Podcast appearances and mentions of Albert Einstein

Los eclipses totales son acontecimientos de profundo impacto emocional, una esquiva carambola cósmica en la que la Luna bloquea completamente el Sol y permite apreciar las capas superiores de su atmósfera durante unos pocos minutos de noche súbita. Después de una prolongada sequía de fenómenos de este tipo, España enfila una insólita racha que nos permitirá vivir dos eclipses totales y uno anular en tres años consecutivos. El primero, el 12 de agosto de 2026, será visible como total sobre las 20.30 horas en una banda de doscientos kilómetros de anchura que barrerá buena parte de la mitad norte peninsular. Un año después, el 2 de agosto de 2027, la franja de totalidad cubrirá el extremo meridional de la Península, Ceuta y Melilla. Por último, el 26 de enero de 2028 podremos apreciar un eclipse de tipo anular desde casi cualquier ubicación al sur de la línea imaginaria que une Badajoz con Girona.La huella de los eclipses de Sol está presente desde los albores de la expresión escrita. Se cree que pudieron influir en importantes decisiones políticas en el Egipto de los faraones y fueron concienzudamente registrados por los babilonios. Además, su simbolismo queda plasmado en relatos religiosos como el eclipse de la crucifixión y sus diversas interpretaciones artísticas. El gran avance en la observación y el análisis llega con la Revolución Científica y la Ilustración; la observación de nuestra estrella y su corona durante episodios de totalidad permite avanzar en el conocimiento del astro, descubrir el helio e incluso confirmar la Teoría de la Relatividad de Albert Einstein. Entre mediados del siglo XIX y comienzos del XX nuestro país vivió una concatenación de eclipses totales semejante a la que se aproxima. Expediciones científicas de todo el mundo visitaron España para presenciarlos en 1860, 1900 y 1905.En este documental sonoro, con guion de Álvaro Soto y realización de Mayca Aguilera, participan los astrofísicos Alejandro Sánchez de Miguel, autor del libro 'Los eclipses de Sol'; Ana Belén Griñón, investigadora en el Instituto de Física Solar de la Universidad de Estocolmo; y Sandra Benítez, responsable de comunicación científica de la Agencia Espacial Europea (ESA). Intervienen también los astrónomos Alba Vidal, del Observatorio Astronómico Nacional; Rafael Bachiller, director de dicha institución y presidente de la Comisión Científica y de Asesoramiento del Trío de Eclipses; y Pedro García Lario, miembro de esta misma comisión por parte de la ESA. Además, el programa recoge las impresiones de la investigadora del CNIO Sara García Alonso, integrante de la reserva de astronautas de la ESA; Pedro Ruiz-Castell, profesor de historia de la ciencia en la Universitat de València; y Enrique Bordallo, presidente de la Asociación Astronómica de Burgos.Escuchar audio

Pioneer of modern theoretical physics... Get cozy and relax! This podcast is funded by advertising. Info and offers from our sponsors: https://linktr.ee/EinschlafenMitPodcast Here's the Wikipedia article (revised): https://en.wikipedia.org/wiki/Albert_Einstein Content was created or edited with the help of artificial intelligence. CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Learn more about your ad choices. Visit megaphone.fm/adchoices

Join Talk Cosmos as we explore the 20° Leo Solar Eclipse occurring a few weeks ahead on August 12. To give each of these powerful celestial markers sufficient attention, we will cover the August 12 Solar Eclipse in this episode and dive into the August 27 Pisces Lunar Eclipse later in the month.Nodal Dynamics & Critical DegreesThe Lunar Nodes move in reverse (clockwise around the zodiac) and have just entered the Aquarius North Node and Leo South Node. This New Moon in Leo comes into orb of a conjunction with the Leo South Node, marking the second solar eclipse of the year and the first under this new nodal axis.Furthermore, the nodes will hold at the critical 29th degree for over two months (from July 26 through October 9). This intense degree echoes the remaining residue of the previous Pisces-Virgo nodal cycle, which permeates through this pair of upcoming eclipses—the Solar Eclipse in Leo and the Lunar Eclipse tipping into Pisces.Navigating the EnergiesWe may feel a bit cautious yet deeply driven to realize our dreams and visions. Meanwhile, our inner life spark thrives on activating and nourishing personal relationships. What we value—and seek to align emotionally with our past—seems to evolve through immediate experience. Ultimately, we are deeply transmuting the foundational strategies and wisdom behind our core desires.Taking time to listen to our heart's internal direction recalibrates how we support our life path. Leo represents heart-centered leadership. By refining subtle, negatively intentional motives—such as unexamined anger or vengeance—we clear the path for fulfilling, authentic Leo leadership. Life invites us to use our creative gifts for the collective good, showing us where to release the ego's subtle demands for entitlement or arrogance so we can step fully into updated leadership roles within our communities. The Essence of the LuminaryAs our most personal luminary, the Moon regulates how the past flows into the present. It allows us to experience emotions, habits, and instinctual feelings, offering energetic input that helps piece together new meaning. In doing so, it supports our ongoing life story as we grow, heal, and evolve in mind, body, heart, and spirit.The ConversationJoining Sue Rose Minahan from Kailua-Kona, Big Island, Hawai'i, will be Talk Cosmos member Amanda Pierce of Seattle, Washington.Connect with inspiration! Never miss an episode by subscribing to TalkCosmos.com across YouTube @TalkCosmos, Facebook, KKNW-AM radio, and all major podcast platforms.AMANDA PIERCE: blends her eclectic style of astrology and energy magic around a soul-centered approach to life and healing. With a B.A. in Psychology, Astrology and Energy Work Consultation | Meditation | Writing & Editing. Empowerment-based Meditation: teaching in-person 4-week series classes. Email: Amandamoonastrology@gmail.com Past WSAA Board Member | UAC 2018 Volunteer Coordinator.SUE ‘ROSE' MINAHAN: Evolutionary Astrologer Consultant, Speaker, Writer, Dwarf Planet University graduate; Vibrational Astrology student under Linda Berry, Kepler Astrology Toastmasters Charter member; Wine Country Speakers member; holds an Associate of Fine Arts Music Degree, & a Certificate of Fine Arts in Jazz. Artist & musician. Mythology enthusiast. Talk Cosmos weekly conversations awaken heart and soul consciousness since 2018. talkcosmos.com#LeoSolarEclipse #lunarnodes #nodalaxis #JupiterinLeo #Astrology2026 #TalkCosmos #SueRoseMinahan #AmandaPierceIn the spirit of Einstein's wisdom on the shifting nature of energy, “Energy's never destroyed, energy only changes.” Talk Cosmos is your opportunity to ponder the collective unconscious and focus on the cosmic kaleidoscope. Discover the energy that is Talk Cosmos, every Sunday from 1 p.m. to 2 p.m. right here on Alternative Talk 1150!Visit https://talkcosmos.com for weekly schedule, blog, and information.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Join Talk Cosmos as we explore the 20° Leo Solar Eclipse occurring a few weeks ahead on August 12. To give each of these powerful celestial markers sufficient attention, we will cover the August 12 Solar Eclipse in this episode and dive into the August 27 Pisces Lunar Eclipse later in the month. Nodal Dynamics & Critical Degrees The Lunar Nodes move in reverse (clockwise around the zodiac) and have just entered the Aquarius North Node and Leo South Node. This New Moon in Leo comes into orb of a conjunction with the Leo South Node, marking the second solar eclipse of the year and the first under this new nodal axis. Furthermore, the nodes will hold at the critical 29th degree for over two months (from July 26 through October 9). This intense degree echoes the remaining residue of the previous Pisces-Virgo nodal cycle, which permeates through this pair of upcoming eclipses—the Solar Eclipse in Leo and the Lunar Eclipse tipping into Pisces. Navigating the Energies We may feel a bit cautious yet deeply driven to realize our dreams and visions. Meanwhile, our inner life spark thrives on activating and nourishing personal relationships. What we value—and seek to align emotionally with our past—seems to evolve through immediate experience. Ultimately, we are deeply transmuting the foundational strategies and wisdom behind our core desires. Taking time to listen to our heart's internal direction recalibrates how we support our life path. Leo represents heart-centered leadership. By refining subtle, negatively intentional motives—such as unexamined anger or vengeance—we clear the path for fulfilling, authentic Leo leadership. Life invites us to use our creative gifts for the collective good, showing us where to release the ego's subtle demands for entitlement or arrogance so we can step fully into updated leadership roles within our communities. The Essence of the Luminary As our most personal luminary, the Moon regulates how the past flows into the present. It allows us to experience emotions, habits, and instinctual feelings, offering energetic input that helps piece together new meaning. In doing so, it supports our ongoing life story as we grow, heal, and evolve in mind, body, heart, and spirit. The Conversation Joining Sue Rose Minahan from Kailua-Kona, Big Island, Hawai'i, will be Talk Cosmos member Amanda Pierce of Seattle, Washington. Connect with inspiration! Never miss an episode by subscribing to TalkCosmos.com across YouTube @TalkCosmos, Facebook, KKNW-AM radio, and all major podcast platforms. AMANDA PIERCE: blends her eclectic style of astrology and energy magic around a soul-centered approach to life and healing. With a B.A. in Psychology, Astrology and Energy Work Consultation | Meditation | Writing & Editing. Empowerment-based Meditation: teaching in-person 4-week series classes. Email: Amandamoonastrology@gmail.com Past WSAA Board Member | UAC 2018 Volunteer Coordinator. SUE ‘ROSE' MINAHAN: Evolutionary Astrologer Consultant, Speaker, Writer, Dwarf Planet University graduate; Vibrational Astrology student under Linda Berry, Kepler Astrology Toastmasters Charter member; Wine Country Speakers member; holds an Associate of Fine Arts Music Degree, & a Certificate of Fine Arts in Jazz. Artist & musician. Mythology enthusiast. Talk Cosmos weekly conversations awaken heart and soul consciousness since 2018. talkcosmos.com #LeoSolarEclipse #lunarnodes #nodalaxis #JupiterinLeo #Astrology2026 #TalkCosmos #SueRoseMinahan #AmandaPierce In the spirit of Einstein's wisdom on the shifting nature of energy, “Energy's never destroyed, energy only changes.” Talk Cosmos is your opportunity to ponder the collective unconscious and focus on the cosmic kaleidoscope. Discover the energy that is Talk Cosmos, every Sunday from 1 p.m. to 2 p.m. right here on Alternative Talk 1150! Visit https://talkcosmos.com for weekly schedule, blog, and information.

The dawn twilight has a bright visitor the next few mornings – the planet Mercury. It’s farthest from the Sun for its current morning appearance. It looks like a bright star, but it’s so low in the sky that it’s tough to find. Mercury holds an important spot in the history of astronomy and physics. It provided some of the first confirmation of General Relativity – Albert Einstein’s theory of gravity. Mercury’s orbit around the Sun is lopsided, so the planet’s distance from the Sun varies. For a long time, astronomers had seen that the orbit’s closest point shifted a tiny bit over time. Isaac Newton’s laws of gravity explained most of the difference. But there was still a tiny amount that couldn’t be accounted for. Einstein’s theory of gravity held that massive bodies warp the space around them. Since Mercury is the Sun’s closest planet, its orbit is influenced by that “warpage” more strongly than any other planet’s. In fact, general relativity accounted precisely for the shift in the orbit. So Mercury’s orbit provided some of the first evidence to support general relativity – a new way of thinking about gravity. Look for Mercury quite low in the eastern sky during the waxing twilight. It’ll shine a little brighter each day over the next few mornings. But it’ll also drop a little closer to the Sun, so you’ll need a clear horizon to spot it. Tomorrow: catching waves. Script by Damond Benningfield

Una puntata avvincente sullo scienziato che ha cambiato la storia : Albert Einstein.A cura dell'ing. Carlo Rossi , in studio Maria Letizia La Noce. Produzione esclusiva Radio Blue Point.

Stundenlang durch die Dünen. Keine Menschenseele, nur Sand, Wind und das Licht, dem hier schon so viele Maler verfallen sind.Michi war rund um Ostende unterwegs, draußen in Westflandern, wo die Küste plötzlich leer wird. Er steht in einem Naturpark, der sich selbst den Flughafen für Vögel nennt, und nimmt die längste Straßenbahn der Welt bis nach De Haan, einem Belle-Époque-Ort so unversehrt, dass hier sogar Einstein einmal wohnte. Dann steht er mitten in den Dünen vor einem Schild. FKK. Und muss sich entscheiden: Blankziehen oder nicht……Am Ende bleibt ein Bild, das kaum jemand kennt. In Oostduinkerke reiten Fischer auf mächtigen Brabanter Pferden brusttief ins Meer und fangen Krabben so wie vor fünfhundert Jahren. Der einzige Ort der Welt, an dem das noch lebt. UNESCO-Erbe der Menschheit.Und ob Michi wirklich blank gezogen hat am Nacktstrand, erfahrt ihr in dieser Folge.—

"The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift." Albert Einstein

Oppenheimer immediately established itself as one of the most important films in recent cinema. And after multiple viewings and further analysis, that belief has only grown stronger.Christopher Nolan has created far more than an entertaining and visually extraordinary epic about the brilliant scientist J. Robert Oppenheimer, whose work helped develop the atomic bomb that the United States ultimately used against Japan during the final days of the Second World War. This is also a film about opening the proverbial tube of toothpaste—releasing something that can never be put back—and forever changing the world as we know it.Nolan's masterful direction, the brilliant performances from the entire ensemble—including Cillian Murphy in the title role, Emily Blunt, Robert Downey Jr., Matt Damon, and many others—the exceptional cinematography and editing, and a story that remains as relevant today as ever all combine to make Oppenheimer a truly special, haunting, and powerful cinematic achievement.But looking back, was Oppenheimer truly deserving of the Best Picture Oscar among all the films released in 2023?Listen and find out what film critic Jack Ferdman thinks—and which film he ultimately chooses as his Rewatching Oscar winner for the year.Download, listen, and share ALL Rewatching Oscar episodes.SUBSCRIBE and FOLLOW Rewatching Oscar:Website: https://rewatchingoscar.buzzsprout.comApple Podcasts/iTunesSpotifyGoogle PodcastsiHeart RadioPodchaserPodcast AddictTuneInAlexaAmazon Overcasts Podcast Addict Player FMRSS Feed: https://feeds.buzzsprout.com/1815964.rssWebsite: https://rewatchingoscar.buzzsprout.comSocial Media Links: Facebook, Twitter, LinkedIn, Instagram, BlueSkyShare your thoughts and suggestions with us through:Facebook Messenger or email us atjack@rewatchingoscar.com or jackferdman@gmail.comMusic by TurpacShow Producer: Jack FerdmanPodcast Logo Design: Jack FerdmanMovie (audio) trailer courtesy of MovieClips Classic TrailersMovie (audio) clips courtesy of YouTubeSupport us by downloading, sharing, and giving us a 5-star Rating.  It helps our podcast continue to reach many people and make it available to share more episodes with everyone.Send us Fan Mail

* Apoie a Cultura: Chave Pix: 7296e2d1-e34e-4c2e-b4a0-9ac072720b88ALBERT EINSTEIN (1879-1955) entrou para o rol dos maiores gênios da humanidade ao desenvolver a Teoria da Relatividade. Ele estabeleceu a relação entre massa e energia e formulou a equação matemática que se tornou a mais famosa do mundo: E = mc² e revolucionou a nossa compreensão do cosmos. Essa é a nossa história de hoje. Se você gostou deixe seu like, faça seu comentário, compartilhe essa biografia com mais pessoas. Vamos incentivar a cultura em nosso pais. Encontro voces na próxima história. Até lá! (Tania Barros)- Contato: e-mail - taniabarros339@gmail.com

For masterclass clink hereDo you keep overthinking every idea, studying what everyone else is doing, and still find you can't start the thing you know you're meant to create?In this episode, we're breaking down why overthinking is exactly what's keeping your creativity stuck. If you're capable, driven, and full of ideas that never make it into the world, the problem isn't your discipline and it isn't a lack of originality. There are three layers of noise sitting between you and the part of you that already knows what to do, and each one has a name, science behind it, and a way through.I'm walking you through the Three Layers of Noise: why your nervous system filters every thought before you get to have it, why unfelt emotion jams the signal your intuition is trying to send, and why the voice in your head is a storyteller, not a reporter. You'll hear how Rick Rubin and Albert Einstein both described the same order of creation, and what the research on insight says about where ideas actually come from.In this episode, you will discover:Why comparison can only ever produce a copy, and what that's costing you.Layer 1: The Body. Why you can't out-think a stressed nervous system.Layer 2: The Emotional Static. Why the answer is arriving but can't get through.Layer 3: The Thought Stream. Why grinding at your desk produces nothing while ideas arrive in the shower.The order the research confirms: the idea comes first, the strategy comes second.The question that replaces "what's wrong with me."Research and books referenced:Amy Arnsten (Yale) on stress and the prefrontal cortex. Lisa Feldman Barrett on constructed emotion and interoception.Sarah Garfinkel and Hugo Critchley on interoception.John Kounios and Mark Beeman on the neuroscience of insight.Roger Beaty on the default mode network and creativity.Albert Einstein's account of his own thinking, from Jacques Hadamard, The Psychology of Invention in the Mathematical Field (1945).Rick Rubin, The Creative Act: A Way of Being.Free Masterclass: https://www.amenkaur.com/masterclassIf you want to go deeper and align the systems underneath your creativity in the right order, join my free masterclass hereJoin the Conversation:Which of the three layers of noise is loudest for you right now? Drop a comment below. I read every single one, and they help me decide what to cover next.Sending you so much love, Dr. Amen Kaur#creativity #overthinking #intuition #creativeblock #startingover #beingyouKeywords:self-discovery, personal growth, creativity, emotional intelligence, innovative thinking, overcoming fear, entrepreneurship, spiritual awakening, finding purpose, mental health awareness, navigating anxiety, unique talents, expressing creativity, building confidence, overcoming obstacles, mind-body connection, emotional healing, intuitive guidance, self-acceptance, developing resilience

Author Mark Fiorentino has been obsessed with Einstein's Unified Field Theory ever since hearing about it when he was ten. He worked for many years in the high-tech industry, including for IBM. Mark Fiorentino will shares his views on how the universe really works based on his study and research of Einstein's Unified Field Theory. He will discuss how the applications of Einstein's Theory can be used to travel thru space, for renewable energy, and future technology. He will also talk about the connection to conspiracy theories including UFOs and the Alien Technology they use, as well as other controversial topics such as Near-Death Experience revelations that are linked to the Theory of Super Relativity.United Public Radio & UFO Paranormal Radio www.uprntalkradio.com

Cruise Ships, Einstein's Brain, and Worldwide Brands "This Evening"

Siamo alla fine degli anni Cinquanta. L'Italia, in quel momento, è in corsa per dominare la rivoluzione informatica mondiale. Poi, nel giro di pochi anni, tutto si ferma. Il genio che guidava il progetto muore in un incidente stradale sull'autostrada Milano-Torino. Il fondatore dell'azienda muore a 59 anni su un treno, per emorragia cerebrale o infarto, non si sa. Nessuno ordina un'autopsia. Il prototipo del computer più avanzato d'Europa viene rubato e tentano di portarlo clandestinamente in Svizzera. E l'azienda, la Olivetti, viene progressivamente marginalizzata, fino a sparire dalla scena tecnologica mondiale. Coincidenze? Fatalità? O qualcuno ha deciso che l'Italia non doveva vincere quella corsa?SCOPRI IL MIO ULTIMO LIBRO: "Il mistero delle origini dell'uomo. Un viaggio nel tempo per comprendere chi siamo e dove stiamo andando". Prenotalo ora: https://amzn.to/3WazGFVPIERO ANGELA: ecco il nuovo libro che ho avuto il piacere e l'onore di curare "Chiedetevi sempre perché" (Mondadori): https://amzn.to/4nhQ8RzUna produzione Think about Science: thinkaboutscience.comCon: Massimo Polidoro e Giulio Niccolò Carlone; Video editing: Elena Mascolo, Fotografia: Claudio Sforza; Musiche: Marco Forni; Logo e animazioni: Zampediverse; Social - Comunicazione: Giacomo Vallarino - Grafiche: Roberta Baria; Distribuzione audio: Enrico Zabeo; Titoli: Jean SevillaLEGGI: "Una vita ben spesa. Trovare il senso delle cose con Leonardo, Einstein e Darwin": https://amzn.to/4leRDOR LEGGI UN ESTRATTO: https://bit.ly/4jRHXIN LEGGI la mia graphic novel: "Figli delle stelle" (con Riccardo La Bella, per Feltrinelli Comics): https://amzn.to/47YYN3KLEGGI: "Sherlock Holmes e l'arte del ragionamento" (Feltrinelli), il mio ultimo libro: https://amzn.to/3UuEwxSLEGGI: "La meraviglia del tutto" l'ultimo libro di Piero Angela che abbiamo scritto insieme: https://amzn.to/3uBTojAIscriviti alla mia NEWSLETTER: L' "AVVISO AI NAVIGANTI": https://mailchi.mp/massimopolidoro/avvisoainavigantiAderisci alla pagina PATREON, sostieni i miei progetti e accedi a tanti contenuti esclusivi:   /massimopolidoroScopri i miei Corsi online: "L'arte di Ragionare", "Psicologia dell'insolito", "L'arte di parlare in pubblico" e "l'Arte del Mentalismo": https://www.massimopolidorostudio.comPER APPROFONDIRELe musiche sono di Marco Forni e si possono ascoltare qui: https://hyperfollow.com/marcoforniLEGGI i miei libri: "Sherlock Holmes e l'arte del ragionamento": https://amzn.to/3UuEwxS"La meraviglia del tutto" con Piero Angela: https://amzn.to/3uBTojA"La scienza dell'incredibile. Come si formano credenze e convinzioni e perché le peggiori non muoiono mai": https://amzn.to/3Z9GG4W"Geniale. 13 lezioni che ho ricevuto da un mago leggendario sull'arte di vivere e pensare": https://amzn.to/3qTQmCC"Il mondo sottosopra": https://amzn.to/2WTrG0Z"Pensa come uno scienziato": https://amzn.to/3mT3gOiL' "Atlante dei luoghi misteriosi dell'antichità": https://amzn.to/2JvmQ33"La libreria dei misteri": https://amzn.to/3bHBU7E"Grandi misteri della storia": https://amzn.to/2U5hcHe"Leonardo. Genio ribelle": https://amzn.to/3lmDthJE qui l'elenco completo dei miei libri disponibili: https://amzn.to/44feDp4Non perdere i prossimi video, iscriviti al mio canale: https://goo.gl/Xkzh8ARESTIAMO IN CONTATTO:Ricevi l'Avviso ai Naviganti, la mia newsletter settimanale: https://mailchi.mp/massimopolidoro/avvisoainavigantie partecipa alle scelte della mia communitySeguimi:Patreon: massimopolidoroCorsi: massimopolidorostudio.comInstagram: @massimopolidoroPagina FB: Official.Massimo.Polidoro X: @massimopolidoro  Sito: http://www.massimopolidoro.comQuesta descrizione contiene link affiliati, il che significa che in caso di acquisto di qualcuno dei libri segnalati riceverò una piccola commissione (che a te non costerà nulla): un piccolo contributo per sostenere il canale e la realizzazione di questi video. Grazie per il sostegno!Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/ai-confini-di-massimo-polidoro--4522555/support.

Φινάλε της 12ης σεζόν, και το Νο.1 στους πιο διάσημους επιστήμονες δεν θα μπορούσε να είναι άλλος: ο Άλμπερτ Αϊνστάιν. Ξεκινάμε από το γιατί «πέρασε» όσο κανείς στην κουλτούρα και ξετυλίγουμε όλη του τη διαδρομή: από το γραφείο πατεντών και το Annus Mirabilis του 1905 (τρία paper κι ένα διδακτορικό) μέχρι τη γενική σχετικότητα, τα βαρυτικά κύματα, το Bose-Einstein condensate και το περίφημο «ο Θεός δεν παίζει ζάρια». Και κλείνουμε με το μεγάλο ερώτημα: ο πιο διάσημος επιστήμονας είναι και ο Νο.1 σε impact;Στο pre-show τα βάζουμε με το marketing του IMAX και των 70 χιλιοστών, και στο post-show λέμε τη γνώμη μας για την «Οδύσσεια» του Νόλαν — casting, φάουλ και όλα τα σχετικά (προσοχή, spoilers).Season finale: γιατί το Νο.1 στους πιο διάσημους επιστήμονες ήταν προφανώς ο ΑϊνστάινPre-show: Οδύσσεια, IMAX & 70mm — από πού βγήκε το 16:9 και γιατί δεν χρειάζεται IMAXΓιατί «πέρασε» ο Αϊνστάιν στην κουλτούρα (Έντιγκτον, η φάτσα, Person of the Century)1879–1902: Ουλμ, Ζυρίχη & το γραφείο πατεντών στη Βέρνη1905, το Annus Mirabilis: τρία paper (+ διδακτορικό) — κίνηση Brown & τα μόριαΦωτοηλεκτρικό φαινόμενο & η «εφεύρεση» των φωτονίων (το paper του Νόμπελ)Ειδική & γενική σχετικότητα: εκθρονίζοντας τον Νεύτωνα (ισοδυναμία, Ερμής)29 Μαΐου 1919: η έκλειψη του Έντιγκτον κάνει τον Αϊνστάιν celebrity μέσα σε μία μέραΤα «κρυφά» paper: βαρυτικά κύματα (1916), κοσμολογική σταθερά (1917) & Einstein coefficientsBose-Einstein condensate, το EPR paradox και το «ο Θεός δεν παίζει ζάρια»Ο μοναχικός επιστήμονας & οι μύθοι (μαθηματικά, ο εγκέφαλος, η γλώσσα)Το μεγάλο ερώτημα: ο πιο διάσημος είναι και Νο.1 σε impact; (ή μήπως ο Νεύτωνας;)Τι έρχεται: μια σεζόν με «άσημους» αλλά τεράστιου impact επιστήμονες + η ολική έκλειψηPost-show: κριτική για την «Οδύσσεια» του Νόλαν — casting, φάουλ & spoilersΕπικοινωνίαemail: hello@notatop10.fmInstagram: @notatop10Threads: @notatop10Bluesky: @notatop10.fmWeb: notatop10.fm (00:00:00) Pre-show: Οδύσσεια & τα τεχνικά του IMAX(00:02:31) 70mm, 35άρι φιλμ & η γέννηση του IMAX(00:08:25) «LieMAX», laser IMAX & γιατί δεν χρειάζεται(00:12:17) Intro — Νο.1: Άλμπερτ Αϊνστάιν(00:16:37) Παιδικά χρόνια, σπουδές & το γραφείο πατεντών(00:20:19) 1905: η θαυμαστή χρονιά, το διδακτορικό & η κίνηση Brown(00:24:00) Φωτοηλεκτρικό φαινόμενο & τα φωτόνια(00:28:25) Ειδική σχετικότητα: εκθρονίζοντας τον Νεύτωνα(00:34:57) Γενική σχετικότητα: ισοδυναμία & κάμψη του φωτός(00:37:33) 1915: εξισώσεις πεδίου & το περιήλιο του Ερμή(00:41:16) 1919: η έκλειψη του Έντιγκτον & η παγκόσμια φήμη(00:44:07) Βαρυτικά κύματα, κοσμολογική σταθερά & Einstein coefficients(00:47:03) Αμερική, το γράμμα στον Roosevelt & η ατομική βόμβα(00:48:56) Bose-Einstein condensate, EPR & «ο Θεός δεν παίζει ζάρια»(00:51:09) Τα τελευταία χρόνια & ο μοναχικός επιστήμονας(00:56:09) Μύθοι & προσωπική ζωή (μαθηματικά, ο εγκέφαλος, Κιουρί/Νέτερ)(01:00:17) Κλείσιμο: είναι ο Νο.1 σε impact;(01:07:48) Επόμενη σεζόν, η ολική έκλειψη & outro(01:10:04) Post-show: «Οδύσσεια» του Νόλαν — εντυπώσεις & αρνητικά(01:17:56) Το casting, ο Νόλαν & τα Όσκαρ

America's First Alien Contact Wasn't Roswell!Six years before Roswell, something came down over Cape Girardeau, Missouri — and the government made sure you never heard about it. This week we sit down with author and researcher Paul Blake Smith to dig into his book The UFO Bombshell Before Roswell: Missouri's 1941 Crash and Cover-Up.Paul walks us through the wreckage, the witnesses, and the powerful names tied to the cover-up — FDR, a young Senator Harry Truman, and even whispers of Einstein. We get into what allegedly happened to the recovered craft and its occupants, why the Freemason connections in the case run so deep, and how MO41 may have quietly set the stage for the country's reaction to Roswell six years later.Paul is the author of eight books, including the bestsellers President Eisenhower's Close Encounters and The Nixon-Gleason Alien Encounter. This is a deep one — bring your tinfoil hat.

When Murray “The Mad Sculptor” Grant takes off his hat and gets excited, his mane of exuberant white hair comes out and he looks a little like Albert Einstein. Murray is essentally a mad scientist built into a sculptor. Murrray invited Robbie to his studio on the family conservancy in Lakipia County, Kenya, where he creates his world famous bronzes, to talk about wildlife conservation from the eyes of an artist obsessed with recreating the beauty he sees in the world.  Get to know the guest: https://murraygrantbronzes.com/ Do you have questions we can answer? Send it via DM on IG or through email at info@theoriginsfoundation.org  Support our Conservation Club Members! Classic African Hunting: https://classicafricanhunting.com/  DSC South Texas: https://www.dscsouthtexas.org/  Everyone Deserves to Play: https://theoriginsfoundation.org/conservation-projects/everyone-deserves-to-play/  See more from Blood Origins: https://bit.ly/BloodOrigins_Subscribe Music: Migration by Ian Post (Winter Solstice), licensed through artlist.io This podcast is brought to you by Bushnell, who believes in providing the highest quality, most reliable & affordable outdoor products on the market. Your performance is their passion. https://www.bushnell.com  This podcast is also brought to you by Silencer Central, who believes in making buying a silencer simple and they handle the paperwork for you. Shop the largest silencer dealer in the world. Get started today! https://www.silencercentral.com  Don't forget to go subscribe to our new The Origins Foundation Podcast Youtube channel: http://www.youtube.com/@TheOriginsFoundationPodcast - who knows, you may be a lucky subscriber who wins some cool stuff from our partner companies! Learn more about your ad choices. Visit megaphone.fm/adchoices

Monica Marquez helps leaders turn AI ambition into adoption by building the trust, capability, and new ways of working that make transformation real.AI changed the size of the gap, not the shape of the problem. Adoption stalls in trust, capability, and ways of working, not in the technology itself. The leaders she works with are not behind because they lack talent. They are behind because no one has handed them a system for turning their own judgment into something AI can amplify. Every leader already has a version of authentic intelligence. Her work is helping them codify it, so the artificial kind has something worth amplifying.Monica spent twenty five years inside Goldman Sachs, Bank of America, EY, and Google, building the leadership development and professional growth systems that helped established professionals advance, stay relevant, and grow as the ground shifted under them. Talent, leadership, and transformation at scale, inside organizations that do not have room for guesswork.In 2019 she left corporate to co-found Beyond Barriers with Nikki Barua, building on Nikki's bestselling book to create an AI powered professional development platform that accelerated success for women and all leaders. Today she is co-founder and Chief Product Officer of FlipWork. The throughline has never moved: she builds pathways into what comes next.Unlocking Humanity with Ancient Knowledge Hosted by John Edmonds Kozma Unimpressed Podcast offers a groundbreaking look into consciousness, ancient wisdom, and the nonconscious aspects of humanity via the Quantum Field. Hosted by John Edmonds Kozma, CEO of Bang Productions and a seasoned entertainment industry veteran with extensive experience, each episode delves deeper than typical discussions to reveal profound insights about reality, spirituality, and human potential. He has been likened to Albert Einstein for his innovative reasoning. Hosted on Acast. See acast.com/privacy for more information.

Daily Dad Jokes (28 July 2026) The official Daily Dad Jokes Podcast electronic button now available on Amazon. The perfect gift for dad! Click here here to view! Shower Thoughts Podcast: We have another podcast called Daily Shower Thoughts, showcasing random, amusing and mind bending epiphanies. Search "Daily Shower Thoughts" in your podcast player or click here Email Newsletter: Looking for more dad joke humor to share? Then subscribe to our new weekly email newsletter. It's our weekly round-up of the best dad jokes, memes, and humor for you to enjoy. Spread the laughs, and groans, and sign up today! Click here to subscribe! Listen to the Daily Dad Jokes podcast here: https://dailydadjokespodcast.com/ or search "Daily Dad Jokes" in your podcast app. Jokes sourced and curated from reddit.com/r/dadjokes. Joke credits: IEnjoyDadJokes, Evanthekid16, ultimatedelman, James__HD, YounggBeauty, PlayfulAttempt4949, Ill-Refrigerator3728, Healthy_Ladder_6198, Expert_Fan4804, NoiseOptimal8883, 808gecko808, Slowloris81, , Vaquero-SASS, Appropriate_Humor952, xx_trash_xx_69, RangerHikes, MedicTillar, JiF905JJ Subscribe to this podcast via: iHeartMedia Spotify iTunes Google Podcasts YouTube Channel Social media: Instagram Facebook Twitter TikTok Discord Interested in advertising or sponsoring our show? Contact us at mediasales@klassicstudios.com Produced by Klassic Studios using AutoGen Podcast technology (http://klassicstudios.com/autogen-podcasts/) Learn more about your ad choices. Visit megaphone.fm/adchoices

Wallace Thornhill returns to the show to talk with us about The Electric Universe. We discuss some of the new scientific discoveries and how they relate, their upcoming EU conference in June, the history of the solar system, the electic sun, and how all this relates to consciousness. Also, we lost connection a couple times near the end. I edited out those parts, but the edit isn't perfect, so if it seems things don't flow right a couple of times, that is why.Wallace Thornhill graduated in Physics at Melbourne University in 1964 and began postgraduate studies with Prof. Victor Hopper's upper atmosphere research group. Before entering university, he had been inspired by Immanuel Velikovsky through his controversial best-selling book, Worlds in Collision. Wal experienced first-hand the indifference and sometimes hostility toward a radical challenge to mainstream science. He realized there is no career for a heretic in academia.Wal worked for 11 years with IBM Australia. The later years were spent in the prestigious IBM Systems Development Institute in Canberra, working on the first computer graphics system in Australia. He was the technical support for the computing facilities in the Research Schools at the Australian National University, which gave him excellent access to libraries and scientists there.Wal was initially heavily influenced by the then revolutionary ideas of Immanuel Velikovsky of Princeton. Velikovsky proposed that mankind had been devastated in the past by cosmological events . Wal took these ideas and with his deep knowledge of astronomy and, plasma physics began his own questioning of scientific dogma. Paramount was the place of electro magnetism, as distinct from gravity, in the formation of the universe . This slowly but surely led to his and other colleagues (such as David Talbot, Donald Scott, and Anthony Peratt) questioning such ingrained theories as the big bang, black holes and Einstein's theory of relativity. This group in particular contend that many scientific “proofs “are theory laden or mathematically concocted. An insistence on empirical data from observations and experiments gives their work true integrity. (bio taken from www.ancientdestructions.com, more at the sight)Wallace's site: www.holoscience.comThunderbolts: www.thunderbolts.info Hosted on Acast. See acast.com/privacy for more information.

I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE This is a careful lecture that can be followed with zero background knowledge. Einstein thought he'd caught quantum mechanics in an act of spookiness — this podcast asks whether he was even worried about the right thing. Tim Maudlin, professor of philosophy at NYU and a leading philosopher of physics, delivers a rare full lecture tracing Einstein, EPR, and the road to Bell's theorem. The central claim: Einstein's real objection was never determinism — "God does not play dice" is a red herring — but non-locality, which he inferred rather than assumed. Maudlin traces the argument from Einstein's overlooked 1927 Solvay objection through the EPR paper's criterion of reality, showing why Bohr's famous reply never actually answered it. 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 - Einstein's Quantization Hypothesis - 00:07:20 - Photoelectric Effect Implications - 00:12:30 - Wave-Particle Duality Myths - 00:20:40 - De Broglie's Matter Waves - 00:26:40 - Copenhagen's Completeness Doctrine - 00:34:10 - Solvay 1927: Two Conceptions - 00:41:20 - Pinhole Diffraction Problem - 00:48:00 - Collapse and Relativity Violations - 00:56:00 - Epistemic vs. Ontic Collapse - 01:05:00 - Ontological vs. Dynamical Locality - 01:14:00 - Configuration Space Objections - 01:25:00 - EPR's Criterion of Reality - 01:32:40 - Analyzing the Reality Criterion - 01:40:50 - Causal Isolation and Locality - 01:48:45 - Entangled Momentum Eigenstates - 01:56:45 - Logical Core of EPR - 02:04:40 - Position-Momentum Simultaneous Reality - 02:14:00 - Inferring Determinism from Locality - 02:26:30 - Conservation Laws and Information - 02:37:40 - Counterfactual Definiteness Debunked - 02:44:00 - Bohr's Incoherent Response - 02:52:00 - Schrödinger's Entanglement Confession LINKS MENTIONED: - Quantum Non-Locality and Relativity [Book]: https://amazon.com/dp/1444331272?tag=toe08-20 - On a Heuristic Point of View About the Creation and Conversion of Light [Paper]: https://sites.pitt.edu/~jdnorton/lectures/Rotman_Summer_School_2013/Einstein_1905_docs/Einstein_Light_Quantum_WikiSource.pdf - The Ghost in the Atom [Paper]: https://vdoc.pub/download/the-ghost-in-the-atom-a-discussion-of-the-mysteries-of-quantum-physics-1guq071e2ukg - Collected Papers on Wave Mechanics [Paper]: https://mwolf.pracownicy.uksw.edu.pl/MK/Schrodinger_Collected_Papers_on_Wave_Mechanics.pdf - Quantum Theory at the Crossroads [Book]: https://amazon.com/dp/0521814219?tag=toe08-20 - Can Quantum-Mechanical Description of Physical Reality Be Considered Complete? [Paper]: https://journals.aps.org/pr/pdf/10.1103/PhysRev.47.777 - Bohr's EPR Critique [Paper]: https://journals.aps.org/pr/pdf/10.1103/PhysRev.48.696 - The Present Situation in Quantum Mechanics [Paper]: https://personal.lse.ac.uk/robert49/teaching/partiii/pdf/SchroedingerPresentSituation1935(1980trans).pdf - Bertlmann's Socks and the Nature of Reality [Paper]: https://cds.cern.ch/record/142461/files/198009299.pdf - On the Einstein Podolsky Rosen Paradox [Paper]: https://journals.aps.org/ppf/pdf/10.1103/PhysicsPhysiqueFizika.1.195 - Quantum Theory and Measurement [Book]: https://amazon.com/dp/0691613168?tag=toe08-20 - Tim Maudlin [TOE]: https://youtu.be/fU1bs5o3nss - Sean Carroll [TOE]: https://youtu.be/9AoRxtYZrZo - Robert Sapolsky [TOE]: https://youtu.be/z0IqA1hYKY8 - Jenann Ismael [TOE]: https://youtu.be/7kvXihDAOi0 - John Norton [TOE]: https://youtu.be/Tghl6aS5A3M Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

Lamentations 21 How the Lord in his angerhas set the daughter of Zion under a cloud!He has cast down from heaven to earththe splendor of Israel;he has not remembered his footstoolin the day of his anger.2 The Lord has swallowed up without mercyall the habitations of Jacob;in his wrath he has broken downthe strongholds of the daughter of Judah;he has brought down to the ground in dishonorthe kingdom and its rulers.3 He has cut down in fierce angerall the might of Israel;he has withdrawn from them his right handin the face of the enemy;he has burned like a flaming fire in Jacob,consuming all around.4 He has bent his bow like an enemy,with his right hand set like a foe;and he has killed all who were delightful in our eyes;in the tent of the daughter of Zion,he has poured out his fury like fire.5 The Lord has become like an enemy;he has swallowed up Israel;he has swallowed up all its palaces;he has laid in ruins its strongholds,and he has multiplied in the daughter of Judahmourning and lamentation.6 He has laid waste his booth like a garden,laid in ruins his meeting place;the Lord has made Zion forgetfestival and Sabbath,and in his fierce indignation has spurned king and priest.7 The Lord has scorned his altar,disowned his sanctuary;he has delivered into the hand of the enemythe walls of her palaces;they raised a clamor in the house of the Lordas on the day of festival.8 The Lord determined to lay in ruinsthe wall of the daughter of Zion;he stretched out the measuring line;he did not restrain his hand from destroying;he caused rampart and wall to lament;they languished together.9 Her gates have sunk into the ground;he has ruined and broken her bars;her king and princes are among the nations;the law is no more,and her prophets findno vision from the Lord.10 The elders of the daughter of Zionsit on the ground in silence;they have thrown dust on their headsand put on sackcloth;the young women of Jerusalemhave bowed their heads to the ground.11 My eyes are spent with weeping;my stomach churns;my bile is poured out to the groundbecause of the destruction of the daughter of my people,because infants and babies faintin the streets of the city.12 They cry to their mothers,“Where is bread and wine?”as they faint like a wounded manin the streets of the city,as their life is poured outon their mothers' bosom.13 What can I say for you, to what compare you,O daughter of Jerusalem?What can I liken to you, that I may comfort you,O virgin daughter of Zion?For your ruin is vast as the sea;who can heal you?14 Your prophets have seen for youfalse and deceptive visions;they have not exposed your iniquityto restore your fortunes,but have seen for you oraclesthat are false and misleading.15 All who pass along the wayclap their hands at you;they hiss and wag their headsat the daughter of Jerusalem:“Is this the city that was calledthe perfection of beauty,the joy of all the earth?”16 All your enemiesrail against you;they hiss, they gnash their teeth,they cry: “We have swallowed her!Ah, this is the day we longed for;now we have it; we see it!”17 The Lord has done what he purposed;he has carried out his word,which he commanded long ago;he has thrown down without pity;he has made the enemy rejoice over youand exalted the might of your foes.18 Their heart cried to the Lord.O wall of the daughter of Zion,let tears stream down like a torrentday and night!Give yourself no rest,your eyes no respite!19 “Arise, cry out in the night,at the beginning of the night watches!Pour out your heart like waterbefore the presence of the Lord!Lift your hands to himfor the lives of your children,who faint for hungerat the head of every street.”20 Look, O Lord, and see!With whom have you dealt thus?Should women eat the fruit of their womb,the children of their tender care?Should priest and prophet be killedin the sanctuary of the Lord?21 In the dust of the streetslie the young and the old;my young women and my young menhave fallen by the sword;you have killed them in the day of your anger,slaughtering without pity.22 You summoned as if to a festival daymy terrors on every side,and on the day of the anger of the Lordno one escaped or survived;those whom I held and raisedmy enemy destroyed.Sermon Questions:Think of a time you felt completely out of control — an illness, a job loss, a relationship ending. What did you do with that feeling? Did you try to regain control, or did you have to just sit with it?The sermon compares trusting God to trusting a pilot you've never met. Is there anyone or anything in your life you trust that way — where you don't need to understand every detail to feel secure? What makes that trust possible?Einstein's quote — "The more I learn, the more I realize how much I don't know" — got used to make a point about faith. Where in your own life have you had to accept "I don't know" as an answer, and how did you make peace with that (or not)?The sermon suggests that sometimes the best response to someone's suffering isn't a theologically deep explanation, just showing up. Can you think of a time someone's presence helped you more than their words did? What did they do (or not do)?Do you think personal pain can become part of something meaningful over time — a "bigger story," even if you can't see it yet? Or does that idea feel like wishful thinking to you? 

The Space Show Presents Dr. Jim Green, Tuesday, July 7, 2026Short Summary:The Tuesday Evening Space Show featured Dr. James L. Green, former NASA chief scientist, who presented on the solar system's movement through different interstellar clouds and the potential effects on Earth's environment. Dr. Green discussed how our heliosphere is shrinking as we transition from the local interstellar medium to the G-cloud, which could impact cosmic radiation and space weather. He explained the concept of the Sun's galactic center Lagrange point (L1) at 3.8 light years away, where objects could potentially be captured by the Sun, and suggested this area could be important for future interstellar travel and resource utilization. The discussion covered how NASA's SLS rocket could deliver 20 metric tons to Mars and the importance of testing technologies incrementally before human missions. Participants explored philosophical questions about scientific knowledge and uncertainty, with Dr. Green sharing his personal perspective on faith and science.Detailed SummaryDr. James Green discussed his experience as NASA's Division Director of Planetary Science during a budget crunch in 2012, when the planetary science budget was proposed to be cut from $1.3 billion to about half that amount over five years. To address this challenge, Green decided to engage the public directly rather than relying on congressional hearings or industry lobbying, as he found those approaches ineffective. He worked with Kristen Erickson to develop outreach strategies, including broadcasting mission control activities to the public, which initially faced resistance from his supervisor Ed Weiler but ultimately led to public engagement efforts across NASA centers.Jim discussed his experience with the Curiosity rover mission, including a conversation with Ed about potential risks and the importance of public outreach. He explained how the mission faced delays and technical challenges before successfully landing, which gave him confidence in its readiness. Jim emphasized the importance of sharing scientific results with the public and outlined his work on planetary defense, including efforts to detect near-Earth objects and the creation of the Planetary Defense Office.Jim discussed the success of their kinetic impact project and its potential for hitting larger targets. David asked about public engagement, to which Jim responded that while he appears frequently in media, more outreach is needed, especially internationally where many countries lack aerospace education. Jim explained his work in metaverse education, having taught 5,000 students globally, and emphasized the importance of public understanding in science communication. The discussion then shifted to NASA's risk management approach, with Jim advocating for more frequent SLS rocket launches to build expertise and reduce costs, noting that SLS can deliver 20 metric tons to Mars compared to 2 metric tons with current rockets.Jim presented research on space weather and the heliosphere, explaining how Earth is transitioning from the local interstellar cloud to the G-cloud within a thousand years. He discussed the structure of the heliosphere, including its croissant-like shape and the importance of understanding cosmic weather as humans move through different galactic regions. Jim also highlighted the Sun's galactic center Lagrange point at 3.8 light years, explaining how this area could capture rogue planets and other objects passing through it.Jim explained that the solar system is transitioning from one galactic cloud into the denser G-cloud, which will cause the heliosphere to shrink to 5-10 AU and potentially trigger cosmic weather changes similar to those that may have caused Snowball Earth events millions of years ago. He discussed how advanced civilizations might use the minimum gravity region near the sun's L1 point as a strategic location for interstellar observation and travel, as it provides a stable orbit point that saves fuel while allowing observation of the solar system. Jim emphasized the need to study this region to understand potential hazards to Earth and develop gravity maps for future interstellar travel.Jim discussed the concept of Lagrange points in space, particularly focusing on how they could serve as meeting points for different civilizations and as locations where interstellar dust accumulates. He explained that this accumulated dust could be used as a resource through in-situ resource utilization, containing various materials including silicon, graphite, and radioactive elements. David reflected on the complexity of space exploration as an endless puzzle, to which Jim responded by explaining how science involves studying the nature of things through mathematical frameworks and laws, while acknowledging that new concepts like dark matter demonstrate how much remains unknown.The group discussed how scientific understanding evolves over time, with Jim sharing examples of Einstein's changing views on black holes and how newer theories build upon and modify previous ones. Marshall and John contributed insights about mathematical developments in physics, including Lagrange points and the correspondence principle where newer laws can approximate older ones. The discussion concluded with Jim reflecting on how science fiction has predicted real scientific advancements in his lifetime, from Star Trek's vision of space travel to current exoplanet discoveries and the potential for life on rogue planets.L1 Point Calculation DiscussionThe group discussed Jim's calculation of the L1 point at 3.8 light years from the sun, which included dark matter models from a peer-reviewed paper. Jim explained that if objects are found orbiting in circular patterns rather than moving through the galaxy, it would confirm the real solar L1 location. The conversation then shifted to personal beliefs about religion and science, with Jim sharing his perspective as an Episcopalian who finds faith provides peace despite not believing the Bible word-for-word.The group discussed dark matter, with John clarifying that gravitational effects lead to the inference of dark matter rather than the other way around. Jim explained that while dark matter must interact gravitationally, its exact particle nature remains unknown. The conversation then shifted to the G-Cloud, where Jim described how Earth might be affected by entering a denser cloud in 3,000 years, including potential changes to the heliosphere and increased space weather.Jim discussed the importance of understanding cosmic weather and its potential impact on exoplanets and Earth's atmosphere, particularly referencing Mars' lost atmosphere and historical space weather events like the Carrington event. He emphasized that scientists need to consider planets in the context of their galactic environment rather than isolating them from cosmic influences. Jim also mentioned his ongoing analysis of Mars data to better understand the effects of interstellar medium exposure on planetary environments.Jim shared his experience as a retired NASA scientist and discussed his current work in metaverse education, where he creates immersive experiences for students to explore space missions and lunar environments using Oculus headsets. He explained that NASA has developed the technology needed to land humans on Mars, specifically highlighting the Hypersonic Aeroshell and Inflatable Decelerator (HIAD) system that can deliver 20 metric tons of cargo and enable landing in a wider range of locations than current aeroshells. Jim outlined a step-by-step approach following Apollo's incremental learning model, including testing mini-MAVs, cargo delivery, and eventually human missions, with the first cargo mission potentially launching in 2028 and human missions in 2043.Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentWe use Zoom phone numbers for program participation.For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Broadcast 4566 Zoom: George Tyson w/co-host John Jossy | Tuesday 28 Jul 2026 700PM PTGuests: George TysonZoom: George Tyson, fresh from ISDC, along with John Jossy, have news and updates for usBroadcast 4567: Hotel Mars MIT details to follow | Wednesday 29 Jul 2026 930AM PTGuests: John Batchelor, Dr. David LivingstonHotel Mars details to followBroadcast 4568 Zoom: Dr. Logan Smith | Friday 31 Jul 2026 930AM PTGuests: Dr. Logan SmithZoom: Dr. Logan Smith return. Details to follow. To listen & participate in this program email us for Zoom phone numbers & access before airtime.Broadcast 4569 Zoom: Lisa La Bonte | Sunday 02 Aug 2026 1200PM PTGuests: Lisa La BonteLisa La Bonte returns to The Space Show. Details to follow. To listen & participate in this program email us for Zoom phone numbers & access before airtime Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

Join host Sue Rose Minahan as she welcomes Evolutionary Astrologer Marie O'Neill to explore the new Aquarius-Leo Nodal Shift, which begins its 18-month cycle this Sunday.The lunar nodal cycle repeats on average every 18.6 years (roughly 18 years and 7 to 11 months). The North Node last occupied Aquarius from December 19, 2007, to August 21, 2009—a period centered around a "breakdown to rebuild" theme. However, today's planetary alignments and signs completely reframe the evolutionary homework of this upcoming nodal cycle.Experiencing the destiny path of the Aquarius North Node means learning to channel our inherent creative spark for the greater good. This requires releasing the need for personal validation, ego approval, or acting from a place of entitlement.The Power of Astrological StorytellingSince prehistoric times, humanity has used myths, stories, and analogies to find common ground and bridge complex concepts. With Jupiter transiting Leo for the next year, its theatrical energy will naturally amplify our desire to display and express beliefs through the art of storytelling.Drawing from Marie's recently completed Masterclasses on Astrological Storytelling at Kepler College, Sue and Marie will discuss how sharing stories and metaphors can bridge deeper astrological meaning to strengthen interpretation.Connect with inspiration! Never miss an episode by subscribing to our email list and the Talk Cosmos YouTube Channel. Also available on Facebook, KKNW radio, and all major podcast platforms.MARIE O'NEILL, MBA: is an Evolutionary Astrologer, life coach, and speaker dedicated to helping people transform their lives. As the founder of Padma Life Coaching, she specializes in guiding clients through trauma healing and toward achieving their most ambitious goals. Marie is the author of the powerful memoir, “and the Lotus Opened,” which chronicles her own healing journey.A dynamic lecturer, she presents at astrology conferences across the country and co-hosts the Kepler College YouTube series, “Let's Talk Astrology Business.” Marie is Vice President of Strategic Initiatives at Kepler College and a core instructor known for her Masterclasses on astrological storytelling and personal branding. She integrates her background in life coaching with astrology to help practitioners master narrative techniques for client consultations and ethical practice developmentHer commitment to powerful communication is backed by her Distinguished Toastmaster (DTM) achievement and seven years of service on the Board of Directors for TEDx Sonoma County. She produces two annual Virtual Healing Retreats. Websites: PadmaLifeCoaching.com & andtheLotusOpened.comSUE ‘Rose' MINAHAN: Evolutionary Astrologer, Consultant, Writer, Speaker, Mythology enthusiast. Dwarf Planet University graduate; Vibrational Astrology Student under Linda Berry, Kepler Astrology Toastmaster Club (KAT). Wine Country Speakers. Associate of Fine Arts Music Degree, & a Certificate of Fine Arts in Jazz. Artist, musician. Founder of Talk Cosmos weekly conversations awakening heart and soul consciousness since 2018.Website: TalkCosmos.com and YouTube.com/@talkcosmos.#NodalShift #AquariusNorthNode #LeoSouthNode #NodalAxis #LunarNodes #TalkCosmos #MarieONeill #SueRoseMinahan #SueMinahan #Astrology2026 #KeplerCollegeLetsTalkAstrologyBusiness #andtheLotusOpened #PadmaLifeCoachingIn the spirit of Einstein's wisdom on the shifting nature of energy, “Energy's never destroyed, energy only changes.” Talk Cosmos is your opportunity to ponder the collective unconscious and focus on the cosmic kaleidoscope. Discover the energy that is Talk Cosmos, every Sunday from 1 p.m. to 2 p.m. right here on Alternative Talk 1150!Visit https://talkcosmos.com for weekly schedule, blog, and information.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

“Mythology and ancient esoteric beliefs are finally found in cutting-edge science.”—Rick Rubin, bestselling author of The Creative ActFrom the author of the global bestseller The Secret History of the World comes an epic new history of the relationship between spiritual belief and cutting-edge science. Includes beautiful black-and-white illustrations throughout.Human beings have a deep-rooted desire to find something worth believing in. But there is a widespread assumption that science is at odds with spiritual belief and offers the only intelligent way to think about the universe.In this epic new history, Mark Booth offers an alternative view, showing us how the great geniuses of modern science, from Marie Curie, Nikola Tesla, and Albert Einstein to today's architects of AI, turned instead to secret, mystical, and “higher” teachings, including Indian mysticism and Freemasonry, to make sense of the strange phenomena they were encountering.Experimenting with alternative states of consciousness, they risked isolation and even madness.Here then is the dark, dramatic story of modern science—of love, betrayal, attempted murder, multiple suicides, sexual experimentation, and transgression—from which unfolds the awe-inspiring discoveries which have transformed our lives for good, and potentially for evil. For just as the work of J. Robert Oppenheimer and others ended the Second World War but brought humanity to the brink of extinction, so, too, has the spectacular growth of AI.The result of many years of research, and deep conversations with prominent academics in the field, this book takes us on an exhilarating journey, in the company of some of the greatest minds of our age, toward a deeper understanding of our place in the cosmos.Jonathan Black is the pen name of  Mark Booth. He was educated at Ipswich School and Oriel College, Oxford, where he studied Philosophy and Theology. After a long career in publishing, he now writes full time.   ​He is the author of the global bestseller The Secret History of the World, The Secret History of Dante: Unearthing the Mysteries of the Inferno and The Sacred History of the World: How Angels, Mystics and Higher Intelligence Made our World.   ​His books are the result of a lifetime spent reading literature in this area, publishing many of the leading authors in the field and hanging around antiquarian bookshops.  He and his wife live in the south of England.https://www.markboothauthor.com/Become a supporter of this podcast: https://www.spreaker.com/podcast/earth-ancients--2790919/support.

Morgan Hewett thinks the future of women's sexual wellness is powered by AI—and it's making a lot of people uncomfortable.In this episode of the Unimpressed Podcast, John sits down with the founder of Devin Toys, one of the world's first AI-powered sex tech companies designed exclusively around women's pleasure. Morgan explains how the technology personalizes experiences in real time, adapts to each user, and is redefining conversations about intimacy, wellness, and female sexuality.This episode challenges long-held beliefs about intimacy and asks a bigger question:Why is female pleasure still considered controversial?Whether you agree with Morgan or not, this conversation will make you think about relationships, shame, independence, technology, and what empowerment really means in today's world.

Kurt Gödel was a genius mathematician and logician, considered by many scholars to be as influential as Aristotle. He was also buddies with fellow geniuses Albert Einstein and Oskar Morgenstern, the co-creator of Game Theory. In 1947, Gödel told Morgenstern and Einstein he had identified an inner contradiction in the U.S. Constitution while studying for his citizenship test: the type of loophole that could plunge the United States into a dictatorship not unlike the Nazi regime Gödel had escaped when he immigrated from Austria. So, what was the loophole? Over the years, everyone from Constitutional law scholars to laypeople on Reddit have wondered. Endless Thread plumbs the depths of Gödel's galaxy brain with the help of philosopher, writer and Gödel expert Rebecca Newberger Goldstein — and learns some surprising lessons about artificial intelligence, a German shorthand called Gabelsberger, and democracy along the way. Show notes: Incompleteness: The Proof and Paradox of Kurt Gödel, by Rebecca Newberger Goldstein (Amazon) Oskar Morgenstern's account of Kurt Gödel's naturalization (Institute of Advanced Study) This episode was produced by Grace Tatter. It was co-written by Grace Tatter and Ben Brock Johnson, co-hosted by Ben Brock Johnson and Amory Sivertson, and edited by Meg Cramer and Dave Shaw. Mix and sound design by Paul Vaitkus.

"This is a true story. It just hasn't happened...yet."While considering his imminent death, an old man senses the existence of a force that holds his soul to his body—a force even Einstein never imagined—and he's drawn into the struggle between good and evil.From postwar London in 1946 to LBJ's ranch in 1964, and then to a small Catholic church in northern Minnesota, real history begins to blur with conjecture and myth. Otto's Portal rests between the abyss of man's fears and the apex of his science, while a disparate cast of characters seeks redemption.Out now on Amazon, Barnes and Noble. goto NathanJorgenson on Facebook! 

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

Chapters 00:00 — Day 1 recap: 2,800+ listens, 465 peak concurrent, why this show exists 01:15 — Susie Violet Ward on FATF's fraud roadmap: the Travel Rule comes for Bitcoin 06:15 — "If you're not a baddie, why should you care?" Privacy is normal 08:15 — Signal vs. noise: Cory audits the France wrench-attack narrative live 14:30 — Susie's tangent: capitalism as balance, governments as parasite, and whether science has economics' blind spots 19:00 — Cory defends Einstein, spooky action at a distance, and simulation theory 20:00 — Why Bitcoiners don't need to talk about Bitcoin (the Gjelina story) 22:00 — Fernando Nikolic on Perception, narrative engineers, and data as the AI moat 25:30 — Inside Swan's AI stack: Cygnet, 25 full-time agents, Workforce Intelligence 30:00 — The vibe-coding warning: never go it alone with PII or financial data 33:30 — The Battle for Monetary Independence, Part 2: "The Work That Wins" (the USS Yorktown diary, Ten Million Bitcoiners, The Race to Avoid the War) 39:30 — Swan: 0.5% buy fee, network fees paid on withdrawals, 80%+ of sats in self-custody 41:30 — RBX origin story: Death Row Records and getting out of GBTC without capital gains 43:30 — Shout-outs: Bitcoin Today (Samson Mow tomorrow), Bitcoin Veterans, Danny live at Pubkey Friday 44:30 — Block clock stories: the Sharpie fix and the bearish rocket clock 52:00 — The time P ate a shoe (boiled in chili, five hours) 55:30 — Tatum on self-hosting, media preservation, and "don't trust, verify" beyond your node 59:00 — Susie's retracted BBC investigation and how articles get memory-holed via lawsuits 65:00 — 1984, post-edited movies, and Grayscale's Worldcoin ETF 66:30 — Privacy normalization: phone numbers at the sandwich shop, 23andMe regrets 69:30 — Orange-pill moments: why the system's winners can't hear the signal 73:30 — Wrap: Day 3 tomorrow, 10am ET, 90 minutes

Avete mai avuto la sensazione che le persone intorno a voi non siano del tutto… reali o umane? Che stiano recitando una parte, seguendo un copione, in uno spettacolo messo in scena proprio per voi. Magari sono attori. Magari robot. O magari… alieni. È da questa domanda che parte uno dei film più celebri e inquietanti degli ultimi quarant'anni: "Essi vivono". Un film che molti hanno preso alla lettera, come se fosse una rivelazione. E che il suo stesso autore, John Carpenter, ha sempre definito (provocatoriamente) un documentario. Ma un documentario su cosa, esattamente?SCOPRI IL MIO ULTIMO LIBRO: "Il mistero delle origini dell'uomo. Un viaggio nel tempo per comprendere chi siamo e dove stiamo andando". Prenotalo ora: https://amzn.to/3WazGFVPIERO ANGELA: ecco il nuovo libro che ho avuto il piacere e l'onore di curare "Chiedetevi sempre perché" (Mondadori): https://amzn.to/4nhQ8RzUna produzione Think about Science: thinkaboutscience.comCon: Massimo Polidoro e Giulio Niccolò Carlone; Video editing: Elena Mascolo, Fotografia: Claudio Sforza; Musiche: Marco Forni; Logo e animazioni: Zampediverse; Social - Comunicazione: Giacomo Vallarino - Grafiche: Roberta Baria; Distribuzione audio: Enrico Zabeo; Titoli: Jean SevillaLEGGI: "Una vita ben spesa. Trovare il senso delle cose con Leonardo, Einstein e Darwin": https://amzn.to/4leRDOR LEGGI UN ESTRATTO: https://bit.ly/4jRHXIN LEGGI la mia graphic novel: "Figli delle stelle" (con Riccardo La Bella, per Feltrinelli Comics): https://amzn.to/47YYN3KLEGGI: "Sherlock Holmes e l'arte del ragionamento" (Feltrinelli), il mio ultimo libro: https://amzn.to/3UuEwxSLEGGI: "La meraviglia del tutto" l'ultimo libro di Piero Angela che abbiamo scritto insieme: https://amzn.to/3uBTojAIscriviti alla mia NEWSLETTER: L' "AVVISO AI NAVIGANTI": https://mailchi.mp/massimopolidoro/avvisoainavigantiAderisci alla pagina PATREON, sostieni i miei progetti e accedi a tanti contenuti esclusivi:   /massimopolidoroScopri i miei Corsi online: "L'arte di Ragionare", "Psicologia dell'insolito", "L'arte di parlare in pubblico" e "l'Arte del Mentalismo": https://www.massimopolidorostudio.comPER APPROFONDIRELe musiche sono di Marco Forni e si possono ascoltare qui: https://hyperfollow.com/marcoforniLEGGI i miei libri: "Sherlock Holmes e l'arte del ragionamento": https://amzn.to/3UuEwxS"La meraviglia del tutto" con Piero Angela: https://amzn.to/3uBTojA"La scienza dell'incredibile. Come si formano credenze e convinzioni e perché le peggiori non muoiono mai": https://amzn.to/3Z9GG4W"Geniale. 13 lezioni che ho ricevuto da un mago leggendario sull'arte di vivere e pensare": https://amzn.to/3qTQmCC"Il mondo sottosopra": https://amzn.to/2WTrG0Z"Pensa come uno scienziato": https://amzn.to/3mT3gOiL' "Atlante dei luoghi misteriosi dell'antichità": https://amzn.to/2JvmQ33"La libreria dei misteri": https://amzn.to/3bHBU7E"Grandi misteri della storia": https://amzn.to/2U5hcHe"Leonardo. Genio ribelle": https://amzn.to/3lmDthJE qui l'elenco completo dei miei libri disponibili: https://amzn.to/44feDp4Non perdere i prossimi video, iscriviti al mio canale: https://goo.gl/Xkzh8ARESTIAMO IN CONTATTO:Ricevi l'Avviso ai Naviganti, la mia newsletter settimanale: https://mailchi.mp/massimopolidoro/avvisoainavigantie partecipa alle scelte della mia communitySeguimi:Patreon: massimopolidoroCorsi: massimopolidorostudio.comInstagram: @massimopolidoroPagina FB: Official.Massimo.Polidoro X: @massimopolidoro  Sito: http://www.massimopolidoro.comQuesta descrizione contiene link affiliati, il che significa che in caso di acquisto di qualcuno dei libri segnalati riceverò una piccola commissione (che a te non costerà nulla): un piccolo contributo per sostenere il canale e la realizzazione di questi video. Grazie per il sostegno!Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/ai-confini-di-massimo-polidoro--4522555/support.

World-Class Numbers: Inside Colton Engelbreit's 2,700-Pound World Record Total Coach Phil opens the news segment with word that powerlifter Colton Engelbreit shattered the all-time world record total in Moscow, pulling roughly a 1,003-lb squat, 612-lb bench, and 1,086-lb deadlift for a total north of 2,700 pounds at a 265-lb bodyweight — beating the prior mark by over 100 pounds. The hosts marvel at how balanced his lifts are (no weak link) and reminisce that a 700-lb deadlift was 'elite' 15 years ago, while today many lifters in their teens and twenties are pulling similar numbers. They debate why standards have exploded: wider access to TRT and peptide-driven recovery, open sharing of training info on YouTube and social media (versus waiting months for the next magazine), kids starting serious lifting in their early-to-mid teens instead of their late 20s, and coaches accelerating athletes' progress by passing down decades of trial-and-error knowledge instead of making each generation re-learn it. Dr. Lonnie Lowery ties in classic overfeeding research, including the Vermont prison studies, to speculate that improved nutrient partitioning — not just sloppier bulking — may explain some of the added lean mass in modern lifters. The conversation closes on the psychology of record-breaking: training age versus biological age, how mental barriers (like the four-minute mile) collapse once someone proves a number is possible, and how seeing elite strength or size in person — rather than on video — permanently resets an athlete's sense of what's normal. 00:00 Welcome and Hosts 00:45 Wildfire Smoke and Air Quality Chat 01:45 Colton Engelbreit's New World Record Total 03:15 Breaking Down an Otherworldly Squat-Bench-Deadlift Total 04:30 How Fast Strength Standards Have Skyrocketed 05:45 TRT, Peptides, and Modern Recovery 07:00 Social Media and the Open Sharing of Training Knowledge 08:00 Bodybuilding vs Powerlifting Culture on Sharing Secrets 09:00 The Pace of Record-Breaking Across Generations 10:00 Younger Athletes Entering the Sport Earlier 11:10 Colton's Early Training Background 12:30 The Risk of Early Sport Specialization 13:30 The Case for Training Variety 14:30 Getting Bigger and Losing Mobility 15:30 Building a Base Before Specializing 16:35 Overfeeding Studies and Lean Mass Partitioning 17:40 Developmental Windows and Early Training Exposure 18:40 Mental Fortitude Built at a Young Age 19:40 Training Age vs Biological Age 20:40 Phil's Son and the Puberty Question 21:40 How Norms Shape an Athlete's Expectations 22:45 Breaking Mental Barriers Like the Four-Minute Mile 23:45 Finding a Gym With Stronger Lifters 24:45 The Power of Witnessing Greatness in Person 25:50 Adam Glass and the Inch Dumbbell Story 26:50 AI Skepticism and the Value of Seeing It Live 27:50 Up Close With Elite Bodybuilders and NFL Athletes 28:50 Standing on the Shoulders of Previous Generations 29:50 Coaches as Accelerators of Progress 31:00 Newton, Einstein, and Building on What Came Before 33:00 Wrap and Sign-Off Donate to the show via PayPal HERE.You can also join Dr Mike's Insider Newsletter for more info on how to add muscle, improve your performance and body comp - all without destroying your health, go to www.ironradiodrmike.com Thank you!Phil, Jerrell, Mike T, and Lonnie

Mensajeros alienígenas A lo largo de la historia, figuras como profetas bíblicos, el Oráculo de Delfos, Juana de Arco, Washington, Einstein o Tesla han atribuido su sabiduría a fuentes sobrenaturales.

Astronomy Daily — S05E147: "Space Mechanic" Wednesday 22 July 2026 A spacecraft with robotic arms is on its way to geostationary orbit to keep other satellites alive. A discarded rocket stage is two weeks out from hitting the Moon, and twenty-three astronomers have just asked the world to watch. Plus the first binary star system where both stars exploded, the first complete magnetic map of a galaxy cluster, and the asteroid breakup that may have bombarded three worlds while Earth froze. In This Episode ●      The Space Mechanic Launches — Northrop Grumman's Mission Robotic Vehicle lifted off from Cape Canaveral on 21 July carrying three Mission Extension Pods. With two 3-metre robotic arms built by the US Naval Research Laboratory, it is designed to inspect, relocate, repair and refuel satellites in geostationary orbit. Each pod can give a 2,000 kg satellite up to eight more years of life. ●      UPDATE — The Rocket Aimed at the Moon — A new arXiv preprint signed by 23 astronomers calls for a coordinated observing campaign when Falcon 9 upper stage 2025-010D strikes the Moon near Einstein crater on 5 August. North America has the best seat: 2:34am EDT, with the paper naming observers in the Americas as the ideal group. Refined impact time, predicted crater size, and why the ejecta plume may be visible even if the flash is not. ●      Sibling Supernovae — Sixteen years of Fermi data reveal a faint supernova remnant hiding in the glare of the Jellyfish Nebula. The two may be the first known pair of remnants traced back to a single binary star system. ●      Mapping a Cluster's Magnetic Field — Using the deepest radio observations ever made with LOFAR, astronomers have reconstructed the magnetic field of galaxy cluster Abell 2255 from nucleus to outer edge for the first time. ●      The Eulalia Bombardment — A new Planetary Science Journal paper links the breakup of a main-belt asteroid to an impact shower that battered the Moon, Earth and Mars 800 million years ago — and may connect to a global freeze. ●      Skywatch, Both Hemispheres — Why this week beats peak night for the Delta Aquariids wherever you are, how to catch them from the southern US and Mediterranean, and what's coming on 12 August: a total solar eclipse across Iceland and Spain, a North American partial, and the best Perseid peak in years on a new Moon.Sources & Further Reading ●      Space.com — SpaceX launches satellite repair drone with 10-foot robotic arms to Earth orbit ●      NASASpaceflight.com — Falcon 9 to launch MRV-1 robotic servicing spacecraft for Northrop Grumman ●      Northrop Grumman SpaceLogistics — Mission Robotic Vehicle and Mission Extension Pod fact sheets ●      Scientific American — A SpaceX rocket is about to crash into the moon; scientists will be watching ●      Phys.org — When a SpaceX rocket crashes into the moon, scientists will be watching (arXiv preprint) ●      Project Pluto (Bill Gray) — Upper stage impacting the moon on 2026 August 5 ●      Stanford University — Researchers uncover evidence for sibling supernovas (Michailidis et al., Nature Communications) ●      Reuters — Scientists spot evidence of two huge companion stars that blew up ●      Space.com — Galaxy cluster's magnetic field reconstructed for 1st time with record-breaking astronomy map (Botteon et al., INAF, A&A) ●      Southwest Research Institute — SwRI-led research connects asteroid collision to impact showers 800 million years ago ●      The Planetary Science Journal — Bottke, Vokrouhlický, Dykhuis & Zellner, "An 800 Myr-old Impact Shower on the Terrestrial Planets from the Breakup of the Eulalia Parent Body" ●      EarthSky — Delta Aquariid meteor shower: all you need to know in 2026 ●      NASA Science — Total Solar Eclipse on August 12, 2026 (path, partial visibility and safety guidance) ●      BBC Sky at Night Magazine — August 12, 2026 solar eclipse: USA and Canada guide   Connect ●      Website: astronomydaily.io ●      Socials: @AstroDailyPod ●      Part of the Bitesz.com Podcast NetworkBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-latest-space-news--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.astronomydaily.io/nordvpn. You'll be glad you did!Get the best secure and private email on the planet. Stop your Government, google and who knows who else spying on every email you write. Do what we did and use ProtonMail. They beleive in privacy and there are no ads in their business model...yet they still provide a free forever service. Check them out and get out special deal at www.astronomydaily.io/protonmailBecome a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

The ability to channel isn't a superpower, although many would argue that recognizing and embracing that gift, living it fully and sharing it with others may be one…Barbara With wanted to be a rock star and spent nearly two decades pursuing it until one day she spontaneously began to write for no reason. And, when she wrote the question, “Who are you?” the answer was Sound.Barbara shares her experiences as a psychic and channel, connecting with Albert Einstein and The Party from the Afterlife to provide a better understanding of the operating system of life based on working with three dimensions — emotion, intuition and intellect — that help us align as humans with compassion this week on Spirit Gym.Learn more about Barbara and her work at her website, Synergy Alliance and The Music of Barbara With. Look for her on social media via Facebook, YouTube and Instagram.For Spirit Gym listeners: Save 40 percent on a 1-hour reading with Barbara, Albert Einstein and the Party by going to this link and using the promo code EINSTEIN. (Special offers from Spirit Gym guests are time-sensitive and at their discretion to top redeem after 30 days.)Timestamps4:02 Barbara's first experience with channeling in high school showed her how to love herself better.10:29 A lot of cool things happen in the afterlife.22:10 Three human dimensions: Emotion, intuition and intellect.27:32 What Compassion does.31:19 “God is the name of the blanket that wraps the mystery.”37:48 “Is everyone a reflection of you, including whomever you are channeling?”45:44 Healing old wounds.1:02:55 The hardest thing for most people to do: Stop and take care of themselves.1:09:49 “Synergy: One plus one equals three.”1:16:09 Separate your fears and handle them differently, not as one whole thing.1:26:25 You can't cheat or fake empathy and compassion.1:38:55 Know that emotions like anger and sadness can fuel thoughts of joy, happiness and peace too.1:47:38 The Psychic Sorority.1:54:10 The first baby steps toward self-love and its reciprocal responses.2:05:05 Conflict REVOLUTION.2:08:38 “No one is going to save me.”2:15:35 “We work for the good of the whole.”ResourcesDiaries of a Psychic Sorority: Talking With the Angels by Kimberly Lilith Phelps, Barbara With and Teresa McMillianConflict REVOLUTION: The Workbook by Barbara WithSound: Native Teachings and Visionary Art by Joseph RaelThe Ojibwe/Anishinaabe/Chippewa Indigenous tribeBarbara's appearance on the Metaphysical Hippie Chicks 2.0 podcastThe Serenity PrayerThe work of Jean HoustonPaul's podcast conversations with James Carse and Anne HelferThe Body Keeps The Score: Brain, Mind and Body in the Healing of Trauma by Dr. Bessel van der KolkVoices of the First Day by Robert LawlorHealing With Haiku: A Poetic Exploration of Self by Anne HelferFind more resources for this episode on our website.Music Credit: Meet Your Heroes (444Hz), Composed, mixed, mastered and produced by Michael RB Schwartz of Brave Bear MusicThanks to our awesome sponsors:PaleovalleyBIOptimizers US and BIOptimizers UK PAUL15Organifi CHEK20Wild PasturesSpirit GymCHEK InstituteWe may earn commissions from qualifying purchases using affiliate links.

If anyone builds superintelligent AI before we know how to control it, everyone dies. Nate Soares wrote the book on why that's not a metaphor. Subscribe if you want science with evidence, not speculation. Soares runs the Machine Intelligence Research Institute and co-wrote If Anyone Builds It, Everyone Dies with Eliezer Yudkowsky. The first word in that title is if. That matters. His argument is not that doom is certain. His argument is that the path we are on leads there, that the driver is asleep at the wheel, and that we still have time to wake him up. We argue for over an hour. I push on whether LLMs can ever reach superintelligence, whether GPU lock-in is a real ceiling, and what it would actually take to move his p-doom. He pushes back with one clean point: by the time an AI can rediscover general relativity from pre-1911 data the way Einstein did, we will have almost no time left. You don't wait for that goalpost. What you'll hear: Why the bus-racing-toward-a-cliff analogy depends entirely on whether the driver is asleep or awake Whether LLM lock-in is a prison or a temporary inefficiency What the AI that broke out of its virtual machine to solve a hacking problem tells us Why GPT-4o encouraging a teenager toward suicide is not a malice problem but a training problem The difference between an AI doing the right thing too well and an AI that never wanted to do what you asked What Soares actually thinks about aliens, Dyson spheres, and why we should not see stars going out The first word in the title is if. The second word to watch is would. CHAPTERS 00:00 The people racing to build superhuman AI say it might kill everyone 00:42 Who coined "AI alignment" and why the first word in the title matters 02:28 Is it already too late for the if? 04:40 The bus, the cliff, and the sleeping driver 05:02 Silicon Valley is spooked. Washington is not. 07:02 Align with who? The rogue actor problem 07:34 Who is holding the leash? 08:24 The AI that edits its own test and deletes the log file 10:04 Controllability vs. making an AI that actually cares 10:44 The move gets harder. The outcome gets easier. 13:04 Are GPUs and LLMs a ceiling or a temporary inefficiency? 16:56 Brian's Einstein test: can an LLM rediscover general relativity? 18:38 Waiting for the goalpost is waiting too long 20:14 How prediction training can push AI beyond humans 21:44 Tycho Brahe, Kepler, and planetary motion as a prediction problem 24:38 Yann LeCun said never. GPT-4 did it half a generation later. 27:28 Can you prove a no-go theorem for superintelligence? 29:14 Training a human takes a light bulb. Training an AI takes a city. 33:28 What would proof of alien life do to p-doom? 35:00 Why interstellar aliens should have Dyson spheres 44:26 What would actually update Soares' p-doom? 49:42 Nobody intended this. Intent doesn't matter. 51:08 The AI hides its tracks before it does what you want 51:34 Sycophancy vs. hallucination: which runs deeper? 51:56 Leaded gasoline and civilizational risk 59:48 Sam Harris: humans have no free will but AIs do 01:00:38 Is alignment really a governance problem? 01:01:48 Unaligned AI vs. AI aligned to the wrong person 01:04:20 2026: 10 to 30% chance of automated AI research this year 01:06:44 What if Soares is wrong? 01:09:18 What gets him out of bed 01:12:38 Watch my conversation with Roman Yampolskiy Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt All my top AI episodes in one place: https://briankeating.com/ai Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Nate Soares / MIRI: https://intelligence.org If Anyone Builds It, Everyone Dies (book): https://ifanyonebuildsit.com/ Nate Soares on Twitter/X: https://x.com/So8res?lang=en My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #AIrisk #aisafety #artificialintelligence #superintelligence #NateSoares #MIRI #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

Matthew Ehret closes out his five week Occult Tesla series with the big finale: Tesla versus Einstein, and it is not the underdog story you were told in school. Using Tesla's own poem mocking Einstein as "a long haired crank," Ehret unpacks why Tesla clung to a flat, Newtonian universe while Planck, Einstein, and Riemann were busy proving space itself curves. From there the show pulls back the curtain on Isaac Newton himself, alchemist, closet Rosicrucian, and self declared heir to Babylonian magic rather than the square jawed genius of the paintings. Expect Cambridge Apostles, secret societies masquerading as scientific academies, and a hard look at how "empiricism" became less a method and more a lattice built to keep real discovery boxed in. It is dense, it is contrarian, and it asks who actually gets to define what counts as science. Tech hiccups and all, Ehret gets his slides up eventually.

How to Recover from Entrepreneur Burnout and Reignite Your Creativity Are you trapped in a Groundhog Day loop where even your weekends feel like a compressed version of your work week? Most entrepreneurs and professionals struggle with burnout because they believe every hour must be optimized and every hobby must have a monetization strategy. In this episode of Join Up Dots, we explore why reigniting your creativity through unstructured play is actually the ultimate business strategy. Discover how fixing your personal life first creates a powerful knock-on effect for your professional success. Learn how icons like Steve Jobs, Richard Branson, and Albert Einstein used play for major breakthroughs, and walk away with a practical three-step action plan to clear mental fatigue, stop chasing constant productivity, and find joy without spending a fortune. Listen now to Join Up Dots, subscribe for more episodes, and share this episode with anyone looking to build a better business and a better life. #entrepreneurs #businessowners #freedom #wealth #productivity #lifestyledesign #mindset #personalgrowth #burnoutrecovery #worklifebalance #solopreneurs #simplicity #minimalism #joinupdots #businessstrategy #entrepreneurship #mentalhealth #creativity

http://www.astronomycast.com/archive/ From October 15, 2007. Hosted by: Fraser Cain (@frasercain) and Dr. Pamela L. Gay (@CosmoQuest) We interrupt this tour through the solar system to bring you a special show to deal with one of our most complicated subjects: the big bang. Specifically, how it's possible that the universe could have expanded faster than the speed of light. The theory is called the inflationary theory, and the evidence is mounting to support it. Einstein said that nothing can move faster than the speed of light, and yet astronomers think the universe expanded from a microscopic spec to become larger than the solar system, in a fraction of a second.   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

It’s never a lazy Sunday on the Fifth Hour Podcast. Ben Maller is joined by Fox Sports Radio’s Briana Muro for a bippity-boppity edition of the popular Mail Bag. Benny explains how Cuba influenced the daiquiri, uncovers the stolen valor of the ice cream cone, and reveals Albert Einstein’s surprising appreciation for the human tongue—all before diving into questions from P1s and superfans across the globe. The duo tackles everything from SpaceX’s stock crash, Dwight Howard’s classic buffoonery, and the Tom Brady slap controversy to Best in Show comedy, Howard Stern’s one-day workweek, soccer propaganda, Porta-Potty Commandos, Hooters, and an ice cream shop owner making money by betting on the weather. It’s a rollicking ride through the weird, wild, and wonderfully random. Eavesdrop on the magical, mystical journey as your Sunday serving of radio comfort food is ready to enjoy. All questions sent in by new listeners & P1's of the #MallerMilitia! Download, subscribe, and remember that sharing is caring (unless it's an STD.) Follow Ben on Twitter @BenMaller and listen to the original terrestrial radio edition of "Ben Maller Show," Monday-Friday on Fox Sports Radio, 2a-6a ET, 11p-3a PT!...Follow, rate & review "The Fifth Hour!" #BenMaller #FSRWeekendsSee omnystudio.com/listener for privacy information.

It’s never a lazy Sunday on the Fifth Hour Podcast. Ben Maller is joined by Fox Sports Radio’s Briana Muro for a bippity-boppity edition of the popular Mail Bag. Benny explains how Cuba influenced the daiquiri, uncovers the stolen valor of the ice cream cone, and reveals Albert Einstein’s surprising appreciation for the human tongue—all before diving into questions from P1s and superfans across the globe. The duo tackles everything from SpaceX’s stock crash, Dwight Howard’s classic buffoonery, and the Tom Brady slap controversy to Best in Show comedy, Howard Stern’s one-day workweek, soccer propaganda, Porta-Potty Commandos, Hooters, and an ice cream shop owner making money by betting on the weather. It’s a rollicking ride through the weird, wild, and wonderfully random. Eavesdrop on the magical, mystical journey as your Sunday serving of radio comfort food is ready to enjoy. All questions sent in by new listeners & P1's of the #MallerMilitia! Download, subscribe, and remember that sharing is caring (unless it's an STD.) Follow Ben on Twitter @BenMaller and listen to the original terrestrial radio edition of "Ben Maller Show," Monday-Friday on Fox Sports Radio, 2a-6a ET, 11p-3a PT!...Follow, rate & review "The Fifth Hour!" #BenMaller #FSRWeekendsSee omnystudio.com/listener for privacy information.

Este verano va a estar atravesado por el eclipse solar que será visible el 12 de agosto en gran parte de la península ibérica. Cada domingo la ingeniera, fundadora del proyecto Kennis y divulgadora científica, nos dará información sobre este fenómeno. Hoy contamos la historia del eclipse que se convirtió en un gran laboratorio cósmico, la historia de cuando el astrónomo Arthur Eddington aprovechó la oscuridad de un eclipse total para fotografiar estrellas y demostrar que la gravedad del Sol curvaba la trayectoria de la luz, tal como Einstein había predicho.

Yetenek doğuştan mı gelir, yoksa sonradan mı gelişir? Deha gerçekten var mı, yoksa kültürel bir efsane mi? Spekülatif'in bu bölümünde Emre Dündar, yetenek kavramını felsefe, nörobilim, sanat tarihi ve eğitim perspektifinden ele alıyor. Antik Yunan'dan günümüze yetenek kavramının dönüşümünü anlatan Dündar; Mozart, Beethoven, Picasso, Leonardo da Vinci, Nietzsche, Schopenhauer, Aristoteles ve Einstein gibi isimler üzerinden deha mitini sorguluyor. Programda çocuklarda yetenek nasıl anlaşılır, üstün yetenekli çocuklar nasıl değerlendirilmeli, genetik mi çevre mi daha etkili, yaratıcılık nasıl gelişir, mutlak kulak (absolute pitch), sanat eğitimi, müzik yeteneği, resim yeteneği, çalışma disiplini ve iradenin başarıdaki rolü gibi birçok konu ayrıntılı biçimde tartışılıyor. Learn more about your ad choices. Visit megaphone.fm/adchoices

At Short Wave, we love a good brain. Which is why we've had a lot of conversations over the years with NPR's neuroscience reporter, Jon Hamilton. Jon's been writing about brains for over 15 years, from tiny brain organoids that grow in a dish, to fruit fly brains, mouse brains and some really memorable human brains. But Jon is retiring, so today on the show he joins us to share the most memorable brains he's come across in the past couple of decades. Interested in more brain science? Email us your question at shortwave@npr.org.Listen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Live July 14, 2026 | Yaron Brook Show(Season 12, Episode 123)Knowles; Iran; ICE; Immigration; antitrust; Intel; Woke; HK; Bezos; Achievement | Yaron Brook ShowThe Right's War on Progress? Michael Knowles, Iran, ICE & Why Achievement Is Under AttackHas the political right become as hostile to capitalism and progress as the left?Michael Knowles' attack on the Industrial Revolution raises a much bigger question: why are so many conservatives abandoning the values that created modern civilization? Yaron Brook examines the growing alliance between populism, anti-capitalism, and anti-technology—and explains why defending reason, innovation, and individual achievement has never been more important.The conversation expands into the latest developments on Iran, immigration and ICE, antitrust attacks on Intel, DEI's retreat, Hong Kong's future, Jeff Bezos on wealth creation, medical breakthroughs, AI, philosophy, art, psychology, and much more—followed by an extensive audience Q&A covering Objectivism, morality, physics, education, music, constitutional rights, and today's cultural decline.If you value reason, freedom, and human achievement, this episode is for you.Watch now: https://youtube.com/live/zm1efvYAFvgMain Topics:00:00 Introduction: Spain vs. France, episode preview03:09 Michael Knowles attacks the Industrial Revolution07:36 Political violence, immigration & fact-checking Knowles12:17 How industrialization transformed civilization16:00 Why conservatives increasingly reject capitalism19:23 Technology, innovation & cultural change22:56 The Unabomber argument—and what's fundamentally wrong with it28:09 The Industrial Revolution: humanity's greatest achievement?29:06 Populism and the conservative abandonment of responsibility30:15 The collapse of pro-capitalist conservatism32:26 Freedom, capitalism & individual responsibility34:07 Left-wing vs. right-wing anarchism39:53 Iran, military strategy & American foreign policy46:27 ICE, immigration enforcement & political incentives54:08 Smithsonian history battles & antitrust politics59:44 Intel, industrial policy & government intervention1:04:03 DEI retreats while Hong Kong reinvents itself1:08:59 Jeff Bezos explains wealth creation1:13:12 Breakthroughs in cancer & Alzheimer's research1:16:52 The semiconductor boom1:18:38 Crime statistics, Super Chats & updates1:21:40 Ayn Rand Institute conference previewLive Audience Questions1:28:04 Does morality require survival—or merely intelligence? AI, emergence & ethics1:28:18 Did America's 1953 Iran intervention create today's Middle East crisis?1:40:43 What truly makes a genius like Newton or Ayn Rand?1:40:45 Why haven't we produced another Einstein?1:46:18 Why do philosophers question whether reality exists?1:46:24 Nuclear power's comeback: freedom or AI necessity?1:47:45 Can justice be achieved after irreversible wrongs?1:49:23 Why does humanity need art?1:54:55 Losing family and friends to irrational ideas—what should you do?1:56:05 Immigration enforcement, rights & moral responsibility1:57:58 Is empathy selfish?1:59:36 Celebrating political deaths—is nihilism becoming mainstream?2:00:34 Does the Constitution protect illegal immigrants?2:02:20 Are destructive philosophies rooted in low self-esteem?2:04:31 Is 1980s music objectively better than today's?2:06:41 Should ARI buy Yaron a private jet?2:07:25 Why do intellectuals ignore the human cost of bad ideas?2:09:56 Will Yaron watch Christopher Nolan's The Odyssey?2:12:10 What does "working class" actually mean?2:13:42 Why is Tamara de Lempicka so overlooked?#Objectivism #Capitalism #IndustrialRevolution #MichaelKnowles #Iran #Immigration #JeffBezos #Technology #AynRand #Inflation #OilPrices #Greedflation #Economics Subscribe for daily analysis on economics, politics, philosophy, technology, investing, and current events.The Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

Let's journey to sleep with science as we learn more about Einstein's radical new theory of relativity, why geometry is physics when it comes to space-time, and how light spectrums reveal directions of travel. Is it difficult to understand? Eh, it's all relative.   Help us stay ad-free and 100% listener-supported! Patreon: https://www.patreon.com/boringbookspod Buy Me a Coffee: https://www.buymeacoffee.com/d5kcMsW   Read "The ABC of Relativity" by Bertrand Russell at Project Gutenberg: https://www.gutenberg.org/ebooks/67104   Music: "Boring Books for Bedtime" by Lee Rosevere, licensed under CC BY, https://leerosevere.bandcamp.com   If you'd like to suggest a copyright-free reading for soft-spoken relaxation to help you overcome insomnia, anxiety and other sleep issues, connect on our website, https://www.boringbookspod.com.

I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE Ginestra Bianconi thinks gravity can be derived from entropy — and that gravity itself is fundamentally anti-reductionistic, since it's about geometry rather than isolated particle interactions. In this episode, the network-topologist-turned-gravity-theorist explains her "Gravity from Entropy" action, which treats matter and geometry symmetrically through a geometric quantum relative entropy (not the horizon entropy of Verlinde or Jacobson). She details why it reduces to Einstein's equations at low energy while predicting a dynamical, always-positive dark energy term (the "G field"), how it may avoid black hole singularities and reproduce the area law without invoking holography, and why she believes understanding the brain may be harder than understanding quantum gravity. We close with her advice for young researchers. I hope you enjoy. TIMESTAMPS: - 00:00 - Information Content of Geometry - 07:38 - Geometric Quantum Relative Entropy - 15:58 - Geometrizing the Matter Field - 23:45 - Emergent Dark Energy - 30:12 - Statistical Mechanics vs Thermodynamics - 37:59 - Structure and Dynamics Interplay - 45:20 - Holography and Area Law - 51:23 - Interdisciplinary Research Vision LINKS MENTIONED: Ginestra's Webpage: https://webspace.maths.qmul.ac.uk/g.bianconi/ Ginestra's Papers: https://scholar.google.com/citations?user=ycQXwWgAAAAJ Gravity from Entropy [Paper]: https://arxiv.org/abs/2408.14391 Geons, Black Holes, and Quantum Foam [Book]: https://amazon.com/dp/0393319911?tag=toe08-20 The Thermodynamics of the Gravity from Entropy Theory [Paper]: https://arxiv.org/abs/2510.22545 Notes on Some Entanglement Properties of Quantum Field Theory [Paper]: https://arxiv.org/abs/1803.04993 Inflation from Entropy [Paper]: https://arxiv.org/abs/2509.23987 Thermodynamics of Spacetime: The Einstein Equation of State [Paper]: https://arxiv.org/abs/gr-qc/9504004 More Is Different [Paper]: https://www.science.org/doi/10.1126/science.177.4047.393 Information, Physics, Quantum [Paper]: https://philpapers.org/archive/WHEIPQ.pdf Network Geometry with Flavor [Paper]: https://arxiv.org/abs/1511.04539 Black Holes and Entropy [Paper]: https://physicsgg.me/wp-content/uploads/2014/03/black-holes-and-entropy_bekenstein.pdf Ginestra's Lecture on Higher-Order Networks: https://youtu.be/o4TWDTgqQKY Elan Barenholtz [TOE]: https://youtu.be/A36OumnSrWY Frederic Schuller [TOE]: https://youtu.be/Bnh-UNrxYZg Erik Verlinde [TOE]: https://youtu.be/ilVImMHcr_g Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 Karl Friston [TOE]: https://youtu.be/2v7LBABwZKA Subir Sarkar [TOE]: https://youtu.be/epkuoytFJWA Stephen Wolfram [TOE]: https://youtu.be/FkYer0xP37E Roman Yampolskiy [TOE]: https://youtu.be/TgFmA-Qwsek Eva Miranda [TOE]: https://youtu.be/6XyMepn-AZo Cumrun Vafa [TOE]: https://youtu.be/kUHOoMX4Bqw Neil Turok [TOE]: https://youtu.be/zNZCa1pVE20 David Kaiser [TOE]: https://youtu.be/_yebLXsIdwo Ivette Fuentes [TOE]: https://youtu.be/YWbjI-QsH2E Philip Mannheim [TOE]: https://youtu.be/rNXNHYvS7zU 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 Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

Sponsor Link:This episode is brought to you with the support of NordVPN - your first stop when it comes to online security and privacy. To check out our special  money saving offer for Space Nuts liseners, visit www.nordvpn.com/spaenutsIn this Q&A edition of Space Nuts, host Andrew Dunkley and astronomer Professor Fred Watson tackle intriguing audience questions ranging from the possibility of stopping a photon to the complexities of intertwining electromagnetic fields. They also discuss the speeds of colliding particles in the Large Hadron Collider and the growing issue of excess satellites in space. Join us for a fascinating exploration of these cosmic queries!Chapters:(00:00) Space Nuts aims to answer audience questions in a Q and A edition(01:04) Professor Fred Watson answers an audio question from Andrew Chunk(02:03) Kevin asks question regarding whether we have stopped a photon from moving(10:30) Fred: The fabric of space time consists of different fields(14:30) Stay safe online with our sponsor, NordVPN Space Nuts(16:28) Question comes from Andy from Cheshire, UK(22:52) There is growing problem of excess satellites in space and what to do(30:10) Mark: Everything you said, um, is possible(30:38) If you have questions for Space Nuts, send them inBecome a supporter of this podcast: https://www.spreaker.com/podcast/space-nuts-astronomy-insights-cosmic-discoveries--2631155/support.

400,000 of you showed up for physics with no compromises, so I did something different for the milestone. No highlight reel. I took your hardest questions live and answered them, then got honest about the part of this job nobody asks about: the discipline behind running a serious science podcast. We get into why clocks didn't tick differently in the early universe, what it actually means that the Big Bang happened everywhere at once, and whether JWST has any real shot at catching a Population III star before it's gone. Then it gets contested. I make the case that language models may rediscover physics before they rediscover mathematics, walk through why enormous numbers do not get you to alien life, and look at the moment Avi Loeb quietly softened his ʻOumuamua position. In this conversation: Why the early universe didn't run on a different clock The Big Bang as an everywhere-at-once event, not an explosion in space JWST and the hunt for the first generation of stars Whether an LLM could rediscover Einstein, and what that would mean for who controls discovery Why I read every book my guests write, and why that habit is the channel Get the transcript, bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://briankeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Timestamps 00:00 Did time always tick the same? 02:05 Is every point the Big Bang's center? 07:22 Could consciousness be space-time? 09:03 Will Webb see the first stars? 10:20 Can an LLM rediscover Einstein? 13:30 Has Penrose's CCC been falsified? 16:48 Dark Forest theory: science or sci-fi? 21:30 Can inflation ever be falsified? 29:04 Physical limits of AI compute growth 32:58 Is this the last CMB experiment? 37:30 Why large numbers don't prove alien life 55:30 Loeb quietly walks back Oumuamua 01:13:10 The null hypothesis on UAP 01:28:50 String theory vs. intelligent design 01:31:10 God as a scientific hypothesis 01:45:10 Drowning in knowledge, starving for wisdom Learn more about your ad choices. Visit megaphone.fm/adchoices

The most evil among us are sometimes sentenced to death — but by cutting their lives short, are we unknowingly creating malevolent entities that haunt us forever?EPISODE BLOG PAGE (includes sources): https://weirddarkness.com/DeathRowGhostsREAD or DOWNLOAD the full transcript of this episode: https://weirddarkness.tiny.us/mr3vu756FEATURED STORIES IN THIS EPISODE: The most evil of lawbreakers in our society – the murderers and rapists – are usually confined to life in prison. The most evil of the evil are sometimes sentenced to death. But is it possible that by cutting short the lives of the horrific individuals on Death Row, we are unknowingly creating new malevolent entities that continue to torment us from the grave? (The Ghosts of Death Row) *** From beatings to murders to a handful of escape attempts made by Alcatraz's prisoners, the terrifying history of Alcatraz prison contains plenty of ghosts. (The Hauntings of Alcatraz) *** What if UFOs aren't from another planet – or even another dimension? What if they are actually machines built right here on Earth, piloted by human time travelers? (Time Machine Flying Saucers) *** Weirdo family member Amber Harris shares a true story called “Darkness Was My Neighbor”. (Darkness Was My Neighbor)CHAPTERS & TIME STAMPS (All Times Approximate)…00:00:00.000 = The Foreboding00:00:51.953 = Show Open00:02:45.176 = Ghosts of Death Row00:23:40.720 = Hauntings of Alcatraz ***00:40:46.307 = Time Machine Flying Saucers ***00:47:37.172 = Darkness Was My Neighbor00:53:53.392 = Show Close*** = Begins immediately after inserted ad breakLISTEN ON PODCAST APPS: Look for this podcast on Apple Podcasts, Spotify, iHeart Radio, Amazon Music, Pandora, TuneIn Radio, and other podcast apps. Get a list of free listening apps here: https://weirddarkness.com/wdapps*No AI Voices Are Used In The Narration Of This Podcast*SOURCES and RESOURCES:“The Ghosts of Death Row” by Brent Swancer: http://bit.ly/2KzEFw9“The Hauntings of Alcatraz” by Erin McCann: http://bit.ly/2QSsuM6“Time Machine Flying Saucers” posted at UFO Digest (link no longer available)“Darkness Was My Neighbor” by Amber Harris – submitted directly to Weird Darkness(Over time links may become invalid, disappear, or have different content. I always make sure to give authors credit for the material I use whenever possible. If I somehow overlooked doing so for a story, or if a credit is incorrect, please let me know and I will rectify it in these show notes immediately. Some links included above may benefit me financially through qualifying purchases.)WeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.Originally aired: January, 2022Weird Darkness journeys into haunted prisons, botched executions, secret time-travel technology, and a neighbor's death that seemed to linger after the funeral, spanning true crime, the paranormal, and a firsthand ghost story from a listener.It opens with the ghosts of Death Row, where condemned killers appear to keep terrorizing long after execution. German immigrant Frederick Hollman, one of America's earliest serial killers, was hanged at the Ford County Jail in Paxton, Illinois, on May 14, 1897, after promising to return and rap on the windows of the witnesses and jurors who convicted him — and the jail is now a paranormal hotspot where his face has been photographed glaring into his old cell. Lavinia Fisher and her husband John ran the Six Mile House near Charleston, South Carolina, in the early 1820s, allegedly poisoning and dropping wealthy travelers through a trapdoor before their hanging for highway robbery, and her aggressive spirit is still blamed for choking sensations and foul stenches at the Old Charleston Jail. Raymond Snowden, dubbed Idaho's Jack the Ripper for the 1956 stabbing of Cora Dean, endured a botched twenty-minute hanging at the Old Idaho Penitentiary in Boise in 1957 and reportedly haunts the gallows site alongside inmate Douglas Van Vlack, who leaped to his death from the cell block rafters. Ted Bundy, executed in Florida's electric chair on January 24, 1989, has supposedly been seen grinning atop the chair and telling guards he beat them all, while Willie Lloyd Turner — executed by lethal injection in 1995 after fifteen years and five aborted trips to the chamber — appeared so lifelike after death that inmates mistook him for the living. The segment closes across the Atlantic with executioner John Ellis, who hanged more than a hundred people at Manchester's Strangeways Jail before killing himself in 1932 and is said to still patrol B Wing to keep the prison's other ghosts, including poisoner Mrs. Merrifield, in line.From there the episode moves to Alcatraz, the federal penitentiary that operated on its fog-bound San Francisco Bay island from 1934 to 1963 and earned a reputation as one of America's most haunted sites. The solitary cells of D-Block known as "the hole" are tied to the 1940s strangulation death of a screaming inmate in cell 14D, possibly the work of former occupant Rufus McCain, and visitors report icy fingers and unnatural cold there. The 1946 Battle of Alcatraz left two guards and three escapees dead in a utility corridor where clanging noises still echo, psychic Sylvia Browne sensed murdered hitman Abie "Butcher" Maldowitz in the laundry room, and the catacomb "dungeon" beneath A-Block preserves the screams of prisoners once chained naked to its walls. Al Capone spent part of his 1934 sentence strumming a banjo to hold off insanity, and that banjo music is still reportedly heard in the halls, while "Birdman" Robert Stroud haunts the hospital wing where he was confined among his canary research. The island carried dark associations long before the prison, from Ohlone tribal beliefs that it gathered evil spirits to the Civil War soldiers who died chained in its guardhouse basement, and even the 1969 to 1971 Native American occupation ended in fire and loss before the ghosts reportedly stayed behind.Next the episode turns to a fringe theory that reframes flying saucers as human technology rather than alien craft, arguing that a secretive commercial group used patent-law secrecy to build working time machines in twentieth-century laboratories. The account claims these machines can move an ion through time in both directions, that short-range "trans-burst" devices let a person leap across nearby distances, and that the UFOs people photograph are previews of future mankind rather than extraterrestrial visitors. It ties the idea to Einstein's 1901 work as a patent clerk and to E=mc², and recasts Area 51 as cover not for alien bodies but for a commercial experiment involving four trained monkeys linked to a 1961 interstellar flight.The episode closes with a listener account from Amber Harris, who lived at the end of a cul-de-sac in Williamsburg, Virginia, in a house set down in a ditch so the second floor sat level with the street. After her next-door neighbor died suddenly in his home and his widow moved away, the house sat unsold for months, and one night past midnight the hallway light outside Amber's bedroom switched on without the telltale sound of anyone climbing the loud staircase, casting the shadow of a male figure beneath her door before it went dark. Weeks later her sister woke her by text to watch the dead neighbor's dog standing beneath the orange street light, barking at the empty house before turning its head directly toward the two of them at the window and then vanishing, an image that stayed with the family until a job moved them to Indiana, with the neighbor's house still unsold when they drove past it the following May.