Podcasts about Reuse

Direkteur van die Regshulpsentrum, Toni Hancox, waarsku dat die laerhowe onder ernstige hulpbron- en personeeltekorte wankel, wat kritieke grondvlak geregtelike kwessies negatief raak. Hancox sê dat landdroste en aanklaers onveilige langafstandreise en ernstige operasionele tekorte in die gesig staar, insluitend 'n gebrek aan basiese opnametoerusting. Sy het aan Kosmos 94.1 Nuus gesê dat vertragings in ondersoeke ook kwesbare verhoorafwagtendes vasvang in oorvol polisieselle. Sy het meer:

The New York State Government continues to help support the cost of household energy bills. Governor Hochul announced an energy affordability plan. WFUV's Anne Jackson explains. New York City is disinfecting ten cooling towers in the Bronx that might contain legionnaires' disease after testing positive. WFUV's Tahlia Pearson has more on what the city is doing to prevent the spread of the bacteria. The Mayor's Office released new plans to reduce traffic deaths and severe injuries. At Fordham University, thousands of unused meal swipes expire every week. But a group of students has figured out how to turn that waste into meals for Bronx residents facing food insecurity. WFUV's Andrew McDonald has more. Host/Producer: Livia Regina Editor: Robin Shannon Editor: Sienna Reinders Reporter: Anne Jackson Reporter: Nora Malone Reporter: Tahlia Pearson Reporter: Andrew McDonald Theme Music: Joe Bergsieker

What if the biggest mistake we make about waste is simply calling it waste?This week on The Lexy Show, Lexy sits down with Kirsten Junor, CEO of Reverse Garbage, a creative reuse nonprofit in Sydney, Australia that has spent more than 50 years helping keep useful materials out of landfills and putting them into the hands of artists, educators, makers, and the community. Kirsten brings a unique perspective shaped by her background in theater, costume design, and a lifelong practice of reusing materials creatively.In this episode, Kirsten shares how creative reuse became part of her life before she even had a name for it, from transforming costumes and fabrics to helping people see discarded materials as resources rather than trash. She explains why reuse should come before recycling, how overconsumption impacts sustainability, and why asking “Do I really need this?” can change the way we shop. Lexy and Kirsten also discuss repairing clothing, learning hands-on skills like sewing and making, the rise of community sharing spaces, and some of the unexpected materials Reverse Garbage has rescued and helped transform into something new.Kirsten also talks about the importance of making sustainability feel accessible, creative, and community-driven rather than overwhelming or impossible. Whether you love thrifting, crafting, sustainability, or simply want to rethink your relationship with the things you already own, this episode is full of practical inspiration.Subscribe to The Lexy Show and follow along for more conversations with changemakers creating a more sustainable future.Social Media Tags:Reverse Garbage: @reversegarbage (Instagram/Facebook)If you enjoyed this conversation, be sure to subscribe to The Lexy Show, leave a review, and share this episode with someone who's ready to make more mindful choices for themselves and the planet. For more information visit https://lexysilverstein.com/

Bonus walk-thru of the fabulous Akiya (abandoned house) to stylish retro guesthouse - talking through the designs and renovation while I was staying at the wonderful Benton Homestead Guesthouse in #omishima island #ehime - join me for a walk-through to hear about all the charming designs of this #renovatedhome #akiya abandoned house into fun, comfortabe & stylish #japan #sustainable #japanesehouse #guesthouse #sustainabletravel‪@bentonhomestead‬ #bentonhomesteadjapan #sustainablestyle #japan #ruraljapan

Grab the Secondary Teacher Systems Toolkit here: https://khristenmassic.thrivecart.com/systemstoolkit/?ref=pod Too many preps and not enough time? Let's make your planning period actually work for you. Hop into the Unit Planning Lab here: https://khristenmassic.thrivecart.com/augustlab/?ref=podcast Planning for the next school year? If your day is organized by class period, your planning calendar should be too. Grab my Editable Class Period Calendar here: https://khristenmassic.com/secondarycalendarpodGet the Planning Period Reset Toolkit—a free set of quick-start tools to help you protect your time, focus faster, and finally finish something… even during chaotic school days. https://khristenmassic.com/resetShop my Teachers Pay Teachers store: https://www.teacherspayteachers.com/Store/Khristen-Massic-Cte-Teacher-CoachA repeatable planning process across multiple preps can change the way secondary teachers approach the school year. The Secondary Teacher Podcast explores how to take the planning process from one finished unit and carry it into the next course, the next standard, and the next prep without rebuilding everything from scratch.The common mistake is assuming that planning one unit means planning the whole year. That was never the goal. One unit gives you something more useful: a practiced decision-making process. You've identified what students are working toward, mapped the flow of the unit, sorted the materials you already have, and decided what is ready enough to teach. The next unit still has different content, different standards, and possibly a completely different course. But you're no longer facing a blank page. You have a process to follow.That matters because secondary teachers rarely have the luxury of repeating the same course all day. A middle school or high school teacher might move from an intro engineering class to a foods class, an elective, or a mixed-level course. The content can feel unrelated. The planning decisions aren't. Every course asks you to figure out what students should be able to do by the end, what materials are already available, what can be adapted, what genuinely needs to be created, and what can wait. A repeatable planning process across multiple preps gives those questions a home.The episode points to a familiar scene in teacher Facebook groups. Someone asks, “What is everyone using for this course?” Another teacher asks whether anyone has a unit for a particular standard. Someone else has just found out they're teaching something new and needs help immediately. Those aren't bad questions. They come from teachers who care and are trying to make a responsible plan. But a borrowed curriculum or shared unit rarely solves the long-term problem by itself. The stronger answer is knowing how to look at what you already have, evaluate what you find, and make decisions quickly and confidently.That shift starts with one unit at a time. Before opening another resource site or collecting more ideas, write down what students should be able to do by the end of the unit. Then pull together the materials you already have. Use the Ready Enough Sort to separate what is ready to teach, what is close enough to adapt, what needs more work, and what can wait. The point isn't to produce a perfect unit with every minute planned. The point is to reach a real finish line called ready enough to teach.The same questions can guide a second unit, a third unit, and a second prep. What are students working toward? What do you already have that can be reused or adapted? What genuinely needs to be created? What can wait? What does ready enough look like for this unit? The decision-making framework stays steady while the content rotates. That's why the process works for a CTE class, an elective, and a mixed-level course, even when those classrooms appear to have little in common.The first unit in each course will usually feel like the hardest one because you're making content decisions while also learning the planning process. The second unit becomes lighter because you already understand more about that classroom. You know what students are working toward. You know what materials you have. You have a clearer sense of what ready enough looks like in that context. The workload doesn't disappear, but the decision-making gets faster. Instead of asking, “Where do I even start?” you can ask, “What's the next decision?”That is the practical value of a repeatable planning process across multiple preps. Four preps still mean four sets of content. The process doesn't pretend otherwise. But four preps don't have to mean four complete reinventions of planning. The same structure can travel from one course to another. You can clarify the produce, map the introduce, practice, produce flow, sort what you have, decide what is ready enough, and keep moving. One unit teaches you how to plan the next one without consuming every planning period and Sunday night.The episode also connects this process to teacher work life balance, especially the low hum of unfinished planning. That background process can follow you out of school, continue through dinner, and show up on Sunday night as the feeling that next week still isn't quite ready. Early in the school year, that hum may still be present. Nobody has everything figured out after a few days or a few weeks. A planning process doesn't magically make every task disappear. It gives uncertainty somewhere to go. You know the questions to ask, the order to ask them in, and the next decision in front of you. That can make the background noise quieter.The approach is especially useful for teachers who keep collecting ideas but still don't feel ready to teach. More curriculum doesn't automatically create clarity. More tabs open on a computer don't necessarily create a unit. The better move is to stop searching long enough to identify the finish line. Decide what students should be able to do. Gather what is already there. Sort it. Reuse what works. Adapt what is close. Identify what is actually missing. Let the rest wait. Planning becomes a sequence of decisions instead of an endless hunt for the perfect resource.This is also where the idea of ready enough matters. Ready enough doesn't mean careless, incomplete in a way that harms students, or unwilling to improve. It means you have made the decisions needed to teach the unit and have stopped treating every possible improvement as an emergency. You can teach what is ready, notice what students need, and make adjustments from there. The finish line protects planning from expanding forever. Without a finish line, every unit can remain half-planned indefinitely.For middle and high school teachers, that distinction has real weight. A single-prep mindset can make it seem as if every course needs the same depth of preparation at the same time. A multi-prep teacher doesn't have that option. There are several classrooms, several sets of students, and several unit sequences competing for attention. The answer isn't to plan every unit in every course all at once. The answer is to carry the same questions from one unit to the next and work through them in order.The episode's practical advice is simple: before asking what everyone else is doing, ask what students need to be able to do by the end of the unit. Before creating new materials, inspect what you already have. Before rebuilding an entire course, decide what this unit needs. Before spending another evening mentally solving the plan, identify the next decision. Those teacher tips are grounded in the actual work of a secondary classroom, where planning time is limited and the next course is already waiting.A repeatable planning process across multiple preps also helps teachers stop measuring progress by how much of the year is complete. The better measure is whether you can make the next unit's planning decisions with less friction than the last one. Maybe you now recognize which materials are ready enough without rereading every page. Maybe you can identify the produce before getting lost in activities. Maybe you can tell the difference between a missing resource and a resource that simply needs adaptation. Those small decisions compound across the year.The Ready Enough Sort is part of that practical structure. It gives you a way to handle the materials already sitting in your files, folders, drives, or classroom. Not everything deserves equal attention. Some resources are ready to teach. Some are close and need adaptation. Some need to be created. Some can wait. When every item feels equally urgent, planning stretches into the evening. When materials are sorted by what they require, the next action becomes clearer.That clarity matters across courses. In an intro engineering class, students might be working toward a design or production task. In a foods class, the unit might have a different kind of produce. In a course you've taught for several years, the content may be familiar while the unit still feels underdeveloped. The examples change, but the planning questions hold. What is the destination? What is the flow from introduce to practice to produce? What do students need to do? What materials can support that work? What can be ready enough for this unit?The...

The Japanese government has begun working with an industry group to take steps against malicious businesses among those that collect used goods for sale at secondhand shops, as the reuse market continues to boom.

Send us a text and chime in!The City of Mesa's Central Mesa Reuse Pipeline has been named the 2026 American Public Works Association (APWA) Arizona Chapter Project of the Year in the Environment category for projects exceeding million. The state award recognizes this innovative infrastructure project designed to strengthen Mesa's water resilience. The project includes approximately 10.5 miles of 36-inch pipeline and a new booster pump station that delivers reclaimed water to the Gila River Indian Community as part of an exchange that provides Mesa with higher-priority Colorado River water. The pipeline began delivering reclaimed water in March 2026, marking a major milestone in a...   For the written story, read here >> https://www.signalsaz.com/articles/central-mesa-reuse-pipeline-wins-award/ Check out the CAST11.com Website at: https://CAST11.com Follow the CAST11 Podcast Network on Facebook at: https://Facebook.com/CAST11AZFollow Cast11 Instagram at: https://www.instagram.com/cast11_podcast_network

In this episode, Cherise is joined by Patricia Rhee, FAIA, DBIA, LEED AP – Partner/Partner-in-Charge at EYRC Architects in Los Angeles, California. Patricia is also joined by Chad-Jamie Rigaud, LEED Green Associate, Designer, and Project Manager, also at EYRC. They discuss the Institute for Quantum Studies at Chapman University in Orange, California.You can see the project here as you listen along.The Institute for Quantum Studies at Chapman University transforms a historically and culturally significant site into a new center for quantum research, collaboration, and public engagement. Bringing the Institute's previously dispersed programs together, the project pairs the adaptive reuse of the historic Lydia D. Killefer School with a new Experimental Lab Building, creating a cohesive research environment that balances advanced science with a distinctly human-centered character.If you enjoy this episode, visit arcat.com/podcast for more.If you're a frequent listener of Detailed, you might enjoy similar content at Gābl Media.

In this episode of Usap Tayo, we explore practical ways to lower fashion waste and keep old clothes out of landfills. - Sa talakayan ng Usap Tayo, inihayag ang iba't ibang hakbang upang mabawasan ang pagtatapon ng mga damit sa landfill.

Solid mining waste represents a significant quantity of waste material in the United States and around the world. Solid mining waste has a range of physical and chemical properties that make it both potentially valuable and potentially hazardous to human health and the environment. From a commercial perspective, mining removes most of the primary minerals of interest; however, waste materials can still contain valuable minerals and other materials that can be recovered. The different types of mining sites and potential wastes for reuse provide a significant challenge but also an opportunity for innovation. Improvements in extraction and mineral processing technologies have occurred over time making it possible to recover minerals present in low concentrations. Interest in trace metals and rare earth elements (REEs) has increased, especially with the drive towards renewable energy sources increasing demand for key minerals required for solar panels and batteries. The reuse of solid mining waste can consist of reprocessing and repurposing the waste for resource recovery or a new application or product. This reuse serves as a solution to two significant needs:a domestic supply of minerals and materials for sustainable development and national defense purposesthe reclamation and remediation of land to reduce risks to human and environmental health The ITRC Reuse of Solid Mining Waste training and guidance document is geared towards state regulators and environmental consultants, mining and manufacturing stakeholders, community and tribal stakeholders, and other who have an interest in the potential reuse of solid mining waste. The guidance and this associated training course includes:Mining wastes introductionConsiderations for reusing mining waste: waste characterization, economic and market considerations, life cycle and risk assessment, regulatory considerations, & stakeholder considerationsPotential applications for the reuse of solid mining waste: examples of construction, environmental, and industrial reusesReview of technologies used in mineral beneficiation and processing Additionally, the guidance includes several case studies illustrating a range of current mining waste reuse scenarios. Prior to attending the training class, participants are encouraged to view the associated ITRC Reuse of Solid Mining Waste document. To view this archive online or download the slides associated with this seminar, please visit http://www.clu-in.org/conf/itrc/MiningWaste_090326/

With support from the nonprofit Community Farmers, the organizations aim to make growing local food approachable by offering hands-on learning and shared produce.

From the vault of the Taking the Lead Media Podcast. Original release date: 06/19/2011. "Episode 21 is streaming now. Listen to the episode here or download it here.       This show also happens to be the One Year Anniversary Show of this podcast. Twenty episodes ago I started this podcast in hopes to share some cool music with anyone who wanted to listen. Twenty episodes isn't exactly a whole lot, and I try to release them whenever I get the chance. But every one of those shows are full of awesome bands and songs I've come to discover, and in a way, rediscover in the past year. I'm hoping by listening to these episodes, you will find a new band or song you've never heard of before and then maybe share them with your friends and support the bands who release these songs. For me, it's all about supporting the local scene. And here's to hoping you feel the same way.       This episode features seven new tracks from seven awesome bands. The opening song comes from Abandon the Raft, from San Diego, California. It's a new mix of the title track from the House of Gold EP, released in 2010. They have a new EP in the works and shows as well. Check out their facebook for details and for download links to their music. Also from San Diego are Eskera and Lands on Fire. Eskera released a new album in April titled, Ruido Debajo del Puente. It's really tight California punk/ska. Lands on Fire released a few new tunes late in December of last year and just recently posted it on their website for download. It's three new tunes set to be released for an upcoming record. Lands on Fire's last release was their self-titled album in 2008 on Fallen Angel Records.  Also on the show is Elba, from Seattle, Washington. I saw these guys twice in the small town of La Grande, Oregon. They were great shows, small in attendance, but awesome performances. I'm glad they're still active and making music. Their new EP is titled, Diplodocus. From Chicago, Illinois, is Voice of Addiction with a track from their new record, Reduce, Reuse, Resist. It's really great punk rock from Chicago. Other punk music on the show comes from Abolitionist, a trio from Portland, Oregon. Their new record, It Used to Rain, is 10 songs of 80's and 90's influenced punk rock. And finally, a track from Thick Shakes, from Massachusetts. The song comes off of their new 7", Why Buy the Cow. It features three short tunes of noisy garage punk rock.         Hope you enjoy the music on this show. Here's to many more episodes for this podcast. Huge thanks to all the bands and listeners. Go out and support local music! Hope everyone is having a wonderful Father's Day! Thank you again. And thanks for listening :-D" View original post HERE Playlist available HERE Playlist Abandon the Raft - House of Gold (House of Gold EP) [1:14] Elba - To the Coast (Diplodocus) [5:59] Eskera - Nuestra Lucha, Nuestra Gente (Ruido Debajo del Puente) [9:06] Voice of Addiction - September Remembered (Reduce, Reuse, Resist) [13:37] Abolitionist - Flaming Barricades (It Used to Rain) [16:52] Thick Shakes - Neighbor's Goods (Why buy the Cow) [20:38] Lands on Fire - Endless Line (single) [24:09] Social Media Website: www.takingtheleadmedia.com YouTube: Taking the Lead Media Instagram: https://instagram.com/takingtheleadmedia Mixcloud: https://www.mixcloud.com/thisisrumorcontrol/ Records Revolution: Exploring the Discography of GC Records Twitter: https://twitter.com/ttlmpodcast Tumblr: https://oneshotsideshow.tumblr.com

What if the most sustainable craft supply is the one you already own?Toban Nichols is the director of education and development at Remainders Creative Reuse, a nonprofit that gives donated fabric, yarn, paper, tools and all kinds of unexpected materials a second life. After 15 years teaching visual arts and photography, including to underserved young people, Toban has seen firsthand how access to creative materials can shape who gets to see themselves as a creative.In this episode, Toban and Lexy get into the surprising ways crafting can fuel overconsumption, from resin and glue-heavy trends to buying supplies for projects that never actually happen. Toban shares some of the wildest things that have landed at Remainders, including a Rolex, a grand piano, a jar of teeth and a very unexpected truck accessory. They also talk about the difference between sustainable reuse and hoarding, why Toban eventually stopped bringing materials home without a current project in mind, and how Remainders is making creativity more affordable and accessible. If you love crafting, thrifting or finding new uses for old things, listen to the full episode and follow The Lexy Show for more conversations about fashion, creativity and demanding better from the things we buy.Find Remainders Creative Reuse:Instagram: @remainders_creative_reuseFacebook: Remainders Creative ReuseWebsite: remainders.orgFollow Lexy: @lexysilverstein on Instagram, TikTok and YouTubeIf you enjoyed this conversation, be sure to subscribe to The Lexy Show, leave a review, and share this episode with someone who's ready to make more mindful choices for themselves and the planet. For more information visit https://lexysilverstein.com/

Spolia, from a Liverpool neighborhood to ancient Rome. Subscribe to SiriusXM Podcasts+ to listen to new episodes of 99% Invisible ad-free and a whole week early. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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)

Maintenant Vous Savez, c'est aussi ⁠⁠Maintenant Vous Savez - Santé⁠⁠ et ⁠⁠Maintenant Vous Savez - Culture⁠⁠. On s'intéresse aux tumeurs, car certaines tumeurs sont cancéreuses. Le mot tumeur vient du latin « tumor ». Ça veut dire gonflement. Une tumeur c'est lorsqu'un groupe de cellules se multiplie et forme une masse. Mais il faut savoir que la majeure partie des tumeurs sont bénignes. Lorsque des cellules normales se multiplient et restent localisées, il n'y a pas d'inquiétude à avoir. Il peut s'agir par exemple de grains de beauté ou de verrues. Encore faut-il le savoir. Quelle est la différence entre tumeurs bénignes et malignes ? Comment peut-on diagnostiquer une tumeur maligne ? Et comment soigner les tissus cancéreux ? Écoutez la suite de cet épisode de "Maintenant Vous Savez - Santé". Un podcast Bababam Originals, écrit et réalisé par Olivia Villamy Première diffusion : février 2022 À écouter aussi : ⁠⁠A partir de quand l'anxiété devient-elle néfaste ?⁠⁠ ⁠⁠Qu'est-ce que le syndrome des ovaires polykystiques ?⁠⁠ ⁠⁠Qu'est-ce que le Flurona ?⁠⁠ Retrouvez tous les épisodes de ⁠"Maintenant vous savez".⁠ Pour rester informé et recevoir le meilleur de “Maintenant vous savez” chaque semaine, abonnez-vous à notre ⁠newsletter⁠. Suivez Bababam sur ⁠Instagram⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of This Is Ag!, Hilary Craig, Director of Produce Category Management at Misfits Market, joins me to explore the connection between agriculture, sustainability, and the people who eat the food farmers grow. Hilary shares how her work helps connect growers with consumers by finding a market for quality produce that may be overlooked because of cosmetic imperfections. From scarred blueberries to unusually shaped fruits and vegetables, the conversation explores how our expectations of perfect-looking produce contribute to food waste and how education and storytelling can help change that. Together, we discuss the importance of understanding where our food comes from, appreciating the work and relationships behind every piece of produce, and choosing flavor and quality over appearance. We also look ahead at the future of agriculture, where technology can help reduce waste, but the human connection to food, farming, and one another remains irreplaceable. Misfits Market: https://www.misfitsmarket.com Kirti Mutatkar, President and CEO of UnitedAg: Reach me at kmutatkar@unitedag.org, www.linkedin.com/in/kirtimutatkarUnitedAg - www.unitedag.org UnitedAg Health and Wellness Centers - https://www.unitedag.org/health-benefits/united-agricultural-benefit-trust/health-centers/ Episode Contributors - Hilary Craig, Kirti Mutatkar, Dave Visaya, Mickayla Ursini The episode is also sponsored by Brent Eastman Insurance Services Inc. - https://brenteastman.com Blue Shield of California - https://www.blueshieldca.com Elite Medical - https://www.elitecorpmed.com Gallagher - https://www.ajg.com/ SAIN Medical - https://sainmedical.com/ MDI Network - https://www.mdinetworx.com/about-us

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

For years, marketers have been helping to shape how consumers think and feel about products that are driving the linear economy. However in this episode of the Circular Economy Show, we explore how they can harness their skills to unlock the opportunities that the circular economy provides.  We're joined by experts Deb Caldow, former Global Marketing Director at Diageo, and Rachel O'Reilly, Global Research Lead at Accenture Song. Their experiences provide an insight into how we can turn ideas into impactful actions that deliver both economic growth and environmental benefits. Join us to find out: How marketers are leveraging storytelling to inspire interest in circular products Why they should engage closely supply teams to ensure innovations align with market demand The importance of internal buy-in and a willingness to experiment when scaling circular solutions 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  

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

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.

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:

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.

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.

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

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