Prussian philosopher
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SPONSORS: - Nobody likes a call center — ElevenAgents by ElevenLabs fixes that with voice AI that sounds human and works 24/7 in 30+ languages. Try it: elevenlabs.io/TOE - I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE This podcast explores the uncanny link between two of the twentieth century's great visionary works. Becca Tarnas, assistant professor at the California Institute of Integral Studies, joins to discuss her doctoral research into the parallels between Carl Jung's Red Book and J.R.R. Tolkien's lesser-known visionary writings. The central claim: working independently, both men produced strikingly similar imagery, timing, and figures — Jung's Philemon and Tolkien's Gandalf, the Eye of the Evil One and the Eye of Sauron — while each insisted their material felt discovered rather than invented. From there the conversation moves through active imagination, the reality of archetypes, psyche versus mind, and the line between mystical insight and psychosis — a wide-ranging look at where imagination ends and reality begins. FOLLOW: - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://nowpayments.io/donation/TOE - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 TIMESTAMPS: - 00:00:00 - Jung and Tolkien's Red Books - 00:05:42 - Archetypal Rhymes and Parallels - 00:11:26 - Defining the Imaginal Realm - 00:16:38 - Active Imagination vs. Dreaming - 00:22:00 - Active Imagination Protocol - 00:27:00 - Psyche's Spontaneous Expression - 00:33:57 - Ontology of Imaginal Figures - 00:39:03 - Psychologizing vs. Mythologizing - 00:45:57 - Planetary Archetypes Experience - 00:51:34 - Perceiving Universal Beauty - 00:58:23 - Universal vs. Objective Reality - 01:04:00 - Psychosis vs. Gnosis - 01:09:00 - Grounding the Visionary - 01:15:00 - Symbolic Language of Madness - 01:21:00 - Unearned Wisdom and Psychedelics - 01:26:30 - Motherhood as Expanded State - 01:33:57 - Meaning in Disconnection - 01:40:56 - Intuition in Science - 01:46:55 - Leonardo's Light and Shade LINKS MENTIONED: - Journey To The Imaginal Realm [Book]: https://amazon.com/dp/1947544217?tag=toe08-20 - Participatory Imagination [Lecture]: https://beccatarnas.com/2020/08/28/jungs-participatory-imagination/ - Becca's PhD Defense: https://youtu.be/3soOYajHUBM - The Red Book [Book]: https://amazon.com/dp/0393089088?tag=toe08-20 - The Holy Grail Of The Unconscious [Article]: https://www.nytimes.com/2009/09/20/magazine/20jung-t.html - Active Imagination: https://jungiancenter.org/jung-on-active-imagination-features-methods-and-warnings/ - Jung On Active Imagination [Book]: https://amazon.com/dp/0691015767?tag=toe08-20 - Richard Tarnas's Website: https://cosmosandpsyche.com/ - The Passion Of The Western Mind [Book]: https://amazon.com/dp/0345368096?tag=toe08-20 - Psychological Types [Book]: https://amazon.com/dp/1614279705?tag=toe08-20 - The Fellowship Of The Ring [Book]: https://amazon.com/dp/0547928211?tag=toe08-20 - Kant's 'Categories': https://plato.stanford.edu/entries/kant/ - The Imaginary And The Imaginal [Paper]: http://www.bahaistudies.net/asma/mundus_imaginalis.pdf - Toni Wolff: https://en.wikipedia.org/wiki/Toni_Wolff - The Call Of Cthulhu [Book]: https://www.hplovecraft.com/writings/texts/fiction/cc.aspx - The Shadow: https://iaap.org/jung-analytical-psychology/short-articles-on-analytical-psychology/the-shadow/ - BBC Interviews Curt: https://www.bbc.com/audio/play/m002s4hd - Andres Emilsson [TOE]: https://youtu.be/gi08eVU_-f8 - Leo Gura [TOE]: https://youtu.be/R-w8k4smC74 - Iain McGilchrist [TOE]: https://youtu.be/Q9sBKCd2HD0 - Greg Kondrak [TOE]: https://youtu.be/FFW14zSYiFY - John Vervaeke [TOE]: https://youtu.be/3p8o3-7mvQc - Bernardo Kastrup & Susan Blackmore [TOE]: https://youtu.be/jrVnAWP2XEs - Consciousness Iceberg [TOE]: https://youtu.be/65yjqIDghEk Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
Un recorrido filosófico sobre la idea del bien
Send us Fan MailMost messages aimed at young men today split into two dead ends: become a sensitive, apologetic non-threat, or go to war and reclaim dominance. Zan thinks both camps hold a fragment of truth and neither offers a boy anything to actually hope for — which is why, more and more, he finds himself wanting to talk to thirteen-to-eighteen-year-olds instead of grown men.In this episode, Jordan asks Zan directly: what's the message? What do you actually say to a generation of boys who've been told their masculinity is the problem, who are retreating to their rooms, whose only two options seem to be surrender or rage?Zan's answer moves through some of the widest philosophical territory this podcast has covered — the concept of worship, stripped from religion and rebuilt around beauty itself. Petrarch and Dante, both of whom had their entire life's work ignited by seeing a woman he barely knew. The 1,500-year-old volcanic winter that blacked out the sun across Europe, and how people found meaning through it anyway. The uniculture problem — why knowing about every disaster on Earth simultaneously might be worse for the human nervous system than knowing only what happened in your own village. And a genuinely moving true story: a twelve-year-old boy, a short walk to a candy store, and one sentence from Zan that a mother said changed her son for weeks afterward.Watch until the end for what Zan calls the real message underneath everything — not optimism, not denial, but gratitude for what you have precisely because it won't last.
Descartes a Kant (DAK) — the theatrical rock band from Guadalajara, Mexico — joins Fox Cities Core on Code Zero Radio during their Mile of Music run in Appleton, WI. Band members Sandra, Memo, and Leo dig into the philosophy-inspired origin of their name, the wild theatrical concept behind their live show (choreography, and "the machine"), and the story behind recording with legendary producer Steve Albini on their album "Victims of Love Propaganda."In this interview, the band talks about:- Where the name "Descartes a Kant" actually comes from- Starting the band in 2001 as a school project inspired by riot grrrl bands- How the band's theatrical, costume-driven concept evolved album by album- Writing their album "After Destruction" through a lineup change, isolation, and grief- Recording on analog tape with Steve Albini and remembering his legacy- Getting vouched for by Mike Patton to play with Mr. Bungle- Sharing stages with Sonic Youth, the Melvins, St. Vincent, and Slayer's Dave Lombardo- How they get their massive live sound without traditional amps- Their first impressions of Appleton, Wisconsin — tornado damage, sound baths, and Wisconsin gifts- What's next for the band, including upcoming UK, Mexico, and US tour datesDescartes a Kant played Mile of Music in Appleton, Wisconsin. Catch their music videos, including their Steve Albini recording sessions, on their YouTube channel.Fox Cities Core is Code Zero Radio's show covering the Fox Cities and Wisconsin live music scene through artist interviews, venue spotlights, and show previews.Subscribe for more Fox Cities Core interviews with touring and local artists.#DescartesaKant #DAK #FoxCitiesCore #CodeZeroRadio #MileOfMusic #AppletonWisconsin #SteveAlbini #MikePatton #MexicanRockBand #TheatricalRock #IndieRock #TouringMusicians #GuadalajaraMusic #LiveMusic #AfterDestruction
Ich habe mir von Jan Kerkmann von der Uni Freiburg sprach Nihilismus erklären lassen. Dies ist die erste Folge einer Mini-Serie zum Thema, da das Gespräch sehr dicht an Informationen ist. Heute geht es um die Frage, was Nihilismus ist und wie Jan auf das Thema gestoßen ist. Wir mahchen einen wilden Ritt durch die Philosophiegeschichte von den Vorsokratikern über Platon, Berkeley, Kant, Schopenhauer und ganz viel Nietzsche. Jans Web-Präsenz: https://uni-freiburg.de/philosophie/startseite/einrichtung/pd-dr-jan-kerkmann/ Lesenswert Hans-Jürgen Gawoll – Nihilismus und Metaphysik https://amzn.to/456LWvE * (das Buch ist überall quasi unbezahlbar, ich habe es aber über die Uni Frankfurt ausleihen können. Schaut in euren Bibliotheken!) Jan Kerkmann – Unendliches Bewusstsein. Berkeleys Idealismus und dessen kritische Weiterentwicklung bei Kant und Schopenhauer https://amzn.to/4yQpe8y * Jan Kerkmann – Geschichtlichkeit und Lebensverständnis. Heideggers Auslegung von Nietzsches II. Unzeitgemäßer Betrachtung. https://amzn.to/4yQpe8y * Stanford Encyclopedia of Philosophy über Berkeley https://plato.stanford.edu/entries/berkeley/ Stanford Encyclopedia of Philosophy über Schopenhauer https://plato.stanford.edu/entries/schopenhauer/ Wikipedia über Amor Fati: https://de.wikipedia.org/wiki/Amor_fati ====== abonniert meinen Kanal! :) ======= Webseite: https://privatsprache.de/ BlueSky: https://bsky.app/profile/privatsprache.bsky.social Mastodon: https://mastodon.social/@privatsprache Instagram: https://www.instagram.com/privatsprache TikTok: https://www.tiktok.com/@privatsprache Mehr Videos: Aristoteles – Metaphysik – Form und Materie: https://www.youtube.com/watch?v=ClGtkztDRvE Aristoteles – Metaphysik der Substanzen: https://www.youtube.com/watch?v=4YJUkOwHPhM Platons Ideenlehre: https://www.youtube.com/watch?v=oNramSm3470&list=PL1L_CFjFbZ9b8Kcv03zL0xVAb7rKNKuiF Aristoteles – Kritik an Platons Ideenlehre: https://youtu.be/Hjghct9d8yo?si=RjCXpiSkQlOdz5Rh Aristoteles – Metaphysik: https://youtu.be/OB5viElv5_o?si=nSz9BvhkWF6W04Jt Alle Philosophie-Folgen: https://www.youtube.com/watch?v=MhvEH9NjuPs&list=PL1L_CFjFbZ9aRfcEW6avxSgvxr9Q2jBrH Wie das mit der Philosophie angefangen hat: https://www.youtube.com/watch?v=MhvEH9NjuPs&t *Das ist ein Affiliate-Link: Wenn ihr das Buch kauft, bekomme ich eine winzige Provision und freue mich. Oder in Amazons Formulierung: Als Amazon-Partner verdiene ich an qualifizierten Verkäufen.
The word “intelligence” is a loaded word. Here's what Elon Musk said to the Economist last week: “I think AI may exceed the sum of human intelligence in around five years. There really won't be anything that AI can't do better than humans, apart from being human, perhaps.” Well “being human” is what intelligence is all about. So I read up on how Aristotle, Descartes, Kant, James, and many of the eastern philosophers define the term. And what you find is that deep thinkers define intelligence far beyond the ability to recall and use facts, but focus heavily on judgement, problem solving, and dealing with uncertainties in life. So after thinking about this for a few years, I decided to posit my ideas, and also reflect on the intelligence of trees, as described in “The Hidden Life of Trees.” What I came up with is four parts to this puzzling idea: horsepower, information, judgement, and wisdom. And as you'll hear, we all have various levels of intelligence in each area, and AI may lack quite a bit. As we all grapple with the exaggerated claims from AI engineers and the real use of these tools, I hope this discussion gives you some perspective, and also calms your fears that AI is going to steal your job. Our new book, Superpowered, is coming out in November, so sign up for early preview – it's all about how AI Superpowers us all, in our work, careers, businesses, and life. Additional Information The Hidden Life of Trees: What They Feel, How They Communicate—Discoveries from A Secret World The full-length interview with Elon Musk | The Economist Are Frontier Models Becoming A Commodity? Galileo: The AI Superintelligence for HR Chapters (00:00:00) - What is Intelligence?(00:10:02) - Does an AI Have Enough Intelligence to Plan?
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
David Blacker sat down to talk with Cara about psychedelics and learning. They highlighted that care occurs within contexts and systems. He moved between specifics about psychedelics, a wider discussion of learning, Kant, and quite a bit of Socrates. For more check out: DEEPER LEARNING WITH PSYCHEDELICS: PHILOSOPHICAL PATHWAYS THROUGHALTERED STATES (State University of New York Press, 2024)https://urldefense.com/v3/__https://psyche.co/ideas/psychedelics-are-philosophical-tools-for-demolishing-assumptions__;!!LAh5qUgpm5Y!GIuPy9Ydbh6qwhBh2vWXbdl5AwX1NqIt7uB46rxQRne-7I3KBPXXqNyz-o6r2PJ2PZkMkBD_152le7DxwIB_NXH8iA$https://urldefense.com/v3/__https://djblacker.scholar.st/__;!!LAh5qUgpm5Y!GIuPy9Ydbh6qwhBh2vWXbdl5AwX1NqIt7uB46rxQRne-7I3KBPXXqNyz-o6r2PJ2PZkMkBD_152le7DxwIDBl610xw$
A ten-year-old boy gallops across the Russian steppe on a Kalmyk horse, collecting plants for his father while German settlers starve in earth dugouts along the Volga. Seven years later, that same boy is standing on the deck of Captain Cook's Resolution as it crosses the Antarctic Circle for the first time in history, sketching birds and icebergs in a cabin so small he can barely turn around. By twenty-two he has circumnavigated the globe, published a book that Christoph Martin Wieland called a masterpiece and Samuel Johnson praised for its prose, dined with Benjamin Franklin in Paris, and been elected to the Royal Society. By thirty he has become the intellectual mentor to a young Prussian named Alexander von Humboldt, who would spend the rest of his life crediting Forster as the man who taught him how to see the natural world. By thirty-five he has helped found the first democratic republic on German soil during the French Revolution. By thirty-nine he is dead in Paris, abandoned by his wife, cut off from his country, and buried in a mass grave during the Terror. Today's guest is Andrea Wulf, author of The Traveller: The Revolutionary Life of George Forster and his Search for Humanity. We discuss how Forster's father dragged him from a parish near Gdansk to Russia at ten and onto Cook's ship at seventeen, why his account of the voyage was considered the finest travel writing of the eighteenth century, and how his travels through the South Pacific convinced him that human diversity was not a hierarchy but a harmony, with all peoples entitled to the same dignity and rights. We look at how he challenged Kant, Rousseau, and Buffon before he was twenty-five, why he threw himself into the French Revolution and helped build the Mainz Republic only to watch the Terror destroy everything he believed in, and why a man who influenced Humboldt, shaped the Romantic movement, and anticipated the concept of universal human rights has been almost completely forgotten.See omnystudio.com/listener for privacy information.
-Estrella de Tabby (0:05)-300 años de Kant (32:50) Hosted on Acast. See acast.com/privacy for more information.
Kann man freigeistiger durch diese unsere Weinwelt gehen als Anna Melissa Eßer? Wohl nicht. Warum? Ihr Wirken erscheint so wunderbar geprägt von konventioneller Unabhängigkeit und vinofeeler Neugier. Diese lebendige Unabhängigkeit ist dabei nicht Ausdruck von Widerspruch um des Widerspruchs willen, sondern einer authentischen Bereitschaft, Dinge differenziert zu betrachten und sich ein eigenes Urteil zu bilden. Und so verkörpert sie die Verbindung von Offenheit mit Verantwortung. Sie lebt ein facettenreiches Interesse, ohne dabei die Bedeutung fundierter Kenntnisse und das einfache Sommelièren-Handwerk aus den Augen zu verlieren. Anna Melissa Eßer ist eine Sommelière, die ihr Weinleben bewusst nach den Prinzipien berufsgeistiger Unabhängigkeit, kritischer Branchenreflexion und persönlicher Alltagsverantwortung gestaltet. Sie ist weder Rebellin um des Widerspruchs willen noch bloße Individualistin, sondern eine Persönlichkeit, die ihre Freiheit nutzt, um eigenständig zu denken, verantwortungsvoll zu handeln und offen für neue Erkenntnisse zu bleiben. Anna versteht Freiheit nicht als Beliebigkeit oder als das Recht, sich jeder Weintradition zu entziehen. Hieraus nährt sie ihre geistige Beweglichkeit und den unbedingten Willen, sich weiterzuentwickeln. Also lehnt sie Regeln oder Traditionen nicht ab. Vielmehr fragt sie nach deren Sinn, ihrer Begründung und ihrer Wirkung. Was sie als vernünftig, gerecht oder hilfreich erkennt, wird gerne durchgewunken. Was sie hingegen für unbegründet, einengend oder ungerecht hält, ist sie bereit, infrage zu stellen und gegebenenfalls abzulehnen. Und genau das braucht diese unsere Weinwelt: eine Leichtigkeit mit Bodenhaftung, eine kreativ fundierte Verrücktheit, ein Bewusstsein für die Weinwirtschaft und einen großartigen Blick über diesen Tellerrand. Und so durchlebt sie ihren Alltag mit ihrer sehr persönlichen, ausgeprägten Selbstständigkeit im Denken, Betrachten und Gestalten. Eine Freigeistin wie Anna lässt sich weder von gesellschaftlichem Druck noch von bloßen Erwartungen anderer bestimmen. Sie sucht den Dialog, schätzt den offenen Austausch und ist bereit, ihre Position zu begründen. Dabei weiß sie, dass Gewissheit selten endgültig ist und dass Erkenntnis ein fortlaufender Prozess bleibt. Also eigentlich könnte man meinen, Kant habe Anna Melissa Eßer vor Augen gehabt, als er notierte: „Du hast den Mut, dich deines eigenen Verstandes zu bedienen.“
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
We're back from vacation and chipping away at the summer backlog. Before the substance: listeners weigh in on our AI-generated cover art, Claude's new prediction scorecard grades our respective forecasting records, and Justices Kagan and Barrett take the Court's budget request — and some docket-terminology talk — to Congress. Then we spend the rest of the show on T.M. v. University of Maryland Medical System Corporation, in which the Rooker-Feldman doctrine, pronounced dead in the Green Bag two decades ago, proves alive enough to reach state-court judgments still on appeal. We try to figure out what the doctrine actually is, whether § 1257 or § 1331 is doing the work, and why the Court's committed formalists split down the middle. Along the way: insider trading as a University of Chicago virtue, a concurrence that may be playing a long game on federal habeas, and a dissent that's "exactly correct and not the law."Highlights[00:00:21] Opening: back from vacation, episode 26 of the season, chasing the all-time record of 29[00:01:15] Listener verdict on the AI back-catalog covers — Proximity Mines in the Facility gets its due, and no, we're not commissioning an artist[00:03:33] Will's Kant remarks draw "especially strenuous criticism"; he declines to recant[00:04:38] Claude's prediction scorecard grades the back catalog: Dan 82.4%, Will 71.2% — hedged singles vs. high-variance swings[00:07:06] Are prediction markets just gambling? Kalshi, Manifold, and insider trading as a University of Chicago virtue[00:10:16] Justices Kagan and Barrett testify before the Appropriations Committees: the security budget, life with a detail, and a remembrance of Lindsey Graham[00:14:03] Kagan on the "terminology nightmare" — shadow vs. emergency vs. interim docket — and why the shadow docket "is not currently shadowy"[00:16:14] The main event: T.M. v. University of Maryland Medical System Corporation, a fed-courts-class case if there ever was one[00:17:44] Doctrine origins: Rooker v. Fidelity Trust, District of Columbia Court of Appeals v. Feldman, Exxon Mobil v. Saudi Basic's attempt to bury them, and Sam Bray's Green Bag obituary[00:20:35] What Rooker actually held: no bill in equity to void a state judgment — only the Supreme Court has appellate jurisdiction over state courts[00:25:17] The unusual lineup: Sotomayor writing for Thomas, Alito, Kavanaugh, and Jackson; Barrett in dissent with the Chief, Kagan, and Gorsuch[00:28:53] Where does the doctrine come from — § 1257 exclusivity, § 1331 "original" jurisdiction, or something constitutional about "inferior" courts?[00:38:42] Will's alternative: you're either in appellate mode or collateral estoppel mode — and the Full Faith and Credit Act already covers this[00:42:44] The Thomas concurrence: Rooker "correct as an original matter" — and footnote 4's possible long game on federal habeas[00:46:34] The puzzle of the missing Feldman: Thomas's concurrence is almost entirely about Rooker[00:48:06] The Barrett dissent — "exactly correct and not the law" — and Rooker-Feldman given an inch[00:51:49] What goes in the fed courts supplement, and whether Hart and Wechsler needs a bigger Rooker-Feldman chapter again[00:53:25] Sign-off: an efficient episode; browse the back-catalog art at dividedargument.comRelevant linksCasesT.M. v. University of Maryland Medical System Corp. — slip opinionRooker v. Fidelity Trust Co., 263 U.S. 413 (1923)District of Columbia Court of Appeals v. Feldman, 460 U.S. 462 (1983)Exxon Mobil Corp. v. Saudi Basic Industries Corp., 544 U.S. 280 (2005)Prentis v. Atlantic Coast Line Co., 211 U.S. 210 (1908)Commentary & articlesSamuel Bray, "Rooker Feldman (1923–2006)," 9 Green Bag 2d (2006) — the obituaryWilliam Baude, "The Interim Docket" (SSRN, forthcoming U. Chi. L. Rev.) — now with Justice Kagan's terminology testimony incorporatedDavid Lat, "Justices Kagan And Barrett Are The Spokeswomen SCOTUS Needs Right Now" (Original Jurisdiction)SCOTUSblog, "Justices Kagan and Barrett testify before Congress"OtherThe custom back-catalog episode art — tell us your favorites
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
Achter de pakketjes die dagelijks bij miljoenen Nederlanders worden bezorgd, gaat een opvallend patroon schuil. Uit onderzoek van het FD blijkt dat onderaannemers van onder meer PostNL en DHL tientallen miljoenen euro’s aan schulden achterlaten en na een faillissement soms gewoon weer opnieuw beginnen. Hoe kan dit systeem blijven bestaan, en wie draait uiteindelijk op voor de rekening? We bespreken het met FD-redacteur Erik van Rein. Lees: Failliete onderaannemers PostNL en DHL laten miljoenenschulden achter AI zou juist de banen van jonge advocaten onder druk zetten. Toch blijkt uit nieuw onderzoek dat de strijd om juridisch talent onverminderd doorgaat. De startsalarissen bij de grootste advocatenkantoren lopen namelijk verder op. Hoe kan dat, en wat zegt dat over de impact van AI op de advocatuur? We bespreken het met redacteur Maud Vroemen. Lees: Strijd om juridisch talent stuwt salarissen bij topkantoren De regering-Trump zet links-extremisme voortaan op één lijn met jihadistisch terrorisme en wil bondgenoten daarin meekrijgen. Opvallend is dat rechts-extremistisch geweld in de nieuwe strategie niet wordt genoemd, terwijl onderzoek een genuanceerder beeld laat zien. Is dit een noodzakelijke koerswijziging of vooral een politieke keuze? We bespreken het met Amerika-correspondent Barbara Noordermeer. Lees: Regering-Trump bestempelt links‑extremisme als topdreiging Redactie: Jort Siemes Presentatie: Floyd Bonder See omnystudio.com/listener for privacy information.
In Geloofsgroei horen we hoe studenten aan de Lifeschool gegroeid zijn in hun geloof. Dit keer is Astrid Kant te gast. Astrid heeft door de jaren heen al een hele groei meegemaakt en vertelt hier voluit over in deze aflevering. Ze vertelt ook hoe ze vanuit de duisternis in Gods licht terechtkwam! Hoe de Lifeschool haar heeft geholpen hoor je ook in deze aflevering.
1944 liepos 31 dieną Raudonoji armija sugriauna labiausiai į Rytus nutolusį Vokietijos miestą Schirwindt (liet. Širvinta), po karo jo sovietai nebeatstato. Iš žemėlapio dingusio miestelio atminimą išgelbėti nusprendžia Antanas Spranaitis, kitapus Širvintos upės esančio Kudirkos Naumiesčio gyventojas. Jis iš po griuvėsių renka daiktus ir atidaro muziejų savo namuose. Vėliau čia lankosi iš miestelio pabėgę gyventojai, nes kitapus sienos jau nėra kur grįžti. Muziejuje apsilanko ir Marielle Vitureau, Prancūzijos tarptautinio radijo žurnalistė ir Antano marti, kuriai sunaikinto miestelio istorija palieka neišdildomą įspūdį.Šiandien Antano surinkti daiktai šeimos leidimu eksponuojami Lietuvos nacionalinio muziejaus parodoje „Noriu namo, į Širvintą. Dingusio miesto beieškant“.Ką byloja tuštuma kitapus tilto ir kodėl Antanas nusprendė saugoti miestelio atmintį? Marielle kalbasi su savo vyru, Antano sūnumi Dariumi Spranaičiu, istoriku ir parodos kuratoriumi Aurimu Kanapkiu bei istoriku Joachim Maenert iš Rytų Prūsijos muziejaus Vokietijoje.Autorė Marielle VitureauRedaktorė Inga Janiulytė-TemporinGarso suvedimas – Justas Pilibaitis
In this unlocked patreon episode Breht traces and explains a subterranean philosophical lineage running from Spinoza through Hegel to Marx and Louis Althusser. Beginning with Spinoza's conception of God or Nature as a single, immanent Substance, he explores how Hegel transforms Substance into a self-developing Subject, how Marx grounds the dialectic in material social life and class struggle, and how Althusser attempts to remove the final hidden Subject from Marxist theory. Along the way, he examines the concept of immanent critique, Kant's transcendental idealism, Hegel's master-slave dialectic, Marx's materialist transformation of idealism, and Althusser's concepts of overdetermination, interpellation, theoretical anti-humanism, and history as a "process without a Subject." What emerges is an ongoing struggle over immanence, contradiction, human agency, and whether history possesses either a sovereign author or a guaranteed destination. Listen to these related episodes to learn more: Economics and Philosophical manuscripts of 1844 Louis Althusser: Ideology and Ideological State Apparatuses The Nature of All Things: Spinoza's Philosophical Odyssey Intro to German Idealism: Kant, Fichte, Schelling, & Hegel Hegelian Dialectics: Contradiction, Marxism, & the Freudian Unconscious Check out our new merch designs and support the show HERE Learn more here: https://revleftradio.com/
Find this episode on YouTube: Jay Dyer joins John Heers to discuss the decline of 2000s New Atheism, shifting internet culture, the Enlightenment, Americanism, Orthodox Christianity, and the political forces shaping the modern West.They examine growing skepticism toward claims of scientific neutrality and political objectivism, arguing that politics often functions as a substitute religion—complete with its own dogmas, sacred myths, and promises of salvation. The conversation also explores whether political systems are ultimately controlled by deeper oligarchic and bureaucratic forces operating behind the scenes.Jay and John critique the Enlightenment as a project that elevated human reason into an idol, connecting its assumptions to liberalism, American civic religion, text-centered religion, and modern ideas about human nature. They contrast abstract systems that promise to save society with the concrete virtue—or vice—of the people who actually operate them.The conversation also covers monarchy and democracy in the Christian tradition, the American Dream, Tucker Carlson's interest in theology, Kant, postmodernism, the decline of New Atheism, and what may come after the Enlightenment project.Along the way, Jay discusses his background, podcasting, internet culture, humor, and the importance of embodied community. They also introduce upcoming Art of Tamada events in Greenville, South Carolina, August 28–29, and Islamorada, October 29–November 2, describing Supra as a communal and experiential practice rooted in hospitality and Orthodox theology.Topics include:- The decline of New Atheism- The Enlightenment and reason as an idol- Americanism and liberal rights- Politics as a substitute religion- Democracy, monarchy, and Christian political thought- Oligarchy, bureaucracy, and political power- Kant, postmodernism, and human nature- Orthodox Christianity and political theology- Supra, hospitality, and the Art of Tamada- Tucker Carlson and the return of theology__
In this unlocked patreon episode Breht traces and explains a subterranean philosophical lineage running from Spinoza through Hegel to Marx and Louis Althusser. Beginning with Spinoza's conception of God or Nature as a single, immanent Substance, he explores how Hegel transforms Substance into a self-developing Subject, how Marx grounds the dialectic in material social life and class struggle, and how Althusser attempts to remove the final hidden Subject from Marxist theory. Along the way, he examines the concept of immanent critique, Kant's transcendental idealism, Hegel's master-slave dialectic, Marx's materialist transformation of idealism, and Althusser's concepts of overdetermination, interpellation, theoretical anti-humanism, and history as a "process without a Subject." What emerges is an ongoing struggle over immanence, contradiction, human agency, and whether history possesses either a sovereign author or a guaranteed destination. Listen to these related episodes to learn more: Economics and Philosophical manuscripts of 1844 Louis Althusser: Ideology and Ideological State Apparatuses The Nature of All Things: Spinoza's Philosophical Odyssey Intro to German Idealism: Kant, Fichte, Schelling, & Hegel Hegelian Dialectics: Contradiction, Marxism, & the Freudian Unconscious Check out our new merch designs and support the show HERE Learn more here: https://revleftradio.com/
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
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
Frank Richter ist zurück im Studiocast. Diesmal aber nicht zum Arbeiten, wie er gleich zu Beginn klarstellt, sondern zum Plaudern. Und das lohnt sich: Der Comedian und Christoph Soltmannowski teilen einen Background im People-Journalismus, und wenn zwei Ehemalige aus dieser Branche zusammensitzen, kommen Geschichten auf den Tisch, die man so noch nie gehört hat.Frank erzählt von seiner Zeit bei «Glanz & Gloria»: von der Managerin, die eine Frage nach Beatrice Eglis Kindheitsinstrument strich, weil sie «zu privat» sei. Von der Anwaltskanzlei, die pro «Schweini» 10 000 Euro forderte, weil man Bastian Schweinsteiger gefälligst beim vollen Namen zu nennen habe. Und von der Erkenntnis, dass die grössten Stars meist die unkompliziertesten sind, während das Management drumherum die Probleme macht. Christoph hält dagegen: mit einem Bond-Girl, einer Aufpasserin hinter seinem Rücken und einem Weltstar, dessen Presseassistentin keine Fotos wollte. Wegen der grauen Haare.Dazwischen geht es um das Handwerk: Wie wird aus einem TV-Journalisten ein Comedian? Frank erzählt, wie ihn das Blick Live Quiz vor die Kamera brachte und warum er sich am Ende zwischen SRF und Blick entscheiden musste. Er erklärt, weshalb schwarzer Humor in der Schweiz mainstreamtauglich verpackt sein muss, warum das deutsche Publikum schneller lacht und wieso er auf dem Land manchmal bremsen muss, damit die Pointen überhaupt ankommen. Und er hat eine Beobachtung mitgebracht, die hängen bleibt: Gen-Z-Zuschauerinnen schauen bei derben Witzen erst nach links und rechts, ob die anderen lachen. Die Lizenz zum Lachen holt man sich heute offenbar beim Sitznachbarn ab.Natürlich ist auch sein aktuelles Programm «Love My Job» Thema. Der Titel ist ironisch gemeint, denn Frank kann den LinkedIn-Hashtag nicht ausstehen: Wer vom Betriebsausflug auf den Pilatus ein Gruppenfoto mit #lovemyjob postet, will laut ihm vor allem eines, nämlich dem Chef Honig ums Maul schmieren. Über Social Media hat er trotzdem seine Meinung geändert. Sein Fazit nach dem späten Einstieg: Am Anfang ist es cringe, später ist es Content. Und es füllt die Theater.Ausserdem: Wie Sacha Baron Cohen als Ali G an sein Trump-Interview kam, warum Donald Trump überhaupt in «Home Alone 2» mitspielt, was Macaulay Culkin heute macht, wie Joel von Mutzenbecher als Regisseur mit Frank 32 Figuren samt eigener Stimme und Körperhaltung erarbeitet hat und weshalb Frank als kokainsüchtiger HR-Chef in einem Kurzfilm einen Ritterschlag erhielt.Eine Stunde über Comedy, Promis und die Frage, wie viel Inszenierung in der Spontaneität steckt. Reinhören lohnt sich.Alle Termine zu «Love My Job» gibt es auf Frank Richters Website und seinen Social-Media-Kanälen.Kapitel:(00:00) Intro: Taylor Swift und der starke Franken(01:40) Von der Lokalzeitung zu «Glanz & Gloria»(03:47) Beatrice Egli und die zwei Managerinnen(05:34) Bond-Girl, Prinz Albert und Paul McCartneys Haare(08:46) Haartransplantationen und graue Bartkränze(10:17) Blick Live Quiz und der Abschied vom SRF(14:37) Schwarzer Humor und die Grenzen der Comedy(16:55) Warum die Gen Z erst nach links und rechts schaut(20:20) Ali G, Trump und «Home Alone 2»(22:52) Kinderstars: Culkin, Jodie Foster, Sofia Coppola(26:01) Schlagfertigkeit: alles inszeniert?(28:53) Lady Gaga im Hallenstadion und Conan O'Brien(30:18) Deutsches Publikum vs. Schweizer Publikum(32:35) Stadt, Land und der Kantönligeist(35:07) «Love My Job»: Warum der Hashtag nervt(38:28) Social Media: Am Anfang cringe, später Content(42:16) Das vierte Programm und die Tour(43:21) Schauspiel: der kokainsüchtige HR-Chef(45:17) Joel von Mutzenbecher und 32 Figuren(46:44) Böse Mails: Schweini und andere EmpörteTickets gibt es bei Ticketcorner:https://www.ticketcorner.ch/search/?a...Mehr zu Frank Richter aufhttps://www.frankrichter.ch/
Desde la filosofía, la intuición no es una corazonada, pero sí una revelación que se realiza a través del pensamiento. Aquí haremos un pasaje de la misma desde Platón, Aristóteles, Tomás de Aquino, Descartes, Kant Husserl y Steiner.
Send us Fan MailMost men have been trained to see the “male gaze” as something to apologize for — a coarse, objectifying reflex to suppress or perform your way out of. Either you're the bro who can't see past a woman's body, or you're the guy in the loafers and matcha latte performing a “female gaze” so hard it becomes its own kind of costume.In this episode, Jordan revisits a line from The Alabaster Girl that's followed him for thirteen years — women have a beauty song — and asks Zan whether it still holds. What follows is one of the most philosophically dense conversations on the podcast: gratitude as the lens that transforms looking into witnessing, the difference between a woman posed as a nude and a woman caught naked and unguarded, and why Zan refuses to be embarrassed about the fact that his eyes are drawn to the female form.They get into the man in the coffee shop who watches a woman order her latte and lets her walk out the door without chasing her — and why Zan calls that moment “code complete.” The phrase Zan wrote in his new book: a woman's walk is the oldest promise in the world. Freud's split between exhibitionism and voyeurism, and why every peep show needs a pervert. And Zan's answer to the discourse around male gaze versus female gaze — which he'd never even heard of before Jordan brought it up.Watch until the end for Zan's real-time reinterpretation of John Berger's “nude vs. naked” distinction — and his own bikini-versus-underwear version of it.__________________________________________________Ars Amorata, Summer '26:Text Her — Stan's course on apps and messaging: from swipe right to archetype. Opens soon.https://amorati.me/courses/text-her/Perception, Openness, Beauty — Online weekend intensive with Jordan, 18-19 July. Inside the Guild.https://arsamorata.com/guild/Desire in the Afternoon — Private salon-consultation with Jordan. 25-26 July, Bali. (Two-day option with embodied women's feedback newly added.)https://desireintheafternoon.carrd.co/Amorati Guild Summer Holiday — A beach weekend in Vama Veche with Zan. 21-23 August, Romania.https://arsamorata.com/guild/____________________________________________________Need a gunslinger? Someone who rides into town, completely solves your problem, then rides off into the sunset. Contact Zan Perrion personally to inquire about his incredibly effective one-on-one Laser Coaching. Find him here: https://arsamorata.com/gunslinger/__________________________________Get instant access to our 4 part mini-course with Zan Perrion
Why do morally good people often hesitate to change other people's minds, viewing persuasion as presumptuous, obnoxious, or downright creepy? The University of Washington's professor of philosophy, Dr. Colin Marshall, joins host PJ Wehry to discuss how to successfully bridge deep divides in our highly polarized climate.Dr. Marshall explores the ethics and psychology of civil persuasion in his book, Just Hear Me Out: How to Change Minds Without Lies, Bullshit, or Moral Compromise. They examine how genuine persuasion isn't about manipulation or aggressive takedowns , but rather requires a delicate balance of respect, compassion, and the right environment.In this conversation they explore: Why the fallout of the 2016 US presidential election pushed Dr. Marshall from pure theoretical philosophy into finding practical tools for civil engagement. The crucial difference between manipulative persuasion—where people are treated as obstacles to maneuver—and civil persuasion that honors a person's complete humanity. How combining Immanuel Kant's emphasis on rational respect with Arthur Schopenhauer's focus on deep compassion creates a coherent framework for changing minds. Why attempting to persuade someone at the Thanksgiving table almost always fails, and the importance of establishing an "attention-friendly context" like grabbing a coffee or a beer. Incredible real-world examples of successful persuasion, including Daryl Davis persuading extremists to leave the KKK and Megan Phelps-Roper leaving the Westboro Baptist Church after patient, charitable interactions on Twitter. The concept of "moral myopia"—the deeply human tendency to become so hyper-focused on one moral issue or our own self-respect that we lose sight of the other person's bandwidth and feelings. The psychology of building trust, and why establishing warmth and shared humanity must always come before proving your intellectual competence. This is a conversation for anyone interested in philosophy, psychology, and healthy communication who wants to understand how to sincerely navigate our modern disagreements without sacrificing their own moral integrity.Make sure to check out Dr. Marshall's book: Just Hear Me Out: How to Change Minds without Lies, Bullshit, or Moral Compromise
Is anyone really out there, or am I just chatting to a very articulate hallucination?In this episode, we juggle Descartes' lonely thinker, Kant's phenomena vs noumena, and Wittgenstein's ban on private language. If all we ever have are mental snapshots of the world, what right do we have to talk about some “real” thing behind them?On the way we hit: Private language & inner voices – are we talking to others, or just different bits of ourselves? Postmodernism & perspective – if everyone reads the world differently, do we still share a world at all? Ethics after God – without a cosmic referee, how do we avoid moral chaos? Featuring Red Dwarf's “Better Than Life,” a brain that sabotages its own happiness, Philip K. Dick and Robert Heinlein as unlikely comrades, and the comforting thought that if this is your solipsistic universe, you've got some serious explaining to do about Hitler, Trump, and broken-down cars full of kids.Plug in and decide whether “we” are really in this together – or whether “we” is already one person too many.
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POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
9 Hours and 15 MinutesPG-13Thomas777 is a revisionist historian and a fiction writer.This is the final 9 episodes of the Continental Philosophy series with Thomas777. He covers Kant, Sombart, Husserl, Wolfgang Smith, Marx and the Frankfurt School.Thomas' SubstackRadio Free Chicago - T777 and J BurdenThomas777 MerchandiseThomas' Book "Steelstorm Pt. 1"Thomas' Book "Steelstorm Pt. 2"Thomas on TwitterThomas' CashApp - $7homas777Pete and Thomas777 'At the Movies'Support Pete on His WebsitePete's PatreonPete's SubstackPete's SubscribestarPete's GUMROADPete's VenmoPete's Buy Me a CoffeePete on FacebookPete on TwitterBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-pete-quinones-show--6071361/support.
Deze week praten Wout Funnekotter, Jurian Ubachs, Arnoud Wokke en Dennis de Vries over Siri, hoe echt voetbal steeds meer lijkt op games, tinkeren aan een 3d-printer, AI gebruiken in een smarthome en het mogelijke verdwijnen van OnePlus. 0:00 Intro0:19 Opening1:15 .post9:39 Knipjes in Siri17:31 Penalty's nemen als in een game22:36 Kant-en-klare 3d-printer blijkt klusproject34:53 Toekomst van smarthome is AI50:23 OnePlus zou verdwijnen, hoezo dan?1:12:02 SneakpeekSee omnystudio.com/listener for privacy information.
Is beauty just personal preference, or is it real? Rev. Kyle Verage, pastor of Good Shepherd Lutheran Church in Pleasant Prairie, WI, joins the podcast to trace how Scripture answers a question philosophy never could. Verage starts with the common phrase "beauty is in the eye of the beholder" and shows where it breaks down — some things are simply more beautiful than others, whether or not we can always articulate why. He walks through the history of aesthetics from Socrates' pragmatism through Aristotle's search for objective standards to Kant's retreat into pure subjectivity, showing how each falls short apart from revelation. The heart of the conversation is word study: Verage searched the Old and New Testaments for every occurrence of "beauty," "beautiful," and "handsome," and found the data points overwhelmingly to the Hebrew term yafeh (used of people) and pa'ar/tif'arah (used of constructed things — garments, crowns, the temple). From there he traces the Greek pairing kalos kai agathos — the beautiful and the good — through figures like David and Abigail, into Zechariah's messianic prophecy, and finally to Christ himself as the true bridegroom who makes His bride beautiful. The conversation closes on the Divine Service: where objective beauty — art, music, architecture, rhetoric — is wedded to God's Word, and where Christ clothes His people in beauty not by their achievement, but by His declaration over them. Topics covered: The history of aesthetics: Socrates, Plato, Aristotle, Kant Word studies: yafeh, pa'ar/tif'arah, kalos, horaios, tov Kalos kai agathos — the beautiful and the good — in David, Abigail, and Zechariah 9 Christ as the exemplar of true beauty How the Word of God declares believers beautiful Beauty in the Divine Service as the marriage of external art and God's Word ----more---- Host: Fr. Paul Schulz Guest: Fr. Kyle Verage ----more---- Become a Patron! You can subscribe to the Journal here: https://www.gottesdienst.org/subscribe/ You can read the Gottesblog here: https://www.gottesdienst.org/gottesblog/ You can support Gottesdienst here: https://www.gottesdienst.org/make-a-donation/ As always, we, at The Gottesdienst Crowd, would be honored if you would Subscribe, Rate, and Review. Thanks for listening and thanks for your support.
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durée : 00:58:30 - Avec philosophie - par : Géraldine Muhlmann - "Ose penser par toi-même !", nous dit Kant. Telle est la devise des Lumières. Mais, s'adressant à Frédéric II de Prusse, il affirme que seule la liberté d'expression favorisera la pensée libre. - équipe : Anna Pheulpin, Carla Michel, Corinne Amar, Nicolas Berger, Nassim El Kabli, Luna Hadjla Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France
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Podcast #789 is all about sunshine with The Mellons, Bob Azzam, Thee Outlets, The Drip Edges, The Circulators, Blackpool Lights, Descartes a Kant, & A-100s.
MDD is here https://www.youtube.com/@ModernDayDebateSuperchats at any time here: https://streamlabs.com/jaydyer/tip Join this channel to get access to perks: https://www.youtube.com/channel/UCnt7Iy8GlmdPwy_Tzyx93bA/join Get started with Bitcoin here: https://www.swanbitcoin.com/jaydyer/ Philosophy Course is here: https://marketplace.autonomyagora.com/philosophy101 Set up recurring Choq subscription with the discount code JAY60LIFE for 60% off now https://choq.com Subscribe to my site here: https://jaysanalysis.com/membership-account/membership-levels/ Music by Dr Evo the Producer, Jay Dyer and Amid the Ruins 1453 https://www.youtube.com/@amidtheruinsOVERHAUL Join this channel to get access to perks: https://www.youtube.com/channel/UCnt7Iy8GlmdPwy_Tzyx93bA/joinBecome a supporter of this podcast: https://www.spreaker.com/podcast/jay-sanalysis--1423846/support.
Send us Fan MailA Guild member sent in a message that stopped this episode cold. A friend of his — married, still in love with his wife — confided that she won't have sex with him anymore. Not even a goodbye kiss. He found out she's been using a vibrator. He can't understand why she won't let him be the one. And the group of friends around him, hearing this, offer the usual advice: book a weekend away, drop the kids at her sister's, get her alone.Jordan and Zan don't offer that advice. Instead, they take the story apart piece by piece — and land somewhere much harder and much more honest than “just try harder.”They get into what it actually means when a woman stops admiring her husband — not respect, something deeper — and why that, once gone, cannot be incremented back with flowers and date nights. The devastating truth that all the desperate gestures a man makes at 3am to save a dying relationship are exactly what he should have been doing all along, when things were good. Why “relationships are work” and “relationships are compromise” are, in Zan's words, bullshit — and what he believes actually holds a marriage together instead. And the brutal timing problem: the moment a man finally feels the fear is usually the exact moment it's already too late to act on it.Watch until the end for what Zan calls his gunslinger coaching — for the men who are in it right now, and need someone to ride in fast and find out if it's still redeemable.
durée : 00:21:25 - Avec philosophie - Quel rapport le philosophe doit-il entretenir avec son temps ? En 1983, Michel Foucault relit Kant pour penser une philosophie attentive au présent et fondée sur l'exercice de la critique. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France
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In this episode of Blind Techie Geek Speaks, host Kathleen Masciana explores the clash between Kant's moral philosophy and Aristotle's Virtue Ethics. She examines whether building character is the key to addressing the impact of technology on attention spans. The episode features social psychologist Jonathan Haidt's research on how digital devices affect young people. It discusses his four recommended reforms: delaying smartphone adoption until high school, restricting social media access until age sixteen, implementing phone-free school environments, and increasing opportunities for independent play and real-world responsibility for youth.
In Episode 156, we enter the unsettling, hypnotic world of David Lynch with acclaimed film scholar Todd McGowan, author of The Impossible David Lynch.Lynch is one of cinema's greatest paradoxes. He worked within Hollywood systems and budgets, yet created films that feel radically independent. From Blue Velvet to Mulholland Drive, from Twin Peaks to Wild at Heart, his cinema resists easy interpretation while remaining deeply watchable.So how did Lynch stay between mainstream cinema and art-house radicality?Todd McGowan unpacks Lynch through Kant, Freud, fantasy theory, and the unconscious — revealing why Lynch's films are not puzzles to solve, but experiences to undergo. We explore whether Lynch was more classical than we assume, how he erased the “wall” between fantasy and reality, and why feeling — not logic — structures his cinema.This episode is essential for filmmakers, theorists, and anyone fascinated by how cinema can disturb, seduce, and transform an audience.⏱️ TIMESTAMPS00:00 Introduction01:28 Kant, Freud & Lynch04:20 Erasing the wall (Blue Velvet)06:57 Staying between mainstream & art film (clip)08:15 His most “mainstream” film – Wild at Heart10:01 Godard & Lynch (clip)11:30 Cinema of feeling vs cinema of reason12:20 On Godard's passing (clip)15:57 Lynch as a traditional Hollywood director? (clip)20:12 Dune — $42 million “disaster”?23:09 Blue Velvet, Wild at Heart, Twin Peaks, Lost Highway29:00 Returning to Kant — the philosophical LynchWatch on YoutubeListen on Spotify/ Apple#TheArtistsPodcast #DavidLynch #ToddMcGowan #TheImpossibleDavidLynch #FilmTheory #CinemaStudies #AuteurCinema #BlueVelvet #TwinPeaks #MulhollandDrive #WildAtHeart #Dune #PsychoanalyticTheory #ArtHouseCinema #FilmPodcast #Cinephile
Ce mois de juillet 2026, l'une des grandes plumes de la littérature francophone aura 85 ans : l'écrivain congolais Emmanuel Dongala, né en 1941. Son parcours l'a fait traverser toute l'histoire du Congo-Brazzaville, de l'indépendance aux déchirements de la guerre civile, en passant par l'âge d'or de la littérature et du théâtre dans les années 70 à 90. C'est cette histoire qu'il a racontée à RFI, à la première personne. RFI : Vous êtes né en juillet 1941, il y a 85 ans. Est-ce que pendant votre enfance se mettent déjà en place des choses importantes pour votre vie d'écrivain ? Emmanuel Dongala : D'abord, quand j'étais petit, à l'époque, il n'y avait pas de télévision et le soir, en saison sèche, il faisait froid. On était dehors, autour du feu, sous le clair de lune, et notre mère, ma mère, nous racontait des histoires, des contes. Et ça me fascinait. Je me disais : « Il faut qu'un jour moi aussi je me mette à inventer mes propres contes. » Et c'est incroyable comme cette femme analphabète m'a poussé à devenir écrivain. Et mieux encore, mon père était instituteur et il avait des livres à la maison que je feuilletais. Je ne comprenais pas la moitié de ce qu'il y avait dans ces livres-là. Mais l'objet était là et fascinant… … Fascinant. Je lisais, je passais de la littérature aux sciences, vous savez, ces grosses encyclopédies de l'époque, Larousse. Je me souviens très bien. Et être fils d'instituteur a été très important pour moi. C'est ce qui a fait ce que je suis aujourd'hui. … Et être bercé par les contes de votre mère. Oui aussi. Il y a un conte qui vous a marqué ? Oui, plusieurs. Et quelquefois la grand-mère, qui était encore vivante, nous racontait ces contes-là sur les animaux de la brousse, les hommes et les sorciers. C'était vraiment fascinant. À lire aussiL'écrivain congolais Emmanuel Dongala Dans les années 70, vous devenez enseignant-chercheur en chimie à l'université de Brazzaville. Le pays est alors en pleine République populaire, pétri de principes marxistes-léninistes, sous la direction de Marien Ngouabi. Quel souvenir personnel est-ce que vous gardez de cette période révolutionnaire ? Ouh là là ! D'abord tout était caporalisé. Parti unique. « Le pouvoir est au bout du fusil » : c'est le slogan qu'on voyait partout dans les rues. « Le parti dirige l'État ». Même à l'université, au Conseil de l'université, il y avait un représentant du parti qui faisait face au recteur. Il y avait quand même une ferveur révolutionnaire. On croyait vraiment qu'on allait changer le monde. Mais à la fin, c'était beaucoup plus des slogans et de la répression qu'autre chose. Est-ce qu'il y a des anecdotes qui vous ont marqué sur ce quotidien pendant la période révolutionnaire ? Oui. Par exemple, une fois, il y avait un grand meeting du parti et le représentant du parti, habillé en col mao, se lève, crie : « À bas l'impérialisme ! » La foule répond : « À bas ! » « À bas le colonialisme ! » La foule crie : « À bas ! » « À bas le néocolonialisme ! » « À bas ! » « C'est bien, nous sommes maintenant libres », a répondu à la suite le représentant du parti. Voilà, c'était fini pour lui. La parole était performative. C'était marrant ! Et puis il y avait beaucoup de ces slogans-là qui nous font rire aujourd'hui. Par exemple : « Il faut se serrer la ceinture aujourd'hui pour mieux vivre demain. » Parce qu'il y avait la pénurie, il n'y avait rien, il y avait des retards de salaires. Donc pour amadouer la population ou pour expliquer, on disait : « Il faut se serrer la ceinture aujourd'hui pour mieux vivre demain. » À lire aussiRetour au Congo, avec Emmanuel Dongala Et quel était justement votre quotidien d'enseignant-chercheur dans ces années 70 à Brazzaville ? Moi, j'étais assez indépendant parce que j'étais en chimie. Mais mes collègues de philosophie, par exemple, disent que la philosophie marxiste primait sur tout. Les autres philosophes, les Kant, les Sartre, c'était des « philosophes bourgeois », ça ne comptait pas. Et c'était terrible cet encadrement de la pensée à l'époque pour des universitaires. Tout le monde devait être marxiste. Ils parlaient du socialisme scientifique et ils parlaient des lois du mouvement social, je ne sais pas quoi. Moi qui suis chimiste et physicien, je ne connais que les trois lois de Newton. Mais eux, ils avaient une loi du socialisme, etc. Il y avait une contrainte intellectuelle. L'espace intellectuel était assez restreint. Est-ce que c'est le moment où vous entrez en écriture ou est-ce que vous êtes entré en écriture plus tôt ? Ah non, j'ai commencé tout simplement, tout bêtement au lycée, en écrivant des poèmes – des mauvais poèmes. Parce qu'à l'époque on envoyait des poèmes aux filles, vous voyez ? J'ai commencé par ça : imiter les Baudelaire, Rimbaud, etc. Et je lisais beaucoup, énormément. C'est ça qui m'a donné vraiment le goût d'écrire. En 1973, vous publiez Un fusil dans la main, un poème dans la poche qui raconte le parcours d'un jeune révolutionnaire africain, de la lutte anticoloniale à l'exercice du pouvoir. Et vous montrez dans ce livre comment l'idéal de libération se déforme jusqu'à produire de nouvelles formes d'oppression. Comment ce texte a-t-il été accueilli à l'époque par vos lecteurs congolais et par le pouvoir ? Les lecteurs congolais, c'était extraordinaire. Une réception incroyable, parce que le livre a été publié par Albin Michel et après on ne le trouvait plus. J'ai vu des gens qui le faisaient circuler par photocopie. Et autre anecdote que je trouve très intéressante : le livre avait été traduit en portugais et des années plus tard, j'ai retrouvé des combattants du MPLA, le parti angolais, et il y en a un qui m'a dit : « J'ai lu votre livre dans le maquis. C'était vraiment très bien. C'était exactement ce qui se passait. Mais vous, vous étiez dans quel maquis ? » Moi j'ai dit : « Non, je n'ai jamais combattu. Je ne sais même pas par quel bout on tient un fusil ! » Il était étonné que j'aie si bien décrit la réalité du maquis. Quand l'imaginaire rencontre la réalité, ça fait vraiment plaisir pour un écrivain. Au Congo, le livre a été censuré, banni parce qu'il y avait une liste de livres interdits. Je ne sais pas pourquoi, je n'ai jamais compris. Autant je comprenais la censure pour Jazz et vin de palme, mais pour Un fusil dans la main, un poème dans la poche, je n'ai pas compris pourquoi c'était censuré. À lire aussi«Un fusil dans la main, un poème dans la poche», par Emmanuel Dongala De quand date votre engagement dans le théâtre, sur la scène brazzavilloise, et pourquoi le théâtre ? Et bien parce que ça fait partie de la littérature, de tout ce que j'aimais et de toute la vie qui était autour de moi. Parce que c'est très théâtral, la vie congolaise, la vie au Congo. Et c'était vraiment une période bénie, cette époque-là des années 70-90 où le théâtre congolais était vraiment florissant. Je ne prétends pas que c'est nous qui avons créé le théâtre congolais. Non, ça existait bien avant nous. Il y avait beaucoup de petites troupes. Il y avait le théâtre national. Mais vraiment, à cette époque-là, parmi les groupes qui existaient, beaucoup montaient plus des scénettes que du théâtre. Et il y a trois groupes qui ont émergé : la troupe de Sony [Sony Labou Tansi, NdlR]. Sony a fait un travail remarquable pour mettre le théâtre congolais sur la scène internationale. La troupe qui s'appelait Ngunga, de mon ami Matondo aussi, qui était très bien, et la mienne s'appelait l'Éclair. Et ça avait tellement d'impact sur les autres que les jeunes, les autres troupes, nous taquinaient ou se moquaient de nous – ils nous jalousaient un peu d'ailleurs – en nous qualifiant de « So-ma-do », acronyme de Sony, Matondo et Dongala. Et chacun de nos groupes travaillait de façon différente. Matondo était spontané, dur, pour confronter la réalité. Sony jouait ses très beaux textes. Il venait à Limoges tout le temps. Et moi je m'intéressais beaucoup plus au théâtre international. Mais j'ai joué des pièces congolaises de Sylvain Bemba et d'autres. Mais tout de suite, j'ai monté « Sartre ». Et quand un de mes livres, Le Feu des origines, a été traduit au Japon, j'ai été invité au Japon. Je me suis mis à lire la littérature japonaise et j'ai découvert une pièce de Yukio Mishima qui s'appelle « Sotoba Komachi », qui m'a plu. Je l'ai montée à Brazzaville. Donc voilà la différence un peu entre nous trois, mais c'était vraiment vibrant. C'était une rivalité fraternelle. Vous diriez qu'il y a eu un âge d'or du théâtre congolais dans ces années 70, peut-être 80 ? Jusqu'en 90, oui. Le théâtre continue aujourd'hui, je ne vais pas dire que ça n'existe plus, mais cette époque particulière a quand même marqué et a été une période importante parce qu'il n'y avait pas que nous. Je parlais du parti unique, il avait aussi des pièces révolutionnaires, et puis ça passait à la radio, c'était très populaire, très suivi. Il n'y avait pas qu'à Brazzaville, il y avait aussi à Pointe-Noire, à Dolisie et puis il y avait des scènes dans les lycées, il y avait des troupes. Jusqu'à aujourd'hui, le théâtre est très aimé au Congo. Est-ce que vous pouvez nous raconter l'histoire du théâtre de l'Éclair dont vous êtes le principal animateur dans les années 80 ? Comment est-ce qu'il est né ? Il y avait un embryon, une petite troupe qui était là, qui végétait un peu, et on est venu me voir. On m'a demandé de prendre la direction de cette troupe. On a commencé à travailler d'abord sur la gestuelle, parce qu'on a eu la chance d'avoir le mime Marceau qui est passé à Brazzaville et on a fait un petit stage avec lui. Ensuite, on a eu d'autres comédiens, d'autres metteurs en scène français qui sont passés à Brazzaville. Donc le théâtre de L'Éclair, ce qui le caractérisait aussi, c'était beaucoup la gestuelle, surtout quand j'ai monté « Sotoba Komachi » de Yukio Mishima, il y avait beaucoup de ces pas glissés. Je tenais beaucoup à ça. J'ai monté « Sartre », comme je l'ai dit tout à l'heure, ce n'était pas rien : « Les mains sales ». S'il y avait cette scène théâtrale aussi riche, ça veut dire aussi qu'il y avait un vivier d'acteurs importants dans le Brazzaville de l'époque ? Oui, oui. Le grand théâtre, à l'époque, c'était le théâtre national. Il était subventionné par l'État et les acteurs étaient payés, professionnels. Donc c'est pour vous dire que l'État donnait une importance à ça. Nous, on était des théâtres indépendants, des groupes indépendants. Moi, comme j'étais professeur à l'université, j'avais un peu d'argent, je finançais ça moi-même et les prix des billets étaient vraiment modiques, évidemment. À lire aussi2. Emmanuel Dongala Est-ce qu'il y a des anecdotes qui vous ont marqué sur cette période théâtrale ? Oui, absolument, parce qu'à cette époque-là, je vous ai dit, on vivait sous une dictature de parti unique et il y avait des flics partout. Ils venaient écouter ce qu'on disait, etc. Évidemment, ils ont mis quelqu'un pour suivre Sony. « Ce qu'il fait dans son théâtre, c'est suspect », disaient-ils. Et Sony les a repérés tout de suite. Ces gens-là, ils n'étaient pas malins et on les repérait tout de suite. C'est lui qui me raconte : il me dit qu'il a conçu une scène avec un comédien. Il allait parcourir trois mètres sur le plateau, s'arrêter, bailler, puis retourner et un autre refaisait la même chose, et la même chose… et la même chose. Et là, au bout d'une demi-heure, le gars commençait à s'ennuyer. Il a dit : « Mais si c'est ça le théâtre, pourquoi on nous demande d'espionner ? » Puis il n'est plus jamais revenu. Mais moi, mon plus grand coup, ce n'était pas dans le théâtre. C'était quand j'étais professeur de chimie à l'université de Brazzaville, directeur des affaires académiques. J'organisais des conférences académiques. Et à l'époque, il y avait un grand débat dans le parti pour savoir qui était de droite, qui était de gauche. Il y avait un combat idéologique dans le parti. Il se trouve que je suis professeur de chimie et en chimie, il y a des molécules qu'on appelle énantiomères. Ça veut dire tout simplement que les molécules sont exactement les mêmes. La seule chose qui les distingue, c'est que, quand vous faites passer la lumière, l'une dévie la lumière à droite, l'autre dévie la lumière à gauche. Donc ce sont des molécules droites et gauches. Donc je me suis dit : « Ah ah ah ! Je vais faire une conférence académique de chimie organique avec le titre "De la notion de droite et de gauche". » Voilà le titre de ma conférence. Oh là là… alors qu'il y a un débat qui était de droite, de gauche dans le parti, les gens se sont dit : « Ça y est, qu'est-ce qu'il va dire celui-là ? » Je n'ai jamais vu un tel engouement parmi les services de sécurité de Brazzaville, il y avait trois ou quatre agents dans la salle pour voir ce que j'allais dire, sur la notion de droite et de gauche. Je me suis bien amusé. Ils se sont ennuyés à mort. D'ailleurs, ils n'ont rien compris. Est-ce que vous pourriez nous parler de vos relations avec Sony Labou Tansi et Matondo Kubu Turé, qui étaient donc les deux autres grands animateurs de la scène théâtrale de l'époque ? Oui, ce sont des frères. Dans le théâtre, on a combattu ensemble, on a fait des choses ensemble. Et ce qui est remarquable, c'est que nous avions des divergences politiques profondes. Par exemple, Sony était dans un parti politique et moi je ne l'étais pas, etc. Mais ça, ça ne nous divisait absolument pas. On se retrouvait, on discutait quelquefois, on copiait un peu les uns sur les autres… parce que j'ai vu quelquefois que Sony piquait quelque chose chez Matondo par exemple. Donc c'est pour ça que ces trois groupes sont restés si emblématiques pour les jeunes Congolais. Il y avait une forme d'émulation aussi entre vous ? Absolument. Rivalité, jalousie fraternelle. Sony avait un peu plus de succès que nous parce qu'il était connu à l'étranger. Il avait des pièces qui étaient jouées à Paris et chaque année il venait à Limoges. Mais c'est bien, parce que ce succès nous a entraînés aussi, nous a fait travailler plus fort encore. Donc voilà ce que c'était le « So-ma-do ». Qu'est-ce que vous retenez de la scène littéraire de cette époque à Brazzaville ? C'est aussi la grande époque de la littérature congolaise. Il y avait des écrivains avant, je vais citer les Letembet Ambily, les Jean Malonga, etc. Mais là, notre génération, ce groupe d'écrivains, je cite quelques noms : Tati Loutard, Henri Lopez, Sony Labou Tansi, Sylvain Bemba, Tchicaya U Tam'si – Bon, il était un peu plus âgé que nous, il n'était pas tout à fait dans notre groupe – et j'en passe… C'est vraiment à ce moment-là qu'il y a eu un véritable corpus de la littérature congolaise, connu à l'étranger, parce qu'on commençait à gagner des prix à l'étranger, à être traduit. Je suis traduit dans une douzaine de langues, par exemple. Et ce qui est encore une fois très intéressant, c'est qu'on était sous la dictature et on arrivait quand même à produire ces œuvres… qui étaient censurées. Comme je vous ai dit, Jazz et vin de palme a été censuré. Sony Labou Tansi a eu des livres censurés, mais on produisait quand même. Il y avait une forme de tolérance, finalement, du pouvoir. Oui, c'est ça qui est paradoxal. En fait, le pouvoir ne savait pas trop. Comme on disait à l'époque : « Il censurait les livres, mais ne censurait pas les écrivains. » C'était ça la formule qu'on avait trouvée. Il y avait une sorte de tolérance et puis je dois dire, quelqu'un qui était très bien, qui était ministre, membre du parti, mais qui nous protégeait, c'est le poète Jean-Baptiste Tati Loutard. Il nous protégeait vraiment. Il a protégé Sony pendant longtemps. Sony était fonctionnaire. Quelquefois, il partait à Limoges pour le théâtre, il abandonnait son boulot… Mais il nous a protégé, Tati Loutard, et je lui suis très reconnaissant pour ça. Est-ce que vous vous rencontriez entre auteurs ? Est-ce que vous formiez un cercle de gens qui se retrouvaient régulièrement ? Oui, on se retrouvait quand un livre des nôtres paraissait et on en discutait. C'est pour ça que Sylvain Bemba a créé ce mot qu'il appelle « la phratrie », la fraternité des lettres entre nous. Et ça a tenu, malgré les guerres civiles, les répressions politiques. C'est le seul endroit où la politique ne nous a pas divisés. Jusqu'à la guerre civile de 98 où nous sommes partis. Sony était mort. Et puis nous sommes partis, dispersés. Quelles sont les anecdotes dont vous vous souvenez sur cette « phratrie » des lettres ? On faisait lire nos manuscrits, les uns aux autres. On se passait les manuscrits. Il y a un point central autour duquel nous tournions, c'était Sylvain Bemba. C'était lui, vraiment le patriarche, c'est lui qu'on allait voir pour passer nos manuscrits. Et j'avais créé aussi une revue, Les Cahiers de théâtre de L'Éclair, qui n'a produit que trois numéros, malheureusement. Dedans, il y avait les contributions de Sony et des autres intellectuels de la place qui s'intéressaient au théâtre. Voilà encore un lieu d'échanges. C'était ça l'époque. Mais bon, on rencontrait des difficultés quand même pour imprimer. Il fallait taper, dactylographier à la machine. Je ne sais pas si vous savez ce que c'est de taper à la machine avec le papier carbone. Et puis quand on voulait faire des tracts, il fallait ronéotyper avec de l'encre sale. Donc c'était difficile, mais on réussissait quand même à faire circuler nos manuscrits, nos idées, les discussions sur la place. Le parti unique avait ce qu'il appelait l'Union nationale des écrivains et artistes du Congo. Tout le monde devait y être, tous les artistes, les écrivains, etc. Et nous, on ne voulait pas faire partie de cette UNEAC caporalisée par le parti. Nous avons créé à part, de façon tout à fait indépendante, ce que nous appelions l'ANEC, Association nationale des écrivains du Congo. Et tous les bons écrivains y étaient… j'exagère, parce que Tati Loutard est resté à l'UNEAC. Mais tous les autres, les Sony, les Matondo, tous les autres écrivains étaient avec nous dans cette association pour laquelle je dis fièrement que j'étais le président et le cocréateur. Et le pouvoir a laissé faire ? Oui, je pense que c'est arrivé au moment où déjà le parti unique était en fin de course. Les années 90 voient une explosion de violence politique au Congo au travers des milices que vous décrivez dans l'une de vos œuvres majeures, Johnny chien méchant. Quelle expérience personnelle, intime, est-ce que vous avez eue de ces milices, vous, Emmanuel Dongala ? Je les ai vécues… Un beau jour, vous voyez dans la rue des gamins avec des fusils plus hauts qu'eux, qui intimident les adultes, qui les font s'agenouiller dans la rue, dans les barrages. Mais vous vous dites : « Dans quel monde nous vivons ? » C'était terrible. Donc, avec ma famille, nous avons fui pour aller vers Pointe-Noire à pied, à travers la forêt. C'est terrible. C'est ça qui m'a donné l'idée d'écrire Johnny, chien méchant. Des gamins comme ça qui ont terrorisé des adultes, leur première expérience amoureuse est un viol. Mais qu'est-ce qu'on peut faire des gamins comme ça ? Comment les récupérer ? C'était vraiment terrible à vivre. Et c'est là où j'ai vraiment vu la souffrance humaine, la cruauté humaine. En ce sens que, par exemple, j'ai vu à un barrage où, pour passer, il fallait donner une pièce de 100 francs. 100 francs CFA, ce n'est rien. Et il y a un pauvre vieil homme, à l'époque, qui n'avait pas d'argent du tout. Et je vois quelqu'un à côté de lui qui dit : « Il n'a pas d'argent, je vais payer, je paie à sa place. » Et le petit milicien, là, avec son fusil, dit : « Non, ce n'est pas ton argent qu'on veut, c'est SON argent à lui. » Vous vous rendez compte… Ils se sont mis à battre le vieil homme. C'était cruel. C'est quelque chose qui a complètement échappé aux politiciens. En plus de ça, ce qui m'a touché personnellement, c'est qu'il y a eu des pillages partout. Ma bibliothèque a été pillée. J'avais des livres signés d'Aimé Césaire, de Jorge Sabato, le Portugais. Et en plus de ça, j'avais de la correspondance parce que j'ai fait des études aux États-Unis à une époque où il y avait les Civil Rights, donc j'ai eu une correspondance avec Malcolm X. Tout ça, ça a été détruit. Mais dans tous ces trucs-là, il y a toujours des moments cocasses. Un jour, en me promenant dans un grand marché de Brazzaville qu'on appelle le marché Total, je vois par terre mes livres pillés. Je dis : « Ce n'est pas possible, il faut que j'aille appeler la police pour récupérer mes bouquins. » Je vais voir la police. Je dis : « Voilà, j'ai vu à Total par terre mes livres. Je veux que vous veniez avec moi en tant que policier pour récupérer mes livres », l'agent de police appelle son chef. Son chef vient, je répète. Il me dit : « Ah, attendez, précisons : vos livres, ils ont été pillés ou volés ? » Je dis : « Pardon ? ». « Ah oui, parce que si ça a été volé, d'accord. Nous faisons une enquête. Mais si ça a été pillé, c'est la guerre, on ne peut rien faire. » [Il rit] Donc, c'est ça la guerre civile… À lire aussiEmmanuel Dongala, une sonate des Lumières
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
POUR COMMANDER MON LIVRE :https://www.editions-larousse.fr/livre/la-philosophie-cest-pour-vous-aussi-9782036070325/POUR COMMANDER MA BANDE DESSINÉE PHILORAMA : https://www.editions-larousse.fr/livre/philorama-9782036082434/Disponible aussi dans toutes les bonnes librairies !
After waking up in a trash pile under the Francis Scott Key Bridge (his favorite haunt), Jonah Goldberg has walked his dogs, stumbled to his American Enterprise Institute office, and is ready to talk shop. Today Jonah gets into the Hinckley Hilton, Washington police, “Kant,” being late, wokery, the art of canine flatulence expression, Darializa Avila Chevalier, weak parties, the evils of primaries, the free beer party, Iran, J.D. Vance beclowning himself, Richard Nixon, “Me Too Republicans,” and his own car. Show Notes: —The Enlightenment: The Pursuit of Happiness, 1680-1790 —92nd Street NY Dispod —Suicide of the West —Wednesday G-File —Nixon G-File —Ben Mankiewicz for The Dispatch's The Next 250 —Dispatch Juntos The Remnant is a production of The Dispatch, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including the Saturday Ruminant, audio versions of all our articles and newsletters, and Jonah's twice-weekly G-File—click here. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member by clicking here. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member by clicking here. Learn more about your ad choices. Visit megaphone.fm/adchoices
Episode 291 - Kant's Dilemma in Ghost of Tsushima
Concluding our treatment of Ch. 2 of Hegel's Faith and Knowledge (1802). Hegel wants to connect various ideas in Kant: The idea of an "intuitive, achetypal intellect" which we have to refer to in explaining biology, the synthesizing imagination that makes experience possible, and the unknown agency that makes things-in-themselves suitable for processing by our knowledge faculties and vice versa. For Hegel, these things all point to Reason as both the way we know God and the activity of God Himself: Hegelian Reason is the bringing together of seemingly opposite things, and so underlying our minds must be some greater kind of mind that brings together mind and world to create experience. Get more at partiallyexaminedlife.com. Visit partiallyexaminedlife.com/support to get ad-free episodes and tons of bonus discussion. Sponsors: Don't get caught running yesterday's security on today's web: visit nordlayer.com/browser. Visit functionhealth.com/PEL to get the data you need to take action for your health. Get a $1/month e-commerce trial at shopify.com/pel.
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