chaos 04:00 Decorating Debate 05:49 Whats Up Battle 08:31 Supermarket Date Night 10:52 Alien Safety Questions 12:27 Celebrity Gossip Roundup 14:54 First Call Nurse Specialist 18:50 Reading Kills Romance 22:37 Extreme Cheapskates Clip 24:02 Dishwasher Salmon Debate 25:27 Library Flight Booking Myth 26:20 Stingy Hacks and Samples 27:06 Extreme Cheapskate Showers 28:19 Rewards Points and Reuse 30:35 Incognito and Free Cash 31:41 Gym Fees Add Up Fast 36:18 Long Distance Bestie Island 37:56 Ball Pit mishap 42:50 Dumb Adult Injury Callers

In 1226 BCE, his sixty-seventh year of rule, the long life of Ramesses II finally ended. We explore his final decades, the difficult life revealed by his mummy, his ascent to status of "living god," and the aftermath of his reign. Music: Luke Chaos. Support the History of Egypt at www.patreon.com/egyptpodcast Select References: Balout, L., Roubet, C., & Desroches-Noblecourt, C. (1985). La momie de Ramsès: Contribution scientifique à l'Egyptologie. Brand, P. (2010). Reuse and Restoration. In W. Wendrich (Ed.), UCLA Encyclopedia of Egyptology online. Brand, P. J. (2023). Ramesses II: Egypt's Ultimate Pharaoh. Demarée, R. J. (2016). Announcement of the passing of Ramesses II. JEOL, 46, 121--125. Academia.edu. Gallet, L. (2013). Karnak: The Temple of Amun-Ra-Who-Hears-Prayers. In W. Wendrich (Ed.), UCLA Encyclopedia of Egyptology online. Hawass, Z. A., & Saleem, S. N. (2016). Scanning the Pharaohs: CT Imaging in the New Kingdom Royal Mummies. Hornung, E., Krauss, R., & Warburton, D. (Eds.). (2006). Ancient Egyptian Chronology. Shehab El-Din, T. (1997). The title, “mdw jAwj”: “the staff of old age” “ 'ukkāza aš-šayḫuḫa.” Discussions in Egyptology, 37, 59--64. Academia.edu. Learn more about your ad choices. Visit megaphone.fm/adchoices

Sponsor Link:This episode of Space Nuts is brought to you by NordVPN, your trusted partner for online security. To access our exclusive offer, including four extra months for free, visit www.nordvpn.com/spacenuts.Q&A: Ultra Hot Jupiters and Rocket Fuel Recycling In this engaging Q&A episode of Space Nuts, hosts Andrew Dunkley and Professor Jonti Horner tackle a variety of intriguing questions from listeners. From the nature of ultra hot Jupiters to the complexities of reusing spent rocket fuel, this episode is packed with insights and cosmic curiosities.Episode Highlights:- Ultra Hot Jupiters Explained: David from the Sunshine Coast asks about the origins of the materials that form stars and their planets, leading to a fascinating discussion about the lifecycle of stars and the cosmic recycling of elements.- Rocket Fuel Reuse: Mark from the UK presents a thought-provoking idea regarding the potential for reusing water ice as rocket fuel, prompting a deep dive into the challenges of capturing exhaust and the physics of propulsion.- Flat Earth Conspiracies: Paul shares his experiences with flat Earth discussions and questions the feasibility of the Artemis mission, allowing Jonty to clarify orbital mechanics and the importance of relative motion in space travel.- Astrophysical Insights: The hosts explore the implications of past star generations on our solar system's composition and the future of space travel technologies, including the potential for innovative propulsion methods beyond traditional rockets.For more Space Nuts, including our continuously updating newsfeed and to listen to all our episodes, visit our website. Follow us on social media at SpaceNutsPod on Facebook, Instagram, and more. We love engaging with our community, so be sure to drop us a message or comment on your favourite platform.If you'd like to help support Space Nuts and join our growing family of insiders for commercial-free episodes and more, visit spacenutspodcast.com/about.Stay curious, keep looking up, and join us next time for more stellar insights and cosmic wonders. Until then, clear skies and happy stargazing.Become a supporter of this podcast: https://www.spreaker.com/podcast/space-nuts-astronomy-insights-cosmic-discoveries--2631155/support.- Origins of Stellar Material- Challenges in Rocket Fuel Reuse- Addressing Flat Earth Theories- Future of Space Propulsion Technologies- Cosmic Recycling of Elements

In Episode 341, Kestrel welcomes Dr. Joanne Brasch, the Assistant Director at the California Product Stewardship Council (CPSC), to the show. A network of local governments, non-government organizations, businesses, and individuals supporting policies and projects where producers share in the responsibility for managing problem products at their end of life, CPSC is California's thought leader and expert on Product Stewardship and the Extended Producer Responsibility (EPR) movement. "We're most proud of our textile EPR program because we achieved a lot in SB 707 that set a new level, a new generation of EPR programs that take a higher priority and implement a lot more reuse and repair throughout the program." -Joanne THEME — EXTENDED PRODUCER RESPONSIBILITY & TEXTILE WASTE DIVERSION This episode is the second in our two-part series dedicated to exploring some of the layers around Extended Producer Responsibility, or EPR, and Textile Waste Diversion.  In line with this conversation, I want to share about an upcoming event I'm collaborating on that's taking place in Los Angeles on June 10th. The Recovered Textile Exhibit is hosted by the City and County of Los Angeles, the California Product Stewardship Council, and the LA Cleantech Incubator. It's funded by CalRecycle, LA Sanitation, and others, and is in collaboration with Afflare.co and Fashion Is Outrageous.  There will be keynote speakers, discussions, and interactive activities that my cofounder Gabi and I helped develop, all with a focus on textile circularity and diverting textiles from the landfill. Additionally, the PRO (Producer Responsibility Organization), Landbell, will be present at the event. If you're interested in attending, you can RSVP here. If you're able to join, I hope to see you there! On the last show, we touched on some of the big picture ideas around EPR, and many of the questions around how we can make these circular systems practical.  On this week's episode, we're diving deep into the first-ever textile EPR bill in the U.S., SB 707: The Responsible Textile Recovery Act of 2024. While this isn't the first EPR policy to hit California or the nation, it is the first to cover TEXTILES.  I chat with someone who played an integral role in the legislative process for the bill – we dive into more on their open-collaborative approach and the importance of community-informed programs, we explore what the "most diverse board requirements" means within the context of SB 707, we learn about how feedback played a distinct role in the legislative process including some from the Or Foundation, and we discuss some of the definitions within the bill like REUSE and RECYCLE, with a focus on unpacking the definition of REPAIR, which is the first global definition that includes upcycling. You will hear words and phrases like PRO, Needs Assessment, dynamic, legislative and regulatory process, and more. We do our best to help contextualize these definitions along the way, but if you have questions, let me know. I don't want this to feel like another policy conversation that leaves you in the dark – I want it to feel like you can feel welcomed into it, as this is a monumental bill for California and the sustainability and fashion industry as a whole. One of my favorite things about this bill is that it's DYNAMIC, meaning it will change over time, and be rewritten every 5 years, based on key findings and learnings. So remember – your voice can play an important role in how this bill continues to evolve moving forward.  Also, to note – when this episode was recorded, our guest's new title had not yet been released publicly. Congrats to her, as she is now the Assistant Director – you'll actually hear her reveal it to us later on in the episode.   Quotes and links from our conversation: "If we're shopping in a different way 20 years from now, this program can adapt to that because the plan is rewritten every five years and has evidence-based decision-making provisions within the program." -Joanne on why SB 707 being a dynamic bill matters "I think the open collaborative approach has really been making sure everyone is using the same language and understands the same process so we can get the best engagement now." -Joanne on CPSC's approach to developing community-informed programs "We know repair costs more. We know that it's labor intensive, but we also know, you know, it's a greater GHG reduction and opportunity to again create new products from existing materials." -Joanne on the importance of incentivizing repair in the bill Recovered Textile Exhibit, June 10th (2026) in Los Angeles, CA California Product Stewardship Website CPSC Instagram

Celtic music never sits still, and episode 758 of the Irish & Celtic Music Podcast is proof of that. We're calling this one O'Neill's Drowsy Irish Town, but don't let that fool you. There is nothing sleepy about this lineup. Fourteen artists are here today, pushing tradition into something fresh and alive. Hit play and let the music take you somewhere  -  -  Subscribe now at CelticMusicPodcast.com! Tartanic, Charlene Adzima, Tara's Folk, Eimear Arkins & Eileen Gannon, The Bordercollies, Jocelyn Pettit & Ellen Gira, The Friel Sisters, Goitse, Rakish, Leevy, Release the Craicen, Katie Jane Band, Low Power Trio, Banshee in the Kitchen GET CELTIC MUSIC NEWS IN YOUR INBOX The Celtic Music Magazine is a quick and easy way to plug yourself into more great Celtic culture. Enjoy seven weekly news items with what's happening with Celtic music and culture online. Subscribe now and get 34 Celtic MP3s for Free. VOTE IN THE CELTIC TOP 20 FOR 2026 This is our way of finding the best songs and artists each year. You can vote for as many songs and tunes that inspire you in each episode. Your vote helps me create this year's Best Celtic music episode. You have just three weeks to vote this year. Vote Now! THIS WEEK IN CELTIC MUSIC 0:06 - Tartanic "Slapping Paddies" from Uncivilized 3:01 - WELCOME 4:43 - Charlene Adzima "Jimmie McGetrick's/John Naughton's/Tom Ward's Downfall" from The Initiation 7:54 - Tara's Folk "O'Neils" from remember how we fall 11:44 - Eimear Arkins & Eileen Gannon "George White's/McGettrick's/Cedars of Lebanon (reels)" from The Belles of St. Louis 15:42 - The Bordercollies "Rollin and Tumblin" from To The Hills and Back 18:31 - FEEDBACK 21:18 - Jocelyn Pettit & Ellen Gira "Bellechasse" from Here To Stay 25:18 - The Friel Sisters "Kelvin's Purling Stream" from Before the Sun 29:04 - Goitse "Margadh an Iúir" from Rosc 31:58 - Rakish "Time Check" from Now, O Now 35:50 - THANKS 37:48 - Release the Craicen "Star of the County Down / Cooley's Reel / Drowsy Maggie" from Live! Songs on a Boat 42:38 - Katie Jane Band "Frank's / Mason's Apron" from Wild One 45:12 - Leevy "The Mountain Spoke" from Baile Mhúirne or the Soldiers March the Paps of Anú 49:53 - Low Power Trio "Loch Lomond" from Dirty Old Town 53:06 - CLOSING 54:53 - Banshee in the Kitchen "King of Laoise" from Band O' Shees 58:44 - CREDITS Support for this program comes from Dr. Annie Lorkowski of Centennial Animal Hospital in Corona, California. Support for this program comes from John Sharkey White, II. Support for this program comes from International speaker, Joseph Dumond, teaching the ancient roots of the Gaelic people. Learn more about their origins at Sightedmoon.com Support for this program comes from Cascadia Cross Border Law Group, Creating Transparent Borders for more than twenty five years, serving Alaska and the world. Find out more at   www.CascadiaLawAlaska.com Support for this program comes from Hank Woodward. The Irish & Celtic Music Podcast was produced by Marc Gunn, The Celtfather and our Patrons on Patreon. The show was edited by Mitchell Petersen with Graphics by Miranda Nelson Designs. Visit our website to follow the show. You'll find links to all of the artists played in this episode. Todd Wiley is the editor of the Celtic Music Magazine. Subscribe to get 34 Celtic MP3s for Free. Plus, you'll get 7 weekly news items about what's happening with Celtic music and culture online. Best of all, you will connect with your Celtic heritage. Please tell one friend about this podcast. Word of mouth is the absolute best way to support any creative endeavor. Here's a thought worth sitting with. The single most powerful thing we can do to fight climate change is move toward clean energy. Solar, wind, hydro. Energy that doesn't cost the earth to produce. But while the big picture shifts, there's plenty we can do right now in our own lives. Think about the 5 Rs of Sustainability. Refuse what you don't need. Reduce what you use. Reuse what you already have. Repurpose the things that still have life in them. And recycle whatever is left. Start with just one of those this week. Pick the easiest one. Then build from there. Small choices, made by millions of people, add up to something enormous. The music we love was born from a culture that respected the land. Let's honor that. Promote Celtic culture through music at http://celticmusicpodcast.com/. WELCOME THE IRISH & CELTIC MUSIC PODCAST * Helping you celebrate Celtic culture through music. I am Marc Gunn. I'm a Celtic musician and also host of Pub Songs & Stories. Every song has a story, every episode is a toast to Celtic and folk songwriters. Discover the stories behind the songs from the heart of the Celtic pub scene. This podcast is for fans of all kinds of Celtic music. We are here to build a diverse Celtic community and help the incredible artists who so generously share their music with you. If you hear music you love, please email the artists to let them know you heard them on the Irish & Celtic Music Podcast. These musicians are not part of some corporation. They are small indie groups that rely on people just like you to support their music so they can keep creating it. Please show your generosity. Buy a CD, Album Pin, Shirt, Digital Download, or join their community on Patreon. You can find a link to all of the artists in the shownotes, along with show times, when you visit our website at celticmusicpodcast.com. ALBUM PINS ARE CHANGING THE WAY WE HEAR CELTIC MUSIC Looking for a fresh way to support the music you love? Meet the Album Pin. Album Pins are lapel pins themed to a specific album — and each one comes with a digital download. Wear your music. All of my latest pins are wood - burned and locally produced, which means a smaller footprint and a one - of - a - kind feel you won't find anywhere else. Pick yours up at magerecords.com THANK YOU PATRONS OF THE PODCAST! Thank you, patrons of the podcast! Because of generous supporters like you, the Irish & Celtic Music Podcast releases a new episode nearly every single week. Your support doesn't just fund the show. It fuels a movement. It helps us share the magic of Celtic music with thousands of new listeners and grow a global community of music lovers. Your contributions pay for everything behind the scenes: audio engineering, stunning graphics, weekly issues of the Celtic Music Magazine, show promotion, and most importantly, buying the music we feature from independent Celtic artists. And if you're not yet a patron? You are missing out. Patrons get early access to episodes, music - only editions, free MP3 downloads, exclusive stories and artist interviews, and a vote in the Celtic Top 20. Join us today keep the music alive, vibrant, and independent. A special thanks to our latest Patron of the Podcast: Jason Schatz HERE IS YOUR THREE STEP PLAN TO SUPPORT THE PODCAST Go to our Patreon page. Decide how much you want to pledge every month, $4, $12, $25. Keep listening to the Irish & Celtic Music Podcast to celebrate Celtic culture through music. You can become a generous Patron of the Podcast on Patreon at SongHenge.com. TRAVEL WITH CELTIC INVASION VACATIONS Every year, I take a small group of Celtic music fans on the relaxing adventure of a lifetime. We don't see everything. Instead, we stay in one area. We get to know the region through its culture, history, and legends. You can join us with an auditory and visual adventure through podcasts and videos. Learn more about the invasion at http://celticinvasion.com/ #celticmusic #irishmusic #celticmusicpodcast I WANT YOUR FEEDBACK What are you doing today while listening to the podcast? Send me a photo. If you're in a Celtic band, send me an audio recording of you performing live. Just audio. I'll use it in a podcast episode later this year. Email me at follow@bestcelticmusic. Daniel Faigin emailed re: Android Shuffle Apps: "Marc: I primarily listen to music via my iPod Classic (extensive smart playlists, all on shuffle). But all my music (around 58,000 tracks), is on my Android phone, and there I used the Gone Mad Music Player. It can play the music downloaded to your device, and has a smart playlist equivalent to the iPod / iTunes capability. It's the backup for my iPod Classic. Just search for it on Google Play; it does require a small payment to support development and unlock full capabilities. Alas, fewer phone manufacturers are supporting an external memory card (critical when you have a lot of music)  -  -  Samsung might at the lowest level, as well as Motorola. Other than that, you need more storage if you download music." Gershon commented on Patreon: "Sitting here in my favorite chair,  listening to #754, two dogs at my feet, reading and sipping my evening tea I heard your commentary regarding our wayward President and feckless Congress. Thank you!  You are on the right side of history. I appreciate this after the horrific week we've had. I am even prouder now to be a supporter and fan. Keep up the good work and make "good trouble."