Podcasts about Painting

Welcome back to When Words Fail, Music Speaks—the show that explores how melody, rhythm, and creative fire can lift us out of the darkest moments. I'm your host, James Cox, a longtime “handicapped” skater‑turned‑podcaster who knows first‑hand how a good track can pull you through the toughest days.In today's episode we sit down with Brett Ashby—a world‑traveling contemporary artist, performance painter, motivational speaker, and ten‑year cancer survivor. Brett has turned a childhood love of skateboarding into a breathtaking art form: he paints vivid portraits while cruising on a skateboard, often on a half‑pipe, with live musicians providing the soundtrack. From his early days drawing in primary school, to collaging a Barack‑Obama piece for Art Basel Miami, to painting Tony Hawk‑inspired canvases on vert ramps, Brett's journey proves that creativity can be far more than self‑expression—it can be a lifeline.We'll hear how a U2 album became his chemotherapy companion, how he transformed a near‑fatal neck injury into a triumphant return to the board, and why he now mentors emerging artists while rolling, painting, and composing his own songs. Expect stories about:The “Energy” live‑skate portrait series that fuses rock, African drumming, Gregorian chant, and the natural sound of wheels on wood.The surreal surf‑board easel that birthed his “Understanding Life and Death” blue‑whale triptych displayed on Times Square's 32‑story billboard.Brett's favorite music that fuels his flow state, the moments when silence is essential, and the bands that helped him survive cancer.Quick‑fire lightning‑round answers that reveal his coffee‑first mornings, sunset preferences, favorite color (blue), and the dream of painting James from Metallica.If you've ever wondered how music can become a healer, how art can turn pain into purpose, or simply want to be inspired by a man who literally paints while he rolls, this episode is for you. Grab your headphones, turn up the volume, and let Brett's story remind us that when words fall short, the beat always carries us forward.Stay tuned—after the break we dive right into the conversation.

Painting contractor marketing, painting business marketing, Facebook Ads, Google Ads, SEO, and painting leads — which marketing strategy should you actually use to grow your painting company?Brandon Pierpont, founder of Painter Marketing Pros, breaks down exactly which marketing channels painting contractors should focus on at each stage of growth — from starting at $0 to scaling beyond $1 million in annual revenue.Instead of throwing money at every new marketing strategy, Brandon lays out a roadmap for choosing between free marketing, Facebook Ads, Google Local Services Ads, Google PPC, SEO, referrals, social media, and more.In this episode, you'll learn:• The marketing fundamentals every painting company needs• How to market a painting business from $0–$500K• When painting contractors should start running paid ads• Facebook Ads vs Google Ads for painting leads• Why some contractors think Facebook Ads “don't work”• When Google PPC actually makes sense• How to use Google Local Services Ads• Why reviews and referrals are so valuable• When to invest in SEO• How to build long-term brand equity• Why your sales system matters just as much as lead generation• How local Facebook groups can generate painting leadsStart your Free 14 Day Trial for Routemize - https://routemize.com/Coaching Session Signup: https://calendly.com/dripjobs/breakthroughPurchase the 31 Days of Value and build an EMPIRE: https://www.amazon.com/31-Days-Value-home-service-businesses/dp/B0FQSH32X7Spotify: https://open.spotify.com/show/2v0D0SNSBofqJJE6zApEE1DripJobs Demo: https://calendly.com/dripjobsteam/dripjobsdemoGusto: https://gusto.com/i/tanner269OpenPhone: https://openph.one/referral/8Kc17aqFacebook Group: https://www.facebook.com/groups/173750747824373/?ref=shareFollow me on Instagram: http://Instagram.com/officialtannermullen#PaintingContractor #ContractorBusiness #ArtificialIntelligence #AI #PaintingBusiness #HomeServices #BusinessGrowth #ContractorSecrets

How do you translate the memory of a landscape into a rich, tactile painting? And how do you sustain a meaningful studio practice when life and work get demanding? In this episode of the Create! Podcast, Kat sits down with Lansing, Michigan-based visual artist and educator Laurén Gerig. Laurén shares her thoughts on place attachment, building expressive paint surfaces, and using poetry to set the stage for intuitive studio sessions. We also dive into the realities of balancing a demanding day job in academia with an active exhibition schedule, and how small micro-moments in the studio can help you overcome creative fatigue and keep momentum alive. In this episode, we talk about: Capturing the psychological bonds we create with landscapes and nature The tactile joy of thick mark-making, paint accumulation, and surface texture Using poetry as a studio ritual to transition into making work Navigating life as both a practicing artist and a university gallery director Making room for low-pressure, small-scale work like 20-minute charcoal drawings How running and stepping away from the easel can serve as the ultimate creative reset Connect with Laurén Gerig: Website: www.laurengerigart.com Instagram: @laurengerigart Stay connected with Create! Magazine: Subscribe to the Create! Substack for more artist stories and reflections: www.createmagazine.substack.com Submit your work for an upcoming issue of Create! Magazine: www.createmagazine.co/call-for-art

What if being a caring person didn't require constantly giving more of yourself away? When you care deeply, it's easy to look around and see need everywhere—and then quietly turn that awareness into another expectation of yourself. I should help. I should do more. I should care more. I should have more to give. But maybe "Where am I needed?" isn't the only question worth asking. In this episode of Don't Cut Your Own Bangs, I'm joined by social worker, author, and community advocate Jalysa King for a conversation about finding the sweet spot between being of service to others and staying connected to what makes you come alive. Jalysa has a remarkable way of showing up for meaningful work without making service synonymous with heaviness. From helping young people find their dream formal wear through The Gifted Gown to supporting grieving families through Brooks Place, she reminds us that helping can look like listening, playing, shopping, creating, smiling, or simply bringing something you already love into a place where it's needed. Because there will always be more good work that could be done. In This Episode, We Explore Why helping doesn't always have to feel heavy How play, creativity, shopping, art, and connection can all become meaningful forms of service How our natural interests can point us toward places where we have something meaningful to contribute Why representation matters—and the power of seeing yourself reflected in books, stories, and possibilities How to recognize when something simply isn't right for this particular season of your life What it might look like to let joy help determine where you invest your energy Jalysa's examples continually expand the definition of what "helping" can look like—from playing with children to helping someone find a gown to simply sitting beside someone experiencing grief. Three Things to Carry With You 1. Where you're needed isn't the only question. If you care deeply and pay attention to the world around you, you can find someone who needs something almost everywhere you look. Instead, try asking: Where do I come alive while helping? Where does something you naturally love, enjoy, understand, or know how to do meet something another person genuinely needs? 2. The way into something heavy can be small, simple, or light. Sometimes a problem feels so enormous that we assume our response needs to feel enormous, too. But service might look like planting flowers. Cuddling a shelter dog. Playing with a child. Helping someone find a dress. Painting beside someone when talking feels impossible. The seriousness of someone's need doesn't necessarily dictate the heaviness of what you have to offer. 3. Being of service doesn't have to mean giving all of yourself away. Your joy doesn't disqualify the contribution. In fact, the things that light you up may contain useful information about what you uniquely have to offer. As Jalysa says about The Gifted Gown, she's doing something she genuinely enjoys while simultaneously meeting a real need—and that experience fills her cup, too. The Heart of This Conversation Maybe you don't need another reminder that the world needs help. Maybe you need permission to remember that you get to exist inside the equation, too. Your capacity matters. Your interests matter. Your energy matters. Your joy matters. Being of service doesn't have to require disappearing from your own life in the process. A Line to Keep "Where do I come alive while helping?" The Clip Moment "When the problem feels big, and hard, and heavy, it almost doesn't feel accessible that…the way into a solution can be either small, or simple, or light." — Danielle A Gentle Reflection Instead of asking yourself what else you should be doing, try asking: What do I genuinely enjoy? And then: Where might that be useful? You don't have to manufacture a passion for something simply because it's important. You might already have something to offer hiding inside the things that make you feel most like yourself. About Jalysa King Jalysa King is a social worker, children's book author, and community advocate whose work spans service, representation, grief support, and community connection. In this conversation, she shares her work with The Gifted Gown and Brooks Place, along with the experiences that first shaped her relationship with service. She is the author of When I Grow Up I Can Be Anything and The World Needs More People Who Smile. Her work as an author grew partly from recognizing how powerful it can be for children to see themselves represented in the possibilities they're imagining for their own lives. Jalysa's Books When I Grow Up I Can Be Anything — Jalysa King The World Needs More People Who Smile — Jalysa King Organizations mentioned in the episode Brooke's Place — grief support and services for children, teens, young adults, and families. The Gifted Gown — provides free formal wear, shoes, and accessories so cost doesn't prevent someone from participating in meaningful milestone events. If This Episode Resonates Send it to the person who cares about everything and is quietly wondering how they're supposed to keep caring without becoming exhausted by it. And if you're that person, perhaps you don't need to leave this conversation asking: How can I do more? Try: Where does what I uniquely have to give meet something I genuinely care about? Because being of service doesn't have to mean giving all of yourself away. Connect With Danielle Danielle Ireland YouTube — Don't Cut Your Own Bangs Instagram — Don't Cut Your Own Bangs The Treasured Journal Wrestling a Walrus Listen on Spotify Listen on Apple Podcasts  

Thousands of languages are used around the world today, and this linguistic diversity is a precious part of our human heritage. But it's also more than that. Linguistic diversity provides clues—a vast crazy quilt of clues—about how language works, about how history has unfolded, about the dynamics of culture, and about the nature of the human mind. My guest today is Dr. Damián Blasi. Damián is an ICREA Research Professor based at the Center for Brain & Cognition at the Pompeu Fabra University in Barcelona. Over the last decade or so, Damián's emerged as one of the most innovative researchers in the study of language and linguistic diversity. He's been widely recognized for this work, and most recently was awarded the 2026 FABSS Early Career Impact Award from the Cognitive Science Society. In this conversation, Damián and talk about why scientists should care about linguistic diversity. We do a bit of linguistic diversity 101—how many languages there are, how we can enumerate them, how they vary from another. We discuss a recent paper in which Damian and his colleagues reconstructed the explosive rise and precipitous fall of human linguistic diversity over the last 12,000 years. We talk about the factors that drive language change. And we discuss how cognitive science relies too much on English and English speakers—and how this may be holding the field back. Along the way, Damián and I touch on the Tower of Babel, a golden age of linguistic diversity, the human bite configuration, scientific aesthetics, whether children shape language, polygenetic theories of language evolution, the pace of language change, schooling, linguistic inequality and behemoth languages, and why Damián thinks that possibility space of human language might be bigger and wilder than we currently imagine. This is a special episode of the Many Minds podcast. As I mentioned, Damián is this year's winner of an award from the Cognitive Science Society, in recognition of his outstanding early-year contributions to the field. He recently gave a keynote at the society's annual conference in Rio de Janeiro, and we've partnered with the Cognitive Science Society to feature this conversation about his work. I'll also mention that this wasn't my first time chatting with Damián—he's an alumnus of the Diverse Intelligences Summer Institute (DISI), the sponsor & incubator of this podcast. We met there a couple years ago, and so it was all the more fun to reconnect with him here. Alright friends, without further ado, on to my conversation with Dr. Damián Blasi. Enjoy!   Notes 10:00 – The UNESCO page on multilingualism and linguistic diversity. 17:00 – The Ethnologue database. 20:00 – For examples of Dr. Blasi's work on "behemoth languages" and on how languages are not represented equally in terms of technological and development investment, see here and here. 26:00 - For the paper in which Dr. Blasi and colleagues introduced Grambank, see here. For the database itself, see here. 29:00 – For Dr. Blasi's earlier work on sound-meaning associations across languages, see here. 33:00 – For discussion of Darwin's views on monogenesis vs polygenesis and race, see our recent episode. 36:00 – For more about pronouns—and how they do and do not vary across the world's languages—see several chapters in the World Atlas of Language Structures. 39:00 – The paper—'Babies aren't persons'—by David Lancy. 41:00 - For Dr. Blasi and colleagues' recent Science paper on the rise and fall of human linguistic diversity, see here. 59:00 – For a recent study of linguistic diversification, including discussion of so-called schismogenesis, see here. For an example of Cristina Moya's work on ethnic identity in the Andes region, see here. 1:00:00 – For an example of Dr. Blasi's work on how ecology shapes language—a topic not discussed in this conversation—see here. 1:01:00 ­– For the study by Dr. Blasi and colleagues about changes in human bite configuration and how these changes have shaped sounds systems, see here. 1:14:00 – For the recent paper by Dr. Blasi and colleagues about the (non-)role of children in language change, see here. 1:22:00 – The paper by Dr. Blasi and colleagues about the over-reliance on English in the cognitive sciences (and how it hinders the field). 1:24:00 – See here for work by Aitor Egurtzegi, in collaboration with Dr. Blasi and others, on ergativity and gaze patterns across languages. 1:27:00 – For the preprint—'Which humans?'—led by Mohammed Atari (and including Dr. Blasi and others), see here. 1:30:00 – For a recent argument that psychology needs to overcome its "ahistorical" bent, see here.   Recommendations A Myriad of Tongues, Caleb Everett The Secret of Our Success, Joseph Henrich Linguistic Diversity in Space and Time, Johanna Nichols   Tile art credit: Painting by Pieter Bruegel the Elder (detail)   Many Minds is a project of the Diverse Intelligences Summer Institute, which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by Kensy Cooperrider, with help from Assistant Producer Urte Laukaityte and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by Ben Oldroyd. Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter here! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com. For updates about the show, visit our website or follow us on Bluesky (@manymindspod.bsky.social).

Look Forward breaks down Trump escalating his trade war with Canada by signing an executive order renaming Lake Ontario to "Lake America;" a genuinely bizarre 400 year old naming dispute born entirely out of tariff spite. Meanwhile, Trump's beef import plan is already causing real problems: 30,000 pounds of Argentine beef just got recalled in Texas and Florida, raising real questions about the plan's quality controls even as ranchers and consumers alike push back.Hakeem Jeffries met privately with Jared Kushner weeks before midterms that could make him Speaker of the House, a decision that's drawing serious criticism from the leftwing of the Democratic Party. SCOTUS handed Trump a technically favorable but ultimately inconsequential ruling on mail voting. A Republican midterm convention is shaping up to be an expensive, tone deaf disaster, with House members reportedly paying up to $100,000 to attend.In corruption news: the State Department is trying to sell a Kehinde Wiley painting because it's "too woke," the Trump administration is threatening to demolish The Kennedy Center if it doesn't get its way on renaming it, and Trump's own crypto ventures have left other investors nearly $5 billion underwater while he personally profits. We cover all of it; the pettiness, the corruption, and the genuinely bizarre.Look Forward is a weekly progressive political podcast covering U.S. politics, government policy, Democratic strategy, elections, voting rights, Supreme Court rulings, and political news. Featuring progressive commentary, political analysis, and unapologetic opinions on the fight for democracy. Hosted by Jay and Brad. A TNP Studios production. New episodes weekly on Spotify, Apple Podcasts, YouTube, and all major platforms. For more TNP Studios content, check out The Nerdpocalypse (movie & TV news), Black on Black Cinema (Black film reviews), and Dense Pixels (video game news).

Take a look at Joni's painting up close here! --------Thank you for listening! Your support of Joni and Friends helps make this show possible. Joni and Friends envisions a world where every person with a disability finds hope, dignity, and their place in the body of Christ. Become part of the global movement today at www.joniandfriends.org. Find more encouragement on Instagram, TikTok, Facebook, and YouTube.

On this episode of Trapped Under Plastic, Scott and Jon discuss questions sent in by our Patrons!Support the Show on Patreon: https://www.patreon.com/trappedunderplasticSupport the Show with Merch: https://teespring.com/stores/trapped-under-plasticFollow Jon: https://www.youtube.com/ninjonFollow Scott: https://www.youtube.com/miniacJoin the FB group: https://www.facebook.com/groups/395664561386239/Listen to the audio versions: http://www.trappedunderplastic.com/On patreon, we offer our patron's the ability to submit topics for us to discuss during a podcast, you get an extended version of the podcast, and you can submit miniatures for us to critique during an episode!TUP PartnersSteamforged Games:https://steamforged.com/The Army Painter:https://thearmypainter.com/Red Grass Games:redgrasscreative.comIwata:https://www.iwata-airbrush.com/Game Envy:https://gameenvy.net/Monument Hobbies:https://monhob.com/TUPhttps://www.twitch.tv/monument_hobbiesRelevant LinksScott's Meeple-A-Thon Classes:https://www.facebook.com/share/1FG8WNnrvU/?mibextid=wwXIfrPatreon Mini Critique - Jake:https://www.instagram.com/p/DH2ZKtCMcik/?img_index=1Ninjon Video:https://www.youtube.com/watch?v=9OoK2uqfSLYMiniac Video:https://www.youtube.com/watch?v=qNuqLW0J7tQ00:00 Start01:28 Preamble Ramble17:54 TUP Partners (Part One)19:30 Hobby Update30:15 Patreon Mini Critique36:17 TUP Partners (Part Two)38:42 Topic DiscussionSupport the showSupport the Show on Patreon: https://www.patreon.com/trappedunderplasticSupport the Show with Merch: https://teespring.com/stores/trapped-under-plasticFollow Jon: https://www.youtube.com/ninjonFollow Scott: https://www.youtube.com/miniacJoin the FB group: https://www.facebook.com/groups/395664561386239/Listen to the audio versions: http://www.trappedunderplastic.com/

We're closing on 2 of our most exciting boutique hotels yet —Catalina Island + Bodega Bay visit somerscapital.com/investJoel Mercado went from being homeless at 16 to building a painting business doing $12 million a year. In this episode, he breaks down why he believes the trades are one of the biggest opportunities of the next decade, how someone can start a painting company with very little money, and why residential painting can be one of the best places to begin.Joel also shares the exact fundamentals behind scaling: finding your first customers through door knocking, building a four-week backlog before starting production, knowing when to hire an admin and sales rep, and creating a business that can eventually operate without you doing every estimate or managing every job yourself.Connect with Rich on Instagram: @rich_somers

Some things are different.. some things are similar..  Did you guess there were going to be so many detachments and psychic rules?  We didn’t.. we give you the high level … Read More

Everyone hates AI, at least that's the perception to some. Alex has been experiencing a lot of real life transgressions featuring AI and it's making him think that the proverbial bubble isn't going to pop anytime soon. The general public acceptance of AI usage is alarming to Alex and he get accused of being a gatekeeper.. Walter catches another creature and dooms the whole house. How well do you know your slogans? Does Jakubs confidence bode well? Something about UK politics is said. Executive Producers: Ian Lotts, Phillip Booker, Lauren Byington, Wes Bradley, & Tim Bland All WYSP Social Link

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

Facebook Ads for painting contractors can generate consistent painting leads — but only if your ads, marketing message, and follow-up system are built correctly.In this episode of the Contractor Secrets Podcast, Tanner Mullen audits Axel's Facebook Ads and explains why his painting business marketing is getting clicks but failing to turn those clicks into painting leads and booked jobs.Axel spent roughly $350–$400 on Facebook Ads and generated around 700 clicks, but received almost no meaningful leads. Tanner pulls up the actual ad and breaks down exactly what's going wrong.In this episode, you'll learn:• How to run better Facebook Ads for painting contractors• Why painting ads get clicks but no leads• How to generate more painting leads• Why your marketing message must match your ideal customer• How authentic video builds trust with homeowners• Why AI-generated ads can hurt your marketing• How speed-to-lead affects conversions• How a CRM helps turn leads into booked estimates• How to budget for contractor marketing• When to DIY marketing vs. hire a marketing agencyTanner explains that successful painting contractor marketing isn't just about getting clicks. Your ads need to attract the right homeowners, build trust, generate qualified painting leads, and have a system behind them that follows up immediately.If you're a painting contractor trying to grow your painting business with Facebook Ads, Meta Ads, lead generation, contractor marketing, or digital marketing, this episode will show you what to fix.VIDEO CHAPTERS:00:00 Why Painting Ads Fail01:16 Meet Axel & His Painting Business02:17 Building the Foundation04:34 Axel's Facebook Ads Strategy05:03 700 Clicks, Almost No Leads06:17 Tanner Audits the Facebook Ad07:09 What's Wrong With the Ad?09:12 The Cost of DIY Marketing10:05 How to Build Trust With Facebook Ads11:31 Stop Trying to Be Perfect on Camera14:37 Should Contractors Hire a Marketing Agency?15:55 Why Speed-to-Lead Matters17:47 Investing in Your Painting Business19:43 Why You Need a Marketing System21:12 CRM & Follow-Up Systems22:28 Building a Better Estimating System25:12 Why Consistent Marketing Matters27:32 How Much Should You Spend on Marketing?31:42 Systems That Free the Business OwnerCoaching Session Signup: https://calendly.com/dripjobs/breakthroughPurchase the 31 Days of Value and build an EMPIRE: https://www.amazon.com/31-Days-Value-home-service-businesses/dp/B0FQSH32X7Spotify: https://open.spotify.com/show/2v0D0SNSBofqJJE6zApEE1DripJobs Demo: https://calendly.com/dripjobsteam/dripjobsdemoGusto: https://gusto.com/i/tanner269OpenPhone: https://openph.one/referral/8Kc17aqFacebook Group: https://www.facebook.com/groups/173750747824373/?ref=shareFollow me on Instagram: http://Instagram.com/officialtannermullen#roofing #roofingbusiness #contractormarketing #localseo #roofingcontractor #homeservices #businessgrowth

Good Morning, Cabalists! Today we kick the show off with a big announcement - Mia the cat has found her forever home with Chris! Then we get into a bunch of the games we've been playing, including Dogfight 1917, Crusaders Thy Will Be Done with the Divine Influence expansion, Darwin's Journey, Moytura, dnup, and a feature review of Slay the Spire. Then, after Tony T's Pokemon Smash-and-Grab Roundup, we dive into some listener-submitted questions about what drives us to buy expansions, why we suck at board games, and what our pet peeves are about ourselves. Dogfight 1917: 00:02:28, Crusaders: Thy Will be Done: 00:11:36, Darwin's Journey: 00:20:17, Moytura: 00:32:17, dnup: 00:42:21, Slay the Spire Review: 00:51:14, News with Tony T: 01:23:04, Short Topic Extravaganza: 02:10:29. Check out our sponsors Restoration Games at https://restorationgames.com/, Game Toppers at https://www.gametoppersllc.com/ and Prester's Painting at https://www.presterspainting.com/

This week, Alexi is joined by music critic and columnist Ella Dorn to unpack her viral essay declaring Giallobrutalism as the defining visual language of mid-2020s pop, informed by the stylish delirium of Italian giallo cinema and a pervasive unease about surveillance and stardom. We discuss the aesthetic's formal signatures—cool temperatures, split diopters, painterly tableaux, and more— as well as the rise of early-onset popstar histrionics, the existential glamour of centripetal girls in centrifugal settings, and the decade's watershed fashion moments so far, from Miu Miu micro-minis to Sandy Liang mega-bows. We also challenge the default Y2K reading of modern pop aesthetics, arguing for a closer look at their 20th-century antecedents, and reflect on our shared past as stans of doomed K-pop girl groups. Links:“‘Giallobrutalism,' or why everything looks like that” by Ella Dorn on Fairyland!Ella Dorn on TwitterFairyland! on InstagramK-Pop Music Video Analysis w/ Alexi One on Top of the Other (1969) dir. Lucio FulciA Lizard in a Woman's Skin (1971) dir. Lucio Fulci“Music, Fashion, Film is Charli xcx at her subversive best” by Ella Dorn in The New StatesmanBalenciaga F/W 2024Film Noir and the Spaces of Modernity by Edward Dimendberg“The realities of being a pop star” by Charli xcx on charli's substackEp. 162: Crayola CasualEp. 102: Just Dance w/ Lexee SmithNicole Kidman wearing Miu Miu S/S 2022 for Vanity Fair's 2022 Hollywood Issue coverSandy Liang S/S 2024Our Aesthetic Categories: Zany, Cute, Interesting by Sianne Ngai“In Defense of the Leopard Print Midi Skirt” in Paper magazine (the original @leopardmidiskirt Instagram account is GONE omg)New Giallobrutalist canon entries we discuss: YENA (최예나) – “캐치 캐치 (Catch Catch)”, aespa 에스파 – “Switchblade (Feat. Ty Dolla $ign)”, Gracie Abrams – “Look at My Life”, ifeye (이프아이) – “r u ok?” , aespa エスパ – “KISS N TELL”Bang Jae Yeob LOONA/ODD EYE CIRCLE – “Sweet Crazy Love” (Official Music Video)LOONA/Kim Lip – “Eclipse” (Official Music Video)The iconic abandoned Brutalist building from every K-pop MVGhosts of My Life: Writings on Depression, Hauntology and Lost Futures by Mark FisherFederico Fellini: Painting in Film, Painting on Film by Hava Aldoubyno na - “honk!” (Official Music Video)no na - “superstitious” (Official Music Video)Elliot Shields on Instagram (creative consultant on Aespa's “Kiss N Tell”) Magic by Jolin Tsai 蔡依林但是又何奈 by Louise Tsuei 崔苔菁 Rocket Girls 101 (火箭少女101) K-pop plagiarism overviewKiiiKiii on Instagram This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.nymphetalumni.com/subscribe

This is the first episode of our Work Series - a three-part look at how painting companies are rethinking their organizational models. When the numbers stopped working, Austin Ilsley and his wife Lacie faced a decision that would change everything about how they run their field team at Ai Painting Plus. Austin walks us through the financial turning point that pushed them away from a full W2 model and what the shift to subcontractors actually looked like on the ground. From rebuilding their team to redefining where control lives in the business, he shares the real lessons behind a transition that's now producing financial health.

Sorry, there's no YouTube episode today. They will be back in a couple of months!In today's episode we share 4 spooky Reddit stories.Stories:https://www.reddit.com/r/Paranormal/s/9dCRtpkHrAhttps://www.reddit.com/r/Paranormal/s/hmjGDiWZtahttps://www.reddit.com/r/Paranormal/s/FB2Lirzw1mhttps://www.reddit.com/r/Paranormal/s/0vDPYw2y43The BOOKBY US A COFFEEJoin Sarah's new FACEBOOK GROUPSubscribe to our PATREONEMAIL us your storiesFollow us on YOUTUBEJoin us on INSTAGRAMJoin us on TWITTERJoin us on FACEBOOKVisit our WEBSITEThanks so much for listening, and we'll catch up with you again on Sunday!Sarah and Rog xx"Spacial Winds" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/SURVEY Hosted on Acast. See acast.com/privacy for more information.

Cathy and Todd discuss Cocktail for Movies We Love and Love to Make Fun of Month. They revisit the 1988 Tom Cruise classic with its flair bartending, strange plot turns, and very serious ideas about money, masculinity, friendship, and success. They share their memories of seeing it when they were younger, explore the darker story hidden beneath the story, unpack Doug's Coughlin's Laws and Kerry's missing backstory, and ask the important questions: Why does everyone make life-changing decisions based on barroom bets? And how did a critically despised Razzie winner become a movie we all still quote, rewatch, and love to make fun of? Subscribe to Cathy's weekly Zen Moment and upgrade to hear a weekly Zen Moment podcast hosted by Cathy and Todd! Some Ways to Support Us Sign up for Cathy's Substack Order Restoring our Girls Links shared in this episode: For the full show notes, visit zenpopparenting.com. This week's sponsor(s): Avid Co DuPage County Area Decorating, Painting, Remodeling by Avid Co includes kitchens, basements, bathrooms, flooring, tiling, fire and flood restoration. MenLiving – A virtual and in-person community of guys connecting deeply and living fully. No requirements, no creeds, no gurus, no judgements Todd Adams Life & Leadership Coaching for Guys Other Ways to Support Us Follow us on social media Instagram YouTube Facebook Buy and leave a review for Cathy’s Book Zen Parenting: Caring for Ourselves and Our Children in an Unpredictable World

Episode: 3401 Samuel Smiles and James Nasmyth: Strange bedfellows.  Today, two views of a polymath.

Amy Gross was born on Long Island, New York, and received her BFA from the Cooper Union in New York City. After a residency at the Skowhegan School of Painting and Sculpture, she started a graphic design company, and worked as a surface designer, specializing in textiles, children's products and toy design. In 2000 she moved to South Florida, and fascinated by the subtropical environment and the intricate natural growth there, began making hand-beaded jewelry. Her work expanded to embroidered canvases, then sculpture merging observed natural elements with invented life forms. In 2006, Gross received a grant from the South Florida Cultural Consortium, was the finalist for the Southern Prize in 2019, and received an Artist Innovation Fellowship in 2020. Her mixed media sculpture is represented by Momentum Gallery in Asheville, NC. Selected exhibits include the Craft and Folk Art Museum of Los Angeles, The Minnesota Museum of Art, the Mesa Contemporary Art Museum, the Asheville Art Museum, the Boca Raton Museum of Art, The Bascom Center, and fairs Art Wynwood and SOFA. Her shadowboxes are part of the permanent collection of the University of North Carolina at Chapel Hill, and, as part of the Art in Embassies program, installed in the new American Embassy in Papua New Guinea. Her work has appeared in Fiber Arts Magazine, American Craft Magazine, The Washington Post, Luxe Magazine, Create Magazine and the art and culture blogs Colossal TreeHugger, ArtMag and The Jealous Curator. She was featured in an article in the Spring 2016 issue of Fiber Art Now Magazine, and was the issue's cover artist. She appears in three Schiffer Press books, Artistry in Fiber, Sculpture, Dimensional Cloth, Sculpture by Contemporary Textile Artists, and Stitched: Journeys with Birds. Her work will appear in the upcoming Phaidon book Fungi: The World of Mushrooms. She lives and works in Delray Beach, Florida. Amy Gross amygross.com Instagram: amyla174 momentumgallery.com

Want to close more painting leads and grow your painting business? Aiden Smith breaks down the exact painting contractor sales funnel his company uses to close around 60% of leads and consistently sell more painting jobs.In this episode of the Contractor Secrets Podcast, we're joined by Aiden Smith, founder of Smith & Co Painting and Paint Pro Coaching, to reveal how he generates painting leads, pre-qualifies prospects, builds trust before the estimate, and closes more painting jobs.Aiden recently sold roughly $50,000 in painting jobs in one week at a 60% close rate. He even shares how he closed a $9,000 painting project over the phone without ever visiting the property.In this episode, you'll learn:• How to build a painting contractor sales funnel• How to close more painting leads• How to pre-qualify homeowners before an estimate• Why speed-to-lead matters for painting contractors• Facebook Ads and Google leads for painting companies• How to build trust before the sales appointment• How Aiden uses "What to Expect" videos to increase sales• How to improve your painting sales closing rate• How to close painting jobs on the spot• How to systemize your painting sales processThe goal is simple: win the painting job before the sales appointment even begins.If you're a painting contractor or painting business owner looking to generate better leads, improve your sales process, increase your close rate, and grow your painting company, this episode is for you.Start your Free 14 Day Trial for Routemize - https://routemize.com/Coaching Session Signup: https://calendly.com/dripjobs/breakthroughPurchase the 31 Days of Value and build an EMPIRE: https://www.amazon.com/31-Days-Value-home-service-businesses/dp/B0FQSH32X7Spotify: https://open.spotify.com/show/2v0D0SNSBofqJJE6zApEE1DripJobs Demo: https://calendly.com/dripjobsteam/dripjobsdemoGusto: https://gusto.com/i/tanner269OpenPhone: https://openph.one/referral/8Kc17aqFacebook Group: https://www.facebook.com/groups/173750747824373/?ref=shareFollow me on Instagram: http://Instagram.com/officialtannermullen#PaintingContractor #ContractorBusiness #ArtificialIntelligence #AI #PaintingBusiness #HomeServices #BusinessGrowth #ContractorSecrets

It's a MONDAY on Big Rich, TD & Fletch, and somehow everybody made it back alive. Rich returns from a wild wedding weekend in Washington, TD decided one concert wasn't enough and hit MULTIPLE shows in the same weekend, and Kat spent a DJ set in the park… painting a picture. Because of course she did. And Fletch? Fletch did Fletch things. Golf. Coors Light. Repeat. Everybody had themselves a weekend. Now STRAP IN and let's party.

Support the sponsor!► This is an ad by Betterhelp. Our listeners get 10% their first month at https://betterhelp.com/mrcreepsTIMESTAMPS:0:00:00 "I Found a Painting with my Childhood Home at Goodwill."0:55:45 "I was Hired to Audit a Dead Company's Assets. I Found Something Disturbing."1:37:14 "We Found an Emergency Distress Buoy Floating in the Pacific. Its Message was Disturbing."

Mr Tumble's guest today is Shopkeeper Tumble. There is some painting fun and join in with the songs Brush Your Teeth, The Sun Has Got His Hat On and See You in the Morning.

Painting a masterpiece is a process. So is transforming you into Christ likeness.Donate to Moody Radio: http://moodyradio.org/donateto/todayssinglechristianSee omnystudio.com/listener for privacy information.

Episode 539 / Adam De BoerAdam de Boer is an artist who graduated with a BFA in Painting from the College of Creative Studies at the University of California, Santa Barbara (2006) and an MA in Fine Art from the Chelsea College of Art, London (2012). His work has been exhibited at Plato, New York (2024); Taymour Grahne Projects, London (2024, 2022); The Pit, Los Angeles (2023); Gajah Gallery, Jakarta and Singapore (2023, 2022); Ben Brown Fine Arts, London and Hong Kong (2022); The Hole, New York and Los Angeles (2023, 2022); Hunter Shaw Fine Art, Los Angeles (2020, 2018); World Trade Centre, Jakarta (2018); and Art Jog, Yogyakarta (2018, 2015).Adam is a Joan Mitchell Foundation Fellow and in 2017 was awarded a Fulbright Research Fellowship to Java, Indonesia. Other grants include The International Art Prize presented by Winsor & Newton and Paul Smith's Foundation, Arts for India from the University of the Arts, London, and those from The Cultural Development Corporation, The DC Commission on the Arts and Humanities, and The Santa Barbara Arts Fund.

AI isn't going to replace painting contractors — but contractors who know how to use AI might.In this special episode of the Contractor Secrets Podcast, Tanner Mullen is joined by Mike Hickman, founder and CEO of Painter Growth, for a deep dive into what an AI-native painting business actually looks like.Mike breaks down practical ways contractors can start using AI across their sales, marketing, estimating, websites, strategic partnerships, and day-to-day operations — including live examples of what's already possible.In this episode, you'll learn:• The 5 levels of AI adoption• How AI can transform your sales process• How to build a website using AI• How to create personalized proposals in minutes• Using AI to visualize projects before the work begins• Automating strategic partner outreach• How AI can help you operate with fewer people and higher margins• Why contractors who wait too long may struggle to catch upThe goal isn't to remove the human element from your business. It's to use AI to handle more of the repetitive work so you can spend more time selling, building relationships, leading your team, and growing the company.If you own a painting or home service business, this is an episode you don't want to miss.Start your Free 14 Day Trial for Routemize - https://routemize.com/Coaching Session Signup: https://calendly.com/dripjobs/breakthroughPurchase the 31 Days of Value and build an EMPIRE: https://www.amazon.com/31-Days-Value-home-service-businesses/dp/B0FQSH32X7Spotify: https://open.spotify.com/show/2v0D0SNSBofqJJE6zApEE1DripJobs Demo: https://calendly.com/dripjobsteam/dripjobsdemoGusto: https://gusto.com/i/tanner269OpenPhone: https://openph.one/referral/8Kc17aqFacebook Group: https://www.facebook.com/groups/173750747824373/?ref=shareFollow me on Instagram: http://Instagram.com/officialtannermullen#PaintingContractor #ContractorBusiness #ArtificialIntelligence #AI #PaintingBusiness #HomeServices #BusinessGrowth #ContractorSecrets

  The annual Perseid Meteor shower is a great excuse to create a mix of spacey ambient music that is perfect for stargazing. The best viewing nights will be August 12 & 13. I believe there will be a new moon this year so we should have nice, dark skies for viewing.   I've done several of these in the past which you can find here: https://www.mixcloud.com/lowlight/music-for-the-perseid-meteor-shower/ https://www.mixcloud.com/lowlight/perfect-perseid-playlist/ https://www.mixcloud.com/lowlight/soundtrack-for-perseid-meteor-shower-2025/   This mix is almost all music from 2026, with the opening track from 2025. You can find all that music at these links: https://hiraeth-records.bandcamp.com/album/blueberries-on-mars https://tonepoet.bandcamp.com/album/the-cloud-diaries https://homenormal.bandcamp.com/album/la-paura-del-vuoto https://coldtearrecords.bandcamp.com/album/the-sound-of-nature-3 https://zakedrone.bandcamp.com/album/microliths-and-momentary-drifts https://whitelabrecs.bandcamp.com/album/solitarium https://slowcraft.bandcamp.com/album/solitude-and-silence https://stateazure.bandcamp.com/album/loom-2 https://phelios.bandcamp.com/album/starfields https://circ.bandcamp.com/album/rite https://thelonelybell.bandcamp.com/album/time-lost   Get out there and watch some stuff burn up in the atmosphere. ☄️

Cathy and Todd discuss Top Gun: Maverick to complete their summer blockbuster series, the 2022 legacy sequel that brought Tom Cruise back to the role that made him famous, three decades (and a pandemic delay) later. They talk about Val Kilmer’s real battle with throat cancer and how it shaped his return as Iceman, discuss the film’s themes of grief, legacy, and mentorship, debate why Jon Hamm’s character is such a dick, and share Cathy’s deeply conflicted feelings about Tom Cruise, who happens to look a lot like Todd. They also discussed the original Top Gun (1986) earlier in the summer, click here to listen: https://podcasts.apple.com/gb/podcast/top-gun-episode-871/id414414318?i=1000770790944 Subscribe to Cathy's weekly Zen Moment and upgrade to hear a weekly Zen Moment podcast hosted by Cathy and Todd! Some Ways to Support Us Sign up for Cathy's Substack Order Restoring our Girls Links shared in this episode: For the full show notes, visit zenpopparenting.com. This week's sponsor(s): Avid Co DuPage County Area Decorating, Painting, Remodeling by Avid Co includes kitchens, basements, bathrooms, flooring, tiling, fire and flood restoration. MenLiving – A virtual and in-person community of guys connecting deeply and living fully. No requirements, no creeds, no gurus, no judgements Todd Adams Life & Leadership Coaching for Guys Other Ways to Support Us Follow us on social media Instagram YouTube Facebook Buy and leave a review for Cathy’s Book Zen Parenting: Caring for Ourselves and Our Children in an Unpredictable World

Grettelyn Darkey and Joe Grabowski take up the most neuralgic subject in G.K. Chesterton scholarship: his defense of the French Revolution. Working through a 1921 Illustrated London News column, a 1905 Daily News essay setting the French and Russian revolutions side by side, and a 1918 New Witness piece that turns without warning to Prussianism, they trace his distinction between the ideals of a revolution and its working out, and his insistence that what a revolution exists to restore is the rights of man and not the wrongs of man.  In This Episode: Why G.K. Chesterton distinguished the motivations behind the French Revolution from its working out under Robespierre and the Terror The negative equality G.K. Chesterton faulted the revolutionaries for—abolishing things for everybody instead of extending them to everybody Why reading G.K. Chesterton on this subject without his British and monarchical audience in view produces anachronism, and what he held against Edmund Burke G.K. Chesterton on positive and negative liberalism, and his warning that the modern revolutionist is free to hate anything, liberty itself included The white post from Orthodoxy, radicalism as a return to the roots, and G.K. Chesterton's claim that revolution is the divine part of man Resources Mentioned: The Rightness of the French Revolution by G.K. Chesterton Two Revolutions by G.K. Chesterton The Two Feasts of Freedom The Outline of Sanity by G.K. Chesterton (100th anniversary edition) The Man Who Was Thursday by G.K. Chesterton Chapters: 00:00: Introduction 00:30: Welcome—Revolutions, Independence Day, and Bastille Day 03:55: "The Rightness of the French Revolution" (1921) 05:26: Negative Equality and the Rationalist Epoch 07:40: G.K. Chesterton's Audience and the Danger of Anachronism 10:01: Politically Homeless—Belloc, the Vendée, and Edmund Burke 13:12: Knowing What You Are Talking About 17:11: "Two Revolutions" (1905) and the Songs of the Revolution 23:46: The White Post and the Job of the Conservative 26:27: "The Two Feasts of Freedom" (1918) and the Prussian Paralysis 28:00: Revolution as the Divine Part of Man 35:38: Watt Tyler and Painting the Fence Post FOLLOW US Instagram Facebook X SUPPORT Consider making a donation Visit our Shop Produced by Saint Kolbe Studios

How and why do art and Summer join forces? Art, and visual arts especially, show imminent change–but so does Summer and its ideal of creative flow. How did–or did not–art speak of the climate emergency, gender policing, the war–in short, of loss? What role does this hot and luscious season play in the hubris of conquest, or the black hole of grief? Art curator and writer Joana P. R. Neves, our host, reminisces about Agnes Martin and John Cheever, and their opposing ideas of Summer and of creation, touching on two paintings by David Hockney and a poignant text by Luigi Pirandello. Joana loved a text sent to her by its author, Vittorio Zappa, on Substack, that guided the last part of the episode, leading to its denouement. Is Summer the season of creative flow of life and art making, full of light and sensorial stimuli? Is creativity about desire or meditative art? Or are all these myths, perpetuated by capitalist simplifications? Has art contributed to create them? Or has it always been alerting us to the cruelty of change via Art Wonderment, the art of paradox?Did you enjoy the episode? Think about supporting Exhibitionistas by leaving a small donation: https://buymeacoffee.com/exhibitionistaThe painting A Bigger Splash, 1967, by David Hockney, is the epitome of hubristic summer. But it hides another painting, Portrait of an Artist (Pool with two figures), 1972, with a far more poignant story: they both tell the extremes of summer, and investigate the very special artifice telling a story of desire, change, loss and healing? Sign up for a tiered monthly support: https://www.buzzsprout.com/2624749/monetization/supporter_settings Your contribution keeps us going.Joana P. R. Neves reads a text by Vittorio Zappa: https://substack.com/@vittoriozappa/p-209760240Other references include:The Swimmer, John Cheever, 1964, New Yorker.The Swimmer, a film by Frank Perry, 1968.Did the episode stir Art Wonderment memories? Share them via text or voice message.Support the showCreated & Hosted by Dr. Joana P. R. Neves, art curator and writer with over 20 years of experience in the contemporary art field. Artistic director of Drawing Now Paris since 2018, she has worked across the industry, from the art market to education. She co-launched the art residency and project space Worlding in 2020. Exhibitionistas' first year offered exhibition discussions with guest co-host Emily Harding; organically, it grew into a more experimental show exploring art topics, stories and interviews complemented by Joana's publication Art Thinkosaurus on Substack. She champions ‘Art Wonderment's' embrace of complexity against the lure of ready-made opinions. A polyglot, she grew up in Lisbon, studied and lived in Paris, to finally settle in London with her artist husband, four children and two cats. Find us:On Instagram – @exhibitionistas_podcastOn Substack (NEWSLETTER: sign up!): Art Thinkosaurus > Exhibitionistas FilesOnline: www.exhibitionistaspodcast.comDo you want to be a guest on Art Etiquette? Reach out: joana@exhibitionistaspodcast.comCopyright: Joana P. R. Neves, 2024.

On this week's episode of Trapped Under Plastic, Scott and Jon (but mostly Jon) talk about GenCon 2026!Support the Show on Patreon: https://www.patreon.com/trappedunderplasticSupport the Show with Merch: https://teespring.com/stores/trapped-under-plasticFollow Jon: https://www.youtube.com/ninjonFollow Scott: https://www.youtube.com/miniacJoin the FB group: https://www.facebook.com/groups/395664561386239/Listen to the audio versions: http://www.trappedunderplastic.com/On patreon, we offer our patron's the ability to submit topics for us to discuss during a podcast, you get an extended version of the podcast, and you can submit miniatures for us to critique during an episode!TUP Partners:Steamforged Games:https://steamforged.com/The Army Painter:https://thearmypainter.com/Red Grass Games:redgrasscreative.comIwata:https://www.iwata-airbrush.com/Game Envy:https://gameenvy.net/Monument Hobbies:https://monhob.com/TUPhttps://www.twitch.tv/monument_hobbiesRelevant Linksimp3dsion Instagram:https://www.instagram.com/imp3dsion/jandro3d Website:https://jandro3d.gumroad.com/l/wnxdmh?layout=profilePatreon Mini Critique - Neil:https://www.instagram.com/p/DI5rKx7pPjh/?img_index=1Ninjon GenCon Video:https://www.youtube.com/watch?v=Ol3HyS8UX4UMiniac Legion Video:https://www.youtube.com/watch?v=fAJ3V9ujyw4Jon's GenCon Goodies:Role 4 Initiative:https://role4initiative.com/Scented Dice:https://studiowoe.com/Dragonlance:https://dragonlancenexus.com/Dragonbane:https://freeleaguepublishing.com/shop/dragonbane/rulebook/Shadowdark RPG:https://www.thearcanelibrary.com/collections/shadowdark-rpg?srsltid=AfmBOoqDq1LHSMKlgczp7UtbGyeqqt-BMa77Ym-EiH0SH6a13VLDLewTKingdom Death:https://kingdomdeath.com/00:00 Start00:42 Preamble Ramble12:25 TUP Partners (Part One)13:47 Hobby Update27:43 Patreon Mini Critique34:50 TUP Partners (Part Two)37:05 Topic DiscussionSupport the showSupport the Show on Patreon: https://www.patreon.com/trappedunderplasticSupport the Show with Merch: https://teespring.com/stores/trapped-under-plasticFollow Jon: https://www.youtube.com/ninjonFollow Scott: https://www.youtube.com/miniacJoin the FB group: https://www.facebook.com/groups/395664561386239/Listen to the audio versions: http://www.trappedunderplastic.com/

A great hobby setup can make all the difference. In this video, we're taking a look at our miniature painting desks, the tools we use every hobby session, and the small upgrades that have made painting more enjoyable and efficient.From lighting and storage solutions to essential hobby tools and workspace organisation, we'll share what works for us, what we'd change, and hopefully give you some ideas for improving your own hobby space—whether you're just getting started or looking to level up your current setup.Everyone's hobby desk is different, so we'd love to hear about yours!Huge thanks to our sponsors! You can check them out here:EVERYTHING AIRBRUSH ➡️ https://shorturl.at/bdX07SIEGE SCENICS ➡️ https://shorturl.at/TF7FLRECOMMENDED PRODUCTS ➡️ https://shorturl.at/N9k7PCOMMISSIONS ➡️ https://shorturl.at/xBKW8WARHAMMER BITS ➡️ https://shorturl.at/Z44SqBOOK A CLASS ➡️  https://shorturl.at/ory37LISTEN ON THE GO ➡️ https://pod.link/1690786293DISCORD ➡️ https://shorturl.at/bwM68PATREON ➡️ https://www.patreon.com/siegestudiosCUSTOM SERVICE ➡️ https://shorturl.at/kGH79FREE PDF TUTORIAL ➡️ https://shorturl.at/Ovdy4Timestamps:Intro: 00:02:16Our Hobby Setups: 00:03:48Paul's Setup: 00:05:34Wes's Setup: 00:07:10James's Seetup: 00:10:40Favourite Parts of Each Setup: 00:15:16The Importance of a Chair: 00:22:20Organising your Hobby Space: 00:31:42If you Could Add Anything...: 00:44:48Organising your Hobby Space pt2: 00:49:48What's your Most Important Part in your Hobby Space: 00:59:52Aftershow/Outro: 01:05:40》UK 15-25% off Warhammer Element Games: http://bit.ly/2ltN9KV》For Double Crystals Use Code 'SIE193' at checkout!》EU 15-20% off Warhammer https://taschengelddieb.de/?SiSt=affXW9D4M7J9BD2J9NGSupport the showPaint Perspective is a miniature painting podcast brought to you by Siege Studios, featuring James Otero (Siege founder & CEO) & Paul Kidd (Studio office team member). Each week, our co-hosts discuss a new topic from the point of view of people who work in the miniature painting industry.Follow us: @siegestudios / @paintperspectivepodcastWatch on YouTube: https://www.youtube.com/@SiegeStudios/podcasts

Send us Fan MailIn this episode, we finally tackle a topic that folks have been requesting from the very beginning. How to paint and finish your rockets without disastrous results. We're not pros, but we do have some insight, and we also share some of our worst disasters! To keep the show and rocket projects coming, get ad-free early releases of all episodes, and to see exclusive behind-the-scenes content, join us on Patreon!  Get 10% off your order with "RV10" at https://rocket-bits.com  Support the showSupport the podcast by joining our Patreon and get early/ad-free episode releases:https://patreon.com/aggpodFollow Braden Here:https://youtube.com/@rocketvlogshttps://www.instagram.com/bigb1011https://www.tiktok.com/@bradencarlson6Follow Taylor here:https://www.youtube.com/@the_rocketchannelFollow Shane (or as you may know him, Postart) here:https://www.youtube.com/@PostartPropulsionshttps://www.instagram.com/shaneharrisphoto

Today's talk: Painting and the Seahawks. A Midsummer Nights Run review. Patreon My Website.

We’ve gotten some games in recently and it was pretty liberating to ‘play what I wanted to.’  Of course we can always do that but… can we?  The answer is … Read More

Send us Fan MailVictor, Evelyn, and Mark hang out this week to talk about paint and sips, E with short sleeves, Superbad, recasting The Witcher, and the hair of the dog.

Hey Now, Cabalists! We're kicking things off with a look at culling games from our collections before getting into what's been hitting the table lately, including Animal Rescue Team, Entropy, Super Auto Pets Card Battle, Carnegie, and Eschaton. Then we take a closer look at our featured game, Revenant, designed by Allan Kirkeby. Tony T brings us the latest in Tabletop Gaming News, and we close out the show with a discussion about how to set yourself up for success on your very first play of a game. Animal Rescue Team: 00:09:00, Entropy: 00:20:33, Super Auto Pets Card Battle: 00:28:34, Carnegie: 00:36:51, Eschaton: 00:51:08, Revenant Review: 01:07:37, News with Tony T: 01:43:21, Succeeding at Your First Play: 02:47:22. Check out our sponsors Restoration Games at https://restorationgames.com/, Game Toppers at https://www.gametoppersllc.com/ and Prester's Painting at https://www.presterspainting.com/

Neon is one of the most respected graffiti writers from San Francisco. He came up inspired by the masterpieces of the 1980s and started pushing his own new wave style. Neon is a founding member of BA crew, and built bridges between Bay Area locals and writers from around the world. He is responsible for building the original Montana spray paint brand and was its first US distributor. This is a great conversation with dozens of photos from Neon's personal conversation.--Essential Bay Area Rap Playlist: https://fanlink.tv/historyofthebay-playlistJoin the Patreon for exclusive access: https://patreon.com/dregsone--00:00 Growing up in SF07:29 Intro to graff16:13 Cholo writing18:47 First tags25:27 Late 80s graffiti41:09 First pieces57:08 Fights, chases, & racking1:01:18 Painting around the Bay1:09:38 BA Crew1:22:09 Converse commercial1:26:18 Misk & Apex1:37:31 Distributing MTN paint 1:53:57 Splitting with MTN

Halfway through 2026, what's actually moving the needle in marketing and what's just noise? David Kryszczak, Founder & President of Spartan Digital, joins the show for a mid-year reality check on AI, search behavior, and where trades businesses should be spending (and not spending) their marketing dollars. From AEO and "robots tricking robots" to the return of direct mail and the truth about AI voice agents, David breaks down what's working, what's hype, and how to keep your business visible no matter how fast the algorithms change.

Cathy and Todd continue their summer blockbuster series and discuss Wedding Crashers, the 2005 comedy starring Vince Vaughn and Owen Wilson that grossed $288.5 million on a $40 million budget. They cover the cast including Rachel McAdams, Isla Fisher, Christopher Walken, Bradley Cooper, and Jane Seymour, plus behind-the-scenes trivia like the almost-cast Justin Long, the scrapped Daniel Craig sequel, and Owen Wilson’s improvised “Rules” of wedding crashing. They count down the movie’s best quotes and talk through the film’s male friendship theme. Subscribe to Cathy's weekly Zen Moment and upgrade to hear a weekly Zen Moment podcast hosted by Cathy and Todd! Some Ways to Support Us Sign up for Cathy's Substack Order Restoring our Girls Links shared in this episode: For the full show notes, visit zenpopparenting.com. This week's sponsor(s): Avid Co DuPage County Area Decorating, Painting, Remodeling by Avid Co includes kitchens, basements, bathrooms, flooring, tiling, fire and flood restoration. MenLiving – A virtual and in-person community of guys connecting deeply and living fully. No requirements, no creeds, no gurus, no judgements Todd Adams Life & Leadership Coaching for Guys Other Ways to Support Us Follow us on social media Instagram YouTube Facebook Buy and leave a review for Cathy’s Book Zen Parenting: Caring for Ourselves and Our Children in an Unpredictable World

The Florida Department of Environmental "Protection" refuses to investigate a Key West utility company that has been injecting waste water underground only to have it surface elsewhere.Everyone knows 1980s PBS TV painter Bob Ross, but most people don't associate him with Florida. Ross was born, raised, lived and died in Central Florida. The Bob Ross Art Workshop & Gallery in New Smyrna Beach is open to the public and carries on his legacy. Our guest this episode, Nicholas Hankins, carries on Ross' legacy through a new version of Ross' "Joy of Painting" TV show.Ross mostly painted woodland and mountain scenes, but he did paint Florida on the show one time.

Painting a boathouse turns out to have a lot in common with yoga: stretching, balance, and the patience of proper prep work. A meditation on ladders, primer, and the quiet satisfaction of a job done right.

In part one we talk about the RADICAL difference in the popular Army List Dispositions these last couple of weeks.  Purge the Foe was way at the top of the … Read More

This week, we discuss Ray being a cry baby about his new cubicle. Plus, we dive into the idea of bringing back the AUAA, where we're joined by our very own play-by-play guy, Dan Gardella!See omnystudio.com/listener for privacy information.

Before FasterHouse. Before FasterFreedom. Before any of the businesses Sam and Lucas run today — there was Budget Painting.Five summers. Two broke college kids. One ladder. And more near-death experiences than either of them care to admit.In this episode, Sam Primm and Lucas pull back the curtain on the business that started it all. Budget Painting was never supposed to be a real company. It was a way to make beer money, avoid a normal summer job, and have some flexibility during college. What they didn't realize at the time was that five summers of running a scrappy little painting business would teach them every single skill they now use to run a $50M+ real estate operation.

Cathy and Todd discuss the most recent summer blockbuster, The Odyssey, Christopher Nolan’s take on Homer’s epic and why a 2,700-year-old story about identity, home, and longing still hits just as hard today. From the Cyclops to the Sirens to Circe, they trace the myths and monsters and what those stories still reveal about who we are, and why the humanities matter to a thriving culture. It’s also a conversation about disguise and belonging, and why it’s so hard to come home when the choices of war change the world and leave shame behind. Subscribe to Cathy's weekly Zen Moment and upgrade to hear a weekly Zen Moment podcast hosted by Cathy and Todd! Some Ways to Support Us Sign up for Cathy's Substack Order Restoring our Girls Links shared in this episode: For the full show notes, visit zenpopparenting.com. This week's sponsor(s): Avid Co DuPage County Area Decorating, Painting, Remodeling by Avid Co includes kitchens, basements, bathrooms, flooring, tiling, fire and flood restoration. MenLiving – A virtual and in-person community of guys connecting deeply and living fully. No requirements, no creeds, no gurus, no judgements Todd Adams Life & Leadership Coaching for Guys Other Ways to Support Us Follow us on social media Instagram YouTube Facebook Buy and leave a review for Cathy’s Book Zen Parenting: Caring for Ourselves and Our Children in an Unpredictable World

Before FasterHouse. Before FasterFreedom. Before any of the businesses Sam and Lucas run today — there was Budget Painting.Five summers. Two broke college kids. One ladder. And more near-death experiences than either of them care to admit.In this episode, Sam Primm and Lucas pull back the curtain on the business that started it all. Budget Painting was never supposed to be a real company. It was a way to make beer money, avoid a normal summer job, and have some flexibility during college. What they didn't realize at the time was that five summers of running a scrappy little painting business would teach them every single skill they now use to run a $50M+ real estate operation.Freebies-Get our funding sources - www.fasterfreedomcapital.comLearn to flip real estate with a full time job - https://flip.fasterfreedom.com/live

Washington Wednesday on the Nixon revival, Britain's political reset, and America's 50 laboratories of democracy; World Tour looks at Cuba's communist system running on empty; and a portrait artist painting character. Plus, Albert Mohler on gender ideology in medical education, dishonor among thieves, and the Wednesday morning newsSupport The World and Everything in It today at wng.org/donateAdditional support comes from Equip by Unbound. Launching high schoolers through coaching, skills training, and interest-led projects. More at BeUnbound.us/worldFrom Ambassadors Impact Network. Christian entrepreneurs scaling their companies often struggle to find capital sources that respect, and even celebrate, their redemptive mission. Ambassadors Impact Network connects these founders with angel investors seeking both financial outcomes and spiritual fruit. If you're an investor wanting to steward your capital toward gospel-advancing companies, learn about membership at ambassadorsimpact.comAnd from Cedarville University. Located in southwest Ohio, Cedarville University is committed to biblical faithfulness and academic excellence, preparing students to serve Christ with conviction in every profession. Every one of its 175+ undergraduate and graduate programs are grounded in a biblical worldview, equipping graduates with the knowledge, skill, and conviction to serve wherever God leads. New online undergraduate degrees through Cedarville Online offer flexible, affordable education rooted in biblical truth and designed for today's learners. Learn more at cedarville.edu, and explore online programs at cedarville.edu/online