Podcasts about Factory

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

It's not every day that we can be heroes, but here's your chance! Cape not needed, just 3 minutes of your time!! North America's duck factory is under imminent threat. A little-know federal rule now under review could determine whether millions of acres of Prairie pothole wetlands--the breeding grounds that produce about 1/3rd of the continent's ducks and were purchased with generations of duck stamp dollars--will remain intact. DU's Scott Stephens and Dr. Mike Brasher explain in less than 20 minutes what's at stake, why you should care, and what you can do before the August 7 public comment deadline. ACT NOW -- LINKS BELOW OR GO TO DUCKS.ORG.   ********** ACT NOW!!! "I support the 2024 rule. Do not drain our prairie pothole wetlands!"   Federal Registry website to provide comments directly: https://www.federalregister.gov/documents/2026/07/24/2026-14995/national-wildlife-refuge-system-request-for-information-on-implementation-of-drain-tile-setbacks   DU "Sign the Petition" https://www.ducks.org/save-the-duck-factory   **********     Visit the Legendary Brands That Make MOJO's Duck Season Somewhere Podcast Possible: MOJO Outdoors  Alberta Professional Outfitters Society Benelli Shotguns Bow and Arrow Outdoors Create the X Habitat Management App Ducks Unlimited  Flash Back Decoys GetDucks.com Migra Ammunitions onX Maps  Use code GetDucks25 to save 25% Sitka Gear SoundGear Use code GetDucks20 to save 25% Tom Beckbe USHuntList.com   Like what you heard? Let us know! • Tap Subscribe so you never miss an episode. • Drop a rating—it's like a high-five in the duck blind. • Leave a quick comment: What hit home? What made you laugh? What hunt did it remind you of? • Share this episode with a buddy who lives for duck season.   Have or know a story to share? Interested in partnerships? Contact: Ramsey Russell ramsey@getducks.com  

CLICK HERE TO PROTECT THE DUCK FACTORYDecoy collecting isn't just about objects — it's about history, craftsmanship, and the people who built the tradition.In this episode of the Ducks Unlimited Podcast, host Katie Burke sits down with longtime collector Bill Dodge at the North American Decoy Collectors Association show. Recorded in one of the show's unique “room‑to‑room” dealer spaces, the conversation offers an inside look at the world of vintage decoys and the community behind it.Bill shares how a single purchase at a farm auction sparked a lifetime passion for collecting — eventually leading him to focus on factory-made decoys, particularly those tied to the historic Detroit makers. They also break down what defines a “factory decoy,” the evolution of materials and craftsmanship, and the balance between standardization and hand-finished detail. From early catalog sales to regional styles and grading systems, the episode highlights how these decoys bridged utility and artistry during a critical period in waterfowling history.Beyond the technical side, Bill emphasizes the importance of mentorship, relationships, and shared knowledge within the collecting community — a recurring theme that defines both collectors and conservationists.This episode offers a deeper appreciation for decoys not just as tools, but as artifacts that tell the story of hunting, innovation, and tradition.Listen now: www.ducks.org/DUPodcastSend feedback: DUPodcast@ducks.orgSPONSORS:Bird Dog Whiskey and Cocktails:Whether you're winding down with your best friend, or celebrating with your favorite crew, Bird Dog brings award-winning flavor to every moment. Enjoy responsibly. CLICK HERE TO PROTECT THE DUCK FACTORY

CLICK HERE TO PROTECT THE DUCK FACTORYLearn why Ducks Unlimited is mobilizing hunters across North America and what's at stake for the future of duck habitat and conservation. In this special DUX 2026 crossover episode, Jeff Stanfield and Andy Shaver of the Big Honker Podcast sit down with Ducks Unlimited CEO Adam Putnam to discuss record attendance at DUX, the growing influence of Ducks Unlimited, and the urgent conservation issues facing waterfowl hunters today.The conversation focuses heavily on the ongoing effort to protect Prairie Pothole Region wetlands, often referred to as the "Duck Factory," and why DU is asking hunters to speak up regarding proposed changes affecting wetland easements funded by generations of Duck Stamp buyers. Adam also shares his vision for Ducks Unlimited's future, the role of volunteers in the organization's success, the growth of collegiate conservation programs, and what lies ahead as DU approaches its 90th anniversary.Topics CoveredRecord-setting attendance and growth at DUX 2026Why the Prairie Pothole Region is known as the Duck FactoryThe importance of Duck Stamp-funded conservation easementsProposed federal changes affecting wetland protectionsHow agriculture and waterfowl conservation can coexistThe future of Ducks Unlimited and volunteer leadershipRecruiting the next generation of hunters and conservationistsDU's upcoming 90th anniversary celebrationListen now: www.ducks.org/DUPodcastSend feedback: DUPodcast@ducks.orgSPONSORS:Bird Dog Whiskey and Cocktails:Whether you're winding down with your best friend, or celebrating with your favorite crew, Bird Dog brings award-winning flavor to every moment. Enjoy responsibly. CLICK HERE TO PROTECT THE DUCK FACTORY

GET TO THE FIREWORKS FACTORY•Unpacking some questionable comics. •Batman Day 2026! •Upcoming events, plus a tease! •Cakeworthy. •Upcoming exterior transformation. •End-of-the-year ordering structure. •Comics talked about in this episode:      SIX OF US #1      HAMMERFIST #1      TERMINAL #1   ---------- Contest of Challengers #791 This episode is dedicated to David Harper's Eisner win! Theme: Adam WarRock (with Mikal kHill) Intro: James VanOsdol (with Danhausen and Chris Jericho) Outro: James VanOsdol "Patrick" Voices: Richie Kotzen, Christopher Daniels, James Acaster, Sue Marasciulo (Trent's Mom), RJ City, Sebastian Bach, Arune Singh, James VanOsdol "Dal" Voices: James VanOsdol, RJ City, Dalton Castle, Sue Marasciulo (Trent's Mom), Kevin Conroy, Kris Statlander, Skye Blue, Bryce Remsberg, Arune Singh, Colt Cabana (both) Dal and Patrick Artwork: Bella Spagnuolo https://bellaspagnuoloart.myportfolio.com/ This episode was digitally edited by Cleanvoice. ----------Challengers Comics + Conversation 1845 N Western Ave • Chicago, IL 60647 773.278.0155 • ChallengersComics.com

Des poissons électriques, guitare en bandoulière, montent la garde sous les pins de l'ile de Porquerolles à Hyères dans le Var. Grandeur nature, plantés là par l'artiste kényane Cosima Von Bonin, ils accueillent le visiteur jusqu'à la Villa Carmignac, sorte de comité d'accueil rock'n'roll pour une exposition qui n'a pas franchement l'intention de rester sage. Derrière ces sculptures déjantées, Kévin Le Squer, responsable des expositions, décode : « Cosima Von Bonin détourne l'imagerie publicitaire des poissonneries pour mieux interroger, sous ses airs potaches, notre rapport aux océans ». Le ton est donné. Pas de protocole guindé pour « Sea, Pop & Sun ». À l'entrée de la fondation Carmignac, on retire ses chaussures, on empoche un verre, et on file visiter l'art comme on traînerait chez des amis un dimanche d'été. Charles Carmignac, le directeur des lieux, assume la filiation : « Mon père Édouard Carmignac a fréquenté la Factory de Warhol dans le New York des années 1960-1970. Cette exposition tente de restituer, plus qu'une époque, une sensation,  celle d'une liberté qu'on pouvait presque toucher ». Un Summer of Love version Méditerranée Le pop art, ce n'est pas que des produits de consommation de masse et des pin-up. C'est aussi une lumière, une énergie, un souffle contestataire. La fondation a choisi ce versant-là, celui du Summer of Love américain, mais aussi celui de mai 1968, version française sur une île où le soleil tape et où la mer n'est jamais loin, l'équation coule de source : riviera, vacances, insouciance affichée. À l'intérieur de chaque salle, les tubes prennent le relais d'Otis Redding aux Beach Boys. « Surfing in the USA » ouvre sur un espace saturé de couleurs avec un surfeur hyperréaliste de Duane Hanson, une langouste géante de Jeff Koons ou encore les plages kitsch de Martin Parr. L'imagerie populaire dans toute sa splendeur car l'idée n'est pas de faire circuler le visiteur en silence devant des cimaises, mais de lui donner, par moments, l'envie de bouger. Sur les quatre-vingts œuvres réunies, beaucoup sortent rarement des réserves. Roy Lichtenstein propose un lever de soleil version vitrail. Warhol, lui, surprend : pas de boites de conserve ni de  Marilyn Monroe  ici, mais des couchers de soleil sérigraphiés, quatre pièces extraites d'une série de six cent trente-deux, conçue à l'origine pour un hôtel. Une demi-sphère, des nuances brumeuses, jamais deux fois les mêmes, chaque combinaison de couleurs devait, selon l'artiste, produire une émotion différente chez qui la regardait. Respect, et corps noirs libérés Changement de registre dans une salle rythmée par « Respect » d'Aretha Franklin ou des artistes femmes y reprennent la main sur la représentation du corps noir féminin. Des figures voluptueuses, alanguies, oisives, rêveuses plutôt que la femme qui travaille et souffre, motif rarissime dans l'histoire de l'art occidental. Montrer ces peintures la fondation Carmignac, n'a rien d'anodin, c'est un geste politique autant qu'esthétique. Tout du long de l'exposition, le pop art made in USA discute avec les avant-gardes européennes. Et au milieu de ce joyeux bazar coloré trône le château de sable géant de Théo Mercier, monument éphémère qui rappelle la fragilité écologique de nos décors de carte postale. Kévin Le Squer résume : « c'est une exposition solaire, dansante, pop jusqu'au bout des ongles mais qui, sous ses couleurs franches, laisse filtrer quelques murmures plus sombres sur la liberté et la fragilité de notre monde ».

The Factory That Shaped Its Workers Into SilenceBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-unsolved-mysteries-forgotten-secrets-unanswered-questions--5684156/support.Darkest Mysteries Online

The Factory Mirrors Returned Reflections No One RememberedBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-unsolved-mysteries-forgotten-secrets-unanswered-questions--5684156/support.Darkest Mysteries Online

Réécoutez FG Underground avec Factory 93 du mercredi 29 juillet 2026

James Brown, George Clinton, Jimi, and Sly are all contenders for a “Mount Funkmore.” There's one woman whose face should be carved in that stone with the boys.Long before she was ever a wife and “muse,” Betty Davis was an up-and-coming songwriter, Wilhelmina model, and “it” girl of the New York underground. She gave her hit song “Uptown” to the Chambers Brothers and called Jimi Hendrix and Warhol's Factory her friends. With her substantial influence on her husband Miles Davis, jazz fusion as we know it was born. Betty didn't need a famous man to make her groundbreaking albums: she was a singer, songwriter, arranger, and producer all on her own. Her in-your-face persona is still so fresh, she's got Beyoncé name-dropping her 50 years later! Betty was everything: raunchy, loud, unapologetically fierce, driven, sensitive, and spiritual. Above all, she was real. Get ready for Betty, because the queen of FUNK is here to stay!In addition, the Dolls Pod is honored to welcome professor, ethnomusicologist, DJ, and author of the upcoming “Game Is Her Middle Name: The Life and Music of Betty Davis” Dr. Danielle Maggio to the show. We talked about how her new book came to be, her own friendship with Betty, and celebrated Betty's power and soul.“Betty Davis: Queen of Funk” is available wherever you stream your podcasts ⭐️Preorder “Game Is Her Middle Name: The Life and Music of Betty Davis” by Danielle Maggio from Feminist Press: https://feministpress.org/collections/coming-soon/products/9781558613782-game-is-her-middle-nameFollow Danielle on Instagram: @saucequeen_phd(Episode starts at 3:41)Sources used:Personal interview with Danielle Maggio, PhD (7/25/2026)Danielle Maggio, “‘Sound Projector:' Reissuing, Representing, and Reclaiming the Music of Betty Davis” University of Pittsburgh, 2020Danielle Maggio, “Betty Davis In Japan,” parts 1-7. Substack via Light In The Attic, 2024John Ballon, “Betty Davis: Betty Davis” All About Jazz, 10/31/2003John Ballon, “Liberated Funk” Wax Poetics issue 22, March/April 2007John Szwed, “So What: The Biography of Miles Davis” (2002)Miles Davis with Quincy Troupe, “Miles: The Autobiography” (2011 ed.)Neil Spencer, “Miles Davis: The muse who changed him, and the heady Brew that rewrote jazz” The Guardian, 9/4/2010Virginia Vigliar, “Betty Davis: What happens when women have a hold on their own narrative?” Medium, 1/2/2021“Betty: They Say I'm Different” dir. Philip Cox, 2017Song used in this episode:Miles Davis - “Bitches Brew” (1970)Bessie Smith - “Young Woman's Blues” (1926)Betty Davis - “They Say I'm Different” (1974)Beyonce - “Break My Soul (The Queens Remix)” [2025]Betty Mabry - “Get Ready For Betty” (1964)The Chambers Brothers - “Uptown” (live at the 3rd Annual Harlem Cultural Festival, 6/29/1969)Betty Davis - “Steppin' In Her I. Miller Shoes” (1973)Miles Davis - “Mademoiselle Mabry” (1968)Betty Davis - “Politician Man” (1969)Betty Davis - “Ready, Willing, and Able (Take 1)” [1969]Miles Davis - “Pharaoh's Dance” (1970)Betty Davis - “If I'm In Luck I Might Get Picked Up” (1973)Betty Davis - “Don't You Call Her No Tramp” (1974)Betty Davis - “Dedicated To The Press” (1975)Betty Davis - “Stars Starve, You Know” (1976)Etta James - “Baby What You Want Me To Do” (Jimmy Reed cover) [1964]Danielle Maggio - “A Little Bit Hot Tonight” (2019)Follow @thedollspod on Instagram for clips and photos from this episode!

An affiliate of a Canadian aerospace manufacturer has filed for bankruptcy protection for its longtime factory in Southwest Ohio, according to a local report.Magellan Aerospace USA Inc. is seeking Chapter 11 protection for the Magellan plant in Middletown, the Journal-News reports. Documents filed with the court reportedly listed assets of $10 million to $50 million and liabilities of $50 million to $100 million.The Chapter 11 process typically allows companies to continue operating their assets; the filing would reportedly authorize company officials to pursue a restructuring or reorganization, as well as a sale of the facility or liquidation, if necessary.Magellan, based in suburban Toronto, makes complex assemblies and systems for aircraft manufacturers, engine makers and space agencies across the globe from production facilities in North America, Europe and India. The company says the Middletown plant manufactures high-temperature, vacuum-brazed honeycomb structures for exhaust systems and control surfaces and offers assembly of titanium- and nickel-based alloy components and aluminum structures.The facility traces its roots to 1928, when a company eventually known as Aeronca was founded in Cincinnati; it moved to suburban Middletown following a 1937 flood, and was acquired by Magellan predecessor Fleet Aerospace 40 years ago.Magellan officials indicated in a corporate resolution this month that bankruptcy would be in the best interests of the company along with those of its creditors, employees and other stakeholders. The Journal-News said that the company did not respond to multiple requests for comment.#Manufacturing, #Aerospace, #AerospaceManufacturing, #Bankruptcy, #Chapter11, #Ohio, #MagellanAerospace, #Aviation, #SupplyChain, #IndustrialNews, #ManufacturingNews, #Aircraft, #Factory, #BusinessNews, #Industry

Follow DJ Toph: ⚡️ Instagram: https://www.instagram.com/dj_toph_ ⚡️ Spotify: https://bit.ly/4it5a3C ⚡️ Mixcloud: https://www.mixcloud.com/toph/ 1 Benirrás Beach Club, Tommy Jules – Garlands (made us do it) (SILAS (UK) Dig Deep Remix) [Salt & Sand] 2 NESI (ES) – No Signs [Too Many Rules] 3 Luccio B – Groove Control [That's Right Dawg Music] 4 Superlover – Never Stop Believin' [Superlover Music] 5 Mattei & Omich – 1995 Feel The Vibe [Mattei & Omich Music] 6 Earth n Days – El Ritmo [HouseU Records] 7 Aidan Hammond – I Can Feel It [Revoke] 8 Stylex (UK) – Movin' [Crypto Ravers] 9 Dino Sauce – If You Don't Believe [House Heads] 10 Simon Kidzoo, Mellizos – Jammin' [Factory 93 Records] 11 Serge Funk – Keep On [Groove Culture] 12 WEISS, Byron Stingily – Love You [Fool's Paradise] 13 Agua Sin Gas by Antoine Clamaran & Da Coona – Anymore [HouseU Records] 14 Lizzie Curious – The Sunshine [Curious Energy Records] 15 Piero Pirupa, Leon (Italy) – Get On [Toolroom] 16 Jules Vic – Save Me [Soulful Legends] 17 Kristofson – Luvin [There Was Jack] 18 Monty Kiddo – Respect [Pleased As Punch] 19 Pinto (NYC) – For Your Soul [Heattraxx] 20 LEFTI – Muchacho [The Disco Express] 21 Hobbs (UK), Slick Pete Flash – Alright [Disco Infiltrators] 22 Renote – Feel for You [Les Folies Digitales] 23 Red Carpet – Alright (Someguy Remix) [White Label] 24 Sam Frandisco – Gotta Have It [Wh0 Plays Records] 25 Axwell – I Found You (Wh0 Festival Remix) [White Label] 26 Heller & Farley Project – Ultra Flava (David Penn Remix) [Defected]

Grim and James are joined by ThatGuy, Nickie the Dude, RSHarmful, Pirateshipping, Endless, Armouro, and Suzanne! Enjoy the chat!!! Email me for the Guilded chatroom link! Check out our anime review show Shonen Dump www.shonendump.com James Cruz Twitch: https://www.twitch.tv/cruz_controllin Grimsteak Twitch: https://www.twitch.tv/grimcrt Grimsteak Youtube: https://www.youtube.com/@grimsteak Send us hatemail or love mail at grimsteak@gmail.com Live Show Every Tuesday at 9pm est on CwS Radio https://s3.radio.co/s230f698de/listen Check out Jerry's show "Nox Mente' at https://noxmente.simplecast.com/

Sports Factory bonus 767 Thu, 30 Jul 2026 12:12:44 +0000 0RWltI1ISwrcjeekQGMIF2TvgwuzPwQW sports Sports Daily sports Sports Factory Wichita's popular morning local sports talk radio show is Sports Daily with Jacob Albracht and Tommy Castor. Listen live M-F 7a-11a on KFH! 2024 © 2021 Audacy, Inc. Sports https://player.amperwavepodcasting.com?feed-link=https%3A%2F%2Frss.amperwave.net

The deadline is August 7th. Act now. ducks.org/save-the-duck-factory https://www.federalregister.gov/documents/2026/07/24/2026-14995/national-wildlife-refuge-system-request-for-information-on-implementation-of-drain-tile-setbacks The prairie pothole region; stretching across Montana, North and South Dakota, Minnesota, Iowa, and up into Canada, is responsible for producing up to sixty-five percent of the ducks that fly down every flyway in North America. Since the 1960s, duck hunters have invested in 1.7 million acres of wetland easements through duck stamp purchases to protect those wetlands permanently. No drainage. No impact. Forever. That protection is now being threatened. The U.S. Fish and Wildlife Service has published a request for information regarding the 2024 rule that governs tile drain setbacks on those easements, and has signaled an intent to repeal it. If that rule is weakened, those wetlands could lose up to twenty-eight percent of their inundated area in the first year alone. Fewer wetlands means fewer pairs. Fewer pairs means fewer ducks. This is the science. This is what's at stake. Chad sat down with Dr. Scott Stephens, Sr. Director of Prairie and Boreal Forest Conservation Strategy at Ducks Unlimited, and Dr. Mike Brasher, Senior Waterfowl Scientist at Ducks Unlimited, for one of the most important conversations ever recorded on this podcast. They break down what easements are, what tile drainage is, what the 2024 rule does, why it matters, and exactly what you can do right now to protect it. If you've ever bought a duck stamp, you are an investor in this habitat. Your voice matters right now. The comment deadline is August 7th. Go to ducks.org/save-the-duck-factory to sign the petition and submit your comment to the Federal Register. The direct Federal Register comment link is also in the show notes. This episode proudly features Ducks Unlimited and is brought to you by Benelli Shotguns, Nutrien Ag Solutions, MOJO Outdoors, Mickey Thompson Tires & Wheels, Corning Ford, LEER Truck Toppers, and GATR Coolers & Drinkware.

Don’t worry, it’ll grow back!

On Your World of Creativity, we travel around the world talking with creative practitioners, entrepreneurs, artists, and innovators about how they get inspired, organize ideas, and turn imagination into impact.Today's guest didn't just build a successful business—he helped create an entirely new category within the entertainment industry.Richard Foos was the co-founder of Rhino Records and most recently of Shout! Factory, two companies that transformed the way music, television, and film archives are valued, distributed, and monetized. What began as a passion for out-of-print records became a movement that helped define today's streaming culture, vinyl resurgence, and the growing importance of content libraries.Richard's WebsiteRichard's Facebook pageWe'll explore creativity, entrepreneurship, cultural preservation, and what happens when you discover opportunity hiding in plain sight.Guest BioRichard Foos is the co-founder of Rhino Records, one of the most influential reissue labels in music history, and later Shout! Factory, a pioneering company focused on television and film distribution.Beginning with a small record store on Santa Monica Boulevard, Richard and his partner Harold Bronson built Rhino into a globally respected brand known for preserving, restoring, and reintroducing forgotten and out-of-print music to new generations of listeners.The success of Rhino helped establish what many now call reissue culture—a model that transformed entertainment economics by demonstrating the long-term value of existing catalogs. Rhino was eventually acquired by Time Warner and became the catalog development and marketing division of Warner Music Group.Richard later co-founded Shout! Factory, bringing similar innovation to television and film archives. Today he continues advising the entertainment industry while focusing on social impact through organizations including The Narrative Method, Volunteer Collective, Cedars Sinai, and other nonprofit initiatives.His career sits at the intersection of creativity, commerce, culture, memory, and preservation.1: Seeing Opportunity Where Others Saw ObsolescenceRichard, one of the themes we explore on this podcast is creative vision—the ability to see possibilities that others overlook.Back in the 1970s, most of the entertainment industry was focused almost exclusively on the next release, the next hit, the next trend.What led you to recognize value in out-of-print recordings and forgotten catalogs when much of the industry considered them yesterday's news? Was there a specific artist, album, or moment that convinced you this could become a business? How much of that insight came from being a fan versus being an entrepreneur?2: The Soul of the Rhino BrandLooking back at Rhino Records and now Shout! Factory:What was your focus?What work were you really doing beyond selling records and media?Who were you serving?What gave you credibility?What industry assumptions were you challenging?And what larger mission connected all of it?3: Reissue Culture Changed EverythingToday we take streaming libraries, catalog recommendations, and rediscovered classics for granted. But Rhino helped create the blueprint.How did the process of licensing, restoring, packaging, and reintroducing older content evolve into what has now become one of the dominant business models in entertainment? What resistance did you encounter from record labels and rights holders in the early days? When did you realize the model was bigger than a niche collector audience?4: What Audiences Teach Us About CreativityOne of the fascinating aspects of your career is that you've spent decades observing how audiences reconnect with older creative work. What patterns have you seen in how people rediscover music, television, and film from previous generations? And what does that tell us about human creativity, nostalgia, storytelling, and cultural memory?5: The Future of Creativity, Archives, and Cultural LegacyToday, entertainment companies increasingly view their catalogs as some of their most valuable assets.Streaming platforms are built on libraries. Vinyl sales continue to grow. Legacy content often outperforms new releases. What does this shift tell us about the future of creativity and content consumption? And what advice would you give today's creators about building work that remains valuable decades from now?If you enjoyed today's conversation, please subscribe, rate, and review the podcast.

Every week, we cover the three biggest stories in manufacturing, and the implications they have on the industry moving forward. This week:New Balance May Donate Shuttered Factory to the Town it VacatedAbout two years ago, New Balance announced plans to close its plant in Norridgewock, Maine. The move surprised pretty much everyone because the shoemaker is known for its U.S.-based manufacturing footprint.Ammo Factory Fails to Produce Any Shells After $469M Army InvestmentAfter sending many, many shells to Ukraine, the Army tasked General Dynamics and its existing ammunition plant in Mesquite, Texas, with manufacturing 30,000 M795 High Explosive 155 mm shells each month by October 2025. But a new report said the plant hasn't produced a single shell in two years of operation.Northwestern Engineers Made a ‘Phantom' Drone That Vanishes in Plain SightEngineers at Northwestern University have created a drone that appears to vanish in plain sight after it takes flight. The Phantom Twist is a drone that uses a concept called "motion blur" — the effect that makes it seemingly impossible to see fast-spinning blades on fans or Road Runner's legs.In Case You Missed ItPairs of Glasses Are Boosting Productivity in Some Garment FactoriesOperators who received reading glasses also made fewer errors.3D-Printed, Patient-Specific Contact Lenses in Just 20 MinutesIt uses a newly developed silicone material.TRISO-X Plots Massive Advanced Nuclear Fuel Expansion in TennesseeLast week, TRISO-X announced tentative plans to create some 1,140 new jobs in Oak Ridge, Tennessee.Please make sure to like, subscribe and share the podcast. And to email the podcast, you can reach any of us at Jeff, Anna or David@ien.com, with “Email the Podcast” in the subject line. Subscribe to our daily and weekly newsletters.

Wednesday, July 24th 2024 Fact checking claims and combating misinformation about Kamala Harris; the Trump Campaign has filed a complaint with the FEC over Harris taking over the Biden war chest; the Harris Campaign has requested VP vetting materials for a short list of potential candidates; the Trump Campaign is second guessing their choice of JD Vance; Secret Service Director Kimberly Cheatle has resigned her position in the wake of the assassination attempt; President Biden is COVID negative and will address the nation tonight at 8 PM ET; Senator Menendez says his last day in the Senate is August 20th; a conversation with Tennessee Rep Gloria Johnson and her campaign to beat Senator Marsha Blackburn; plus Allison and Dana deliver your Good News. Our Guest Tennessee State House Rep. Gloria Johnson​​ (TN-90)Representative Gloria Johnson StoriesA Reader's Guide To MAGA's Racist And Misogynistic Attacks On Kamala Harris (Talking Points Memo) FACT FOCUS: A look at false claims around Kamala Harris and her campaign for the White House (AP News) Trump campaign files complaint over transfer of Biden funds to Harris (NYT) Harris campaign requests vetting materials from several possible running mates (NBC News) Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Missing Workers Were Never Meant to Leave the Salt FactoryBecome a supporter of this podcast: https://www.spreaker.com/podcast/dark-mysteries-unsolved-mysteries-forgotten-secrets-unanswered-questions--5684156/support.Darkest Mysteries Online

This week, the Queens are back with a miniature version of a longtime favorite. Will Snack Factory Bites secure a spot next to Pop'ums in our regular rotation?

In this China Manufacturing Decoded Gold episode from 2021, host Adrian from the Sofeast Group revisits a practical conversation with Renaud about preventive maintenance and why it is essential for any manufacturing operation. The episode breaks down what preventive maintenance means in practice, contrasts it with reactive maintenance, and explains how a planned approach reduces defects, downtime, and lost capacity. The discussion covers concrete examples and formats for preventive maintenance: daily operator checks (cleaning filters, spotting leaks), weekly technician inspections (belts, pulleys, early wear), and age- or cycle-based actions (tooling or seal replacements before mean time to repair thresholds). Renaud uses simple analogies (like dental checkups) and real-world scenarios to show how small, scheduled interventions keep machinery 'as new' and prevent cascading quality and scheduling problems. Who should listen? Factory managers, operations leaders, procurement and engineering teams, and anyone involved in automation or production planning. Expect a clear, actionable primer on how preventive maintenance reduces cost and risk, helps deliver on customer promises, and enables smarter automation over time.   Show Sections 00:00 – Introduction 01:10 – The reactive maintenance cycle 03:06 – What preventive maintenance means 04:29 – Is preventive maintenance expensive? 07:42 – Daily, weekly, and usage-based maintenance 11:52 – Why factories resist planned maintenance 13:05 – Maintenance skills and automation 15:32 – Capacity, inventory, and delivery benefits 18:17 – Quality, 5S, and process control 20:12 – Planned maintenance versus emergency repairs 20:43 – Conclusion   Related content Preventive Maintenance Plan Template Is Investing In Process Automation In The Factory Worth It? + Implementation Best Practices [Podcast] Two Preventive Maintenance Examples: Go for More Structure Are Preventive Maintenance and Process Control the Same Thing? Manufacturing Cycle Time (Part 1): Machining Optimization Why Inconsistent Component Variation Is Bad for Assembly & Product Quality 10 Key Factors That Affect Supplier Production Capacity 3 Key Process Improvement Tools You Need To Start Using: Flow Chart, FMEA, Control Plan Get in touch with us Connect with us on LinkedIn Contact us via Sofeast's contact page Subscribe to our YouTube channel Prefer Facebook? Check us out on FB

What does Dame Angela Eagle’s appointment as Defra secretary mean for UK agriculture? This week, we examine the new secretary of state’s priorities, political influence and ability to secure the long-term budgets farmers say they need. We also hear from NFU Sugar chairman Kit Papworth following British Sugar’s proposal to stop processing beet at its Cantley factory in Norfolk. At the Royal Welsh Show, farmers respond to a three-year, £340m-a-year funding settlement for the Sustainable Farming Scheme. We pay tribute to NFU livestock board chairman David Barton, hear how the drought is affecting harvest and examine rising farmgate prices. And during Farm Safety Week, combine fire survivor Andrew Williamson explains why machinery can be replaced—but people cannot. This episode is brought to you by seed breeder KWS. Chapters 00:00 – Introduction00:51 – What will Angela Eagle mean for farming?05:58 – A farmer-friendly appointment at Defra?07:37 – Cantley sugar factory faces closure12:30 – Can Britain rebuild its rural economy?18:21 – Imported cane sugar versus home-grown beet23:09 – Welsh farmers receive funding certainty27:48 – Has Wales found a better approach?33:45 – Remembering David Barton36:41 – Harvest and drought update38:52 – Abby’s new investigative role40:03 – Farm markets42:38 – Combine fires and Farm Safety Week46:02 – Why farm safety is not improving Special Guests: Dame Angela Eagle – secretary of state for environment, food and rural affairsKit Papworth – Norfolk sugar beet grower and chairman of the NFU Sugar BoardAndrew Williamson – farmer and vice-chairman of the NFU Combinable Crops Board The episode is presented by Johann Tasker, Abi Kay, Hugh Broom and Anne Dunn. Useful links ⁠British Sugar’s announcement on the proposed Cantley closure ⁠NFU Sugar: Growers need certainty following Cantley proposal ⁠Welsh Government: £1bn multi-year funding commitment ⁠NFU Cymru response to the funding settlement ⁠Farm Safety Foundation – Yellow Wellies ⁠Farm Safety Week 2026 ⁠Farmers Weekly: Level the Field – physical health in farming ⁠KWS UK Email the podcast team at podcast@fwi.co.uk. Listeners in the UK can text FARM, followed by their message, to 88440.See omnystudio.com/listener for privacy information.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

We revisit StarFox 64 as an addendum to our StarFox conversation from last time, as well as talk Rhythm Heaven Groove, Forza Horizon 6, R-rated movies that grossed a billion dollars, a new round of Place Your Bets, and much more on this brand new Big Deal. Fine Time on Bluesky: @fineti.me Andre: @pizzadinosaur.fineti.me Steve: @monotonegent.fineti.me Kevin: @kevinflevin89.fineti.me [00:00] Intro [02:39] Rhythm Heaven Groove [21:20] Forza Horizon 6 [35:14] Shower Time [38:50] StarFox 64 Revisited [01:06:43] Microsoft Studios Go Independent Again [01:14:17] Bethesda's Insane Future Plans [01:29:00] Sony's FlexStrike Goes MIA [01:35:15] Consumer Digital Rights In The Future [01:42:11] Billion-Dollar R-Rated Movies [01:47:15] Place Your Bets! [01:59:53] See Ya!

Today in 1945, the Seattle Daily Times newspaper revealed a great wartime hoax: for several years, a key military factory had been cosplaying as a neighborhood! Plus: Hyve is an autonomous off-road rover that hosts a colony of live bees and lets them sort of go on a pollination tour. Boeing made an entire fake neighborhood to hide its bombers from potential WWII airstrikes (Seattle Times)nicolas nielsen designs self-driving HYVE beehive to pollinate cities (designboom)Join our neighborhood of backers on Patreon today

America has lived through technological and economic upheaval before. Farm workers moved to factories. Factory workers moved into services. New industries replaced old ones. Productivity rose. Living standards improved. But are we ready for the greatest disruption in American history? Ben Gilliland, author of the upcoming book Breaking the Compact, joins Business Security Weekly to discuss why business leaders need to be prepared for the upcoming AI disruption. The impact of AI, which has not fully materialized, goes far beyond security and job displacement. It will impact our economy, our privacy, and our way of life. The closest recent warning is the "China shock," the period of rapidly increasing import competition that followed China's integration into the global trading system. AI will dwarf that. Ben will discuss the human advantage and how we can prepare now. In the leadership and communications segment, Cybersecurity's Economics Are Broken. Automation Alone Won't Fix It, The business case for burning down security debt: A practical approach for CISOs, The last human relationship in cybersecurity, and more! Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-457

British Sugar's factory at Cantley in the Norfolk Broads is to close, in February next year. Hundreds of farmers will now have to transport their crop much further afield for processing. The Cantley factory has been operating for more than a century, and currently processes about 120,000 tonnes of sugar every year, from beet grown by local farmers. It employs just over 100 staff.As prime minister Andy Burnham's new cabinet members settle into their new jobs, the Secretary of State at the Department for Environment, Food and Rural Affairs, Angela Eagle, will feel quite at home. She was Farming Minister at Defra up until six weeks ago when she was transferred briefly to security - but now she's back, and promoted to Secretary of State.This year's harvest is really 'everything, everywhere, all at once' and it's causing a logistical nightmare. Barley, oil seed rape and wheat are being harvested at the same time - and that's putting pressure on the lorries needed to get it to storage. We also hear from farmers who've been affected by big fires on their land, and how farming neighbours have played an essential role in bringing the fires under control.All this week we're taking a closer look at Welsh Agricultural Policy. Agriculture is devolved, so it's up to each of the four nations to decide on their own post-Brexit subsidy policy. In Wales, farmers are paid under the Sustainable Farming Scheme, to bring in conservation practices alongside food production. We speak to a hill farmer to find out what's in it for her.Presenter = Anna Hill Producer = Rebecca Rooney

America has lived through technological and economic upheaval before. Farm workers moved to factories. Factory workers moved into services. New industries replaced old ones. Productivity rose. Living standards improved. But are we ready for the greatest disruption in American history? Ben Gilliland, author of the upcoming book Breaking the Compact, joins Business Security Weekly to discuss why business leaders need to be prepared for the upcoming AI disruption. The impact of AI, which has not fully materialized, goes far beyond security and job displacement. It will impact our economy, our privacy, and our way of life. The closest recent warning is the "China shock," the period of rapidly increasing import competition that followed China's integration into the global trading system. AI will dwarf that. Ben will discuss the human advantage and how we can prepare now. In the leadership and communications segment, Cybersecurity's Economics Are Broken. Automation Alone Won't Fix It, The business case for burning down security debt: A practical approach for CISOs, The last human relationship in cybersecurity, and more! Show Notes: https://securityweekly.com/bsw-457

America has lived through technological and economic upheaval before. Farm workers moved to factories. Factory workers moved into services. New industries replaced old ones. Productivity rose. Living standards improved. But are we ready for the greatest disruption in American history? Ben Gilliland, author of the upcoming book Breaking the Compact, joins Business Security Weekly to discuss why business leaders need to be prepared for the upcoming AI disruption. The impact of AI, which has not fully materialized, goes far beyond security and job displacement. It will impact our economy, our privacy, and our way of life. The closest recent warning is the "China shock," the period of rapidly increasing import competition that followed China's integration into the global trading system. AI will dwarf that. Ben will discuss the human advantage and how we can prepare now. In the leadership and communications segment, Cybersecurity's Economics Are Broken. Automation Alone Won't Fix It, The business case for burning down security debt: A practical approach for CISOs, The last human relationship in cybersecurity, and more! Visit https://www.securityweekly.com/bsw for all the latest episodes! Show Notes: https://securityweekly.com/bsw-457

Send us Fan MailOur Adventurers happen upon a distillery of the Red Water Potion that seems to be permeating the forest floor with its fog. They see a group of sleepwalking zombies surrounding the owner of the Factory, who happens to have access to the party's ride to Sharn. Support the show

Factory started building fully autonomous coding agents in April 2023, two years before enterprises were ready. Matan Grinberg now says this is indistinguishable from being wrong. The Factory co-founder and CEO explains how the company survived its "journey in the desert," including the decision to hand nearly all of its revenue back to customers when the product wasn't making developers obsessed. Matan makes the contrarian technical case that a model-agnostic harness beats the model-and-harness co-design that labs like OpenAI and Anthropic favor, because exposing a harness to many models keeps it from overfitting to any single one. He argues open-weight models like GLM will capture the majority of tokens by staying one generation behind the frontier at a fraction of the cost, and that CIOs will soon justify every incremental token the way they justify headcount. Looking ahead, he predicts 90% of coding tokens will run asynchronously—the "dark factory" where software builds itself. Hosted by Sonya Huang and Pat Grady, Sequoia Capital

What did you have on your face? DJ Will's National Tour Bedroom divorce Battle Of The Gens Midnight ideas See omnystudio.com/listener for privacy information.

Andy Warhol was the first artist to achieve rock star status. He was a Beatle with a silkscreen printer. His work and play space, the Factory, attracted people of all ages; rich and poor, straight and gay, sane and…not so sane. It was in the Factory that he was shot by a would-be assassin. He was rushed to a hospital and pronounced clinically dead. But Andy Warhol's second life began the moment he was resurrected on an operating table. As soon as his heart began to beat again, he became a true cultural icon – bigger than his paintings or his Polaroids or his experimental films, bigger than life itself. Andy Warhol became the future.See omnystudio.com/listener for privacy information.

What if you can't actually manage your people, and every hour you spend trying is the reason your restaurant won't scale?Seth Gerber co-owns MIDA, a six-restaurant Italian group out of Boston, and was a 2024 James Beard semifinalist for Best Restaurateur. He came up through Hillstone, earned his sommelier pin and an MBA, and treats outlearning everyone in the room as his only real edge.In this conversation, we get into why you manage the structure people work in and never the people themselves, why the cinematic version of risk that bets it all on one move is a fantasy that quietly ruins operators, and why the best restaurateurs grow slower than their egos want them to.If you're grinding inside your business and still feel like the bottleneck, this one tells you where the real problem is.To learn more about MIDA and their growing collection of neighborhood Italian restaurants, visit midarestaurant.com._________________________________________________________Free 5-Day Restaurant Marketing Masterclass – This is a live training where you'll learn the exact campaigns Josh has built and tested in real restaurants to attract new guests, increase visit frequency, and generate sales on demand. Save your spot at restaurantbusinessschool.com

Get away! Unless you want to hear us talk about the demos and alternate versions of songs They Might Be Giants cooked up from the Factory Showroom era! There's too many lo-fi delights to list here, but let's just say, incidentally, you might want to buy your Token Back to Brooklyn, head to New York City, and hitch a ride on a Rocket Ship! What are you, some sort of Alternate Take of I Can Hear You?Stop standing around like there's something to get! Sit down and listen!

If your custom fabrication facility looks like a cold, dusty, uninspired warehouse, you are actively losing high-ticket B2B architectural clients. Modern architects and design executives don't want to buy signs out of a catalog; they want to walk into a playground of spatial possibilities.In this episode of The AC Method, Aaron Clippinger sits down with Rob Stewart, a 37-year graphic industry veteran from Cincinnati's Harlan Graphics. Rob reveals how their 40,000 square foot architectural hub has been transformed into a physical "Willy Wonka's Chocolate Factory" where corporate clients touch materials, test custom prototypes, and conceptualize ideas right on the floor.They break down the extreme financial risk of underutilizing your high-end print equipment, the critical sales close that preserves margins during "value engineering" negotiations, and how keeping a physical, $500k in-stock swatch book closes emergency, short-turnaround deals.

How did Moneyball Black go from an experimental mono-black brew to one of Premodern's defining archetypes?In this Masterclass, David Gleicher, the creator of Moneyball Black, sits down with Zac to explain the philosophy behind the deck, its evolution over time, why every card earned its place, and how to approach both deckbuilding and sideboarding. Whether you're sleeving up the deck for your next tournament or simply want to understand why it's become a format staple, this is the definitive guide. We cover:The origin of Moneyball BlackWhy mono-black works in PremodernThe "Moneyball" philosophyRavenous Rats vs. Black KnightUrza's Bauble technologyWhy Dark Ritual is still essentialCursed Scroll's role in the deckGraveborn Muse vs. Phyrexian ArenaMishra's Factory and Spawning PoolPlague Bearer and other key card choicesSideboarding philosophyMatchup guides against the format's top decks Whether you're new to the archetype or have been playing it for years, there's something here for every Moneyball Black pilot.#Premodern #MagicTheGathering #MoneyballBlack #MTG #Masterclass00:00 Introducing Moneyball Black00:22 Why David Built the Deck01:58 Early Suicide Black Lists04:40 The Birth of Moneyball Black07:15 Why Play Moneyball Black?07:53 The "Moneyball" Philosophy09:32 Ravenous Rats Explained12:55 The Evolution of the Deck14:14 Black Knight vs. Ravenous Rats16:47 Urza's Bauble Technology19:07 Why Cursed Scroll Matters23:22 Dark Ritual's Role28:06 Mishra's Factory & Spawning Pool30:05 Plague Bearer Tech31:35 Graveborn Muse vs. Phyrexian Arena34:25 Sideboarding PhilosophyTimestamps

How did Moneyball Black go from an experimental mono-black brew to one of Premodern's defining archetypes?In this Masterclass, David Gleicher, the creator of Moneyball Black, sits down with Zac to explain the philosophy behind the deck, its evolution over time, why every card earned its place, and how to approach both deckbuilding and sideboarding. Whether you're sleeving up the deck for your next tournament or simply want to understand why it's become a format staple, this is the definitive guide. We cover:The origin of Moneyball BlackWhy mono-black works in PremodernThe "Moneyball" philosophyRavenous Rats vs. Black KnightUrza's Bauble technologyWhy Dark Ritual is still essentialCursed Scroll's role in the deckGraveborn Muse vs. Phyrexian ArenaMishra's Factory and Spawning PoolPlague Bearer and other key card choicesSideboarding philosophyMatchup guides against the format's top decks Whether you're new to the archetype or have been playing it for years, there's something here for every Moneyball Black pilot.#Premodern #MagicTheGathering #MoneyballBlack #MTG #Masterclass00:00 Introducing Moneyball Black00:22 Why David Built the Deck01:58 Early Suicide Black Lists04:40 The Birth of Moneyball Black07:15 Why Play Moneyball Black?07:53 The "Moneyball" Philosophy09:32 Ravenous Rats Explained12:55 The Evolution of the Deck14:14 Black Knight vs. Ravenous Rats16:47 Urza's Bauble Technology19:07 Why Cursed Scroll Matters23:22 Dark Ritual's Role28:06 Mishra's Factory & Spawning Pool30:05 Plague Bearer Tech31:35 Graveborn Muse vs. Phyrexian Arena34:25 Sideboarding PhilosophyTimestamps

Stewart Florsheim shares about his most memorable summer job experience.

The Alan Cox ShowSee omnystudio.com/listener for privacy information.

The Alan Cox Show

The era of the "two-pizza" engineering team is officially dead, replaced by the "two-slice" team and a massive token budget. This week, LaunchDarkly CTO Cameron Etezadi joins the show to explain why traditional guardrails are breaking down and how engineering teams can regain control using runtime agent frameworks. He introduces the concept of the "dark factory," a highly automated assembly line for safely observing, flagging, and deploying AI-generated code to production. The conversation turns to the new ROI of software development, why engineers must now act as frontline managers, and how to navigate the build-versus-buy dilemma in the modern token economy. As AI speeds up how code gets written, the real bottleneck moves downstream to review, testing, and release, where software either delivers measurable value or quietly stalls. Check out the latest research from LinearB on how to measure that value.Life Beyond Tokenmaxxing Workshop: Watch the full replay on demand at linearb.io Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:LaunchDarkly AgentControl: Learn how to govern your probabilistic AI with runtime agent frameworks at launchdarkly.com/platform/agent-controlThe Goal by Eliyahu M. Goldratt: Read the quintessential business novel on the theory of constraints at AmazonThe Phoenix Project by Gene Kim: Explore the seminal book on IT, DevOps, and business success at IT RevolutionSimAnt: Dive into the history of the 1991 classic electronic ant colony simulation at Wikipedia.Follow Cameron: LinkedIn OFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

CrossPolitic One-on-One — Show Notes: China Update with Missionary Ben On this episode of CrossPolitic One-on-One, the Waterboy sits down with Missionary Ben — host of China Compass on the Fight Laugh Feast Network and himself a deportee from China — for an update on the underground church. The conversation covers Pastor Ezra Jin's miraculous July 4th release from prison (and the eight still behind bars), the latest crackdown on Pastor Wang Yi's church network, how Christians should think about doing business with China, and what the American church can learn from brothers and sisters who are forced to make hard decisions for their faith every single day. Timestamps 03:35 — Intro — Fight Laugh Feast conference update and Business Makers Network 07:06 — Meet Missionary Ben — deported from China, host of China Compass 08:11 — Pastor Ezra Jin: from Tiananmen Square to Zion Church — and his July 4th release 10:32 — How Zion Church grew and why it became a target — the 2008 Sichuan earthquake and Xi Jinping's crackdown 12:22 — October 2024: Jin's arrest, the simultaneous network raid, and the eight still in prison 13:04 — Did Trump broker the release? The May meeting with Xi and why this one worked when others haven't 14:45 — Jin's Wall Street Journal op-ed: "I'm Free Now. Pray for China." — Hebrews 13:3 16:19 — Zion Church splits into house churches — and may now be larger than before the arrest 17:22 — Why not just join the state-sanctioned church? — a stopgap, not a home 18:16 — How should Christians think about doing business with China? 21:05 — What can ordinary believers do? — going to China, sharing the gospel, meeting the church 23:32 — How should Trump engage Xi? — befriending, influencing those around him, holding cards close 26:46 — What the American church can learn from the underground church in China 28:43 — Pastor Wang Yi update — seven and a half years in, sentence nearing end, no word from him directly 30:01 — Recent crackdown: 50 SWAT officers at a Wang Yi church plant, 20–30 arrested 32:22 — Missionary Ben's book Unbeaten, Mission Catalyst, and the Malaysia conference for Chinese house church leaders Connect with Missionary Ben Podcast: China Compass (weekends) & Prison Pulpit Series (midweek) Network: Fight Laugh Feast Network — available in the FLF app Book: Unbeaten — $4 on Amazon Ministry: Mission Catalyst International — training indigenous pastors and missionaries Give: mci3.org Fight Laugh Feast 2026: Holy Wars Join us October 1–3 in Franklin, Tennessee at The Factory in Franklin for the Fight Laugh Feast Conference. This year's theme is Holy Wars — Just War, the Crusades, and the Christian Life. Featuring Doug Wilson, Joe Boot, Pastor Toby, Ben Merkel, Jared Longshore, and more. Pre-business makers conference Thursday 1–5 PM. Pastor/church leadership luncheon Friday. Business luncheon Saturday. Booths are nearly full — sponsorship opportunities still available. Tickets: https://tickets.flfnetwork.com/holy-wars-conference Sponsorship inquiries: waterboy@crosspolitic.com Also Mentioned Business Makers Network A network for courageous Christian business leaders — building a Christian economy for the good of businesses, churches, and America. Over 100 members, quarterly CEO calls, and a national conference each May. Website: businessmakers.network Contact: waterboy@crosspolitic.com About CrossPolitic CrossPolitic exists to put Jesus over Politics and reclaim the public square through bold, joyful, biblically grounded media. We confront the chaos discipling America and build the next generation of Christian media infrastructure. Our mission is simple: all of Christ for all of media for all of America. Mainstream media is collapsing. Eighty-seven percent of journalists identify as progressive, and even many conservative outlets prioritize profit over principle. Meanwhile, billions of hours of digital content are discipling the world every day. CrossPolitic stands in that gap, producing courageous, entertaining, truth-filled media for households, churches, and leaders across the nation. Become a CrossPolitic Club Member Support the mission and unlock exclusive content, behind-the-scenes shows, and theology series. https://pubtv.flfnetwork.com/menu/checkout Subscribe & Share! Every like, comment, and share helps push Christian media back into the algorithm where it belongs. Follow CrossPolitic YouTube: https://www.youtube.com/@CROSSPOLITIC X: https://x.com/CrossPolitic Facebook: https://facebook.com/crosspolitic Instagram: https://instagram.com/crosspolitic Join our Email List: https://crosspolitic.com/ Available on Apple Podcasts, Spotify, NRBTV, DirecTV, Dish, and everywhere podcasts are found. #CrossPolitic #ChinaChurch #UndergroundChurch #PastorEzraJin #WangYi #MissionaryBen #ChristianPersecution #ChristianMedia

CrossPolitic One-on-One — Show Notes: China Update with Missionary Ben On this episode of CrossPolitic One-on-One, the Waterboy sits down with Missionary Ben — host of China Compass on the Fight Laugh Feast Network and himself a deportee from China — for an update on the underground church. The conversation covers Pastor Ezra Jin's miraculous July 4th release from prison (and the eight still behind bars), the latest crackdown on Pastor Wang Yi's church network, how Christians should think about doing business with China, and what the American church can learn from brothers and sisters who are forced to make hard decisions for their faith every single day. Timestamps 03:35 — Intro — Fight Laugh Feast conference update and Business Makers Network 07:06 — Meet Missionary Ben — deported from China, host of China Compass 08:11 — Pastor Ezra Jin: from Tiananmen Square to Zion Church — and his July 4th release 10:32 — How Zion Church grew and why it became a target — the 2008 Sichuan earthquake and Xi Jinping's crackdown 12:22 — October 2024: Jin's arrest, the simultaneous network raid, and the eight still in prison 13:04 — Did Trump broker the release? The May meeting with Xi and why this one worked when others haven't 14:45 — Jin's Wall Street Journal op-ed: "I'm Free Now. Pray for China." — Hebrews 13:3 16:19 — Zion Church splits into house churches — and may now be larger than before the arrest 17:22 — Why not just join the state-sanctioned church? — a stopgap, not a home 18:16 — How should Christians think about doing business with China? 21:05 — What can ordinary believers do? — going to China, sharing the gospel, meeting the church 23:32 — How should Trump engage Xi? — befriending, influencing those around him, holding cards close 26:46 — What the American church can learn from the underground church in China 28:43 — Pastor Wang Yi update — seven and a half years in, sentence nearing end, no word from him directly 30:01 — Recent crackdown: 50 SWAT officers at a Wang Yi church plant, 20–30 arrested 32:22 — Missionary Ben's book Unbeaten, Mission Catalyst, and the Malaysia conference for Chinese house church leaders Connect with Missionary Ben Podcast: China Compass (weekends) & Prison Pulpit Series (midweek) Network: Fight Laugh Feast Network — available in the FLF app Book: Unbeaten — $4 on Amazon Ministry: Mission Catalyst International — training indigenous pastors and missionaries Give: mci3.org Fight Laugh Feast 2026: Holy Wars Join us October 1–3 in Franklin, Tennessee at The Factory in Franklin for the Fight Laugh Feast Conference. This year's theme is Holy Wars — Just War, the Crusades, and the Christian Life. Featuring Doug Wilson, Joe Boot, Pastor Toby, Ben Merkel, Jared Longshore, and more. Pre-business makers conference Thursday 1–5 PM. Pastor/church leadership luncheon Friday. Business luncheon Saturday. Booths are nearly full — sponsorship opportunities still available. Tickets: https://tickets.flfnetwork.com/holy-wars-conference Sponsorship inquiries: waterboy@crosspolitic.com Also Mentioned Business Makers Network A network for courageous Christian business leaders — building a Christian economy for the good of businesses, churches, and America. Over 100 members, quarterly CEO calls, and a national conference each May. Website: businessmakers.network Contact: waterboy@crosspolitic.com About CrossPolitic CrossPolitic exists to put Jesus over Politics and reclaim the public square through bold, joyful, biblically grounded media. We confront the chaos discipling America and build the next generation of Christian media infrastructure. Our mission is simple: all of Christ for all of media for all of America. Mainstream media is collapsing. Eighty-seven percent of journalists identify as progressive, and even many conservative outlets prioritize profit over principle. Meanwhile, billions of hours of digital content are discipling the world every day. CrossPolitic stands in that gap, producing courageous, entertaining, truth-filled media for households, churches, and leaders across the nation. Become a CrossPolitic Club Member Support the mission and unlock exclusive content, behind-the-scenes shows, and theology series. https://pubtv.flfnetwork.com/menu/checkout Subscribe & Share! Every like, comment, and share helps push Christian media back into the algorithm where it belongs. Follow CrossPolitic YouTube: https://www.youtube.com/@CROSSPOLITIC X: https://x.com/CrossPolitic Facebook: https://facebook.com/crosspolitic Instagram: https://instagram.com/crosspolitic Join our Email List: https://crosspolitic.com/ Available on Apple Podcasts, Spotify, NRBTV, DirecTV, Dish, and everywhere podcasts are found. #CrossPolitic #ChinaChurch #UndergroundChurch #PastorEzraJin #WangYi #MissionaryBen #ChristianPersecution #ChristianMedia

What are you really worshiping? Every city has its idols. Athens had temples to gods of wisdom, power, and victory. We have our own. The idol of intellect. The idol of political power. The idol of approval. An idol is anything you trust more than God, fear more than God, or love more than God. And here is the sobering truth from Psalm 115: you become what you worship. The unknown God of ancient Athens is not unknown anymore. He has a name. Are you ready to find out what it means to worship Him alone? Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Robach and Holmes cover the latest news headlines and entertainment updates and give perspective on current events in their daily “Morning Run.”See omnystudio.com/listener for privacy information.

Robach and Holmes cover the latest news headlines and entertainment updates and give perspective on current events in their daily “Morning Run.”See omnystudio.com/listener for privacy information.

Robach and Holmes cover the latest news headlines and entertainment updates and give perspective on current events in their daily “Morning Run.”See omnystudio.com/listener for privacy information.

Marty sits down with Francis Pouliot to discuss Bull Bitcoin's legal fight against DAC8 and MiCA in France, the dangers of FATF-driven surveillance regimes, and the Bitcoin privacy tools like PayJoin and Silent Payments that builders are deploying to resist global KYC overreach. Francis on X: https://x.com/francispouliot_ Bull Bitcoin: https://www.bullbitcoin.com/ Stop DAC8: https://dac8.com/en/ Find the Home Mining Playbook here: https://www.tftc.io/home-mining-energy-playbook STACK SATS hat: https://tftcmerch.io/ Our newsletter: https://www.tftc.io/bitcoin-brief/ TFTC Elite (Ad-free & Discord): https://www.tftc.io/#/portal/signup/ Discord: https://discord.gg/yHGkvYxdqT Opportunity Cost Extension: https://www.opportunitycost.app/ Shoutout to our sponsors: Bitkey https://bitkey.world/ Aven https://www.aven.com/bitcoin CrowdHealth https://www.joincrowdhealth.com/tftc Unchained https://unchained.com/tftc/ Salt of the Earth: https://drinksote.com/tftc Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Newsletter tftc.io/bitcoin-brief/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Nostr https://primal.net/tftc Follow Marty Bent: Twitter https://twitter.com/martybent Nostr https://primal.net/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://www.tftc.io/tag/podcasts/