Podcasts about SF

Marti, August 4 - Sfintii 7 tineri din Efes; Sf. Cuv. Mc. Evdochia

Silver and JD react to the breaking news that SF is shipping Robbie Ray to the Padres and we get the fellas' thoughts on SF's trade with Philly regarding Luis ArraezSee omnystudio.com/listener for privacy information.

Luni, August 3 - Cuv. Isachie, Dalmat si Faust; Sf. Mironosita Salomeea

The Blasters & Blades PodcastReclaiming the Lost Space Age: Inside John E. Siers' Lunar Free State SeriesWhat if humanity never gave up on the dream of the Moon? Inspired by the legacy of Apollo 11 and classic hard science fiction giants like Robert A. Heinlein, author John E. Siers crafted a massive, 12+ book reality where humanity didn't stop in 1972. In this episode, we sit down with Siers to discuss his expansive Lunar Free State universe. We're tracking the journey from the first secret lunar base in The Moon and Beyond to a massive interstellar playground filled with political tension, deep space exploration, and human grit. Tune in as we discuss his 25+ year journey as a writer, how his military background shapes his tactical space combat, and why a self-sustaining lunar civilization remains the ultimate sci-fi frontier. This was a fun interview, so go check out this episode. Join us for a fun show! We're just a couple of nerdy Army veterans geeking out on things that go "abracadabra," "pew," "zoom," "boop-beep" and rhyme with Science Fiction & Fantasy. Co-Hosts: JR Handley (Author) (Grunt)Nick Garber (Comic Book Artist) (Super Grunt)Madam Stabby Stab (Uber Fan) (Horror Nerd)Jana S Brown (Author) (Chief Shenanigator)We work for free, so if you wanna throw a few pennies our way there is a linked Buy Me A Coffee site where you can do so. Just mention the podcast in the comments when you donate, and I'll keep the sacred bean water boiling!Support the Show: https://www.buymeacoffee.com/AuthorJRHandley Our LinkTree: https://linktr.ee/blastersandbladespodcast Today's SponsorSupercarrier by Scott Bartlett: https://www.amazon.com/gp/product/B0DKGB6GQ2/ The Lunar Free State Series by John E. Siers: https://www.amazon.com/dp/B097R12215 Follow John E. Siers on social mediaJohn's Amazon: https://www.amazon.com/stores/John-E.-Siers/author/B00J9YV6LU John's Website: https://lunarfreestate.com/ John's Twitter: https://x.com/JohnSiers7987 John's Facebook: https://www.facebook.com/John.Siers/ John's Patreon: https://www.patreon.com/user?u=48552283John's GoodReads: https://www.goodreads.com/author/show/6591389.John_E_Siers John's Store: https://lunarfreestate.com/shop-the-lfs-store The Lunar Free State Facebook: https://www.facebook.com/SF.Worlds.of.John.E.Siers#scifishenanigans #scifishenaniganspodcast #bbp #blastersandblades #blastersandbladespodcast #podcast #scifipodcast #fantasypodcast #scifi #fantasy #books #rpg #comics #fandom #literature #comedy #veteran #army #armyranger #ranger #scififan #redshirts #scifiworld #sciencefiction #scifidaily #scificoncept #podcastersofinstagram #scificons #podcastlife #podcastsofinstagram #scifibooks #awardwinningscifi #newepisode #podcastersofinstagram #podcastaddict #podcast #scifigeek #scifibook #sfv #scifivisionaries #firesidechat #chat #panel #fireside #religionquestion #coffee #tea #coffeeortea #CoffeeBrandCoffee #JRHandley #NickGarber #MadamStabby #JenaRey #JanaSBrown #OpalKingdomPress #JohnESiers #TheLunarFreeStateSeries #LunarFreeState #starwars #jedi #georgelucas #lucasfilms #startrek #trekkie #firefly #serenity #browncoat #wheeloftime #wot #robertjordan #brandonsanderson #gameofthrones #got #grrm #georgerrmartin #ChroniclesofNarnia #CSLewis #Trex #TyrannosaurusRex #ChrisKennedy #TheMoonIsAHarshMistress #FlashGordon #TheShipWhoSang #AnneMcCaffery #Existentialism #ExistentialLiterature #kafkaesque #FranzKafka #Heinlein #RobertAHeinlein #IsaacAsimov #Asimov #ArthurCClarke #LunarFreeState #JohnESiers #LunarColony #MoonBase #ColonizeTheMoon #SpaceExploration #HardSciFi #HardScienceFiction #SpaceOpera #PoliticalSciFi #LibertarianSciFi #FreeState #ClassicSciFiVibes #HeinleinFans #VeteranAuthor #AirForceVeteran #Worldbuilding #SciFiAuthor #AuthorInterview #BookTok #Bookstagram #SciFiReads #HardSciFiBooks #IndieSciFi

Christine Hentschel zu Sicherheit und Aktivismus in katastrophischen Zeiten Future Histories LIVE Das Gespräch mit Christine Hentschel ist Teil des Formats ‚Future Histories LIVE‘. In unregelmäßigen Abständen werden hierbei einzelne Episoden live – soll heißen vor Publikum – aufgezeichnet. Diese Folge Future Histories ist am 12. Juli 2026 auf Einladung des Hamburger Künstler*innenkollektivs Zollo entstanden. Shownotes Christine Hentschel Prof. Dr. Christine Hentschel an der Universität Hamburg: https://www.wiso.uni-hamburg.de/fachbereich-sowi/professuren/hentschel/team/hentschel-christine.html DFG Humanities Centre for Advanced Studies “Futures of Sustainability”: https://www.zukuenfte-nachhaltigkeit.uni-hamburg.de zur DFG-Forschungsgruppe "The Promise of Security in Catastrophic Times“: https://www.wiso.uni-hamburg.de/en/forschung/verbundprojekte/ru-promise/welcome.html Nina Perkowski zu Reimagining Security: Perkowski, N. (2025). Radical Imagination and Vernacular Security: Creating Spaces for Alternative Security Futures, International Political Sociology, Volume 19, Issue 4.  https://academic.oup.com/ips/article/19/4/olaf033/8255613 Garcés, M. (2024). Versprechen Können. Turia + Kant.  https://www.turia.at/titel/garces3.html zu Tadzio Müller und solidarischem Preppen: Müller, T. (2024) Zwischen friedlicher Sabotage und Kollaps. Wie ich lernte, die Zukunft wieder zu lieben. Mandelbaum. https://www.mandelbaum.at/buecher/tadzio-mueller/zwischen-friedlicher-sabotage-und-kollaps/ https://www.collapseclub.com/blog/five-elements-of-solidarity-prepping/ zur Letzten Generation: https://letztegeneration.org/ zu Just Stop Oil: https://juststopoil.org/ zu ‘Dernière Rénovation' (Riposte Alimentaire): https://en.wikipedia.org/wiki/Riposte_Alimentaire Christine Hentschel zu Endzeitaktivismus und Edgework:  Hentschel, C. (2023). Edgework in post/apokalyptischen Zeiten. Soziopolis.  https://www.soziopolis.de/edgework-in-post-apokalyptischen-zeiten.html zur Kollapsbewegung: https://taz.de/Kollapsbewegung-in-der-Klimakrise/!6110821/ zu Heat Strike: https://heatstrike.uk/ zu Les Soulèvements de la Terre: https://de.wikipedia.org/wiki/Les_Soul%C3%A8vements_de_la_Terre Bröckling, U. (2026). Invertierte Zukunft. Konturen der Zuspätmoderne. WestEnd, 23(1), 3–18. https://doi.org/10.5771/1860-2177-2026-1-3 https://www.inlibra.com/de/document/view/pdf/uuid/dd3d743d-12ac-3db9-8d17-af67ddb150ba?page=1&toc=5916333 Reckwitz, A. (2024). Verlust: Ein Grundproblem der Moderne. Suhrkamp Verlag. https://www.suhrkamp.de/buch/andreas-reckwitz-verlust-t-9783518588222 Folkers, A. (2022). Nach der Nachhaltigkeit: Resilienz und Revolte in der dritten Moderne. Leviathan 50, 239–262. https://doi.org/10.5771/0340-0425-2022-2-239 https://www.jstor.org/stable/27295246?seq=1 zu Mutual Aid: https://en.wikipedia.org/wiki/Mutual_aid Christine Hentschel und Susanne Krasmann über gegenwärtige „Collapse Awareness“: Hentschel, C., & Krasmann, S. (2024). Collapse awareness in the face of climate breakdown and urbicide. Critical Studies on Security, 14(1), 2–16. https://doi.org/10.1080/21624887.2024.2403795 https://www.tandfonline.com/doi/full/10.1080/21624887.2024.2403795 Vortrag von Christine Hentschel „Vom Retten der Welt zum Vorbereiten auf den Kollaps“ in der Ringvorlesung “Am Scheidepunkt: Zur Krise der Demokratie” in Frankfurt (22.01.2026): https://www.youtube.com/watch?v=tmn7g3vJPKg Marina Garcés zur postumen Kondition und Metapher der Nachspielzeit: Garcés, M. (2019). Neue radikale Aufklärung. Verlag Turia+ Kant. https://www.turia.at/titel/garces.html zu postapokalyptischem Umweltaktivismus: Cassegård, C., & Thörn, H. (2022). Post-Apocalyptic environmentalism. palgrave macmillan.  https://link.springer.com/book/10.1007/978-3-031-13203-2 Anaïs Maurer zur postapokalyptischen Agency im Pazifik & ‘fighting for what's left': Maurer, A. (2024). The ocean on fire: Pacific stories from nuclear survivors and climate activists. Duke University Press.  https://www.dukeupress.edu/the-ocean-on-fire zu climate change mitigation: https://climatepromise.undp.org/news-and-stories/what-climate-change-mitigation-and-why-it-urgent Jacob Blumenfeld zu Managing Decline:  Blumenfeld, J. (2024). Managing Decline. Cured Quail. https://www.academia.edu/121062536/Managing_Decline zu Climate Barbarism: Blumenfeld, J. (2023). Climate barbarism: Adapting to a wrong world. Constellations, 30, 162–178.  https://onlinelibrary.wiley.com/doi/full/10.1111/1467-8675.12596 zu Christine Hentschels und Ursula Schröders Forschungsprojekt „Infrastructures of Protection. Planning for Catastrophes to Come“: https://www.wiso.uni-hamburg.de/en/forschung/verbundprojekte/ru-promise/research-projects/rp2.html zu Adaptation: https://climatepromise.undp.org/news-and-stories/what-climate-change-adaptation-and-why-it-crucial zum Forschungsprojekt „Extreme Adaptation to Climate Change“ im DFG-Exzellenzcluster CLICCS: https://www.cliccs.uni-hamburg.de/research/projects-themes/projects/a4-extreme-adaptation.html Lacbawan, M. (2025). Escaping the Climate Crisis: Speculative Wealth and the Selling of a Smart City. Journal of Business Anthropology, 14(2), 94-117. https://www.fis.uni-hamburg.de/en/publikationen/detail.html?id=13f172b2-6f23-44cc-adc7-31b42fb376b4 Rothe, D., Boas, I., Farbotko, C., & Kitara, T. (2024). Digital Tuvalu: state sovereignty in a world of climate loss. International Affairs, 100(4), 1491-1509. https://academic.oup.com/ia/article/100/4/1491/7710472 zu Muscular Adaptationism: Carton, W. & Malm, A. (2025). The long heat: climate politics when it's too late. Verso Books. https://www.versobooks.com/products/3317-the-long-heat?srsltid=AfmBOort8KE2C2ggxjCJE1Q7GTyEoyzLypZO0Wd75hcCmJUTuuW0zGa6 Joshua KaewnetarasForschung zu Wiederaufbau nach der Flutkatastrophe in Valencia: https://urban-future-making.hcu-hamburg.de/research/disrupted-mobilities Solar Geo Engineering: https://www.umweltbundesamt.de/presse/pressemitteilungen/unberechenbare-risiken-solares-geoengineering-keine Geoengineering: https://www.umweltbundesamt.de/en/topics/climate-energy/geoengineering zur Securitisation Theory: https://criticallegalthinking.com/2025/03/31/key-concept-securitization-copenhagen-school/ Schröder, U. (2026). Schutz in Krisenzeiten. Piper. https://www.piper.de/buecher/schutz-in-krisenzeiten-isbn-978-3-492-07396-7 zu Sicherheit im Anthropozän:  Rothe, D., Hentschel, C., & Schröder, U. (2025). Recomposing the climate-security nexus: A conceptual introduction. Geoforum, 159, 104195. https://www.sciencedirect.com/science/article/pii/S0016718524002562 Von Redecker, E. (2023). Bleibefreiheit. S. Fischer Verlag. https://www.fischerverlage.de/buch/eva-von-redecker-bleibefreiheit-9783596711260   zum Begriff der Infrastrukturen in der Sicherheitsforschung: Hentschel, C., Schröder, U., 2020. Democratising Security in Turbulent Times: An Infrastructural Lens. S+F 38, 191–194. https://doi.org/10.5771/0175-274X-2020-4-191 https://www.jstor.org/stable/pdf/27260104.pdf?refreqid=fastly-default%3Abf575832edd748b4b77595527737cd25&ab_segments=&initiator=&acceptTC=1 zu Nina Perkowskis Forschungsprojekt zu „Counter-Communities' Protection Practices and Security Utopias in Catastrophic Times“: https://www.wiso.uni-hamburg.de/en/forschung/verbundprojekte/ru-promise/research-projects/rp8.html Staab, P. (2022). Anpassung. Leitmotiv der nächsten Gesellschaft. Suhrkamp, Berlin. https://www.suhrkamp.de/buch/philipp-staab-anpassung-t-9783518127797 Saito, K. (2026). Am Ende des Fortschritts. Überleben in den Ruinen des Kapitalismus. dtv. https://www.dtv.de/buch/am-ende-des-fortschritts-28534 Garcés, M. (2022). Critical Imagination. Artnodes.  https://www.academia.edu/73676938/Critical_Imagination zum Thüringen Projekt: https://verfassungsblog.de/wp-content/uploads/2025/06/Thueringen-Projekt_Abschlussbericht.pdf zu Verwaltung für Demokratie: https://verwaltung-fuer-demokratie.de/erste-hilfe-kit-demokratie/ zu Zukunftswerkstätten nach Robert Jungk: https://www.sowi-online.de/praxis/methode/zukunftswerkstatt.html_2 zu climate change boredom: Anderson, B. (2023). Boredom and the politics of climate change. Scottish Geographical Journal, 139(1–2), 133–141. https://doi.org/10.1080/14702541.2023.2197869 https://www.tandfonline.com/doi/full/10.1080/14702541.2023.2197869 Christine Hentschel zu „Apokalypse-Indifferenz“ (Günther Anders) im Kontext der Klimakrise: Hentschel, C. (2024). Im Angesicht der planetaren Zerstörung. Soziologische Perspektiven gegen die Indifferenz., in: Lessenich, S., Scheffer, T. (Eds.), Gesellschaften Unter Handlungszwang: Existenzielle Probleme, Normalität Und Kritik, IfS-Aus Der Reihe. Bertz + Fischer, Berlin, pp. 78–92. https://bertz-fischer.b-cdn.net/wp-content/uploads/2026/06/bertz-fischer-ifs-02-gesellschaften-unter-handlungszwang-9783865056528.pdf zu Verleugnung nach Alenka Zupančič: Zupančič, A.(2024) Disavowal. Polity Books. https://www.politybooks.com/bookdetail?book_slug=disavowal--9781509561193 Christine Hentschel zu affectiven investments in den Protesten gegen die Coronamaßnamen Hentschel, C.(2021) »Das große Erwachen«: Affekt und Narrativ in der Bewegung gegen die Corona-Maßnahmen. Leviathan 49, 62–85.  https://www.inlibra.com/de/document/view/pdf/uuid/1ed695c2-d459-32aa-a482-bf64a34a412c zur mobilisierenden Kraft von Wut bei Audre Lorde: Lorde, A. (1997). The uses of anger. Women's Studies Quarterly, 25(1/2), 278-285. https://americanstudies.yale.edu/sites/default/files/files/Lorde%20-%20The%20Uses%20of%20Anger.pdf zu Everyone hates Elon: https://www.everyonehateselon.com zu strategischem Hass nach Seyda Kurt: Kurt, S. (2023). Hass. Von der Macht eines widerständigen Gefühls. HarperCollins. https://www.harpercollins.de/products/hass-von-der-macht-eines-widerstandigen-gefuhls zum Begriff des Prozesses nach Rahel Jaeggi: https://www.fes.de/asd/buch-essenz/jaeggi-fortschritt-und-regression Jaeggi, R. (2023). Fortschritt und Regression. Suhrkamp. https://www.suhrkamp.de/buch/rahel-jaeggi-fortschritt-und-regression-t-9783518587140 White, J. (2024). In the long run: The future as a political idea. Profile Books. https://www.academia.edu/115206928/In_the_Long_Run_the_Future_as_a_Political_Idea_Profile_Books_2024_ Relevante Future Histories Folgen S04E07 | Kohei Saito, Christoph Sorg and Jan Groos on Creative Construction and the Struggle over Progress https://www.futurehistories.today/episoden-blog/s04/e07-creative-construction-and-the-struggle-over-progress/ S03E46 | Rahel Jaeggi zur Krise des Liberalismus, Fortschritt als Prozess und sozialistischem Utopisieren https://www.futurehistories.today/episoden-blog/s03/e46-rahel-jaeggi-zur-krise-des-liberalismus-fortschritt-als-prozess-und-sozialistischem-utopisieren/ S03E33 | Tadzio Müller zu solidarischem Preppen im Kollaps https://www.futurehistories.today/episoden-blog/s03/e33-tadzio-mueller-zu-solidarischem-preppen-im-kollaps/ S03E32 | Jacob Blumenfeld on Climate Barbarism and Managing Decline https://www.futurehistories.today/episoden-blog/s03/e32-jacob-blumenfeld-on-climate-barbarism-and-managing-decline/ S03E23 | Andreas Malm on Overshooting into Climate Breakdown https://www.futurehistories.today/episoden-blog/s03/e23-andreas-malm-on-overshooting-into-climate-breakdown/ S02E56 | Şeyda Kurt zu strategischem Hass https://www.futurehistories.today/episoden-blog/s02/e56-seyda-kurt-zu-strategischem-hass/ S02E38 | Eva von Redecker zu Bleibefreiheit und Demokratischer Planung https://www.futurehistories.today/episoden-blog/s02/e38-eva-von-redecker-zu-bleibefreiheit-und-demokratischer-planung/ — Future Histories Kontakt & Unterstützung: Wenn euch Future Histories gefällt, dann erwägt doch bitte eine Unterstützung auf Patreon: https://www.patreon.com/join/FutureHistories Schreibt mir unter: office@futurehistories.today Instagram: https://www.instagram.com/futurehpodcast/ Mastodon: https://mstdn.social/@FutureHistories Website mit allen Folgen: www.futurehistories.today   Episode Keywords #ChristineHentschel, #JanGroos, #Interview, #FutureHistories, #FutureHistoriesLIVE, #Sicherheit, #Aktivismus, #Katastrophe, #Klimakollaps, #Zukunft, #SolidarischesPreppen, #Kollaps, #MutualAid, #Adaptation, #Mitigation, #ExtremeAdaptaion, #Klimaaktivismus, #Klimakrise 

Duminica, August 2 - Aducerea moastelor Sf. Intai Mc. si Arhid. Stefan; Binecredinciosul Imparat Justinian

Everyone wants to know the next hot stock, the best ETF or where they should invest their money.But after two decades in the finance industry, I've learned that the people who build wealth fastest aren't focused on investment products, they're asking better financial questions.In this episode, we unpack why your next best financial move may have nothing to do with the share market, and everything to do with your current financial position.You'll discover why strategy always comes before products, how to identify your highest-return financial decision, and why building wealth is less about chasing opportunities and more about making the right decision in the right order.Why "What's the best stock to buy?" is usually the wrong first question.How high-interest consumer debt silently destroys wealth.The concept of your highest-return financial move.Why wealth is built in sequence, not all at once.How the SF-15® System helps you identify your next strategic financial step.Remember:The goal isn't simply to own investments.The goal is to build enough wealth that one day your assets can fund your lifestyle.Download your Freedom Number Guide: www.sf-15.com/numberLearn more about the SF-15 System: www.sf-15.comInstagram: https://www.instagram.com/jessica.conrick/If you enjoyed today's episode, please subscribe, leave a review and share it with someone who desires to be self-funded and make work optional.

This Week on the Toy Power Podcast; even though we are Celebrating our 10th Year of Podcasting; we unfortunately exhausted all our energy on the Previous SDCC Reveals & Announcements Ep. So we are giving you a ReIssue episode instead from the Vault. (Originally Posted as Ep #143 - from October 2019). Original blurb: It’s a very special ep as we’re joined by not one, but two of favourite guests in Davey Damaged and Scotty so that’s right – it’s a Homer episode! Trent takes us through a vintage retrospective look at The Addams family by Playmates. Perfectly timed for Halloween, we wonder what could have been with this short-lived line and look to the future of the franchise as a new movie approaches. What characters didn’t get made, which ones make the camera glow and where does MC Hammer fit into it all? We make the jump to Show and Tell as Davey tries to trump us all with a purchase so large, we doubt it will actually fit in his toy room! There’s some DC, MOTU and even Final Fantasy options to choose from. Frank gives a lovely tribute showing that sometimes, the value of a toy; lays in more than its sticker price. Then it’s Quiz time and with Davey onboard, anything is possible. To say this episode goes off the rails is putting it mildly – but on the plus side we learnt that Trent can’t hold his liquor, Ben thinks photos record sound, Space Jam was an event and Rick Moranis is Trent’s muse. So hold on for this crazy ride that also serves as a farewell for one of the team…Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Strange & Unusual: Flashback to Bam punching Tyler Herro and Tony Romo gets in trouble in Wisconsin.MLB: Who gets moved at the deadline, can teams keep up the hot streak and is PCA the best player in baseball? Plus, Wheel of Teams: Athletics!NCAA: Notre Dame and Ohio State get sponsored jersey patches- sign of the times?NFL: Madden ratings are out, fair or foul? Training camp is buzzing; we discuss all of the storylines like Kyle Shanahan's accident, Deebo returning to SF, Bears WR1 decision, Baker disgruntled in Tampa & more.Follow us & tell your friends to listen!X: @TaxiSquad1Instagram: @taxisquadpodcastTik Tok: @taxisquadpod

Soție și mamă, mereu atentă la nevoile celor din jur, Sfânta Doamnă Maria Brâncoveanu a fost un adevărat model femeie creștină. Pentru smerenia ei, iubirea față de Dumenezeu și curajul cu care a trecut peste greutățile vieții, Sfântul Sinod a hotărât consemnarea sa în rândul sfinților. După evenimentele de la Catedrala Patriarhală istorică din luna februarie a acestui an, sfințenia Sfintei Maria Brâncoveanu va fi recunoscută și pe plan local, la București. Manifestările prilejuite de acest moment vor începe sâmbătă, 15 august, când, de la ora 16:00, va fi sfințită racla în care vor fi așezate moaștele Sfintei Doamne Maria Brâncoveanu. Rânduiala va fi săvârșită în Paraclisul istoric „Sfântul Mare Mucenic Gheorghe” al Reședinței patriarhale. După acest moment, racla va fi dusă la Biserica „Sfântul Gheorghe – Nou”, acolo unde va fi așezată în partea dreaptă a Sfântului Altar. De la ora 17:00, va fi săvârșită slujba Vecerniei unită cu Litia și Utrenia. În data de 16 august, Sfânta Liturghie va fi săvârșită de un sobor de ierarhi, începând cu ora 09:00. Ceremonia de proclamare locală a canonizării Sfintei Doamne Maria Brâncoveanu va avea loc imediat după rugăciunea amvonului. Soția Sfântului Voievod Constantin Brâncoveanu a fost canonizată de Sfântul Sinod al Bisericii Ortodoxe Române, anul trecut, împreună cu alte 15 sfinte femei românce.Vizionare plăcută!Pentru Pomelnice și Donații accesați: https://www.chilieathonita.ro/pomelnice-si-donatii/Pentru mai multe articole (texte, traduceri, podcasturi) vedeți https://www.chilieathonita.ro/

1. 新車（シトロエン・ベルランゴ）の納車と感想 納車: 前回の配信から1週間後、シトロエンのベルランゴが納車されました。 デザインと使用感: 外観は可愛らしく、横幅が広い割に前後が短い独特のバランス（縦横比）を気に入っています。 操作性: 概ね想定内ですが、回転式のシフトスイッチ（パーキング、バック、ドライブなどの切り替え）だけは使いにくく、従来のレバー式の方が扱いやすいと感じています。 燃費と積載性: 燃費は以前の車（シエンタ）よりわずかに良く、横幅があるため、後述する古い洗濯機の運搬もスムーズに行えました。2. 人間関係と教育機関との関わり 金沢での交流: ベルランゴ納車前日に、教え子からの誘いで急遽金沢へ向かい、シエンタでの最後の車中泊を経験しました。 母校（高専）との繋がり: 母校である高専を支援する企業の「工場見学会」に参加し、データセンターを運営する企業を訪問しました。 その後の懇親会では、かつての恩師たちが学科長などの要職に就いている姿を確認しましたが、「先生と学生」という当時の関係性は時を経ても変わらないと感じています。 高専出身者同士、共通の知人がいることで、仕事上の技術的な相談も話しやすい雰囲気があることを再認識しています。3. スマートバンドの導入 購入の目的: 子供の寝かしつけ中に一緒に寝てしまうことが多いため、目覚まし代わりとして「Huawei Band 10」（アルミニウムエディション）を5,000円〜6,000円で購入しました。 使用感: 当初は起きられませんでしたが、数日使ううちに効果を実感し始めています。 ハードとソフト: ハードウェアの質には満足していますが、米中対立の影響でGoogle Playストアが使えず、アプリをブラウザからダウンロードする必要がある点や、Googleヘルスケアとの歩数連動ができない点に不便さを感じています。 モニタリング: 睡眠中の心拍数の変化を可視化できることに面白さを感じています。4. 洗濯機の買い替え 経緯: 奥様が独身時代から使っていた東芝製の古い機種（TW-200V）が、ヒーター詰まりによるエラーで乾燥機能などが停止したため、買い替えを決めました。 新機種: パナソニックの「NA-LX127C」をメルカリで165,000円（型落ちの中古品）で購入しました（新品の最新機種は約30万円）。 満足している点: 洗剤の自動投入機能により、毎回の手間や詰め替え作業が激減したこと。 スマホ連携機能や、洗浄時のドラムの動きが滑らかになったこと。 振動が劇的に少なくなったことに驚いています。 不満点: ドアを閉める時の音が、以前の機種の重厚な「ドスン」という音から、軽い「ガチャ」という高い音に変わった点のみ、前の機種の方が好みでした。5. 業界ニュース：台湾のタングステン関連会社社長の殺害事件 事件の概要: 超硬合金の原料であるタングステンのリサイクルを行う台湾の大手企業（年間4,000トンの処理能力）の社長が殺害されるという、業界に衝撃を与えるニュースがありました。 背景と憶測: 中国がタングステンの出荷制限をかける中、この台湾企業は制限対象外だった可能性があります。 不審な点: 椅子に縛られて暴行された形跡があることや、6月末から監視カメラが作動していなかったことなどから、金品目的ではなく、中国側からの暗殺の可能性も否定できないと考察しています。 所感: 過去の「餃子の王将」社長殺害事件や、SF小説『三体』の権利を所有していた林奇氏の毒殺事件などを引き合いに出し、利害関係による社長殺害事件の怖さを語っています。

Jessica Pegula vs Alexandra Eala — full preview, head-to-head breakdown and prediction for the 2026 Mubadala Citi DC Open FINAL in Washington, D.C. Sunday, August 2, on Stadium Court at Rock Creek Park. A three-time Washington finalist against a 21-year-old chasing the first WTA singles title of her career.Alex Eala's week has become the story of the summer. The World No. 28 beat Olympic gold medalist Qinwen Zheng from a set and a break down, ended defending champion Leylah Fernandez's title defense 6-2, 7-6(1), dismantled No. 2 seed Elina Svitolina 6-3, 6-4, and then took apart four-time Grand Slam champion Naomi Osaka 6-4, 6-2 in their first career meeting. Three straight-set wins in a row to close it out. Three seeded players gone. Her first WTA 500 final — and a first tour-level title on the line after the Eastbourne heartbreak of 2025.Jessica Pegula is the top seed, World No. 3, and the most complete player left. She beat No. 4 seed Diana Shnaider 7-5, 6-4 in 87 minutes to reach her fourth final of 2026, a season that already includes titles in Dubai — her fourth WTA 1000 — and Charleston. She is chasing a third Washington crown.The head-to-head favors the American: Pegula leads 1-0, edging Eala 7-6(3), 5-7, 6-3 in the 2025 Miami semifinals. That was 16 months ago, and Eala is a very different player now. Can she finish the fairytale?

On ce retrouve tous les mois pour debrief les prochaines sorties sur PC et console en jeux SF

Hour 1: Deebo heads back to SF & the Nats fall 2.5 games back / Are we surprised the Commanders didn't have interest in Deebo Samuel? / Sam Hartman on learning David Blough's new offense

Vineri, Iulie 31 - Inaintepraznuirea scoaterii Sfintei Cruci; Sfantul si Dreptul Evdochim; Sf. Iosif din Arimateea (Lasatul secului pentru Postul Adormirii Maicii Domnului)

(03:00): Hvorfor strammer UEFA grebet om FIFA's Infantino denne gang? Medvirkende: Jens Rohde, tidl. folketingspolitiker og formand for Danske Fodbolddommere. (13:00): Er Trump ved at skabe fred mellem Israel og Hamas? Medvirkende: Allan Sørensen, mellemøstkorrespondent for Kristeligt Dagblad. (32:00): Har byråd tillid til, at SF ikke vil løbe fra flere løfter? Medvirkende: Niels Rasmussen, byrådsmedlem og gruppeformand for SF i Ringkøbing-Skjern Kommune. (37:00): Hvordan er stemningen i Ceuta lige nu? Medvirkende: Gunnar Willum, korrespondent i Mellemøsten for Berlingske, bosat i Cairo. Værter: Anne Philipsen og Nicolai Dandanell. See omnystudio.com/listener for privacy information.

Ascultați chiar vocea Sfântului Sofronie Athonitul, care ne istorisește minunata poveste a vieții unuia dintre părinții din Vechiul Russikon, pe care sfântul l-a cunoscut în perioada când viețuia în Sfântul Munte.Vizionare plăcută!Pentru Pomelnice și Donații accesați: https://www.chilieathonita.ro/pomelnice-si-donatii/Pentru mai multe articole (texte, traduceri, podcasturi) vedeți https://www.chilieathonita.ro/

Could tonight be Robbie Ray's last start as a Giant? As the trade deadline nears, SF looks to shake up roster and refill farm system; Krueger and Silver debate front office strategy and if team should try to move off Heliot Ramos. We pivot to football and welcome Matt Lively from CBS to break down the latest on Ricky Pearsall as his knee continues to be problematic ahead of the 2026 season.See omnystudio.com/listener for privacy information.

In Part 2, Aft joins us to chat about the band—Top Chefs. We start with the story of how TJ met Aft. A mutual friend of his and Loveberry's, Lucas Hanson, put on a show at DNA Lounge called "Liam Berry and Friends." TJ was just getting out of high school at the time. They asked him to join them on stage, something he'd never really done before. TJ had been playing smaller sets in restaurants, but joining a band at a bona fide venue was new for him. Loveberry (Liam) had met Aft at something called a "sound gig" for a set they would play as part of the effort to keep People's Park in Berkeley from being closed. They called Loveberry because he had a PA and they needed someone to run sound. Loveberry didn't really know what that meant at the time, and he might've been mistaken for a guy named Jeff whom they'd been expecting. Nevertheless, Aft was one of the few people who was nice to Loveberry that day in Berkeley. The band ended up needing a guitar player, and so a mutual friend told Aft that he should have this guy, Loveberry, play guitar with him. They agreed and ending up borrowing a guitar from a member of the crowd. Aft later invited Loveberry to a rehearsal back in SF. Afterthough, aka Aft, joins the conversation at this point. He recounts his version of that day in Berkeley. It was still early pandemic enough that folks were wearing masks. He liked Loveberry's playing that day enough to ask him over. They played a couple of shows as "Aft and His Band" or "Aft … backed up by (various musicians.)" Aft was also asked to play with "Liam Berry and Friends." At that DNA Lounge show, Aft met TJ and the trombone player Will, who's also in Top Chefs now. Aft had played in bands for years, including with a set of twins who played piano and bass. But those guys moved to New York around the time he connected with Loveberry. And soon after that, TJ and other members of today's Top Chefs. There's some collective fuzziness around when this all went down, but that doesn't matter. It was the beginning of coming out of a worldwide pandemic when time began to get totally skewed for everyone. But also, they were just busy playing music and making friends. I ask the three of them who came up with the idea to play shows on the stoop of Aft's childhood home on Scott Street. Loveberry and TJ credit Aft with the idea. Liam talks about arrangements that Aft was putting together with complex horn sections. And in those sessions, one of the musicians started a habit of saying, "Yes, chef" to Aft when he was asked to do something. That came from watching cooking competition shows. The dude was messing with Aft, so at the end of a show one night, when Aft was introducing band members, he called this guy "the top chef" to get him back. The name stuck for the entire band after that. There's a story about TJ's Filipina grandma, who lives near DNA Lounge, making Filipino BBQ on a stick and madeleine cookies for the band. TJ wasn't sure what to do with the food, so he set it out in the crowd while they played a song called, "Everybody Eats." It was a whole thing, and something Aft says he wants to bring back. I ask the fellas whether anyone has ever compared their Stoop Sessions to NPR's Tiny Desk music series. There's a resounding "yes" from all three. Then Aft shares that Top Chefs actually shot a Tiny Desk audition video on the stoop. It was early 2024, and despite not making it onto the hit public media show, they realized that they had something in shows on the stoop. In 2025, thanks to a fundraiser show, the band went to South by Southwest in Austin. After that, they played in the Haight, Hunters Point, Bernal Heights, downtown. Then Top Chefs started ramping up the quality of the videos. In late 2025, just before they planned to take a break from the band, Aft called a media day. The video of that performance went viral. Top Chefs' Stoop Sessions had arrived. It must've been around that time that my wife and I discovered them and immediately knew we'd found something special. And it was basically in our backyard. This coming Sunday, Aug. 2, they'll be at Chapel of the Flowers in Berkeley. If you're in the South Bay next weekend, they're playing San Jose Jazz on Friday, Aug. 7. There's a show in Levi's Plaza in The City on Saturday, Sept. 12. And for more shows and info, and to lose yourself in videos of past Stoop Sessions, follow @stoopsessionslive. The band account is @topchefsband. Their website is topchefsband.com. Sign up for their email list to learn about upcoming Stoop Sessions. That's the only space they share that info with folks. That's a wrap on Season 8: Every Kinda People. Thank you for listening and supporting our effort to uplift and share the stories of artists, activists, and working people who make San Francisco what it is. Photography by Marcella Sanchez

At least four San Francisco Grocery Outlet stores have deployed SAFR facial recognition technology to identify repeat shoplifters — and privacy advocates are predictably outraged. This in a city that banned government use of facial recognition back in 2019, while simultaneously watching retailers hemorrhage thousands of dollars a day to brazen, consequence-free theft.The numbers tell the story clearly: a nearby Safeway was losing an estimated $7,000 per day to shoplifting before corporate pulled the plug. One documented offender had 77 known incidents on record. When the stores close, the neighborhood loses its grocery access — but the critics are busy worrying about camera databases. San Francisco's soft-on-crime political culture created this crisis; facial recognition is what fills the vacuum when the will to prosecute simply isn't there.The legal picture is complicated but revealing. Private businesses can still deploy this technology even after SF's 2019 public-sector ban. The EFF has raised misidentification concerns. The SAFR president has pushed back on fears about ICE data-sharing. It's a genuine debate — but it only exists because Sacramento and City Hall spent years refusing to enforce the law. Corporate fights back with cameras because city government checked out a long time ago.CHAPTERS0:00 San Francisco Grocery Outlets Deploy…1:44 SF Grocers Deploy Facial Recognition…2:15 Grocery Outlet Deploys Facial…3:08 We Already Have No Privacy Anywhere3:47 SF Grocery Outlets Build Shoplifter…4:30 Repeat Shoplifter Had 77 Known Incidents5:58 Can Stores Legally Ban Known Shoplifters6:44 SAFR on Data Sharing With Law…7:15 Facial Recognition Misidentifies Black…8:09 Security Cameras Already Dominate…10:10 Soft-on-Crime Laws Leave Stores…12:06 What Happens After Facial Recognition…13:01 Defund the Police Made Shoplifting Worse14:32 Is Crime Finally Trending Down in SFSubscribe to @reasonablenews for daily commentary on Pacific Northwest politics and national stories the mainstream media won't tell you straight.#NFRP #SanFrancisco #FacialRecognitionGO PREMIUM WITH REASONABLE+ FOR UNCENSORED ACCESS

Comprehensive coverage of the day's news with a focus on war and peace; social, environmental and economic justice. Image by Yohan Navarro, License CC BY SA 3.0 Trump says he'll delay Blanche nomination for Attorney General until 2 dissenting Republicans leave office next year; Dem Senator Schumer introduces anti-corruption act taking aim at Trump actions, CA senator Padilla say Trump is normalizing his corruption; UN Security Council holds hearing on Palestine as violence surges in West Bank and continues in Gaza; Mayor Lurie signs new SF budget closing $642 million deficit, restoring his proposed cuts to service programs; Senate committee hears testimony on AI-powered scams targeting Seniors; Mono Lake at center of conservation crisis with declining shorebirds birds, CA Water Board may decide fate this year; Last suspect arrested in murder of Victor Jara, Chilean activist folksinger tortured and executed after US-backed coup overthrew socialist president Allende The post Trump says he'll delay Blanche nomination for Attorney General; Last suspect arrested in murder of Chilean activist folksinger Victor Jara – July 30, 2026 appeared first on KPFA.

Could tonight be Robbie Ray's last start as a Giant? As the trade deadline nears, SF looks to shake up roster and refill farm system; Krueger and Silver debate front office strategy and if team should try to move off Heliot Ramos. We pivot to football and welcome Matt Lively from CBS to break down the latest on Ricky Pearsall as his knee continues to be problematic ahead of the 2026 season.See omnystudio.com/listener for privacy information.

Joi, Iulie 30 - Sf. Apostoli Sila, Silvan, Crescen si Andronic; Sf. Mc. Iulita

John Shea of The San Francisco Standard speaks to Jeff Kent's induction the Hall of Fame and his thoughts on the Giants roster ahead of trade deadline as players are likely to be moved. After injuries to Casey Schmitt and Harrison Bader, SF looks to replenish system in lost year.See omnystudio.com/listener for privacy information.

$0 -> $100M ARR. How Gamma Scaled Quickly without a Sales Team 100 million in ARR. A team of 50. Zero sales reps. Grant Lee, Co-founder and CEO of Gamma, shares the exact playbook behind one of the most capital-efficient growth stories in SaaS - from pitching investors out of a London kitchenette to going viral with a single tweet that got Paul Graham throwing shade. In this session, Grant breaks down four lessons: 1. Product-market fit isn't a checkbox. After winning Product of the Day on Product Hunt and watching signups plateau, Gamma went back to the drawing board. They gave themselves three months to make the first 30 seconds of the product feel magical - and word of mouth did the rest (5K signups/day, then 10K, then 50K, zero marketing spend). 2. Creator marketing only works if you've done it yourself. Grant went through "Cringe Valley" to understand what creators actually need - then used that to manually onboard every creator partner and build something that felt authentic, not transactional. 3. Community-led growth is literal. At 50M users, Gamma flew power users to SF, visited customers in Seoul, London, and São Paulo, and created a Gambassador Slack where early feedback shapes the product roadmap. Your users are not a faceless entity. 4. Dogfooding the future builds conviction. How Gamma killed their virtual office idea after six months and went all-in on presentations - and why testing your own product is the fastest path to knowing what to build next. If you're building a product-led company and wondering whether to invest in marketing or go back to the product - watch this first.

John Shea of The San Francisco Standard speaks to Jeff Kent's induction the Hall of Fame and his thoughts on the Giants roster ahead of trade deadline as players are likely to be moved. After injuries to Casey Schmitt and Harrison Bader, SF looks to replenish system in lost year.See omnystudio.com/listener for privacy information.

在忙碌中迷失了節奏？讓心靈深呼吸。《城市使命》每日 7–10 分鐘短篇靈修，為你的日常靈性充電！我們透過經文與生命見證，把你的通勤與休息時間，轉化為與神對話的神聖時刻。不長篇大論，只給你最純粹的屬靈養分。現在就收聽，在城市的喧囂中找回你的屬靈方向！Overwhelmed by the hustle? Take a deep breath. We offer 7–10 minute short devotionals to recharge your spirit on the go. Through quick biblical insights and powerful testimonies, we turn your commute or coffee break into a divine dialogue. Simple, deep, and exactly what your soul needs today. Tune in now, quiet the noise, and realign your spiritual compass!

Miercuri, Iulie 29 - Sf. Mc. Calinic, Veniamin, Mamant si Teodota

Purchase the BDGE Fantasy membership here: https://bdge.co/membership0:00 - it's rude to skip introductions0:24 - josh downs, WR, IND (round 9)3:24 - jk dobbins, RB, DEN (round 9)6:09 - round 87:00 - quentin johnston, WR, LAC (round 8)8:19 - round 79:27 - sam laporta, TE, DET (round 7)10:39 - tucker kraft, TE, GB (round 7)12:45 - jordyn tyson, WR, NO (round 7)13:15 - jayden daniels, QB, WAS (round 6)15:41 - mike evans, WR, SF (round 5)18:05 - rladd mcconkey, WR, LAC (round 4)20:02 - cam skattebo, RB, NYG (round 4)22:45 - davante adams, WR, LAR (round 4)23:22 - devonta smith, WR, PHI (round 3)24:24 - javonte williams, RB, DAL (round 3)24:48 - josh jacobs, RB, GB (round 3)26:28 - chase brown, RB, CIN (round 2)27:50 - kenneth walker, RB, KC (round 2)28:59 - round 1subscribe to the bdge dynasty channel: https://ytube.io/3pZklisten to the bdge dynasty podcast: https://bityl.co/NzJ1bdge nfl trivia youtube channel: https://ytube.io/3jmJjoin the BDGE discord: https://discord.gg/77BxrqCF6Fsubscribe to the BDGE podcast | https://linktr.ee/bdgefollow me on the socials | https://linktr.ee/nickercolanoContact▪️ business inquiries | business@bdge.co▪️ customer support/help | help@bdge.co▪️ fantasy questions can go in our discord | https://discord.gg/AvpY3QJTAythis video is about (Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Susan Slusser of the San Francisco Chronicle discusses the impact of Casey Schmitt's injury ahead of the MLB trade deadline and how it could affect SF's strategy of moving on from other talent. She suggests moving on from Heliot Ramos could be in the cards, if for nothing else than to make playing time for other young talent.See omnystudio.com/listener for privacy information.

There's a band in San Francisco comprising musicians from The City and whom I discovered on social media playing shows on a stoop here in SF. I can't quite be sure when I discovered them, or whether it was me or my wife, but either way, right from the start, we knew we'd stumbled upon something special. Something truly unique. Something joyful, beautiful, free … something for the people. People could gather around with neighbors, some they know, some they might be meeting for the first time. Community is something of a buzz word lately, especially in light of things like gentrification, neo-tech fascism, and just plain ol' fascism. But for good reason—it's one of the most powerful tools we have not only to resist all the nonsense being forced on us, but also to overcome that crap and move on toward creating a world of inclusion, equity, diversity … and good tunes. The band's name is Top Chefs. This last episode of Season 8 of Storied: San Francisco is, not by accident, all about them and the magic they create. We start things off with TJ Milan, lead arranger for Top Chefs and co-editor of what they call "Stoop Sessions." TJ was born and raised in San Francisco. His dad came out here from Connecticut, and his mom is from The Philippines. His parents lived in South of Market, and TJ lives in that neighborhood today. TJ's mom came to The City with her parents when she was young. She later joined the Army, serving with a support group for the Green Berets. His dad had moved to New York first for work. Then he got offered a job in San Francisco in the late Eighties and took it. In the mid-Nineties, the two met at a law firm where they both worked. In 2002, they had TJ, the youngest of their three kids—he has an older sister and an older brother. His dad is an amateur saxophone player. More on that in a bit. The family lore goes that his sax stylings played a part in his parents' union, because duh. I give TJ the opportunity to rattle off his SF schools, something he's proud to do—Clarendon Elementary, Hoover Middle School, and School of the Arts (SOTA). He describes his time at SOTA as "very chaotic." There was beef between the SOTA kids and the Academy kids in his days. Academy is sort of a school-within-a-school for SOTA, a majority/minority arrangement. Despite that situation, though, TJ met the person who's still his significant other to this day. The only time TJ left San Francisco for any significant time was to go to college at The New School in New York City, where he studied jazz. But he credits most of his education with Community Music Center in The Mission. The entire time TJ spent in NYC, he felt in his heart that he would return to his hometown. He played out a lot while he lived in New York, but the market for saxophonists was saturated. He came back to SF around September 2024. It started off rough, as he puts it. He started busking to make ends meet. Then we turn to Liam Berry, aka Loveberry, the head of audio production and guitar player in Top Chefs and co-editor of Stoop Sessions. His mom came here from San Diego. Her mom came to the US from Ecuador, where she was raised. His mom's dad is from Illinois. Loveberry's grandfather was on a work trip in South America when he met his grandmother. That couple went to Philadelphia before landing in San Diego. Their daughter, Loveberry's mom, moved to The Bay to go to UC Berkeley to study biology. I take us on a sidebar about how San Diego can be cool to visit for like two days, max. But there's only so much that good Mexican food and nice weather can do for me before I need to get back home. His dad is from Cupertino. His dad's dad is from Philadelphia and his dad's mom is from Houston. They met when his grandfather worked for NASA and was stationed at the Johnson Space Center in Texas. His grandfather was transferred to the Ames facility on the Peninsula and brought his wife with him. Loveberry's dad wanted to get out of the area where he grew up, but he didn't want to go too far. So he got into SF State for meteorology. He ended up working in the data science field after discovering that meteorologists don''t make great money. His parents met at a party through mutual Cupertino friends who'd moved up to Berkeley and Oakland. They were still together until a couple years ago, when Loveberry's dad passed away. He was almost raised in Oakland. His dad worked a low-paying job at a tech company and his mom, back then at least, worked different admin jobs (today, she's a librarian). They'd lived in the Inner Sunset and put bids in on a couple houses in Oakland, where they could afford to buy in the late Nineties/early 2000s. They also bid on one house here in The City, in The Castro. And that's the one they got. That's the house Loveberry grew up in, and when his parents first bought it, the house needed a lot of work. There are family stories about moving in and a mysterious tub of some sort of toxic goo. Schools he went to and music intertwine for Loveberry. He started at Rooftop K–8, where kids worked on a different art project every year. When he was in second grade there, Loveberry started to gravitate toward guitar and started taking lessons. He went to high school at Gateway, which didn't have a music program at the time. So he and some friends started the school's music club. In the summer between his sophomore and junior years of high school, Loveberry smoked pot for the first time. He also did an internship at NASA over that same summer. Going back and forth between those two wildly different worlds, he preferred the community that came with smoking pot with friends. Music became more and more a thing, but so did partying. Shit got out of control. He got out of high school to get himself into rehab. After rehab, he wanted to go to music school in Los Angeles, but his parents nixed that idea. Instead, he stayed in The City and worked at a few different local eateries. So he never left. A guy he met playing open mics in SF offered Loveberry $1,000 a month to open a recording studio and music business. It didn't work out, but it showed him that he could persue music and art as a lifestyle. He worked at some nonprofits and did visual art as well as music. He met Afterthough (Aft) in 2021. Top Chefs formed soon after that. Check back Thursday for Part 2 and the final podcast of Season 8. We recorded this podcast on Scott Street near Alamo Square in June 2026. Photography by Marcella Sanchez

Susan Slusser of the San Francisco Chronicle discusses the impact of Casey Schmitt's injury ahead of the MLB trade deadline and how it could affect SF's strategy of moving on from other talent. She suggests moving on from Heliot Ramos could be in the cards, if for nothing else than to make playing time for other young talent.See omnystudio.com/listener for privacy information.

在忙碌中迷失了節奏？讓心靈深呼吸。《城市使命》每日 7–10 分鐘短篇靈修，為你的日常靈性充電！我們透過經文與生命見證，把你的通勤與休息時間，轉化為與神對話的神聖時刻。不長篇大論，只給你最純粹的屬靈養分。現在就收聽，在城市的喧囂中找回你的屬靈方向！Overwhelmed by the hustle? Take a deep breath. We offer 7–10 minute short devotionals to recharge your spirit on the go. Through quick biblical insights and powerful testimonies, we turn your commute or coffee break into a divine dialogue. Simple, deep, and exactly what your soul needs today. Tune in now, quiet the noise, and realign your spiritual compass!

Marti, Iulie 28 - Sf. Apostoli si diaconi Prohor, Nicanor, Timon si Parmena

Sent lørdag aften blev en Pride-fejring i Berlin angrebet i det, tysk politi efterforsker som et islamistisk terrorangreb. Danske politikere siger efterfølgende, at hadet skal bekæmpes med alt, hvad vi har. Men hvem skal bekæmpes - og hvordan? Findes der en berøringsangst over for islamismen? Eller bliver en tysk tragedie misbrugt til dansk politik? Du kan blande dig i debatten ved at ringe ind fra 12:15-13:30 på 70 21 19 19 eller send en sms til 1212. Medvirkende: Mohammad Rona, politisk ordfører, M. Susanne Branner, sekretariatschef LGBT+ Danmark. Mikkel Bjørn, udlændingeordfører, Dansk Folkeparti. Christian Marcussen, medlem af Det Nationale Integrationsråd. Lise Müller, udlændingeordfører, SF. Peder Hvelplund, udlændingeordfører, Enhedslisten. Vært: Kaare Svejstrup. Producer: Vilhelm Juhler Kjær. Redaktion: Mathias Pedersen, Thomas Roig og Vilhelm Juhler Kjær.

Danae ran the SF marathon! And Bob cheered her on! Santa Cruz had its own little race too. Plus, Sarah and Vinnie both caught up on some movies.

Dylan Aaron and Nate react to the biggest SF news out of Los Angeles, Draft which playoff series they would be most excited to see and get their crystal balls ready and predict the top 10 players for the 2026-27 season.

Luni, Iulie 27 - + Sf. Mare Mc. si Tamaduitor Pantelimon; Cuv. Antuza

This week on my podcast, I read Post-political, a recent essay from my Pluralistic newsletter, about the material, irreconcilable differences between leftism and other political beliefs. But when it comes to a “post-politics that is neither right nor left,” the definition I turn to most often comes from science fiction writer Steven Brust, who once... more

There's so so SO much stuff to cover, we just hit record and took our eyes off the clock. We go through all the big player companies (and a few surprises as well) to bring you the biggest review of San Diego Comic-Con 2026 ever! Who wins? Who surprises us? Who dissapoints us? How many Shuttup and Take my Money's are we throwing out there? Strap in because this is hands down our longest episode EVER! Best of luck to Lego Master Trent who finds out if he's winner today! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

Duminica, Iulie 26 - Sf. Sfintit Mc. Ermolae; Sf. Cuv. Mc. Paraschevi

 After a bit of political talk--what can one do against such reckless hate?--Rish talks about a possible intruder in the building.Also, dealing with criticism, a book signing, A.I., sickness, a possible Halloween program, a couple with unbelievable stamina. And Trump--did he need to be brought up in this episode? Probably.If you want to download the episode, Right-Click HERE.If you want to support me on Patreon, click HERE.Logo by Gino "Are You Peeing This?" Moretto.

Comprehensive coverage of the day's news with a focus on war and peace; social, environmental and economic justice. Image by Wickey-nl Situation dire in Occupied Palestinian Territories as settler violence escalates in West Bank, Israel builds barrier across Gaza; SF activist arrested by ICE at airport in SF, following ICE arrest of Oakland resident at Denver airport, both now in private Geo Group detention facilities; A month after devastating Venezuela earthquake, rescue workers still at work in the rubble; Aid from solidarity workers and US trickles into Cuba as blackout, economic troubles continues amid US pressure and embargo; White Sturgeon federal endangered listing delayed, threatening bay fish after lawsuit says listing is warranted The post West Bank settler violence increasing as Israeli builds barrier across Gaza; ICE arrests SF activists at airports in SF, Denver – July 24, 2026 appeared first on KPFA.

Bryan Smiley is CEO of Hard Carry Media (HCM), a digital-first media company dedicated to building multi-platform brands and IP specifically for Gen Z men. Under Smiley's leadership, HCM bridges the content gap for this demographic with plans to create and acquire a portfolio of digitally native brands for every stage of Gen Z male life across verticals, including sports, entertainment, comedy, and lifestyle. A veteran Hollywood executive with nearly two decades of experience spanning content development, production, and studio leadership, Smiley has a proven track record of scaling global media powerhouses. At HCM, Smiley oversees a seasoned team responsible for the company's core offerings in development, sales, audience analytics, and in-house production out of its 20,000 SF studio in Los Angeles. HCM's flagship entertainment brand, Full Squad, is one of YouTube's leading variety and social entertainment channels for Gen Z with more than 11 million followers and 260 million monthly views. Prior to HCM, Smiley served as President & Chief Content Officer of Kevin Hart's Hartbeat, where he oversaw more than $500 million in production and helped grow the company into a global studio with hits across TV, film, audio and digital for partners including Netflix, Peacock, Audible, and SiriusXM. Prior to that, he held an executive role at Columbia Pictures, where he led talent-driven deals including Stephen Curry's Unanimous Media and a multi-picture pact with Issa Rae. He has also consulted for Fox Digital Studio, 20th Century Fox's digital arm for early web-first series and films with social influencers.

Scott and Jason are back to answer your most important Star Trek and horse-related questions! And they’re so excited they could punch a dinosaur! We break down the pulp-SF-flavored Season 4 premiere of “Strange New Worlds,” featuring martians and dinos and also a bonus jerk Vulcan! Scott McNulty and Jason Snell.

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

Episode Summary      On January 18, 2018, in Helmand Province, Afghanistan, Green Beret combat medic Luke Sciulli stopped being the medic and became the patient. A booby-trapped building collapsed on him during a personnel-recovery operation, fracturing his C4, C5, and C6 vertebrae, both scapulas, and his pelvis, and leaving him with a spinal cord injury at C5. In this episode of WarDocs, SFC(R) Luke Sciulli walks through that day and the MEDEVAC chain that followed — forward surgical team, Kandahar, Landstuhl, and Walter Reed — and explains how living through the system he once operated permanently reshaped his view of casualty care, pain management, and patient advocacy.   SFC(R) Sciulli is candid about the recovery that nearly broke him. He describes the culture shock of moving from elite combat operations to a 30-bed spinal cord injury ward populated largely by elderly veterans, and the slow, deliberate realization that, in his words, the only person who was going to make his life better was him. He maps the framework that pulled him back — physical health, mental health, and career — and offers a direct message to wounded operators who cannot get out of bed: the discipline that earned the Beret, the Tab, or the Trident is still inside them.   The conversation then widens into the operational and strategic challenges Sciulli has tackled across an extraordinary career. He compares civilian and military trauma systems and argues that surgeons in major American cities now see more penetrating trauma than many military providers — making military-civilian partnerships essential to keeping skills sharp. He recounts building a joint, combined MEDEVAC architecture across five African nations, and embedding at forward surgical teams in Ukraine just kilometers from the front, where 60 to 90 casualties a day exposed gaps in Role 1 and Role 2 doctrine, risk tolerance, and interoperability that U.S. tabletop exercises rarely surface.   SFC(R) Sciulli also draws on his time supervising a 216-bed field hospital at Columbia University during the 2020 COVID surge and his current work with Vigilant Consulting and Valinor Enterprises advising on tactical medical technology. He makes the case for predictive analytics, wearables, and automated resuscitation systems to force-multiply providers in large-scale combat operations — while confronting the risk-acceptance and accountability questions that slow adoption. He closes with the cause he intends to define his legacy: fixing the negligent disconnect that leaves SOF medics and operators underutilized in the civilian, defense, and healthcare worlds, and expanding the transition programs that turn their hard-won skills into lives saved at home. Chapters (00:00-01:10) Cold Open and Introduction (01:10-05:30) Becoming the Casualty in Helmand Province (05:30-11:30) The Recovery That Rewired Everything (11:30-15:45) Civilian and Military Medicine, Two Ways (15:45-22:45) Distance, Mass Casualties, and Lessons from Ukraine (22:45-32:30) Field Hospitals, Tactical Technology, and Risk (32:30-36:15) Recruiting the Next Force and a SOF Medic Legacy Chapter Summaries (00:00-01:10) Introduction Dr. Soderdahl frames the journey ahead: a Green Beret combat medic who became the patient on the wrong side of an IED blast and now drives technology and policy for the next war. The standard WarDocs welcome sets up a firsthand look at military medicine from the austere edge of combat to the front lines of innovation. (01:10-05:30) Becoming the Casualty in Helmand Province SFC(R) Sciulli recounts volunteering to backfill a buddy's ODA as senior medic and the January 18, 2018 operation in which a booby-trapped building collapsed onto him. He details catastrophic injuries — broken C4-C6, a C5 spinal cord injury, fractured scapulas and pelvis — and the MEDEVAC through the forward surgical team toward higher care, including how his teammates stabilized and moved him. (05:30-11:30) The Recovery That Rewired Everything He describes six months split between Walter Reed and the Tampa VA, the jolt of moving from combat to a spinal cord injury ward, and the loss of career, health, and identity overnight. The turning point came when he accepted that no one else could make his life better, and built a deliberate framework around physical health, mental health, and career. (11:30-15:45) Civilian and Military Medicine, Two Ways Drawing on his paramedic, firefighter, and critical-care flight background plus SF service, Sciulli argues civilian surgeons now out-rep many military providers on penetrating trauma, making military-civilian partnerships essential. He also reflects on serving as senior medical provider for SEAL Teams Two and Eight and why interoperability comes down to people and personality. (15:45-22:45) Distance, Mass Casualties, and Lessons from Ukraine He explains building a joint, combined MEDEVAC system across Chad, Niger, Nigeria, Cameroon, and Libya, then scales the problem to the Indo-Pacific and Eastern Europe. Embedded at forward surgical teams in Ukraine, he saw 60 to 90 casualties a day and learned hard lessons about volume, interoperability, civilian EMS integration, and U.S. risk tolerance. (22:45-32:30) Field Hospitals, Tactical Technology, and Risk SFC(R) Sciulli describes supervising a 216-bed COVID field hospital at Columbia University and why a military-style community made it work. He then identifies penetrating and shrapnel trauma as a top killer and makes the case for wearables, predictive analytics, and automated resuscitation — confronting the risk acceptance and human trust that slow adoption. (32:30-36:15) Recruiting the Next Force and a SOF Medic Legacy Speaking to students, residents, and recruiters, Sciulli reframes the pitch: the military needs people willing to operate beyond their comfort zone and scope, not just another credential. He closes on the legacy he intends to leave — fixing the negligent disconnect that leaves SOF medics and operators underutilized after service. Take Home Messages Experiencing Care Builds the Best Advocates: Living through the casualty evacuation chain as the patient teaches lessons no provider can learn from the other side of the litter. Empathy, pain management, and patient advocacy take on new meaning once a clinician has been the one strapped to the litter. Recovery Is a Decision You Make Daily: The hardest part of catastrophic injury is mental, not physical. Progress comes from a conscious choice to get up and be better than yesterday, supported by resources but driven by personal ownership of physical health, mental health, and career. Keep Military Providers Sharp in Civilian Trauma: Civilian surgeons in major cities now see more penetrating trauma and polytrauma than many military clinicians. An integrated military-civilian system that rotates providers through high-volume civilian centers is essential to keeping wartime skills ready. Plan for Volume, Distance, and Interoperability: Future large-scale combat means moving casualties across oceans and continents while relying on host-nation, partner, and civilian systems. High casualty volumes and contested distance demand generalist skill, civilian EMS integration, and a higher tolerance for operational risk. Close the Gap Between Research and the Warfighter: Government funds enormous amounts of combat casualty research and technology that never reaches frontline providers fast enough. The mission is to translate proven findings and devices into the warfighter's hands tomorrow — not years from now.   Episode Keywords military medicine, combat medicine, Green Beret medic, 18D combat medic, special forces medic, combat casualty care, TCCC, spinal cord injury recovery, polytrauma, Helmand Province, Walter Reed, MEDEVAC, forward surgical team, Ukraine combat medicine, mass casualty, large scale combat operations, LSCO, prolonged field care, tactical medicine, medical device innovation, SOF medic, military civilian partnership, WarDocs, Luke Sciulli, veteran resilience, penetrating trauma   Hashtags #MilitaryMedicine, #CombatMedicine, #GreenBeret, #WarDocs, #CombatCasualtyCare, #SOFMedic, #VeteranResilience, #TacticalMedicine   Honoring the Legacy and Preserving the History of Military Medicine The WarDocs Mission- WarDocs exists to honor the legacy of Military Medicine, preserve its history, and inspire every generation — across all Services, Corps, and Ranks — to serve with excellence and pride. Through mentorship, coaching, and education, we equip those considering, entering, and serving in military medicine with the knowledge, connections, and community they need to thrive. We celebrate Who we are, What we do, and, most importantly, How we serve Our Patients, the DoW, and Our Nation. Find out more and join Team WarDocs at https://www.wardocspodcast.com/ Check our list of previous guest episodes at https://www.wardocspodcast.com/our-guests Subscribe and Like our Videos on our YouTube Channel: https://www.youtube.com/@wardocspodcast Listen to the "What We Are For" Episode 47. https://bit.ly/3r87Afm WarDocs- The Military Medicine Podcast is a Non-Profit, Tax-exempt-501(c)(3) Veteran Run Organization run by volunteers. All donations are tax-deductible and go to honoring and preserving the history, experiences, successes, and lessons learned in Military Medicine. A tax receipt will be sent to you. WARDOCS documents the experiences, contributions, and innovations of all military medicine Services, ranks, and Corps who are affectionately called "Docs" as a sign of respect, trust, and confidence on and off the battlefield, demonstrating dedication to the medical care of fellow comrades in arms. Follow Us on Social Media Twitter: @wardocspodcast Facebook: WarDocs Podcast Instagram: @wardocspodcast LinkedIn: WarDocs-The Military Medicine Podcast YouTube Channel: https://www.youtube.com/@wardocspodcast  

Follow the Podcast Here: https://www.instagram.com/ctc.podcast/ Follow Tennis Athlete Here: https://www.instagram.com/tennisathlete_sta/ Follow SotoTennis Academy Here: https://www.instagram.com/sototennis/?hl=en Email us: info@sototennis.com Meet our panellists:Denmark's Davis Cup Captain and 2012 Wimbledon Doubles Champion, Freddie Nielsen - currently coaching August Holmgren, who is in the final round of qualifying as we record.GB Coach Calvin Betton who is currently working with Henry Patten & Harri Heliövaara.Top S&C Coach Kieron Vorster who has worked with Liam Broady, Dan Evans, Tim Henman and Wayne Ferreira.Sports broadcaster and former CNN anchor Candy Reid, Tennessee Vols alumna and one of the great voices of tennis.The ATP is proposing to reshape men's doubles from 2028. Halved draws. Halved prize money. Doubles specialists effectively pushed out of the sport. In this episode, Dan takes on the proposals in a full debate segment, then reviews the fortnight at Wimbledon 2026 that has just wrapped up. Recorded after the Wimbledon Live series ended.THE MAIN DEBATE: Should the ATP be allowed to kill men's doubles?The 2028 proposal in detail: doubles draws halved at Masters 1000s, prize money split shifted from 80-20 to 90-10 in favour of singles, Challenger Tour entry handed to singles players ahead of doubles specialists.Our panelist discuss these proposals in depth.In the Wimbledon 2026 Review:Sinner defends his title, 5th career Grand Slam, only 10th man in the Open Era to defend WimbledonLinda Nosková becomes the youngest women's champion since Kvitova in 2011, in the first all-Czech final in the Open EraArthur Fery's historic wildcard SF run, the deepest British men's Wimbledon run since Cameron Norrie in 2022Djokovic equals Federer's all-time record of 105 Wimbledon match winsAlfie Hewett and Gordon Reid win their 7th Wimbledon men's wheelchair doubles titleThe 3 Czech Wimbledon women's champions in the last four years: Vondrousova (2023), Krejcikova (2024), and now Nosková (2026)The women's draw wide open: 10 different champions in 10 years, the longest streak in Wimbledon historyTimestamps05:21 -Inside Sinner's fifth Slam: why he wins even when he's not at his best, and Vozzi breaks down his movement09:06 - Arthur Fery's fairytale run: turning heroic losses into gritty wins, and what his future really looks like16:36 - A new Alexander Zverev? How Roland Garros changed him, and dissecting that stunning first set of the final22:14 - Djokovic at 39: the chase for 25, why his body can't do back-to-back matches anymore, and the Sinner semi27:53 - Nosková's astonishing comeback from 6-2, 5-2 down, plus the truth about her "friendship" with Muchová36:20 - The Doubles Debate Begins About Control the ControllablesHosted by Dan Kiernan, Director of SotoTennis Academy and coach to top ATP and WTA doubles players. Control the Controllables was voted x3 Best Tennis Podcast.

We're still surprised people did this but... 50+ founders worth $10M to $4B reveal their personal finances. Here it is: https://joinhampton.com/mw-wrWhy do we do this? Because if you're an aspirational person or someone who runs a business and is making money, it's incredibly challenging to figure out what to do. Information is impossible to find — and that's what we put together: the net worth reveal and why we do this podcast, Moneywise.He got his first $5M check and expected to feel superhuman. The next day was one of the most disappointing of his life.Jesse Pujji walked away from a Goldman Sachs job where he made $500K at 25 — with a boss making $3M and a group head making $20M — to bootstrap an ad agency on $33K per partner and a stack of Amex cards. Ampush cracked the Facebook arbitrage before almost anyone: $100K in monthly revenue in June 2010 became $2M a month with $600K in EBITDA fourteen months later. He scaled it to half a billion in annual ad spend and 250 employees without raising a dollar, turned down $25M at 27, sold 20% to Red Ventures in 2015, and sold the whole thing to New Mountain Capital in 2022 for somewhere between $40M and $60M on a 35% stake. He never got the nine-figure number he made up in his head, and he says chasing it was the mistake.This episode gets into the exact allocation of a post-exit portfolio, why Jesse refuses to let his advisors put illiquid startup equity on his balance sheet, what $500K a year of "normal" spending actually buys, and why he asked his financial advisor how people possibly spend more than that. He's honest about the gap between the money he expected to change him and the money that didn't. And we spend real time on the part most founders avoid: three kids who never saw him grind, a Greenlight allowance split into thirds, a $63 JCPenney paycheck at 16 that taught him more than any of it, and the question of whether to leave them anything at all.Also, this podcast is made by Hampton, which is a community for founders doing on average $20 million a year in revenue. We saw a lot of these money conversations happening privately behind closed doors and we thought, "What the heck, let's make it public." If you are a founder, apply here: http://joinhampton.com/mwTimestamps:00:00 — Jesse's origin story: immigrant household in St. Louis, a snow shoveling business in middle school, and $33K each plus Amex cards to start Ampush02:00 — The Facebook arbitrage that changed everything: $100K/month in June 2010 to $2M in revenue and $600K in EBITDA fourteen months later02:49 — "Sandbox entrepreneurship" — Facebook cold-calls them: "Who the hell are you guys? You're one of our top 100 advertisers"04:24 — Why he left Goldman at 25 making $500K: "I would rather make half of my future expected earnings and do something I feel excited about"06:18 — The $25M offer two years in, why they said no, and the $3M dividend they took instead — $1M each, which bought his SF house07:30 — The made-up number that wrecked them: hoping for $150M, getting $60–75M offers, and turning down $190M in Marin stock09:24 — The Red Ventures deal and $5M after tax: "I thought I would get wings or superhuman strength... nothing changed"11:16 — 2022: selling to New Mountain and walking away without going with the deal13:12 — The exit number, on the record: a $40–60M range on a stake "a little bit more than a third"16:04 — The Zone of Genius framework, and why being a CEO sat in his zone of excellence — good at it, drained by it17:52 — Gateway X by the numbers19:06 — Whether the scarcity ever goes away: "nine days out of ten" became "one day out of ten," and the coach question he couldn't answer20:16 — The Deer Valley condo, and finally understanding why people buy vacation homes21:08 — Full portfolio breakdown and why he tells his advisors to mark his startup equity at zero23:24 — Annual spend 26:52 — The schedule that makes it work: Tuesdays and Thursdays he misses bedtime, Monday/Wednesday/Friday he doesn't, and he deletes Slack on vacation28:16 — The thing that keeps him up: "They've gotten all the fruits of the grind without actually observing the grind"29:23 — Greenlight, allowance equal to their age, and splitting it into thirds — spend, save, give30:19 — Running a Starbucks P&L with his 9-year-old daughter in the store32:30 — The four-bucket framework: spend it, give it to the government, give it to charity, or give it to your kids34:44 — A Schnucks family board member on generational wealth: "Money doesn't ruin kids. Lack of values does."35:36 — What Jesse wants said at his funeralSponsors: Daily Body Coach - achieve your dream body with https://moneywise.dailybodycoach.comSubscribe to Moneywise: https://www.youtube.com/@themoneywisepodcastFollow Daniel on X: https://x.com/danielcberkListen on Spotify / Apple Podcasts: [search "Moneywise Hampton"]

In today's episode Nick talks about A Dog Rescued in SF, Locked Up Meat, SD Card Exposes Pedo, A Tranny Loses It and Maher Defends Louis! The FULL SHOW is live streaming & FREE-ONLY on Rumble! Join our LIVE CHAT at 6pm ET every Mon-Thu or watch the FULL EPISODE anytime on demand after 7pm ET. Follow my Channel and get notified! https://rumble.com/c/TheNickDiPaoloShow GET TOUR DATES & TICKETS - https://www.nickdip.com/tour NOVEMBER 5TH - The Punchline: ATLANTA, GA NOVEMBER 6TH - Rivers Casino: PHILADELPHIA, PA NOVEMBER 7TH - Soul Joel's: POTTSTOWN, PA MERCH - Grab some mugs, hats, hoodies, shirts, stickers etc… https://shop.nickdip.com/ PERSONAL VIDEO FROM ME – Send someone a personal video from me! Go to https://shoutout.us/nickdipaolo  or www.cameo.com/nickdipaolo SOCIALS/COMEDY- Follow me on Socials or Stream some of my Comedy!  https://nickdipaolo.komi.io/