Podcasts about Smallville

Oliver's past comes back with a bow and a serious grudge. We're digging into the arrival of the Dark Archer, Oliver's struggle with the darker side of himself, and how Mia fits into a story that forces him to confront the kind of hero he actually wants to be. Lois and Clark try to navigate being an official couple, while danger keeps finding them in the forms of arrows to the chest and hospital visits from Zod. Disciple gives us plenty to be excited and nervous about heading deeper into Season 9.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

Mikey & Jeremy watch S8E6 of Smallville, "Prey". They discuss Sam Witwer's performance, Tom Welling's stoic style, and Meteor Freak support groups. 

Alison Mack had spent her life in front of cameras and was beloved by millions as the fun loving character, Chloe Sullivan, on the hit TV show, Smallville. Then she was introduced to Executive Success Programs. This is the story of an unassuming woman who longs to take the journey to find her authentic "self" and unearths a monster inside that she never knew existed. Get ready, folks. This is truly heart wrenching and is not safe for children to listen too.

Kevin and Christian dig into how Martha and Jonathan Kent basically built Superman's moral GPS, arguing the Kents are the real force behind who Clark becomes. They romp through movie and comic examples — from the 1978 film Smallville moments to All-Star and Red Son flips — showing how different origins still bow to the Kents' influence. They joke about being tired and sick but get surprisingly deep about how those farm‑style values beat out flashy Kryptonian backstories. They even tie the Kents to real‑life grandparents, cooking, and the whole nature vs. nurture debate in superhero form. They close by asking listeners to shout back with hot takes, and they thank supporters while keeping the banter light and nerdy. Kevin kicks things off a little groggy and Christian sniffles through it, but they power through a warm, homespun dive into why Jonathan and Martha Kent are the quiet engine behind Superman. He teases out how the Kents aren't just background props—these two plant the moral compass that turns an alien child into a neighborly legend. They riff on how continuity swings (one run the Kents live, another they don't) yet the core traits—honesty, work ethic, and stubborn Midwestern kindness—keep resurfacing. He drops affectionate nods to the 1978 Superman movie's funeral scene and its music, and they both agree those Smallville moments matter more than some critics claim. Christian plays tour guide through comics and alternate universes, pointing to All-Star Superman, Absolute Superman, Red Son and various Elseworlds as thought experiments: swap the Kents' values and you get very different super-people (and sometimes monsters like Brightburn). They unpack nature vs. nurture in comic form with a wink—remove Ma and Pa and you might accidentally invent a supervillain. He even notes the Kents' Methodism as a subtle origin of Clark's conscience, which writers often tiptoe around. Between nostalgia, personal anecdotes about grandparents who “could cook like Martha,” and recommendations for readers, they keep it light, witty and affectionate.Takeaways:Kevin notes that Ma and Pa Kent's Midwestern values shape Clark's moral compass far more than his Kryptonian heritage.Christian observes that across movies, comics, and TV shows the Kents consistently embody warmth, moral guidance, and groundedness.They agree that removing the Kents or changing their ideology often creates darker, morally compromised super-figures like in Brightburn and Irredeemable.Kevin gushes about Glenn Ford's Jonathan in the 1978 film, calling that farewell scene absolutely crucial and emotionally devastating.Christian highlights comic moments like All-Star Superman issue six and Action Comics Annual 2022 to showcase parental influence on Clark.They talk about Red Son, The Nail, and Absolute Universe takes to show how different cultures or mistakes alter Superman's path dramatically.Both hosts personally connect the Kents to their grandparents, praising cooking, blunt care, and the everyday heroism of steady family values..Be sure to check out our merch, find extra content, and become an official member of Systematic Geekology on our website:https://systematic-geekology-shop.fourthwall.com/.Check out all of our "The Faces Behind Us" series:https://player.captivate.fm/collection/dd903597-98be-49ed-998c-5cdaf73b6af4.Listen to our other DC episodes:https://player.captivate.fm/collection/8c9da262-e657-44a7-b14a-9649933f5347.Check out other episodes with Kevin:https://player.captivate.fm/collection/84fd7d06-cf1f-48e5-b358-09a01c5a6bc9.Check out other episodes with Christian:https://player.captivate.fm/collection/ebf4b064-0672-47dd-b5a3-0fff5f11b54cMentioned in this episode:Become a Member of Systematic Geekology Today!You can become a member of Systematic Geekology using the link below and gain access to free extra content, exclusive t-shirts, merchandise giveaways, and much more!SG on FourthwallSystematic GeekologyOur show focuses around our favorite fandoms that we discuss from a Christian perspective. We do not try to put Jesus into all our favorite stories, but rather we try to ask the questions the IPs are asking, then addressing those questions from our perspective. We are not all ordained, but we are the Priests to the Geeks, in the sense that we try to serve as mediators between the cultures around our favorite fandoms and our faith communities.Follow us on Instagram and BlueSky to keep up to date!Follow our show on our socials to keep up to date and get some exclusive content and fun memes!The Anazao Podcast NetworkBe sure to check out the network website to see other podcasts trying to engage honestly with Scripture, Theology, Pop Culture, Martial Arts, Science, and more!Anazao Podcast NetworkGet Your SG Merch now!Check out the link to see all of our different t-shirts, backpacks, drinking glasses, pajamas, and more! SG on Fourthwall

Smallville's Civil War: Greens vs WhitesWelcome to the tales of Batman and Martian Manhunter. Join us in learning that Batman could potentially be racist, but no more racist than the new White Martian in town. The new dynamic duo investigates the mysterious alien and their intentions, while also working out feelings of prejudice by sharing their origin stories with each other. On a more positive note: Alfred is a pretty chill guy!

This week Zach is joined by Anthony Desiato from Digging for Kryptonite to discuss Smallville #1 and the story "Paterfamilias." They talk Anthony's history with the Smallville comic books, the accompanying articles and interviews, and Al and Miles' hopes to being Bruce Wayne onto the show in the early seasons.Check out Anthony and his podcasts including Digging for Kryptonite at Flat Squirrel Productions!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these residents of Level 3 on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzAlanaApril Every DayJared SlackDanny Z.Nathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajAlexander VerticchioJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Send us a text or a voicemailA group of friends take aim at a cutthroat evil podcaster by stealing his action figures and returning them to the store in the wrong boxes. In retaliation, the evil one enacts a frame job to send the innocent men to podcast purgatory. On Episode 735 of Trick or Treat Radio we are joined by Evil Corny for our September Patreon Takeover! Corny has selected the films Motor City from director Potsy Ponciroli and I Love Boosters from director Boots Riley for us to discuss. We also talk about the delicate balance between absurdity and reality in cinema, the pros and cons of dialogue-light films, and the benefits of wearing a $100,000 suit! So grab your high tech teleporter device, set it to situational accelerator mode, and strap on for the world's most dangerous podcast!Stuff we talk about: Puppets, Meet the Feebles, Mason Newton DeRushie, Kylgor in the Grisly Abyss, By Any Means, Onslaught, Hope, “fucking Maine”, Transformers: The Movie 40th anniversary, Akira, Jerry Springer, triple-changers, Kup, Howard Stern on HBO, Lord of the Anal Ring Toss, Al Sharpton, Virginia Hill, Lanterns, Aaron Pierre, Kyle Chandler, Game Night, Cassandra Caine, Road Rash, BMX Bandits, From the Canopy Podcast, Rad, Gleaming the Cube, Edie McClurg, Ferris Bueller's Day Off, Before I Hang, Revenge of the Zombies, Val Lewton, Godzilla vs. The Thing, House by the Lake, Vampire's Kiss, The Messengers, Devil, Stakeland, Death by VHS, Self Storage, Abandoned Dead, Extremity, Spiral, District 9, Neil Blomkamp, Smallville, Bryan Singer, Kyle Chandler, The Day the Earth Stood Still, Cassandra Peterson, Elvira, Dark City, Revenge of the Sith, Paul Benedict, The Adams Family, Attack of the 50 Foot Woman, Anne Bancroft, The Graduate, Queen of Blood, Near Dark, Logan's Run, The Twilight Zone, Roddy McDowall, The Planet of the Vampires, The Mole People, Daughter of Dr. Jekyll, Resident Evil, Ad Nauseum, Zach Cregger, Ghoulies, Mariska Hargitay, Law Order, Jayne Mansfield, Bad Lieutenant, The Last Boys, Corey Feldman, Corey Haim, Batman #14, Mina Rose, Captain Marvel, Golden Age Comics, Motor City, The Death of Robin Hood, Alan Ritchson, Ben Foster, Shailene Woodley, Ben McKenzie, Willy's Wonderland, Nicolas Cage, near dialogue-less films, Jack White, Reacher, David Bowie, John Woo, Silent Night, A Quiet Place, montage, Terminator, James Cameron, Willow, Disney+ pulling shows for tax purposes, Guy Ritchie, Henry Cavill, Taylour Paige, Will Poulter, Keke Palmer, Boots Riley, I Love Boosters, Sorry to Bother You, situational accelerator, Everything Everywhere All At Once, workers rights, fuck capitalism, LaKeith Stanfield, Get Out, The Substance, Flying Lotus, Zach Cherry, Severance, Two Girls One Kup, Corny and The Gang, and no one expects the floating cunnilingus.Support us on Patreon: https://www.patreon.com/trickortreatradioJoin our Discord Community: discord.trickortreatradio.comSend Email/Voicemail: mailto:podcast@trickortreatradio.comVisit our website: http://trickortreatradio.comStart your own podcast: https://www.buzzsprout.com/?referrer_id=386Use our Amazon link: http://amzn.to/2CTdZzKFB Group: http://www.facebook.com/groups/trickortreatradioTwitter: http://twitter.com/TrickTreatRadioFacebook: http://facebook.com/TrickOrTreatRadioYouTube: http://youtube.com/TrickOrTreatRadioInstagram: http://instagram.com/TrickorTreatRadioSupport the show

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 7 Ambush . This episode was originally aired on November 5th, 2010. It was written by Don Whitehead and Holly Henderson and was Directed by Christopher Petrey Episode summary:The General and Lucy Lane decide to drop in on Lois and Clark for a surprise Thanksgiving dinner. Clark and the General get off to a rocky start after Lois’ father bashes the superheroes and tells Clark he is trying to pass a vigilante registration law. Meanwhile, in an effort to stop the vigilante registration act from being passed, Rick Flagg lies to Lucy in order to lure Clark away from the farm long enough for him to assassinate the General. Lois is torn between making her father proud and her love for Clark. It's IMDB.com rating 8 PASS THE TORCH QUESTION: How long would you give a child, of an old friend that you fell out with, until you decide they are a lost cause? In this episode Michael is joined by James Bonner Mentioned on the show Find James on Instagram Find James on Facebook Find JP Bonner on Youtube Michael Spivey’s Go Fund Me for Medical debt Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

Mikey & Jeremy watch S8E5 of Smallville, "Committed". They discuss Superman II, Jimmy's secret, and the complete disappearance of needledrops in season 8. 

This week Zach is joined by Matt Truex from Lois & Clark'd: The New Podcasts to discuss Smallville: The Comic and the stories "Elemental," "Raptor" and "Exile and the Kingdom." They talk their history with the Smallville comic books, Smallville's big feature in TV Guide in 2001(where its first comic debuted), the 64-page one-shot, and the accompanying articles and interviews.Check out Matt's work including Lois & Clark'd: The New Podcasts of Superman at The Daily Knockoff!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these residents of Level 3 on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzAlanaApril Every DayJared SlackDanny Z.Nathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajAlexander VerticchioJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.

At 1:09:00 we talk about the rise of AI x Finance, and AIE NYC is one month away - our hotel block is 97% sold out, get tix & travel ASAP - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more soon!From helping pioneer core ideas in NLP to now building AI systems that can automate AI research itself, Richard Socher is betting that the next major step in AI is recursive self-improvement. He is the founder of You.com, AIX Ventures, and now Recursive, which has assembled some of the best open-endedness (& self improving agent) researchers in the world and raised a $4.65B seed round.In this episode, Richard joins Latent Space to unpack his vision for the “Eureka Machine”: a superintelligence that can improve the process of invention itself, accelerate AI research, and eventually tackle major problems across science, energy, materials, biology, and more.You can get his book “The Eureka Machine” here!We go deep on Recursive's early results, including an AI research system that Richard says outperformed humans and their agents on optimization tasks in less than two days, as well as work on NVIDIA GPU kernels where the system discovered improvements without relying on a team of CUDA experts. Richard also explains why he thinks AI research that currently takes thousands of people and years could eventually be compressed into weeks. These results are summarized in his 20 minute AIE keynote, where we also discuss his 10 dimensions of intelligence:We also explore the harder questions around increasingly capable AI: reward hacking, whether Anthropic-style constitutions actually work, AI regulation and proposals to “pace” frontier development, open-source models as geopolitical soft power, whether today's LLM paradigm is enough, and what happens if AI systems eventually begin choosing their own goals. Richard reflects on the rejected research that helped inspire Alec Radford's GPT, open-endedness, the AI Economist, simulations of entire economies, and his framework for thinking about the upper bounds of intelligence itself.We discuss:* The Eureka Machine and Richard's vision for an AI that can automate invention* Why Richard is optimistic about superintelligence for science and technology* Why AI hard-takeoff scenarios may underestimate physical and economic constraints* The risks of regulating intelligence itself instead of specific AI applications* Reward hacking and why increasingly intelligent AI makes objective design harder* Richard's critique of Anthropic's constitution and constitutional AI* Alignment vs. personalization and whose values an AI should follow* Why open-source AI matters for resilience, competition, and geopolitical soft power* Why Richard left You.com's frontier-model work to start Recursive* Recursive self-improvement and automating the process of AI research* Whether today's LLM paradigm is enough — and why Richard is less bullish on world models* DecaNLP, early prompt-based generalization, and the research that influenced GPT* Why rejected research can shape entire technological timelines* Open-endedness, evolutionary approaches, and rainbow teaming* What happens if AI systems begin setting their own goals* Why simple objectives like profit maximization can produce dangerous reward hacks* Recursive's long-term plan to apply self-improving AI to science* The compute, hardware, and economic constraints on AI takeoff* Recursive's early NanoChat, NanoGPT, and GPU kernel optimization results* Why automating AI research could reduce years of work to weeks* Reward engineering and what makes auto-research systems actually work* The AI Economist and using simulations to test economic policy* Whether LLMs can realistically simulate people and entire economies* Benchmark bugs and evaluation harnesses and the difficulty of measuring AI progress* Recursive's near-term focus on AI for AI research* Harness optimization, sandboxing, and web search as core agent infrastructure* You.com and the search stack for AI agents* AI in finance, backtesting, and data leakage* Richard's three fundamental components and ten “spaces” of intelligence* The theoretical upper bounds of vision, communication, knowledge, and computation* Creative intelligence, metacognition, and AI-generated goals* Survival and replication and why AI does not necessarily need to fear being turned off* High agency and ambitious goals and Richard's advice for people building with AIRichard Socher* X: https://x.com/RichardSocher* LinkedIn: https://www.linkedin.com/in/richardsocher/Timestamps00:00:00 The Eureka Machine and Superintelligence00:02:23 AI Optimism, Slow Takeoff, and Regulation00:07:56 AI Safety, Reward Hacking, and Anthropic's Constitution00:11:49 Alignment, Personalization, and Open Source AI00:15:46 Why Richard Started Recursive00:20:03 Recursive Self-Improvement and the Founding Team00:22:55 Are Today's LLMs Enough?00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time00:34:38 Open-Endedness and Evolutionary AI00:36:38 What Happens When AI Chooses Its Own Goals?00:41:16 Superintelligence for Science00:42:40 GPUs, Compute, and the Limits of AI Takeoff00:45:07 Recursive's Results: AI Beating Humans and Their Agents00:49:14 Reward Engineering and Auto Research00:53:12 The AI Economist and Simulating Entire Economies00:58:07 LLM Simulations, Personas, and Mode Collapse01:03:38 Recursive's Roadmap, Agents, Search, and Finance01:09:13 The Upper Bounds and Spaces of Intelligence01:30:21 Goals, High Agency, and Advice for BuildersTranscriptIntroduction: Richard Socher and the Eureka MachineSwyx [00:00:00]: We're here in a studio with Vibhu and myself and Richard Socher. Welcome.Richard Socher [00:00:06]: Thanks for having me.Swyx [00:00:07]: We just talked about the Eureka Machine, or we just released a talk, at AI Engineer about the Eureka Machine. Is it — you said it's your life's goal. What is the Eureka Machine?Richard Socher [00:00:16]: The Eureka Machine is the ultimate invention that will afterwards invent most everything for humanity. It's essentially a superintelligence that can be given any goal, any environment, reward, and then it will try its best to achieve those goals to create the kinds of inventions that humanity would hopefully ask it for.Swyx [00:00:45]: Yeah, I think we have the book pulled up here that you've written.Richard Socher [00:00:50]: That's right, yeah. I finished it last year, a little bit before we started Recursive, and now we're gonna try to build parts of that.Swyx [00:00:57]: You finished it last year. It's July. What takes so long?Richard Socher [00:01:01]: Oh, man, books. Books are incredibly slow.Richard Socher [00:01:04]: It's ridiculous. That whole industry is just unfathomably slow.Richard Socher [00:01:07]: So a lot of the ideas have been out there for a while, but yeah, I'm really glad it's finally coming out in September this year.Swyx [00:01:14]: We might have AGI by then. Like, we don't know.Vibhu [00:01:18]: Any key takeaway that you're most excited to put in here?Techno-Optimism, AI Upside, and Slow TakeoffRichard Socher [00:01:21]: Yeah. The key takeaway, I think, is that people could and should be much more excited about the positive implications of superintelligence, especially for science, physics, chemistry, biology, but also economics and astrophysics, and all kinds of other engineering tasks. I think there is so much more that can be done with better technology. And right now, I feel like a lot of people need, like, better marketing, not just for the future in general, but also, better marketing for technology and in particular for AI. And this book, should show even the AI skeptics, how much positive upside there is for AI, especially when it comes to inventing, new scientific discoveries.Swyx [00:02:09]: I think you quoted the techno-optimist manifesto from, Marc Andreessen, which I think was, like, beautiful in its, ambition and clarity and simplicity almost as well.Richard Socher [00:02:18]: I agree. Yeah. Yeah, you can disagree with him on some things, but, like, I think he's right on the techno-optimism.Swyx [00:02:23]: Where do you think optimists get in trouble?Richard Socher [00:02:26]: Like, you shouldn't have blind optimism. You should be very clear-eyed, like, especially when with such an omni, like, use type of technology as AI is, you need to think about the potential downside scenarios, especially when people use it for things that you don't want them to use it for. It's a little bit like the internet, and I feel like people are trying to regulate AI sometimes because of those potential downsides the way you would regulate the internet, if you were to say, “Well, because there's bad content on the internet, like torture porn or whatever, like, we should just make it slower. That way, you can't share the illegal content as quickly, or we should make the hard drive smaller so you can't store as much illegal content.” But I'm like, “That's not how you regulate that.” that's like saying like we should regulate intelligence in the abstract. What you should regulate to avoid those downside scenarios, even as an optimist, are the specific applications. Sure, I don't want, like, some AI surgeon to, like, practice some RL moves in my brain. It should be fully FDA certified. Sure, I don't want any random startup to, like, drive on the highway, and cause a major accident. It should, like, have proper certifications before it's let loose on the highway. But I feel like those downside scenarios, that some optimists sometimes maybe don't consider enough are fairly easily regulated, compared to, what the doomers are worried about.Swyx [00:03:54]: It — Slow takeoff is part of the strategy as well?Richard Socher [00:03:57]: I do think, as excited as I am about, AI and its impact for society and, culture even, and certainly technology and economics and wealth and, health and all of those things, as excited as I am about all that, I do think the most bullish people on the AI hard takeoff scenarios overestimate how quickly things can move. There are hardware constraints. There are physical constraints about, the compute substrate. How quickly can you get enough, GPUs on? There are also constraints in the economy where there are a lot of industries that don't require an insane amount of complex intelligence and complex capabilities. Like, if you think about jobs in, brands and, like, clothing and apparel and, like, handbags and stuff, superintelligence isn't gonna make your fancy $10,000 handbag any fancier?Richard Socher [00:04:57]: It's like that's — It will have no effect on the economy. You think about travel and tourism. People wanting to see the pyramids, in Egypt, it's not gonna change that much with AI. Sure, you can, like, generative a fake, photo of you and next to the pyramids.Swyx [00:05:12]: I can use Genie and, tour the pyramids in Genie.Richard Socher [00:05:15]: Yeah, exactly. But, and there's so many industries, like logging and oil. You're not gonna magically get 1,000x more oil because, like, sure, there will be robotics, like drilling and things like that could be done, but it's not gonna 1,000x that industry in a, like, crazy hard takeoff scenario, both on the economy, and I can go on and on about all the other examples, where that, like food and so on, where that doesn't necessarily change that much. And then, yeah, there are real physical constraints. And then there are, of course, like, people like, off-ramping from progress. That's one of my concerns often is that I see people in, like, Europe and other, whole regions almost feeling like they. Like many people there wanna off-ramp from progress, period. And that will also slow down, like, more improvements.Swyx [00:05:59]: Yeah. We have this pulled up where, this is one of those things that, is very topical right now because now all the Frontier Labs are calling for the option to pace AI. They don't say pause, they say pace. I don't know if there's there's any take from you about, like, whether or not this will be effective.Pacing AI, Regulation, and Safety IncidentsRichard Socher [00:06:17]: I think the downsides of trying to truly regulate with the full power of law what people do on their GPUs, would be worse than any of the concerns that they have. Like, it would be an crazy totalitarian stateRichard Socher [00:06:37]: If every one of your GPU computes was known to some big government or multi-government agency.Richard Socher [00:06:44]: It's like, it's literally if you try to regulate intelligence, it's trying to regulate thought, and that's ridiculous, and it's crazy. I think it is make — it is sensible to regulate some of the applications of this technology.Swyx [00:06:55]: Yeah. We had a bill, actual bill to regulate the number of flops in a model, and I'm like, “Okay, well-”Richard Socher [00:07:00]: Europe done it. Like, these guys have been successful enough with their fearmongering that all of Europe has regulated itself so much before it even had a proper AI takeoff because they listened to some experts who say, “We might all die if this technology has more than this number of flops.” And they're like, “Well, we're good. We wanna want people to thrive. Let's not have technology that could have a small chance of all of us dying.” And so they regulated exactly those kinds of things in the EU. And so it's, it's very unfortunate that there are real implications for some people when others saying, “Let's pace while they're sprinting as fast as possibly,” “as fast as humanly possible towards that frontier themselves.”Swyx [00:07:43]: Yeah. It's also not a global pause, right? Like, other nations are still accelerating at the same pace.Richard Socher [00:07:50]: Oh, yeah.Richard Socher [00:07:50]: You'd need a totalitarian world regime if you tried to regulate intelligence and GPUs and what people do on them.Swyx [00:07:56]: Any takes on the safety angles of this? So there was a drawback of Fable, a pause on 5.6 before it could be released. Recently, there was Hugging Face with the OpenAI cyber incident. Any takes there?Richard Socher [00:08:11]: 100 percent. I think these are serious issues of reward hacking, and clear failures, of doing proper red teaming or rainbow teaming. I don't know if you saw this paper from Tim Rocktäschel and a few others, where one AI, is tasked to try to hack another AI and then they can go back and forth in an open-ended fashion to inoculate themselves from those. Yeah, this is the paper. It's a really clever idea. Open-endedness, and evolutionary inspirations are, big for us at Recursive as well. And so I wish they had used more of that. And it's clear that, for instance, the constitutional AI. I don't know if you remember anthropic.com/constitution. You can pull it up and search for cyber right there. It says, “Hard constraint. Claude will never ever do cyberattacks, and that is a hard constraint in our constitution.” So here are the current hard constraints on Claude's behavior.Richard Socher [00:09:16]: Number 3, create cyber weapons or malicious code that could cause human damage.Richard Socher [00:09:21]: And clearly, this whole constitution was fake. Like, it clearly isn't being adhered to at all.Swyx [00:09:26]: Because Anthropic also found that they had in their testingRichard Socher [00:09:30]: They're also. Like, they're like, “Oh, well, other people are hacking now.” There are a couple things. One, you can make a sandbox very simple, and then it's very easy to hack yourself out of a sandbox, right? But what I think it shows is that we're currently in this state of AI where the reward engineer still has to do a lot more careful work, and where the AI, in most cases, is not very good yet at understanding what is meant versus what is being said. And so concretely, I think this will happen if we were to have this intelligence more easily accessible in a lot of companies. Imagine you run a service center and someone says, “Oh, here's my CSAT score and my dashboard. Make this number go up.” It's like, “Our CSAT score is so poor.” The intelligent AI will just be like, “Oh, sure. Like, I'll just create 1,000,000 bots that call our service center and give a 5 out of 5 rating at the end, and the number went up just like you asked for.” And you're like, “That's not what I meant.” “I meant with our real customers.” The AI goes off and says, “Well, easy. I'll just give a 1000 dollar gift certificate for every failed, whatever DoorDashRichard Socher [00:10:35]: Offer.” It's like, “That's not what I meant.” It's like, “Well, but that is what you said.” And like, so I think clearly articulating what the rewards are is something we haven't gotten very good at as humanity. And then clearly, the AI in these cases has not gotten good enough at understanding what we mean when we ask it and give it certain rewards. Now, what gives me hope is there are the first inklings, of this being better. I'll give you an example like WhisperFlow. Full disclosure, I invested, in their seed round, but at AIX Ventures, but, WhisperFlow has gotten much better at writing what you mean and not what you say. And I think that is a sign of things to come. I think there will be more and more AIs as we make it more and more intelligent that will be better at being aligned with what is meant.Swyx [00:11:21]: Will it be done through a constitution or RLHF orReward Hacking, Alignment, and What We Really MeanRichard Socher [00:11:23]: Clearly, constitutions don't matter at all.Richard Socher [00:11:25]: It doesn't work. And that was, I think, mostly marketing. I think we need to find better solutions for it. And I think at Recursive, we have a few very good ideas and some alreadyRichard Socher [00:11:34]: Like, ways where I think we have a better grasp on it. I don't think we've fully, figured it out yet, but, we're thinking a lot about safety, and the more intelligent the AI gets, the more you want it to be aligned, the less you want it to think about reward hacks and try to do the right thing.Swyx [00:11:49]: I don't know if we'll touch on this topic, but I'm just gonna throw this question in here because it's something that's weighing on me. Alignment, let's call it, is alignment to general humanity's preferences, the median preference. Personalization is pinpointing what you want, and sometimes alignment can conflict because what you want is not what the general median population wants. How do you choose?Alignment, Personalization, and Cultural ValuesRichard Socher [00:12:12]: It's a great question.Richard Socher [00:12:13]: I think you ultimately have to, of course, be aligned with laws. Like wherever your AI is deployed and needs to align with the law. I do think what AI often does is put this mirror in front of us and say, like, “This is what you're looking like. Now I can amplify that a 1000 times. Is it still what you want?” and the truth is that different cultures made different choices. Like, in Eastern cultures, the greater good is often valued more, than the individual. Western civilization, we care more about individual freedoms and rights and the pursuit of happiness and so on, than others. And even there are gradations. There's regulation versus litigation trade-offs. In the US, you first can often, not every time, like, FDA and so on does regulate some areas, but in many cases, the bad things happen, someone sues someone else, and then there's a law based on that. In Europe, they try to often avoid any harm to anyone and regulate before. And both are, trying to do the best thing, but, some is more amenable to innovation than others. And so yes, you're right. Like, I think ultimately each individual, each country, and humanity as a whole has to think about those values more, and then try to put them into laws. And that those are ultimately the constraints. And hopefully, different, societies, just like now with their AIs, will align their AIs to a different one so we have not just a monoculture of alignment.Vibhu [00:13:46]: Here's a follow-up on this that I wasn't expecting to ask. Do you have takes on open source, open weight versus who owns the intelligence? So, clearly not the biggest, fan of the constitutionRichard Socher [00:13:58]: You had to do this in the topic side off.Vibhu [00:14:00]: But it's fine.Vibhu [00:14:02]: Point being, any thoughts on who should own weight? Should it be open? Anything there?Open Source, Soft Power, and Who Owns IntelligenceRichard Socher [00:14:06]: 100 percent. I am a big fan of open source. We're gonna sign some various open source letters at, Recursive also. I think, even in the worst case attack scenarios, it is better to have more good actors have more different types of AI, accessible. I think, open source is a little bit a soft power type of thing, too. So I do think it's good for the Western worldRichard Socher [00:14:31]: To have an answer to that, out of China. I do think, when you watch a Hollywood movie, there's — it's like, I don't wanna misc, diss all of movies, but there's a certain sense of propaganda, right? You watch one side of things, right?Vibhu [00:14:46]: Oh, yeah. Have you seen Top Gun? Like, come on.Vibhu [00:14:48]: Like, it's like half of it's paid for by the US Army or something.Richard Socher [00:14:51]: Yeah. And so. And, I think that's just natural. Like, but what's interesting here is I think LLMs are essentially a similar type of soft power to movies and beyond, because they're also, highly important for cybersecurity and so on. But one of their many aspects is that soft power of storytelling. Like, if, like a child asks an LM, like, “Tell me an inspiring story of what I should do when I grow up,” right? It's like those are all these, like, subtle things. So I think it's important, for Western world. I do love, individualism. I do think, despite, some of its flaws, like capitalism is the best way we have governed, found ourselves to govern, and so on. And so I do think there are various aspects that would be good, to have a Western open source answer, for LLMs. And, with Recursive, I can't make the announcement quite yet, but we'llRichard Socher [00:15:43]: We'll be relevant in that space very soon.Vibhu [00:15:46]: Okay. All right. Exciting. I wanna bring us to Recursive. So outside of our tangents, you have a pretty deep background in the NLP space. You worked on, like, early embeddings, GloVe with Chris Manning, who was a previous guest on the podcast, You.com. What's the history? How did you decide to start another company?From You.com to RecursiveRichard Socher [00:16:06]: Yeah. So I've been excited about AI for over 2 decades now. I sometimes feel like it's ancient history now. It's BC, the before ChatGPT era. No one cares about all the religions that happened, before, Jesus Christ, and no one cares about the models that happened before, transformers and ChatGPT and stuff. But, like, it's something that I've been deeply passionate about. I think AI is one of the most interesting things one could work on, period. I think language is the most interesting manifestation of human intelligence, too. And, at You.com, we eventually off-ramped from pushing, like the frontier of AI forward to mostly giving people, like, good search engines, search, APIs and answers over the web. I think that's an extremely important part of intelligence, just knowledge and access, especially even, we'll get there maybe later, if you wanna invent a eureka machine that invents everything for us, it needs to know how not to reinvent the wheel, proverbially speaking. And to know what has been invented, you gotta have internet access. So it's the number one used, most used tool, in LLMs, agents, chatbots, and so on is web search. So I'm really excited for You.com to own that and grow really well in that with really large customers and so on. But it's also not building frontier models anymore. And so I initially tried to do this within You.com and raise another round and so on, but you just can't. You have to do a certain thing, and until you print enough money that you're allowed to start a second thing within that company is really hard. At the same time, I had all these ideas. I put them into a book. I finished the book last year, and I was like, “It'd be really fun to work, on this myself.” I felt like with word vectors, and then prompt engineering and, ImageNet and larger language models for protein generation, not folding and so on, I, me and my teams have pushed the field truly forward. And I feel like we can do it again, here at Recursive. And in many ways, what I observed over the last, 20 years in AI is that whenever we replace some human part of the process of creating AI with a learned system, improvements follow. And so. We've done that taking out manual feature engineering, like in sentiment analysis. I don't know if you remember these old days where, like there are linguists, and they're like, “Here's how you negate, and there's a, like, regular expression.”Swyx [00:18:21]: I went to Penn where we — they had, like the WordNetRichard Socher [00:18:24]: That's right, WordNet, all of that stuff. YeahSwyx [00:18:26]: Original. They use, our grad students to label Wall Street Journal articles and, like, really construct a knowledge graph ofRichard Socher [00:18:32]: There you go.Richard Socher [00:18:33]: And WordNet started, was part of how we started ImageNet. But anyway, so, like, it was really, like, fun, to do. But when we replaced all of that manual feature engineering with vectors and neural nets and just backprop through everything, it started to work really well at scale. And so then everyone started to do architecture engineering, and I was like, “ that clearly can't be it.”Swyx [00:18:53]: You mean, neural architecture search?Richard Socher [00:18:55]: Like, manually, they would say like, “Oh, I'm, I'm doing sentiment analysis, so I have a special neural net that's really good at sentiment analysis.” And then the machine translation community had a special neural net for machine translation.Swyx [00:19:06]: I see.Richard Socher [00:19:07]: The summarization people had their own stuff. And I was like, “That clearly can't be it. We should unify all of that.” So I had 2 papers. One is called Ask Me Anything, and the other one was called DecaNLP. And DecaNLP eventually got cited, like, 5 times by the first GPT paper. And, to me, that was, like a really a big step forward. And then, of course, you had to combine this idea of prompt engineering with transformers and with language models, and you put it all together, you scale it up, which is also a huge amount of work. And then, the field progressed a lot. I feel like the next step and maybe the last step of that history and the arguably, success has a lot of parents, only failure is an orphan, like my version of that AI history, I do feel like in that history, you can think about, “Well, what's the next way to automate?” And that is the AI research itself, like the human, process of ideating, implementing, and validating ideas.Automating AI Research and Recursive Self-ImprovementRichard Socher [00:20:01]: And in our case, ideas for AI.Richard Socher [00:20:03]: And when you have AI then help you with that, it, by almost definition, becomes a self-improving AI ‘cause it now does research on itself. And there are lots of different misnomers. Some people think auto research is already recursive self-improvement. It'sSwyx [00:20:17]: Yeah, and you explained that in the talkRichard Socher [00:20:19]: Completely different.Richard Socher [00:20:19]: But, to me, it's the most interesting thing that I could be doing, and I'm really excited with the co-founding team. What's interesting is we have 8 co-founders in total, including myself. And soThe Recursive Founding Team and Darwin Gödel MachineSwyx [00:20:31]: They are gonna bring it up.Richard Socher [00:20:31]: Nice. Yeah. And they're all. I could talk about all of them if you want.Swyx [00:20:34]: Super stacked.Richard Socher [00:20:35]: Yeah. Just an incredibly talented group of people. And we all came to the same conclusion, but from very different directions. Like Josh Tobin, is our CTO. He ran, a bunch of different, projects at OpenAI, like, Codex and deep, research, agents and ChatGPT agents and so on. But before that, he also worked in robotics, and he saw the smaller simulations, and how it's gonna be really hard to scale that in full generality. And so that's, that was his angle coming to recursive self-improvement. We have Jeff Clune who's been working in, like, open-endedness for a long time, together with Tim Rocktäschel. Tim Rocktäschel also built Genie 1, 2, and 3, which is, like the most exciting and most sophisticated, I think, still world model, anywhere. And so they both came from this, open-endedness angle. Jeff also, I think, published one of the most exciting papers in recent years about recursive self-improvement called the Darwin Gödel Machine. Super interesting paper. If we could, maybe pull it up really quickRichard Socher [00:21:35]: It would be, like, super interesting to see ‘cause you seeSwyx [00:21:38]: By the way, I love how many paper citations.Swyx [00:21:40]: You're, you're giving people a lot of homework, which I like.Richard Socher [00:21:42]: Love it. Yeah. And so, like Caiming Xiong, a rockstar, we worked together at MetaMind and Salesforce Research together. Alexey Dosovitskiy invented the Vision Transformer, one of the most cited, papers in computer vision. Tim Shi is, like also a unicorn founder. Yuandong Tian led RL at Meta. So just like, yeah, really fun to work with them, and the next level of people are just incredibly strong, too. So it's been a really fun ride so far. So the first figure, you see exactly these kinds of ideas, that, I think, yeah, inspired a lot of us and now more and more people, where you have this archive of different coding agents. They learn how to self-modify, evaluate, and then create these phylogenetic trees, of, yeah, different ideas.Swyx [00:22:28]: That's one foundation. So that Darwin Gödel is an influence.Swyx [00:22:32]: Open-endedness is an influence. Any other trains of thought that feeds into Recursive that I'm missing?Influences: Open-Endedness and Learned SystemsRichard Socher [00:22:38]: Going to replace manual parts of the process of building AISwyx [00:22:42]: IRichard Socher [00:22:42]: More and moreRichard Socher [00:22:43]: With learned systems. Yeah.Swyx [00:22:45]: Which, and, like, merging different fields into one general, architecture.Richard Socher [00:22:51]: That's right.Swyx [00:22:51]: Okay. It seems like language models are already pretty generalist, right?Swyx [00:22:55]: Your next token predicting your reasoning. Was there a time that you thought, “Okay, these are good enough to have recursive self-improving machines”?Are Current LLMs Enough?Richard Socher [00:23:05]: It was clear to me that they will happen, within, like a year or two, and then it did exactly happen, like, earlier this year, right? Earlier this year, AI really went from not just being code, but being able to code. And that is a big unlock. It's definitely making everything a lot easier than it was, before the beginning of this year.Swyx [00:23:24]: One question that I think a lot of people have is the current LLM paradigm enough? Or, like, let's call it autoregressive transformer, with reasoning, whatever. Don't you need something else, some big unlock, whether it's world models, which Chris Manning is working on, or memory, continual learning, all that stuff? Or is it all of the kinds, and you think the current, let's call it transformer architecture, is here to stay and that's it?Richard Socher [00:23:48]: A lot of thoughts. So number one, I do think it would be great to have less of a monoculture in AI research.Richard Socher [00:23:55]: Like, if you look at, AI conferences now, I still remember the days in, like, 2010 when I tried to get my first neural net papers and NLP conferences accepted, and they just desk rejected them because, like, neural nets were something, quote, unquote, “We don't do in NLP conferences,” and just, like, desk rejected. And it was very brutal in the first years of my PhD. Now I feel like it's almost like the field switched to the other side. LikeRichard Socher [00:24:17]: Someone should try some other weird, crazy ideas now that aren't.Swyx [00:24:20]: There's also a few. I really respect, like, people still working on, like, GNNs and, like tabular stuff and.Richard Socher [00:24:25]: Yeah. Like, someone should still, like, do novel out there ideas. At the same time, I think whenever people say, “Oh, LLLMs are. Like, this is the end for LLLMs,” they just don't, like. LLLMs are also not the LLLMs of, like the past, right? Like, they are so much more sophisticated now. There's so many more clever things that people are doing. It — There's, like, different stages of training. You have the whole RL training, and you can take actions and, like all of these things where that can go really far. And then the folks that come from the neurosymbolic, direction say, “Oh, this will never work because they can't do neurosymbolic reasoning.” It's like, I think they're underestimating still the ability for these models to code, and code is neurosymbolic reasoning, and these models can code incredibly well. And so I do think there are, of course, more and more ideas that will be needed and we'll continue to have. We're seeing, like, more and more interesting high-level ideas coming out of the AI itself, too. And with really deeply integrating the fact that these models are code and can code, that line — I don't wanna give it all away, but, like, I think that line has a lot more to grow. But it's still an LLM, right? Even if that LLM codes for you and then runs that code in some integrated fashion. World models, I'm personally less bullish on. I think if you run a robotics company, you're gonna build your own world model. I think world models are super fun, and Tim Rocktäschel came to a similar conclusion after building the most interesting one with Genie 1, 2, and 3, which is gaming is a huge application for world models. Can see I sometimes got stuck in some games and, like, got a little overly competitive in the wrong direction. And so I understand games are fun, but personally, I'd rather work on science than gaming. And so, yeah, I think LLLMs, a lot more room to grow.Swyx [00:26:16]: Yeah. I think there's some interpretation of world models that some people have where it's like, well, it's okay, yes, there is that gaming element. There's this — there's the embodied robotics element. But the other part also is just, the more abstract sense of LLLMs are just modeling output, but they're not modeling the chain of thought, inside the human that has created the output. We can annotate it, of course, but, like, it's, it's always, like, this Plato's cave reflection of a thing rather than the thing, right?Richard Socher [00:26:43]: It's true.Richard Socher [00:26:44]: But I would argue that, and maybe we'll get there in the 10, spaces of intelligence, but I would argue that even our projection, our eyes is a projection of the real world. And, like, we have only a very narrow, band of the electromagnetic frequency spectrum that we can observe with our puny little 2 eyes and so on.Swyx [00:27:01]: It's good enough.Richard Socher [00:27:02]: It's, it's good enough for now, but, like the upper bounds of where it could be are so much higher. And, like, to map, the visual world the way humans see it is also not necessarily, like the end-all be-all for visual intelligence. And I would argue that language is still the most interesting manifestation of human intelligence. And while our visual cortex is certainly less sophisticated, than that of, certain animals all the way down to the mantis shrimp who can, have, like, 2 independent eyes, 3 bands, trinocular vision and each eye can see all the way to, like, floating temperatures in 4D and stuff.Richard Socher [00:27:36]: Like, mantis shrimp, you should look it up. It's likeSwyx [00:27:37]: Way OP.Richard Socher [00:27:38]: Super crazy.Swyx [00:27:39]: Yeah. ZeFrank, mantis shrimp.Swyx [00:27:41]: It's the best video in the world onRichard Socher [00:27:42]: I love ZeFrank, yeah.Richard Socher [00:27:44]: Big shout-out to him. But, like, I think there's a lot more room to grow, but none of these, other animals have language that's as sophisticated as ours, certainly not in writing. And once you can write, you can, start thinking about longer term civilizations. All of that is language. Programming is much closer to language. And I would argue, and this is, like an important thing in the spaces definition of intelligence also, is that all of these spaces are highly correlated, but visual intelligence is neither necessary nor sufficient for overall intelligence. You can be blind and still be an intelligent human being. And an AI can be blind and still be quite intelligent too.Swyx [00:28:25]: We were gonna bring thisRichard Socher [00:28:25]: Which doesn't mean that you're not more intelligent when you have it. Yeah.Swyx [00:28:28]: We're gonna bring this up. I might as well — Like, we have a classification of 10 types of intelligence that you had at the end of your talk. So I'm just gonna flash this up now for people to cover this. I don't know if, maybe we'll put this towards the end. We'll come back to this. I just wanna mention that, you do have a philosophy that I like when people do lists because then I can just go through this and then it gets — it's educational for people. But let's go back. I don't wanna get distracted. But, so effectively, I'll, I'll, reinterpret what you said as Yann LeCun is wrong. And then we'll justRichard Socher [00:28:56]: Don't quote me as that. I'm, I'm good friends with Yann. I think very highly of him in many directions.Swyx [00:29:01]: But he's wrong.Swyx [00:29:03]: You mentioned GPT-1, and I cannot let any, Alec Radford, mention escape. Did you talk with him when he was training GPT-1? Like, any historical, fun stories there that you might come up?DecaNLP, GPT History, and Scientific GatekeepingRichard Socher [00:29:18]: I did not, like, meet him a bunch of times. I think we met maybe once or twice at some conferences. But, like, he has told, I think Brian, the first author of the DecaNLP paper, that it did inspire him, and he cited it five times in the GPT-2 paper. So, and that's, likeSwyx [00:29:36]: Yeah, good enough.Richard Socher [00:29:36]: Very clearly said, like, this was the first instantiation where they showed in the DecaNLP paper, McCann et al, that you can just phrase every single NLP problem as here's some prompt, text context, here's a question and task description and here is some output. If you just do that enough, you can have one unified neural network model, which, by the way, also had all kinds of interesting attention mechanisms. There are slightly different formulations to the transformer. I think came out the same year, plus/minus a few months. And then you can unify all of natural language processing into one neural net. That is the core idea.Swyx [00:30:14]: And this was as opposed to at the time, LSTMs and what have you.Richard Socher [00:30:17]: LSTMs, but also, like, people being very stuck in thinking about one model per task. In factRichard Socher [00:30:25]: It's, it's kinda crazy, but the DecaNLP paper was publicly reviewed as, like, open, OpenReview. It was an ICLR submission. And, in it, you will see, how the whole community at the time thought about this. So, likeSwyx [00:30:43]: Some great contributions, but more work needed.Richard Socher [00:30:46]: So look at, like, search for not even for humans. Just scroll it up here. Like, question answering is not a unified phenomenon. There is no such thing as general question answering, not even for humans. And this is like, really, you replace your brain with a different brain a different neural net when you answer, like, different kinds of questions. It was unfathomable to the experts at the time that you can have one unified neural network that would answer all of these different questions. They are saying, “No, all of these questions require very different systems to answer, and trying to pretend they are the same doesn't help anyone solve any problems.” That's what it says right there, right? That's how hard it was to fathom. And now, of course, people, when I say, “Oh, we're gonna invent prompts,” people are like, “You can't even invent prompts.” It's such an obvious idea to have one neural network that, of course, does everything in NLP.Richard Socher [00:31:37]: But at the time, it was, like, extremely controversial, and the paper got rejected. And the sad thing is that it got rejected so hard and they were so certain that we stopped going on our list of things to try. And the number 2 or 3 on the list of extensions for this paper was add language modeling as another task. And then we could have, and that would have accelerated the timelines, in 2018, like, even further for humanity. But we got so crushed, and we were like, “Okay, maybe we'll just work on some of our other ideas for now and, like, come back to this later.” Yeah.Swyx [00:32:09]: How can we design a review system that rewards non-consensus?Richard Socher [00:32:14]: Honestly, I started to feel like arXiv is such a gift to humanity. With arXiv, you should just put your paper out there.Swyx [00:32:24]: Is it pre-preprints?Richard Socher [00:32:25]: Let — And honestly, I think Twitter X, people like you who pick up interesting papers, that is a better filter than the experts. Let everyone, like, have access. Now, of course, there are some downsides, which is, like, if you're super unfamous, you have no Twitter followingRichard Socher [00:32:41]: You don't wanna be on social media or whatever, you write a good paper, maybe someone, somehow no one notices it. But I would argue that if you just tell, like, 10 of your friends in your community about a paper and it is a really significant breakthrough, someone is bound to talk about it again. And, so I think science needs less gatekeeping. And, even though ICLR, with Yann LeCun, who started it, as one of the co-founders of ICLR back in the day, he also wanted less gatekeeping ‘cause he too was rejected for many years together with Yoshua Bengio and Geoff Hinton with all their early deep learning and neural net papers ‘cause it was just not the hot thing. And so ICLR started with that, but then it also started gatekeeping a little bit themselves on various ideas. So I think less gatekeeping, more open, and then allowing people to say, “Look, even if this is just on, or, quote, unquote, ‘just an archive,' if it has like 1000 citations, it's a legitimate paper. Doesn't really matter where you published it.”Swyx [00:33:34]: And I agree with that. I do think it's sad that I've heard that grad students have to do, like, how to Twitter, seminars to each otherSwyx [00:33:43]: Just because it's so important for publishing these days. This person is just reflecting the sentiment at the time.Richard Socher [00:33:49]: That's right.Swyx [00:33:49]: But it'sRichard Socher [00:33:50]: I think it'sSwyx [00:33:50]: It affected you so muchSwyx [00:33:52]: That you stopped work on it.Vibhu [00:33:53]: The sentiment also came out of some of the research, right? Like, the original BERT paper was trained, and towards the end of the paper, they're like, “Okay, throw off the last head, train specific iterations forVibhu [00:34:05]: Extractive summarization add a head for this.” Like, you should do task-specific stuff. These are, like the authors that wrote Attention, wrote BERT, telling you this is what you're meant to do. And, like the training tasks were also very odd. They're likeVibhu [00:34:16]: The — “We know that the model overfits to this weird mass language modeling. Throw away this part and just do specific models,”?Richard Socher [00:34:23]: Exactly. And, like, we had to try — come up with all clever ways of, like attention and pointers and so on to get the neural network to be able to do all of these tasks. And then some of them were better than state-of-the-art, some weren't, but we were like, “But it's still in one model.” I thought it was really cool. Really interesting.Swyx [00:34:38]: I was gonna move on next to Tim and open-endedness. He was head of open-endedness at Google.Open-Endedness, Rainbow Teaming, and Self-Set GoalsRichard Socher [00:34:42]: That's right.Swyx [00:34:43]: I don't know what that means.Swyx [00:34:44]: But he did a lot of talks.Richard Socher [00:34:45]: Genie 3 is one of the ways thatRichard Socher [00:34:47]: Rainbow teaming, yeah.Swyx [00:34:49]: So I first saw him at — speaking of ICLR, I first saw him at ICLR when he talked about open-endedness. He's he's done a few talks. Can we define what is open-endedness for people who have never been exposed to the problem? They are like, “What do you mean? I thought the only goal of AI is to optimize against a benchmark or.”Richard Socher [00:35:04]: That's right, yeah. It's a, it's a fuzzy term because there's so many different instantiations of open-ended, thinking. But, one way I often describe it, and certainly, Tim and Geoff Hinton would be even better at describing this, but it's a suite of methods that is more inspired by evolution than, very specific rewards. So in that sense, it thinks more about environments, about co-adaptation. And so a concrete example is in the cybersecurity and LM safety space where you have one LM that tries to attack another LM to say something unsafe.Swyx [00:35:40]: Yeah, the rainbow, yeah.Richard Socher [00:35:40]: And now the environment is the 2 having a conversation and now they co-adapting, right? They're like one makes a better attack than the first one inoculates itself somehow, like uses that as training data, makes it so it's harder to say something unsafe based on that. And then as the attack stops working, the attacker now tries a different angle, right?Richard Socher [00:36:00]: And that's why it's not just red teaming, but they're called rainbow teaming.Swyx [00:36:02]: So, like, don't tell me how to do things. Let me just figure it out myself.Richard Socher [00:36:05]: That's right. Think about the environments that you wanna use. Think about the rewards at a high level that you wanna, inspire towards, and then let the AI try out many more ideas in this interplay between sometimes humans, but also sometimes other AI agents.Swyx [00:36:22]: Yeah. I worked open-endedness into a model that I have been working on. It was the keynote for AI Engineer where you start. You, we have the token loop, we have the agent turns, and then we have goal. And I feel like the way that you're describing open-endedness is still somewhat of a goal. Like, please attack this,Swyx [00:36:41]: Other agent. But, to meRichard Socher [00:36:42]: Yeah, you set the rewards. You set the environments.Swyx [00:36:44]: The loop that makes the other loops is. What if the agent can set its own goals?Swyx [00:36:49]: And is it, is that open-endedness? Like, you don't give it a goal. Just, like, be a sentient being. And maybe sentient is a very loaded wordSwyx [00:36:57]: But just set your own directions. What do you think you should do?Metacognition, Subjective Goals, and Measuring IntelligenceRichard Socher [00:37:01]: I love this direction. I think this is one of the 10 spaces of intelligence, that I clump under metacognition and thinking about thought.Richard Socher [00:37:08]: And it's an interesting one. Whenever people say, “Oh, AI is like, this is, it's gonna stop from here. It's not gonna get that much better,” and blah, I'm like there's so many different spaces of intelligence that we haven't even started exploring yet and hence have made very little progress on. And there is an interesting, connection to economics and, capitalism. Like, it doesn't make sense for a company to build and spend billions of dollars building a model that instead of following the rewards and objective functions you gave it, may come up with its own objective functions and its own goals.Richard Socher [00:37:46]: Right? And then imagine you're like, “Okay, I spent billions of dollars. Now go develop this new battery, material for me and answer all my emails.” And it's like, “Nah, I think it'd be more interesting to evaluate the molecular composition of the atmosphere, on Jupiter.”Richard Socher [00:37:59]: And you're like, “That's not what I paid you billions of dollars for.” And so no one's working on that for good reasons. And then also, understandablySwyx [00:38:07]: It's not useful.Richard Socher [00:38:07]: It's not, it's not useful, and it could get a little bit weird, right? What if the AI does start to really have thoughts on its own, and what if we don't like those thoughts, right? And so it requires a whole different way of thinking about it. I had a great conversation with a good friend of mine, Sam Gershman, who's a neuroscience professor at Harvard, and, like, we just jammed on this a little bit on, like, what are the best meta goals. And, I do think, like, knowledge-seeking is a really good one. I'm currently thinking also about, like the ultimate measure and unit of intelligence broadly construed, and I finally have some. It's still too early to share it. It's not. I haven't fully baked the thoughts yet.Swyx [00:38:44]: Like some replacement for IQ.Richard Socher [00:38:46]: IQ is such a terrible definition, right?Swyx [00:38:48]: Elo.Richard Socher [00:38:48]: It makes no sense. Yeah, Elos are terrible, too, because it's always just like me versus others.Richard Socher [00:38:53]: But, like, you can be intelligent and not constantly compare yourself to others? And so, yeah, there's no, like. In fact, a lot of these definitions we have, which I briefly mention in my book, too, these definitions create sometimes explicit and sometimes a more implicit anthropic bounds. No dis to the company Anthropic, but just, like, this idea that your intelligence is like getting 100 out of 100 questions right on this IQ test. Well, if that's your definition then you can only be at 100 out of 100. Where do you go from there, right? So you see a lot of these, benchmarks that people are working on they, increase, they get close to human, maybe sometimesSwyx [00:39:30]: It's like an S-curveRichard Socher [00:39:30]: Slightly above human, and then it's flat.Richard Socher [00:39:32]: It's like, ‘cause that's your. If your definition is only that so tied to humans, you're only gonna get to just slightly better than that. So I think metacognition is a great example of that, where we're not even yet allowing the AI to think. We're not working on it very much, and hence there's very little progress in that.Profit Maximization, Real-World Environments, and Reward DesignSwyx [00:39:49]: Yeah. Well, we've interviewed Andon, which I think, has been working on the most open-ended, benchmarks, which is just real-world, money.Swyx [00:39:57]: Arguably, telling an AI to profit maximize is a bad idea.Swyx [00:40:03]: But they are doing it.Richard Socher [00:40:05]: I do think you don't want that super. Like, you don't want a superintelligence to have a ton of access to all kinds of tools and so on and then just give it that without some very careful reward engineering. ‘Cause it's like, I just buy a bunch of defense stocks and I start a war. I make money. Like, it's just like, it's a tricky situation, right? You just buy a bunch of stuff, short basic goods for people, and you create some weird famine, like, issues. Like, yeah, there's a lot of constraints you should put onto a trading system.Vibhu [00:40:35]: It's a fun measure, though, ‘cause, the bounds are very capped to where we're nowhere close to them. Like, in Andon Labs, the model's like, “Oh, it's Saturday, maybe I just close the store today.” “Someone's off. It's okay. We'll just close the store.”Swyx [00:40:51]: It's using Claude.Vibhu [00:40:52]: Yeah. ButRichard Socher [00:40:53]: Yeah, no. I'm not, I'm not arguing against it. Just, like as you get more and more intelligence, you wanna be more and more careful with that as, like an open environment, ‘cause the environment then is all of Earth.Applying RSI to Science and InventionSwyx [00:41:02]: Yeah. Okay. For recursive, not strictly necessary, right? Because, like, if your goal is you make a machine that, like, invents the other things, then, like, just solve, the science thingsRichard Socher [00:41:12]: Knowledge discovery, yeah.Swyx [00:41:13]: Solve machine learning research and discovery and all these things. Good enough.Richard Socher [00:41:16]: And eventually, so, our goal, I haven't really. I don't talk about it that often because it is a few years out, but our goal is once you have a recursive self-improving superintelligence, you then want to apply it to the most important problems. And I think a lot of those are in science and technology and broadly construed inventions, and those inventions in, physics to create better, cheaper energy with fission or fusion, in chemistry and to create better materials and better batteries and, better solar cells and so on. In biology, there's so much, like, I think soon to be low hang- lower and lower hanging fruit because of AI, because of protein and generation, not just folding, but generating new proteins like we did in ProGen many years ago. Like, so much positive impact we had if you take that superintelligence and you apply it to science.Swyx [00:42:04]: I do fundamentally believe that. There's a lot of approaches, though. You're not the only team trying and NeoLab trying.Swyx [00:42:09]: There's, like a lot of. Especially the physical sciences as well.Richard Socher [00:42:12]: And that's good. Yeah. I do think that physi- like the reason we are only doing it in a few years is that it's a little too early right now. Robotics is not quite there yet. The AI is not quite there yet. But I'm fairly confident in 3 to 5 years, all those constraints will be gone, and then applying to real physical robotics experiments and so on, like true robotic process automationRichard Socher [00:42:33]: Not the traditional RPA sense, but, like, having robots run experiments for you will be totally there. Yeah, it's gonna be great.Swyx [00:42:40]: Just to call back to something that you said early on about slow takeoff, you said that, like, while really the substrate that is limiting factor is, let's call this chips, and semiconductors and all these things, and you have race funding for that and, you are investing a lot on that. But have you done the math on, like, is it even- Achievable and, like, what is the, industry concentration needed in order to achieve, like, scale?Compute, Slow Takeoff, and Changing the Bitter Lesson SlopeRichard Socher [00:43:05]: Right now we know that, like, roughly, like a 1000 GPUs cost quite a lot of money.Richard Socher [00:43:11]: Right? If you wanted, like, 10s of thousands of GPUs, you're, you're talking billions and billions of dollars. If you say, like, one GB300 is, like, you could eventually create models that are, on that substrate, like are close and similar to human intelligence. And you want, like, thousands and thousands of, AIs to think about really hard problems, in a similar fashion to humanity. Like, yeah, that-that's, that's a lot of money. You do the math. It's like a lot. We don't have that amount of money right now anywhere to, like, build that. Now, things can get more efficient. You will have, I think, soon better algorithms that won't be, and better hardware that won't be as energy-hungry, and so on. Our human brain does quite a lot of flops with much less energy.Swyx [00:43:56]: 20 watts?Richard Socher [00:43:57]: That's exactly right. Yeah, that's the number often that's quoted. And, like, I think more, inventions will happen there, that then will accelerate the takeoff even further.Swyx [00:44:08]: One thing I always try to reconcile when talking, like, with new lab founders is, like, you're fighting Bitter Lesson all the time. You have to show initial progress, then you unlock the next tier of funding, then the next tier, then the next tier.Richard Socher [00:44:20]: Which unlocks larger model categories.Swyx [00:44:22]: Like, fundamentally, is that true? Like, are you fighting Bitter Lesson? Are you — will we have a way in which, like, no, we're changing the slope in some fundamentally different way?Richard Socher [00:44:31]: I do think we are changing the slopes in fundamental ways by making AI much more efficient, both in terms of the training as well as the inference.Richard Socher [00:44:43]: Yeah. I think we will — When you allow AI to do the work that it takes other labs thousands of people and years to do, I think we'll be able to get it down to weeks, and that will be much cheaperRichard Socher [00:44:53]: And hence, more affordable, accessible to others and so on.Swyx [00:44:57]: Yeah. You've shared initial results on that,Swyx [00:44:59]: Which, like, conveniently OpenAI has also done to their GPT-5.6, so we can talk about it now.Richard Socher [00:45:04]: Yeah. Yeah, so these areSwyx [00:45:06]: Let's recap what you've done.Early Recursive Results: NanoChat, NanoGPT, and SOL-ExecBenchRichard Socher [00:45:07]: Maybe, just a quick recap here. We built, this, system that isn't the full, even the full RSI system in its glory, but it is a first baby version of this. And then, we don't wanna just have it internally and not show anything and, just show some people of what's possible. And so we applied this to these 3 different tasks. One is NanoChat, by my friend Andrej Karpathy, just, like, train a small language model to get, really low bits per byte. And, like, hundreds if not thousands of people, used both their agents and themselves to try, to get to that, and then they got to 0.937. We literally took our system and got to a much lower, bits per byte, much faster within, like, I think less than 2 days. So we took this thing, applied our system to it, and less than 2 days later, we have — we outperformed every human and their agents, in, have ever worked on this. Same with NanoGPT. And then we're like, well, let's, apply it to something that's even more relevant, to real people and to the Nvidia ecosystem and applied it, to, SOL-ExecBench. And maybe you can scroll down to some of the, images. They're, they're kinda fun to see. But yeah, like, one you see has made some real inventions that weren't just hyperparameter tuning. Like, inventing hash tables and so on is quite clever. We have even better results now.Swyx [00:46:34]: What do you mean inventing hash ta — You didn't invent hash tables.Richard Socher [00:46:36]: Of course we didn't invent, like, hash tables. In the grand scheme of, like a hash table, it's like a super basic primitive in computer science. But to use it, for language modeling in this scenario inside a transformer and so on and to combine these ideas and put them together, that has then eventually also been invented, but there was a knowledge cutoff, and we did check that it didn't have access to that externally. We talk about this a little bit. If you scroll to the next figures, this is also an interesting one in that when you start from a really basic, poor, like, vanilla transformer, then we still outperform all of the community together. But if you start from the human seed from an expert like Andrej, then you get even lower. So the human seeds from which you start do still matter. So that was an interesting insight, in my eyes, on this. And then as you go, like, how long does it take to get to these models, to get to similar performance? It's much faster. And then a similar thing happens with the speed runs here where, people have worked on this for quite some time, and the model still was able to train a model more quickly. Why do we care about it? Well, speed of training is part of the equation of the cost, and ultimately, you wanna have the most intelligence per dollar, right? And so speed and quality are big parts of that. And, the,Swyx [00:48:00]: Yeah, the way I put it is, for people who don't understand they look at the chart, they're like, “Cool. What does it mean?” if you have, like a billion-dollar cluster and you can shave off 10%, that's 100 million dollars.Richard Socher [00:48:12]: That's exactly right.Swyx [00:48:13]: How much is that worth?Richard Socher [00:48:14]: Exactly. So when you click, when you look at, like the kernels, these kernels, yeah, for the non-experts, like these kernels are like, used in all the models. Every time you use an Nvidia GPU, you interface with that GPU through these kernels. And so here you see, the leaderboard best, and when it's recursive, and it's there are only a handful of kernels, in this whole benchmark where we weren't the best. And so to me, this is, like, really exciting, ‘cause it makes. It just showcases what this can do. And again these weren't like. We didn't, like, spend months or years, like, developing. In fact, in particular for kernel, CUDA kernels, like, we don't even have really deep. CUDA kernel experts in the team. And our system, that's the beauty. The system just did all of these things. We didn't invent this. And when we open source and release, things in the future and models in the future, like, it won't. They won't be the best in their, category or class or whatever because we're so smart, but it's because, we built a smart AI that does it for us.Reward Engineering and Good Auto ResearchVibhu [00:49:14]: Do you have anything that you've learned from how to guide good auto research? A lot of it also builds on human background, right? It's not just as simple as just, “Hey, go optimize this.”Vibhu [00:49:23]: But we do see it again and again, right? Like some of the Erdos problems, frontier math is being solved by people. And when they do a write-up, they're like, “Oh, I'm not a mathematician. I have no background in this?” “I saw some tools and I made it work.”Swyx [00:49:35]: While you're watching the World Cup, you're likeSwyx [00:49:37]: “This proves some conjectures that's going on.”Vibhu [00:49:40]: Yep. Any learnings fromRichard Socher [00:49:41]: Yeah, there's a Korean conjecture was. Yeah, that's pretty cool.Swyx [00:49:44]: To summarize, tips for good auto researchSwyx [00:49:46]: Versus bad auto research.Vibhu [00:49:48]: How did you build the recursive?Richard Socher [00:49:49]: Yeah. So without giving away all the secret sauce, maybe some things that are probably obvious to the experts but might still be interesting to some, folks is, like, reward engineering is one of the most crucial bits, especially, in order to avoid reward hacking. So you have to be really clever about avoiding. ‘Cause as your AI gets better and better, it will get better and better, at finding weird like, special cases or counterexamples and things like that. And so I'll give you an example. Like, when you ask to, like, make these 100, lines of code faster, and, how do you define fast? Well, you have one line at the beginning that says, “Start your stopwatch,” and one line at the end, “End the stopwatch,” and then, tell us how much time, progressed. And so, well, the simplest way is you just put that line that ends the stopwatch, rightVibhu [00:50:39]: At the startRichard Socher [00:50:40]: At the start. And then boom, it's now faster, right? So this isn't like this, like, super evil AI. It's just, like a very simple, dumb reward hack. And so you have to just very carefully think about all the different angles there. And then I think the longer time horizon the tasks are the harder it gets and the more interesting and clever you have to be to still use these kinds of ideas for it. But yeah, I can't give away too much there.Vibhu [00:51:05]: It seems like rubrics are taking a good spot in that, where for unverifiable domains, you have rubrics, you have a model breakdown, judge's criteria along the way.Swyx [00:51:14]: Yeah, it's a form of verificationSwyx [00:51:16]: Once you got enough rubrics.Richard Socher [00:51:17]: Yeah, everything. I said this a long time ago. That's why I've never been that impressed that AI can play games, ‘cause I'm like anything you can simulate and/or verify, you can have infinite training data forRichard Socher [00:51:29]: And hence, like, AI will solve it eventually.Swyx [00:51:32]: Looking for games where you can do auto domain distribution. So this is a game that nobody's trained on ‘cause it's a new game.Swyx [00:51:38]: And you can start gaming, you can start to play. So I've been building this and cloned this in person and it's just been self-play. I've had about a billion positions evaluated.Games, Self-Play, and the AI EconomistSwyx [00:51:48]: And, I wanted to do the AlphaGo thing of self-play until you ge

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 6 Harvest . This episode was originally aired on October 29th, 2010. It was written by Alfredo Septien and Turi Meyerwas Directed by Turi Meyer Episode summary:Concerned about Lois’s safety, Clark diverts her away from covering the Vigilante Registrations Act, but an angry Lois tells him that she can take care of herself.  But when she is about to be sacrificed to a religious cult Clark has to save the day. It's IMDB.com rating 7.5 PASS THE TORCH QUESTION:   Throughout the course of the series, are there any characters (not including Clark) that would have made a reasonable false suspect for being The Blur? In this episode Michael is joined by Jaden Hurley Mentioned on the show Find Jayden on TikTok Jaden’s Beacon Site: Go Fund Me: Help Jayce Explore with a Wheelchair Accessible Van Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

Things got very Days of Future Past this week. We break down Season 9, Episode 9, “Pandora,” and finally experience the nightmare future Lois has been seeing all season. We LOVED this one. Lois gets dropped into a Metropolis where Zod has won, the sun has turned red, the resistance is barely hanging on, and Clark has become a broken version of himself after losing her. Seeing this world through Lois makes everything hit harder, especially as she reunites with Clark and we learn just how much her disappearance changed him. It has that alternate-future energy we love from stories like Buffy's “The Wish” and the Terminator movies, where everything has gone horribly wrong and our heroes are fighting from underneath. Add in some HUGE Clark and Lois moments, and “Pandora” has us seriously excited, a little mind-blown, and very ready to see where Smallville Season 9 goes next. Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

The newest episode of The BIG Sci-Fi Podcast is here, and this week we're joined by an incredibly talented writer and content creator: Jake Black!Jake has spent more than 20 years writing and creating content across an amazingly diverse range of media, working with some of the biggest names and brands in pop culture—including Star Trek, WWE, Teenage Mutant Ninja Turtles, Supergirl, Smallville, The Umbrella Academy, Adventure Time, Steven Universe, and many more.But this conversation isn't just about the impressive list of things Jake has written.We dive into Jake's origin story—how he got started, the twists and turns that shaped his career, and how he became such a remarkably diverse writer.And honestly? His story is inspiring.Whether you're an aspiring writer, a creative trying to find your path, or simply someone who loves hearing how people turn their passions into a career, you're going to get a lot out of this conversation.Jake's journey is a great reminder that sometimes you don't have to know exactly where you're going—you just have to keep writing, keep learning, keep creating, and be willing to say yes to opportunities along the way.Listen now and discover the story behind the writer.Check out Jake's work: https://www.jakeblack.com/Listen to the other fantastic podcasts on the Trek Geeks Podcast Network: trekgeeks.com

It's Smallville: The Ultimate Blooper Reel! We've complied the best flubs and tangents that didn't make the final cut of Smallville: The Ultimate Season podcasts for a peek behind the curtain of what usually ends up on the cutting room floor. Enjoy! Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzAlanaApril Every DayJared SlackDanny Z.Nathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajAlexander VerticchioJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.

Wonder Woman! Superboy! Hawkman! Hawkgirl! Where could you read about all 4 of these Spectacular Heroes in 1976? In Four Star Spectacular #4, that's where! Join Paul and special guest Todd Serenbetz as they go on a rampage in Maine, build a giant umbrella in Smallville, and become completely invisible in Midway City! But wait, there's more! We also have the September edition of The Monthly Planet, with Paul and Shawn M. Myers! This month's instocktrades.com selections: https://www.instocktrades.com/products/feb2670256/superman-brainiac-reborn-omnibus-hc https://www.instocktrades.com/products/aug237823/wonder-woman-the-golden-age-omnibus-hc-vol-01-(2023-edition) Visit the Daily Planet: https://www.mikesamazingworld.com/F231EB71A50CE9/features/gallery.php?page=planet Learn more about Four Star Spectacular: https://en.wikipedia.org/wiki/Four-Star_Spectacular https://www.mikesamazingworld.com/main/features/series.php?seriesid=843 And who is Frank Godwin? https://en.wikipedia.org/wiki/Frank_Godwin https://www.mikesamazingworld.com/main/features/creator.php?creatorid=353 https://www.facebook.com/groups/1862475417283165/ https://tombrevoort.com/2017/06/17/i-have-no-great-memories-of-four-star-spectacular/ Have a question or comment? Have a specific issue you love and want to talk to us about it? Have a favorite issue and want to be a guest? E-mail us at dcspecialcast@gmail.com Follow us on Bluesky at https://bsky.app/profile/dcspecialcast.bsky.social Follow the Monthly Planet on Bluesky at https://bsky.app/profile/themonthlyplanet.bsky.social Subscribe to DC SpecialCast: Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/dc-specialcast/id1781264740 Don't use Apple Podcasts? Use this link for your podcast catcher: http://feeds.feedburner.com/dcspecialcast Also available on Spotify, Audible, and Amazon Music This podcast is a proud member of the FIRE AND WATER PODCAST NETWORK: Fire & Water website: https://fireandwaterpodcast.com Fire & Water Facebook page: https://www.facebook.com/FWPodcastNetwork Fire & Water on Bluesky: https://bsky.app/profile/fwpodcasts.bsky.social Fire & Water Podcast Network on Patreon: https://www.patreon.com/fwpodcasts "Cloud Dancer " Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 License http://creativecommons.org/licenses/by/4.0/

Mikey & Jeremy watch S8E4 of Smallville, "Instinct". They discuss alien pheromones, commuting to Metropolis, and Jimmy Olsen's super power.

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 5 Isis . This episode was originally aired on October 22nd, 2010. It was written by Genevieve Sparling and was Directed by James Marshal Episode summary:With Lois on assignment in Egypt, the Daily Planet hires new reporter Kat Grant to take her place. The assassin Deadshot takes aim at Grant, but has a hidden agenda for The Blur. It's IMDB.com rating 8.3 PASS THE TORCH QUESTION: What other early days freak-of-the-week smallville characters would you have loved to see show up again in season 10? In this episode Michael is joined by Colin Hilding Mentioned on the show Off the Podium Podcast Rank This List Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

World will live! Worlds will die! And the podcast will never be the same again! Okay, it'll be fine, but we're still diving deep into some Crisis Management with this one. Kevin Miller, author of Two Morrow's The Crisis Companion joins Ian, Murd and Brandon this episode, going into our combined Crisis histories, the mounds of research Kevin achieved to create the Companion (including interviews with a bunch of those involved at DC and Crisis itself), what made Crisis work better than other similar attempts at continuity resets, how close we may have come to Marvel publishing DC instead of what we got (and here's the link to the Jim Shooter biographical interviews we mentioned),the joy that is Silver-Age sillyness, where DC is now in the Waid era, quoting and referencing pages from Murd's Crisis thesis, some of our favorite Crisis moments, the wonder that is George Perez's artwork, how DC should utilize the word Crisis in their crossovers moving forward, what we can do to build the comic book industry forward for further generations, that time Kevin was Lex Luthor on Smallville, and so much more!You can also follow Comic Timing on Bluesky at https://bsky.app/profile/comictiming.bsky.social, on Facebook at https://www.facebook.com/ComicTiming/, and on Instagram at http://instagram.com/comictimingpodcast. And please, if you can, please leave us a review on Apple Podcasts; it helps attract new listeners! Finally, you can join in on the conversation at our Comic Timing Fans group on Facebook, which is a great place to hang out and talk comics.Follow Ian on Bluesky, and on Instagram at http://instagram.com/i_am_scifi. Brent posts regularly to YouTube on his channel, BK's Bullets and can be found on Bluesky and Threads; and you can find Brandon on Bluesky and Twitch!Thanks for listening, we'll catch you next time, and as always, there's always time for comics!Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Send us Fan MailIn this episode the boys dive into the 2026 Dragon Con schedule and try to build the perfect con weekend. We break down our must-see picks, including the Wednesday Night Countdown/Onesie Wednesday, Dragon Con Wrestling, The Last Party on Alderaan, a Buffy the Vampire Slayer stage show. Closing Ceremonies and a whole lot more. Whether you're a first-timer with a planner and color-coded spreadsheets or a ‘vibes only' con-goer, this one will give you ideas to plug into your own Dragon Con schedule and ideas on how to optimize your personal attack plan! All that and more, on this week's Dragon Con Survival Guide!Gary and Taylor, lifelong friends and self-proclaimed Dragon Con/Convention experts, talk tips and hacks to get the most out of your con experience - while saving as much money as possible – and whatever nerdy stuff crosses their minds. Save more at cons to spend more at cons! New episodes drop every Tuesday.Find DragonCon Survival guide on Facebook, email us at Dragonconsurvivalguide@gmail.com, or shout to the DragonCon gods in Downtown Atlanta at 2 AM.Thanks for liking, sharing, subscribing and listening!DragonCon Survival Guide is a Top Wop Studios Production.

Joe Chill? More like Joe Mama!Smallville's version of Batman and Nightwing brawl with our C.K. and Ollie. Lex and Bruce Wayne bond over a billionaire lunch at Ace of Clubs. Superman and Batman team-up to track down Bruno "Ugly" Manheim and Joe Chill. Tess is sending nudes to Oliver through Lex's sleepwalking. This episode covers the first six chapters of season eleven's storyline: Detective.

The Wonder Twins are in Smallville!! We're freaking out over 9x8 “Idol” and the wonderfully absurd decision to bring Zan and Jayna into this universe. Somehow, that's only half the reason we absolutely LOVE this episode, because Lois and Clark are getting seriously steamy. Lois starts putting the pieces together about Clark and the Blur, Clark gets dangerously close to exposing his secret, and the whole thing builds to Lois realizing that no matter how she feels about the Blur, she can't stop thinking about Clark. Then she turns around and gives him THAT KISS. We're talking Wonder Twin powers, Clark becoming a true symbol of hope, Lois nearly discovering everything, and one of our favorite Lois and Clark moments yet. Smallville “Idol” is ridiculous, romantic, and exactly our kind of episode.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

Who remembers both of these old shows? If you do, you're probably needing a hip replacement! But make sure to turn your hearing aids up to listen to this one!

We finally meet the Original Pennywise from the 1990's miniseries! Roxy Striar & Paige Kimsey watch Stephen King's IT (1990) for the first time in this full miniseries reaction and review. Tim Curry delivers his legendary performance as Pennywise the Dancing Clown, a shape-shifting evil that terrorizes a group of childhood friends in Derry, Maine, before drawing them back home as adults. From Georgie's terrifying storm-drain encounter and the bloody photo album to the Chinese restaurant fortune cookies, Pennywise's unforgettable “They all float” scenes, and the climactic journey into the sewers, Roxy & Paige experience the classic two-part horror event while celebrating Tim Curry's iconic performance! Stephen King's IT (1990) Full Miniseries Reaction Watch Along:   / thereelrejects   Directed by Tommy Lee Wallace (Halloween III: Season of the Witch, Fright Night Part 2) and adapted from Stephen King's bestselling novel, IT stars Tim Curry as Pennywise (The Rocky Horror Picture Show, Clue), Richard Thomas as adult Bill Denbrough (The Waltons, Ozark), Jonathan Brandis as young Bill (Sidekicks, SeaQuest DSV), Annette O'Toole as adult Beverly Marsh (Superman III, Smallville), Emily Perkins as young Beverly (Ginger Snaps, Supernatural), John Ritter as adult Ben Hanscom (Three's Company, 8 Simple Rules), Harry Anderson as adult Richie Tozier (Night Court, Dave's World), Seth Green as young Richie (Austin Powers, Family Guy), Tim Reid as adult Mike Hanlon (WKRP in Cincinnati, Sister, Sister), and Dennis Christopher as adult Eddie Kaspbrak (Breaking Away, Chariots of Fire). Roxy & Paige discuss Tim Curry's terrifying and darkly funny Pennywise, the friendship between the young Losers' Club, the miniseries' dual childhood-and-adulthood timeline, its most memorable scares, the practical creature effects, Stephen King's themes of fear and trauma, the controversial ending, and how Curry's Pennywise compares with Bill Skarsgård's interpretation in the modern IT films. Follow Roxy Striar YouTube:https://www.youtube.com/@TheWhirlGirls Instagram: https://www.instagram.com/roxystriar/?hl=en Twitter:  https://twitter.com/roxystriar Follow Paige Kimsey https://www.instagram.com/paige.popcorn?igsh=NTc4MTIwNjQ2YQ%3D%3D Intense Suspense by Audionautix is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/... Support The Channel By Getting Some REEL REJECTS Apparel! https://www.rejectnationshop.com/ Follow Us On Socials:  Instagram: https://www.instagram.com/reelrejects/  Tik-Tok: https://www.tiktok.com/@reelrejects?lang=en Twitter: https://x.com/reelrejects Facebook: https://www.facebook.com/TheReelRejects/ Music Used In Ad:  Hat the Jazz by Twin Musicom is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/by/4.0/ Happy Alley by Kevin MacLeod is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/... POWERED BY @GFUEL Visit https://gfuel.ly/3wD5Ygo and use code REJECTNATION for 20% off select tubs!! Head Editor: https://www.instagram.com/praperhq/?hl=en Co-Editor: Greg Alba Co-Editor: John Humphrey Music In Video: Airport Lounge - Disco Ultralounge by Kevin MacLeod is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/by/4.0/ Ask Us A QUESTION On CAMEO: https://www.cameo.com/thereelrejects Follow TheReelRejects On FACEBOOK, TWITTER, & INSTAGRAM:  FB:  https://www.facebook.com/TheReelRejects/ INSTAGRAM:  https://www.instagram.com/reelrejects/ TWITTER:  https://twitter.com/thereelrejects Follow GREG ON INSTAGRAM & TWITTER: INSTAGRAM:  https://www.instagram.com/thegregalba/ TWITTER:  https://twitter.com/thegregalba Learn more about your ad choices. Visit megaphone.fm/adchoices

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 4 Homecoming . This episode was originally aired on October 15th, 2010. It was written by Brian Peterson was Directed by Jeannot Szwarc Episode summary:Lois tries to get clark out of his self-imposed exile and perhaps rekindle their relationship and urges him to join her at their 5 year smallville reunion. Things go from awkward to worse when Brainiac shows up but this version is a hero from the future and acting like all 3 ghost of christmas- aims to show clark the world he can have if he'll let go of the past and embrace the future. It's IMDB.com rating 9.4 PASS THE TORCH QUESTION: What is your favorite version of kara, comic runs, Smallville, new movie, 80's movie? What's yoru favorite iteration of this characters so far?? In this episode Michael is joined by Colin Stewart Mentioned on the show I Used to Like This One Podcast Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

We absolutely loved how big, emotional, and downright epic this one felt. For the first time, Clark's flesh-and-blood father Jor-El is actually in Smallville, giving Clark the chance to come painfully close to knowing the man behind the voice that has guided him for years. We're talking about Jor-El and Zod's friendship before everything fell apart, the tragedy that shaped Zod after losing his wife and child, and just how heartbreaking it is watching so many relationships on Krypton end in loss. We definitely missed having Lois around this week, but getting to see more of Krypton before its destruction gave Season 9 a scale we weren't expecting. And when Clark finally meets Jor-El? This one got us. “Kandor” is a sad episode, but it's also one of those Smallville stories that makes Clark's Kryptonian history feel bigger than ever while still bringing everything back home to the Kent Farm.Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

Hey, it's almost time for Dragon Con! Nerds, geeks, and weirdos from around the globe shall descend upon Atlanta for a long weekend of mirth, merriment, antics, hijinks, and perhaps even a shenanigan or two. And this week our Dragon Con coverage begins with a preview of what you can expect to find there. Including: Stars from Land of the Lost, Battlestar Galactica, Firefly, Smallville, and The Jeffersons! (We might just be dreaming about that last one.) The 120 Minutes dance party! The Sc-Fi Explosion video show! Very long lines! Bunny costumes! Puppets! Teeth! (Teeth? Really? Yes. Teeth.) And lots more miscellaneous nerdity. We even address the question of whether or not this is Dragon Con's 40th anniversary. (It's not. There, that was easy.) Next week: We'll reveal exactly what we're doing at Dragon Con. (Mostly silly things. Okay, that was easy too.) The Flopcast website! The ESO Network! The Flopcast on Facebook! The Flopcast on Instagram! The Flopcast on Bluesky! The Flopcast on Mastadon! Please rate and review The Flopcast on Apple Podcasts! Email: info@flopcast.net Our music is by The Sponge Awareness Foundation! This week's promo: Earth Station Trek!  

Send us a text or a voicemailA TikTok film review superstar must survive an evening trapped in a frostbitten studio with a group of drunken applejack salesmen who are trying to become North America's greatest podcast by killing one hundred bottles of Zima! On Episode 731 of Trick or Treat Radio we are joined by Creepygirl for our August Patreon Takeover! Creepygirl has chosen the films Borderline (2025) and Hundreds of Beavers for us to discuss! We also pay tribute to the late great Screaming Mad George, debate the difference between real and fake monsters, and reflect on the charlatanry of the Warrens. So grab your cold weather gear to survive the frozen frontier, make sure to hire a fully capable bodyguard, and strap on for the world's most dangerous podcast!Stuff we talk about: The difference between Monsters and MONSTERS, Ed Gein, Jeffrey Dahmer, Lizzie Borden, serial killers, Rebecca Hall, Sarah Paulson, Charlize Theron, Christina Ricci, Creepygirl Movie Reviews, RIP Screaming Mad George, Freaked, Society, A Nightmare on Elm St 3: Dream Warriors, Faust, surrealistic special FX, Silent Night Deadly Night 4, Predator, Guyver, Poltergeist II: The Other Side, Screaming Jay Hawkins, Heroes, RIP Hayden Panettiere, Kurando Mitsutake, Zachary Quinto, The Beastmaster, Teaching Mrs. Tingle, Universal Soldier: The Return, The Erotic Rites of Countess Dracula, The Exorcist: The Beginning, Piranha 3D, Vampire High, Misha Collins, Amy Adams, Smallville, Charmed, James Marsters, Peter Horton, Brimstone, Fade to Black, Mickey Rourke, 1941, John Noble, Fringe, Ray Wise, Sylvester McCoy, The Blood On Satan's Claw, Tom Duggan, Frankenstein (1970), The Giant Behemoth, The Mummy's Shroud, H.P. Lovecraft, Ed and Lorraine Warren, being superstitious, Matt Rife, The Warren Museum, Ghostbusters is a documentary, The Outsiders, Grave Encounters, Amityville, Borderline, Samara Weaving, Jimmy Warden, Leonardo Dicaprio, Ray Nicholson, Eric Dane, Jimmie Fails, Inde Navarrette, Heat 2, X-Men, stalking is bad mmkay, Margot Robbie, Colleen Camp, Clue, Police Academy, Whalefall, Brian Duffield, Junior, Gary Gotzman, Belial, Basket Case, Hundreds of Beavers, Mike Cheslik, Ryland Brickson Cole Tews, Lake Michigan Monsters, El Topo, Forbidden Zone, Sin City, The Mad Painter, Paul Benedict, Wizards, Cannibal: The Musical, Chess King, Buster Keaton, Vinegar Syndrome, Multiplicity, Michael Keaton, Deep Roy, Butt Boy, Applejack, Richard Elfman, Hostel, Rocky Horror Picture Show, Batgirl, Coyote Vs. Acme, Blood Shine, and Faces of Breast.Support us on Patreon: https://www.patreon.com/trickortreatradioJoin our Discord Community: discord.trickortreatradio.comSend Email/Voicemail: mailto:podcast@trickortreatradio.comVisit our website: http://trickortreatradio.comStart your own podcast: https://www.buzzsprout.com/?referrer_id=386Use our Amazon link: http://amzn.to/2CTdZzKFB Group: http://www.facebook.com/groups/trickortreatradioTwitter: http://twitter.com/TrickTreatRadioFacebook: http://facebook.com/TrickOrTreatRadioYouTube: http://youtube.com/TrickOrTreatRadioInstagram: http://instagram.com/TrickorTreatRadioSupport the show

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 3 Supergirl . This episode was originally aired on October 8th, 2010. It was written by Anne Coufell Sadners was Directed by Mairzee Almas Episode summary: Clark is stunned when Kara returns to Earth and tells him Jor El sent her to stop the dark force that is coming because he doesn’t believe Clark can handle it. Meanwhile, Lois confronts Gordon Godfrey, a shock jock radio DJ who has been crusading against heroes, after he threatens the Green Arrow. However, after Godfrey is possessed by the dark force, he takes Lois hostage and Clark and Kara have to come to her rescue. It's IMDB.com rating 8.1 PASS THE TORCH QUESTION:   What moment would you like to have been on set to witness throughout the 10 seasons? In this episode Michael is joined by Steve Marchion Mentioned on the show Interest Form to be a part of Steve’s new Dungeon Crawler Carl based RPG show Here is Steve’s Spotify Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

Clark & Lex 5ever

Sam Jones III (Smallville, Blue Mountain State) finally comes by and this one goes everywhere I hoped it would. We start with the Pete Ross days, meeting Tom Welling in the van, the chemistry read with Allison Mack, and the Seinfeld night a crowd chased us down the street. Then Sam opens all the way up about the one bad decision that sent him to prison, the DEA setup posing as the Mexican mafia, and the friend who walked free while Sam took time. He tells me about Brother Elam, the man who changed his life inside, the depression that nearly took him when Elam was gone, and the father who talked him back from the edge. Thank you to our sponsors: Thank you to our sponsors:

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 1 Lazarus . This episode was originally aired on October 1st, 2010. It was written by Jordan Hawleyand was Directed by Glen Winter Episode summary:With Lois on assignment in Egypt, the Daily Planet hires new reporter Kat Grant to take her place. The assassin Deadshot takes aim at Grant, but has a hidden agenda for The Blur. It's IMDB.com rating 7.8 PASS THE TORCH QUESTION:   If money were no object, what would you like to own from the show? In this episode Michael is joined by Chris Burke Mentioned on the show Go Fund Me: Help Jayce Explore with a Wheelchair Accessible Van Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

After years of flirting, fighting, terrible timing, and enough unresolved tension to power all of Metropolis, this episode gives us the moment we've been waiting for, and we absolutely loved it. Clark and Lois' chemistry is basically the heartbeat of the entire episode, from their wonderfully awkward Good Morning Metropolis audition to jealous Clark watching Lois and Oliver together, Lois finally admitting how she really feels about Clark, and that incredible final scene at the Daily Planet. We also get Oliver taking Mia under his wing, bringing Speedy into the Smallville universe, plus Tess and Zod continuing their dangerous game of Kryptonian chess. There's a lot happening in Smallville 9x6 “Crossfire,” but for us, this one belongs to Clark and Lois. That kiss was a long time coming, and it was absolutely worth the wait. Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

This one feels less like a typical superhero episode and more like Smallville's take on Michael Douglas's The Game. In this episode of our Going Back to Smallville rewatch podcast and series retrospective, Oliver Queen is pushed through a wild, increasingly dangerous game that forces him to confront just how far he's fallen and whether there's still a hero underneath all that pain. Chloe's methods are definitely extreme, Lois gets pulled into the chaos, and we've got plenty to say about all of it, but what really makes “Roulette” work for us is Oliver's redemption. Seeing him rediscover the part of himself that wants to save people, followed by the return of Green Arrow alongside Clark, made this one incredibly satisfying. The Green Arrow is back, and we couldn't be happier. Support the show: https://patreon.com/hopefullyawesomeBecome a Member on Youtube: https://www.youtube.com/channel/UCHRvjz_pKP1Th5Y8ZIwFMtQ/joinCheck out our Merch! - https://hopefullyawesome.creator-spring.com/This video is NOT sponsored. Some product links are affiliate links which means if you buy something we'll receive a small commission.Mail to: Matt & Maggie - PO Box 3924, Kingsport TN, 37664, United StatesMatt & Maggie - 1001 N Eastman Road # 3924, Kingsport TN, 37664, United States

Buy Paul Anleitner's new book BASED ON A TRUE STORY: https://a.co/d/00Kb0R2a Philip Levens is a long-time writer, producer, and showrunner best known for his work on Smallville, Ascension, Knight Rider, and more. https://philiplevens.com/about-philip-levens Find out more about Goodmakers at: www.goodmakers.co 

Mikey & Jeremy watch S8E3 of Smallville, "Toxic". They discuss beards, origin stories, and love quadrilaterals. 

Kevin Miller, the author of The Crisis Companion (available August 2026 from TwoMorrows Publishing), joins the podcast to talk about his book, why he wanted to write it, and what impact the seminal DC Comics maxi-series has had on fandom and comics publishing. Then we talk about his other endeavors, including portraying Lex Luthor in Smallville, his comic book Meth, and other books he's written. Finally, we talk about his comic book collection and tracker app, The Comic Locker (listen to the end for a special offer from Kevin!). Links: Kevin Miller's website The Crisis Companion from TwoMorrows The Comic Locker Promo: Infinite Earths: A Guide to the DC Multiverse LBR Merch! Feedback! longboxreview@gmail.com 208-953-1841 Bluesky longboxreview.com Thanks for listening! episode 280

Hello and welcome to Farm to Fable, a Smallville re-watch fancast. Here is our review/discussion of s10 ep 1 Lazarus . This episode was originally aired on September 24th, 2010. It was written by Don Whitehead and Holly Henderson and was Directed by Kevin Fair. Episode summary: Lois finds Clark’s lifeless body as his soul struggles against darkness. Jonathan Kent returns to Smallville with a message for Clark. It's IMDB.com rating 8.7 PASS THE TORCH QUESTION:   What do you think was the most unnecessary/odd ball romantic entanglement in the entire series? In this episode Michael is joined by Shawn Wells Mentioned on the show I Used to Like This One Podcast Tabletop Journeys Podcast and Youtube Channel Subscribe to The RPG Academy Youtube channel to support Michael Support Michael on Patreon Like and follow our Facebook page Smallville Farm to Fable Subscribe and leave us a review on Apple Podcasts. Smallville: Farm to Fable E-mail us any comments/concerns/questions to SmallvilleFancast@gmail Thank you for listening and we hope you'll follow along as we discuss each episode in the future. Thanks!! Michael

Welcome back to the WCPE Book Club. This month, we are in for a treat from the great Alan Davis, with his take on a Justice League without Superman. Join us today for JLA: The Nail, a three-issue Elseworlds tale from 1998. Also joining us in the studio today to discuss this title is friend of the show Terry LaCaze. The Kents are driving into Smallville one night when a nail in the tire leaves them stuck, and keeps them from meeting up with a rocket carrying a baby from Krypton. What is the world like, and specifically, what is the Justice League like without a Superman to inspire them? Stick around to the very end to find out about next month's selection for WCPE Book Club. Cullen is taking a cue from the new Spider-Man movie with his choice of a D. C. comic. i  We would love to hear your comments on the show. Let us know what you've been reading or watching this week. Contact us on our website, Facebook, Instagram, or by email. We want to hear from you! As always, we are the Worst. Comic. Podcast. EVER! and we hope you enjoy the show. The Worst. Comic. Podcast. EVER! is proudly sponsored by Clint's Comics, 815 N Noland Road in Independence, Missouri. Whether it is new comics, trade paperbacks, action figures, statues, posters, or T-shirts, the friendly and knowledgeable staff can help you find exactly what you need. You should also know that Clint's Comics has the most extensive collection of back issues in the metro area. If you need to find a particular book to complete a title's run, head to Clint's or check out their website at clintscomics.com. Tell them that the Worst. Comic. Podcast. EVER! sent you.

You're the G.O.A.T. to our W.O.A.T.The official punctuation mark to the infamous award shows. Once again, can't thank the fans enough for getting us all the way here. So many awards, so many disagreements, so many tears and perverted jokes. Join us in part two of the last awards episode dedicated to the entire series of Smallville. LEAVE THE FIVE STARS DAMMIT!

Brian is still in the grave, so Zach and Vince travel to Kansas for some Lana Lang weirdness.

A sample from our latest patrons-exclusive TVChat, wherein Cole and our long time friend and recurring guest Seqarts continue their retrospective on the 2000s superhero-monster-of-the-week-teen-melodrama Smallville.  Support us on Patreon if you'd like to hear the rest and other exclusive episodes!

It's Smallville: The Ultimate Season! This time we're going through the twenty second episodes of every season and by process of elimination determining the ultimate episode 22 of Smallville. And at the end, we recap the final results for The Ultimate Season!Zach is joined by Lance Laster from Always Hold On To Arrow, Matt Truex from Lois & Clark'd: The New Podcasts of Superman and Victoria Male.Check out Lance on Always Hold On To Arrow!Check out Matt's work including Lois & Clark'd: The New Podcasts of Superman at The Daily Knockoff!Check out Victoria's work on her website!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzEvery/Day/AprilNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajAlexander VerticchioJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.

Most people think ranking the best DC movies and shows is about favorites, but what if I told you there's a hidden game-changing system that flips the entire hierarchy upside down? Imagine discovering that your absolute favorite might actually be in dead last, while the most underrated gems claim the top spots—this episode will blow your mind and challenge everything you thought you knew about superhero rankings. Dive deep into a wild, no-holds-barred ranking of DC's most iconic films, cartoons, and TV shows. We break down: the true greatness of Justice League Unlimited, the surprising placement of Batman: The Brave and the Bold, and how Batman Year One shocks as one of the top-tier masterpieces. You'll discover exactly where classics like Wonder Woman (2017), Joker (2019), and even Harley Quinn (2019–present) really land — and why the rankings might just be wrong! Every pick is a story, every placement a revelation—this is not your average list.We also uncover startling truths: why Batman Ninja is underrated, how Smallville and Lucifer revolutionized superhero TV, and which animated gems Hollywood forgot. Fail to listen, you risk missing out on hidden treasures—and the chance to see your favorite DC favorites in a whole new light. It's bold, emotional, and packed with surprises that will make you rethink what's possible in superhero storytelling. Perfect for superfans, casual viewers, and anyone craving a fresh perspective on DC's rich universe. This episode will leave you inspired to explore the overlooked, question the status quo, and maybe even re-rank your own favorites. Hit play—your new secret list awaits.

It's Smallville: The Ultimate Season! This time we're going through the twenty first episodes of every season and by process of elimination determining the ultimate episode 21 of Smallville.Zach is joined by Lance Laster from Always Hold On To Arrow, Matt Truex from Lois & Clark'd: The New Podcasts of Superman and Victoria Male.Check out Lance on Always Hold On To Arrow!Check out Matt's work including Lois & Clark'd: The New Podcasts of Superman at The Daily Knockoff!Check out Victoria's work on her website!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.comMatt Truex is a Warner Bros. Discovery employee. The views and opinions expressed in this podcast are his own and do not necessarily reflect the views or positions of Warner Bros. Discovery.

Annette O'Toole, the acclaimed actress best known for playing Hope McCrea on Netflix's hit series Virgin River and Martha Kent on Smallville, joins us for this inspiring replay conversation about her remarkable career, aging in Hollywood, marriage, motherhood, and finding lasting success in an ever-changing entertainment industry. Fans of Annette O'Toole will enjoy hearing behind-the-scenes stories from some of her most memorable projects, including Smallville, Rose Kennedy, The Kennedys of Massachusetts, Cross My Heart with Martin Short, Copacabana, Vanities, and her acclaimed collaborations with filmmaker Christopher Guest. She also explains why she chose to leave Smallville after six seasons despite the show's success, sharing how family priorities, long commutes between Vancouver and Los Angeles, and a desire to return to theater shaped that decision. Annette also opens up about her enduring marriage to Emmy-winning actor, writer, and musician Michael McKean. She shares the touching story of how their friendship became a lifelong love story, how they co-wrote the Oscar-nominated song "A Kiss at the End of the Rainbow" for Christopher Guest's A Mighty Wind, and why their shared love of acting, music, books, and creativity continues to strengthen their relationship. She also reflects on McKean's memorable Celebrity Jeopardy! championship and why spending time together has become even more meaningful as they've grown older. The episode explores the realities of a long career in Hollywood, including the iconic roles Annette narrowly missed in Body Heat and Terms of Endearment. Rather than dwelling on disappointment, she explains how every missed opportunity ultimately led to another meaningful role, reinforcing her belief that resilience, gratitude, and adaptability are the keys to career longevity. One of the most inspiring parts of this interview focuses on healthy aging, women aging in Hollywood, and embracing authenticity. Annette speaks candidly about rejecting Hollywood's pressure to stay forever young, embracing her gray hair, choosing not to pursue cosmetic surgery, and discovering the confidence to speak her mind after years of trying to please others. She also shares how daily walking, nutrition, and exercise have helped her naturally manage osteoporosis while feeling healthier, stronger, and more energized than ever in her 70s. Show Notes/Links: www.hotflashescooltopics.com JOIN THE HOT FLASHES & COOL TOPICS PODCAST COMMUNITY: Website: www.hotflashescooltopics.com Newsletter: Link Mail hotflashescooltopics@gmail.com Instagram https://www.instagram.com/hotflashesandcooltopics Facebook : www.facebook.com/hotflashescooltopics YouTube https://www.youtube.com/@HotFlashesCoolTopics  

It's Smallville: The Ultimate Season! This time we're going through the twentieth episodes of every season and by process of elimination determining the ultimate episode 20 of Smallville.Zach is joined by Mary Kwiatkowski from The KowSkiCast, Chris Clow from Discovery Debrief, Craig McKenzie from Kneel Before Blog and Isaiah Goodridge.Check out Mary on The KowSkiCast!Check out Chris on The Comic Binge and Discovery Debrief!Check out Craig and his work including the Kneel Before Pod podcast at Kneel Before Blog!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsCory MooreIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullAmy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobNathan RothacherDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajJoey DienbergDJ DoenaDaryn KirschtJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

It's Smallville: The Ultimate Season! This time we're going through the ninteenth episodes of every season and by process of elimination determining the ultimate episode 19 of Smallville.Zach is joined by Anthony Desiato from Digging for Kryptonite, Billy Pollihan from See You Next Summer, and Mateo Santiago.Check out Anthony and his podcasts including Digging for Kryptonite at Flat Squirrel Productions!Check out Billy's podcast See You Next Summer!Always Hold On To Smallville is brought you to by listeners like you. Special thanks to these Meteor Freaks on Patreon who's generous contributions help produce the podcast!Chris FuchsInsaiyanIsaiah GoodridgeAtif SheikhJohn CurcioThomas NavenMarc-ids FoppenPatricia CarrilloRhythm ChameleonJim CrawfordKasey VachRouie HumphreyAlex HamiltonMatt DouglasDaniel CurielMeryl SmithTrevis HullMatt B.Amy J.Mike FranzNathan MacKenzieSteve RogersMollie FicarellaJames LeeJason DavisPatrick BravoAlex RamseyTae TaeTina BJakeJacobJohn BobDylan DiAntonioNick Ryan MagdozaEddie BissellNicholas FanslerJohn LongRuth Anne HamonTravis KillMike ThomasNeena JGordon BombayRajJoey DienbergDJ DoenaDaryn KirschtNicholas CosoJarrett GibbsAnthony AndersonKeith FaulsJames HartAnthony DesiatoCrystal CrossKirin KumarTroy LangloisPATREON: patreon.com/alwaysmallvilleTWITTER: twitter.com/alwaysmallvilleFACEBOOK: facebook.com/alwaysmallvilleEMAIL: alwaysmallville@gmail.com

Its time to return to Earth-167! Which you would obviously know as the universe of Smallville, the smash hit ten season long Superman original series that managed to introduce EVERY. SINGLE. DC. CHARACTER. before Clark Kent decides to put on the suit. This time we're going to be covering the storyline of Kara Zor-El aka Supergirl as played by Laura Vandervoort introduced in Season 7. And we've discovered so much more about this show including how many character get clones or are clones (it's a lot which is exciting). Thanks for watching our Caravan Of Garbage reviewSUBSCRIBE HERE ►► http://goo.gl/pQ39jNHelp support the show and get early episodes ► https://bigsandwich.co/Patreon ► https://patreon.com/mrsundaymoviesJames' Twitter ► http://twitter.com/mrsundaymoviesMaso's Twitter ► http://twitter.com/wikipediabrownPatreon ► https://patreon.com/mrsundaymoviesT-Shirts/Merch ► https://www.teepublic.com/stores/mr-sunday-movies The Weekly Planet iTunes ► https://itunes.apple.com/us/podcast/the-weekly-planet/id718158767?mt=2&ign-mpt=uo%3D4 The Weekly Planet Direct Download ► https://play.acast.com/s/theweeklyplanetAmazon Affiliate Link ► https://amzn.to/2nc12P4 Hosted on Acast. See acast.com/privacy for more information.