Podcasts about Laguna

Scott Gussin grew up in New York, lost his mother at seven years old in a moment that changed the direction of everything that followed, fell into selling weed and hash and LSD coming of age in the 1960s and 70s, and eventually built one of the most specific and most audacious international drug smuggling operations available — bringing hash from Morocco to the United States across a career that spanned thirty years before it finally ended. _____________________________________________ #crimestory #drugsmugglers #truecrimestories _____________________________________________ Thank you to AG1 & HUNGRYROOT for sponsoring this episode: AG1: For a limited time, save 20% on your first subscription order of AG1 Next Gen or AG1 Pro at https://drinkag1.com/lockedin _____________________________________________ Hungryroot: Explore my Hungryroot Digital Cookbook "The Commissary Upgrade" at https://hungryroot.com/LOCKEDIN _____________________________________________ Buy Scott's Book: https://www.amazon.com/Gus-All-Years-Combined-Samson/dp/B0H34MTKSV _____________________________________________ Hosted, Executive Produced & Edited By Ian Bick:  https://www.instagram.com/ian_bick/?hl=en https://ianbick.com/ _____________________________________________ Timestamps: 00:00 Roadblock with hash on board 00:37 Guest intro: Scott's story 00:57 Growing up in Portchester 01:21 Losing his mom at 7 03:37 A parade of housekeepers 06:59 The housekeepers get weird 08:03 Getting into weed at 15 10:42 Meeting Reuben and the LSD 13:58 Hash vs. weed explained 17:28 Pricing the product 20:00 Spotting 'heads' back then 22:45 Only two straight jobs 25:05 The ignorant law enforcement 26:18 Cops don't recognize hash 27:25 A trippy traffic stop 32:26 Acid buddy becomes a JW 37:23 After high school 38:18 Dodging the Vietnam draft 40:56 College for connections 44:40 Hitching with a pound of weed 46:16 The business mindset 47:42 Leaving college for California 48:09 Meeting Cassidy and the Morocco plan 50:47 The cocaine trip that wasn't 54:30 Selling the boat, new plan 57:03 Bringing Stella along 58:59 Buying the VW bus 01:01:39 Buying hash on the mountain 01:05:28 Driving with 44 pounds 01:07:09 Coming upon a roadblock 01:10:47 Getting past the roadblock 01:13:52 Setting up at the beach house 01:16:34 Building the secret stash bed 01:18:57 A month in Morocco 01:20:00 The sign to leave 01:22:45 The Moroccan customs search 01:27:20 The ferry and the dolphins 01:28:24 Easy entry into Spain 01:32:40 Cassidy's anger and laughter 01:33:46 Shipping the bus to Montreal 01:35:28 Swapping the bed in Canada 01:37:53 Crossing into the US with the bed 01:39:13 Selling the hash, big profits 01:43:36 Trust in the old days 01:44:29 Bust risks in Spain and Morocco 01:46:19 The failed second attempt 01:49:01 The near-disastrous boat trip 01:54:15 How long did the career last? 01:54:55 Back to New York, then Arizona 01:59:32 The Laguna Beach connection 02:02:20 Cowboy Neil's operation 02:04:41 The North County partnership 02:07:12 Weighing and distributing 02:09:00 The two-year plan 02:10:16 Thai stick and new markets 02:14:19 The big deal in Laguna 02:17:20 The gift suitcase 02:18:51 The DEA roadblock 02:21:17 Three hours of interrogation 02:25:00 The search comes up empty 02:27:24 Scared straight by the DEA 02:29:04 Overall earnings and taxes 02:32:59 The DEA's advice: leave California 02:36:41 Moving to Florida and new businesses 02:38:00 The mistake that ended it all _____________________________________________ To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/LockedInWithIanBicka

Presentan el segundo modelo del vehículo eléctrico Olinia, diseñado para el transporte de mercancías con capacidad de hasta 650 kilos para trabajadores mexicanos. Por otro lado, la presidenta Claudia Sheinbaum aclara que el traslado a México del contralmirante Fernando Farías Laguna desde Argentina no está en duda y descarta que la presencia de Omar García Harfuch en dicho país esté relacionada con el caso. Hosted on Acast. See acast.com/privacy for more information.

Et si on poursuivait nos vacances italiennes auprès de Lucie Tournebize ? Dans cet épisode, cap sur Rome et Venise avec cette journaliste et autrice française, infatigable exploratrice du Bel Paese !L'Italie elle la raconte dans les pages de la presse française (de Libération au Figaro Voyages), mais aussi à travers des guides devenus des références, comme « L'Italie en train » (Hachette) ou « Venise, petit atlas hédoniste » (Éditions du Chêne). Mais derrière cette plume voyageuse se cache un itinéraire plus intime. Partie en Italie pour étudier, Lucie n'en est jamais repartie. Elle y a construit sa vie, trouvé sa voix et fait de ce pays son plus beau terrain de reportage. Pendant près de dix ans, son blog L'occhio di Lucie a été une véritable fenêtre ouverte sur le quotidien italien, mêlant récits de voyage, bonnes adresses et découvertes insolites. Dans cette conversation, Lucie nous ouvre son carnet de voyage. Elle nous entraîne dans les ruelles de Rome et les calli de Venise, partage ses adresses confidentielles, ses lieux refuges et ces détails du quotidien qui racontent souvent bien mieux un pays que les monuments. Une invitation à regarder l'Italie autrement, avec les yeux de celles et ceux qui l'habitent. Buon viaggio ! · À lire cet été :Sa newsletter L'Italie sans filtre et son blog www.occhiodilucie.com ! · Les inspirations italiennes de Lucie Tournebize :La cuisine sètoise de ses origines aux résonances italiennes : les Brageoles de Sète & les Bracciole des Pouilles, la tielle de Sète et celle di Gaeta.Les musées romains où elle puise l'inspiration : la Villa Borghese, le Palazzo Altemps, et le forum de Trajan.L'église Sant'Ignazio di Loyola et son plafond en trompe l'œil !Ses 3 confidences de voyages à l'italienne :- Le passage « secret » des Este à Ferrare entre le Castello Estense et le Palazzo Ducale,- La plage du Passetto à Ancone,- Le projet Laguna nel bicchiere à Venise sur l'île de San Michele.Conçu, réalisé et présenté par Claire PlantinetMontage Générique : François PraudMusique : Happy Clapping Cinematic Score / PaBlikMM / Envato ElementsCréation visuelle : Thomas JouffritPortrait © Silvia Pasquetto · Archives épisodes :© Extraits morceaux « Rose Bay » Ludovico Einaudi, « Funiculì Funiculà » Luciano Pavarotti, « Elfe », « Ses Canteres », « Idylle » Dario Lessing, Extraits video « Brasciole al sugo » @CucinaGeek, Museo archeologico nazionale di Napoli, Italian Time Zone, Roma ora, Lella Gioielli, Pixabay. Retrouvez Lucie Tournebize sur Instagram @occhiodilucie ainsi qu'allora @allora.lepodcast ! Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

The Hills Season 3 Episode 27We're back recapping The Hills and this week, a familiar Laguna Beach face makes his way into the mix… Stephen Colletti!

Today's guest is the drummer who took punk out of the garage, put it on your radio, then on MTV, then on your body. But his real story isn't the discography. It's what he did with everything that was supposed to end him.From a four-piece kit in Fontana to the center of the album cover, Travis carries three things simultaneously that most musicians never figure out how to hold at once: the craft of writing a drum part as a hook — singing it in his head before his hands ever get there; the conviction that no genre gets to own him, and no one else gets to write his ending; and the contrarian edge to ban Auto-Tune, editing, and drum samples from his own studio at the exact moment the rest of the industry is sprinting toward machine-clean.And The Writer Is... Travis Barker!Hit subscribe and turn on notifications. Every week, we go deep with the most interesting creatives in music.Join us on Patreon: patreon.com/andthewriterisWe're giving away 4 pieces of premium recording gear on zoom on August 25th! Join Patreon for details.Follow us on socials: @andthewriterisA special thank you to our sponsors for making these conversations possible. Our lead sponsor, NMPA — the National Music Publishers' Association. Your support means the world to us. And @splice — the best sample library on the market. Period.CHAPTERS0:00 Intro1:51 Why the documentary sat for nine years3:18 The producers pitch an ending: "Can you just walk onto a plane?"4:51 Survivor's guilt: "They wouldn't want me to sit here and not fly again"6:33 "The doc was dead to me" — and living your life like it's a movie9:41 "I don't make beats." "Now you do. I need it in two days."12:23 Beastie Boys, Bad Brains, Buck Owens — refusing the box15:28 The drums are a hook, not a timekeeper15:54 "That's not music" — the teacher who wouldn't teach him rock17:25 How he actually writes a part: "I sing it in my head"18:21 Learning the whole blink-182 set in an hour21:29 Trash men in Laguna, and one store that sold their tape22:52 The untitled album, and the day the process changed25:57 The lyrics he wrote: "We'll have Halloween on Christmas"27:15 "I Miss You" — the loop he had to convince them to keep28:07 "You have to put your ego away to be a drummer who's a songwriter"35:46 The burn center: "How am I going to play drums?"36:25 The hand he couldn't feel, and why he told no one43:20 "Too heavy for blink" — Boxcar Racer, and Mark56:30 "There was no therapy for what happened"1:10:19 What drummers should actually practice1:19:29 AI: "I hate it" — no Auto-Tune, no editing, no samples1:31:20 "All the Small Things" — the day Tom told him to open the valve1:33:33 "Where's all the hits?" — the note that made Rock Show and First Date1:35:15 "One More Time": writing about your own band breaking upCredits:Hosted by Ross GolanProduced by Joe London and Jad Saad Hosted on Acast. See acast.com/privacy for more information.

Our third episode of the week! Mo and Alex yet again team up to bring two calls from two girls who possibly want something more stable after running through guys. Lets do this!Caller #5 is Kate 23yrs from Laguna. Kate has slept with a few guys but is starting to turn the corner in wanting to be a "higher value" woman and wants to know how to go about it.Caller #6 is Ria 37yrs from Manila. Ria is a hot doctor with a high sex drive. While she enjoys the single life, she may have met her match with a foreigner celeb look-a-like.

Rafael Laguna de la Vera, Direktor der Bundesagentur für Sprunginnovationen SPRIND, gibt dem Ökonomen Moritz Schularick recht: Das Geld der Bundeswehr gehört in günstige Massenware statt in schwere Systeme. Die Ökonomie des Krieges hat sich gedreht, sagt er: „Drohne gegen Panzer, 500 Euro gegen 25 Millionen, dann ist etwas im Ungleichgewicht.“ [15:12]Der russische Krieg gegen die Ukraine dauert an. Trumps Krieg gegen den Iran hat den Nachschub an Patriot-Abwehrflugkörpern für Kiew versiegen lassen. Hier ist die Ukraine im Nachteil - aber ihren Verteidigungskampf mit Drohnen kann sie unverändert fortsetzen. [08:53]Vor den Landtagswahlen im Osten gehen die Wahlkämpfer von CDU und SPD auf Distanz zur eigenen Bundesspitze. Mit den Reformbeschlüssen kurz vor der Sommerpause ist dort nichts zu gewinnen, den Kanzler laden die Landesverbände kaum ein. Aufgegangen ist diese Rechnung nie: Julia Klöckner grenzte sich 2016 von Angela Merkel ab und verlor, Winfried Kretschmann stellte sich hinter die Kanzlerin und gewann. [01:42]Table.Briefings - For better informed decisions.Sie entscheiden besser, weil Sie besser informiert sind – das ist das Ziel von Table.Briefings. Wir verschaffen Ihnen mit jedem Professional Briefing, mit jeder Analyse und mit jedem Hintergrundstück einen Informationsvorsprung, am besten sogar einen Wettbewerbsvorteil. Table.Briefings bietet „Deep Journalism“, wir verbinden den Qualitätsanspruch von Leitmedien mit der Tiefenschärfe von Fachinformationen. Professional Briefings kostenlos kennenlernen: table.media/testenHier geht es zu unseren WerbepartnernHol dir deine persönlichen Daten mit Incogni zurück und hol dir 60 % Rabatt auf ein Jahresabo: https://incogni.com/tabletodayImpressum: https://table.media/impressumDatenschutz: https://table.media/datenschutzerklaerungBei Interesse an Audio-Werbung in diesem Podcast melden Sie sich gerne bei Jan Puhlmann: jan.puhlmann@table.media Hosted on Acast. See acast.com/privacy for more information.

Omar García Harfuch, secretario de Seguridad y Protección Ciudadana, rechazó y calificó como “absurda” la versión atribuida a un testigo protegido sobre una supuesta negociación relacionada con las investigaciones por huachicol fiscal en México. Según una carpeta de investigación contra los hermanos Farías Laguna, presuntamente habría existido un acuerdo para permitir el ingreso de buques por las aduanas de Altamira y Tampico, en el que se menciona a uno de los hijos del expresidente Andrés Manuel López Obrador y al senador Adán Augusto López. Ante estos señalamientos, García Harfuch negó de manera categórica haber sostenido negociaciones con alguno de los hijos de López Obrador para frenar, limitar o interferir en investigaciones relacionadas con el tráfico ilegal de combustibles. En este video te presentamos sus declaraciones y las claves de un caso que vuelve a poner bajo la lupa el combate al huachicol fiscal en México. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Omar García Harfuch, secretario de Seguridad y Protección Ciudadana, rechazó y calificó como “absurda” la versión atribuida a un testigo protegido sobre una supuesta negociación relacionada con las investigaciones por huachicol fiscal en México. Según una carpeta de investigación contra los hermanos Farías Laguna, presuntamente habría existido un acuerdo para permitir el ingreso de buques por las aduanas de Altamira y Tampico, en el que se menciona a uno de los hijos del expresidente Andrés Manuel López Obrador y al senador Adán Augusto López. Ante estos señalamientos, García Harfuch negó de manera categórica haber sostenido negociaciones con alguno de los hijos de López Obrador para frenar, limitar o interferir en investigaciones relacionadas con el tráfico ilegal de combustibles. En este video te presentamos sus declaraciones y las claves de un caso que vuelve a poner bajo la lupa el combate al huachicol fiscal en México. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sopla un viento de desolación sobre la Laguna de Navarro, cuando el último fusil certifica la defunción de Manuel Dorrego y un tiempo de odios profundos inicia el azote sobre la promesa argentina. ¿ Quién fue el Coronel Dorrego? ¿ El milico rebelde? ¿ El valeroso guerrero? ¿ El estadista? ¿ El fundador del populismo vernáculo? ¿Todos ellos? Para echar luz sobre este Héroe no tan conocido, visita Laberinto el notable Gabriel Di Meglio, autor de " Manuel Dorrego. Vida y Muerte de un líder popular" . No se pierdan este esclarecedor episodio.

El Museo Provincial de Historia Natural (MPHN) y la Dirección Provincial de Patrimonio Cultural realizaron una campaña de prospección y rescate en el margen noroeste de la Laguna de Guatraché, donde recuperaron piezas paleontológicas y arqueológicas. La actuación se inició a partir del aviso del vecino Franco Izaurralde Ocampos, quien notificó la presencia de restos fósiles y fragmentos líticos en la playa.El operativo en territorio estuvo integrado por el director del MPHN, Daniel Pincén, el técnico Pablo Tejerina y la arqueóloga Lía Pera, en representación de Patrimonio Cultural. El equipo fue recibido en la localidad por la responsable del área de Cultura y Turismo del municipio, Fabiana Dieser, antes de dirigirse al sitio del hallazgo junto al poblador que realizó la denuncia.

BONUS MONDAYS: Dr. Edwin Barnhart, director of the Maya Exploration Center, has over twenty years of experience in Central and South America as an archaeologist, an explorer and an instructor. He has appeared in over a dozen documentaries and given presentations all over the world.His involvement in Maya studies began in 1990 as an archaeological intern in the ruins of Copan, Honduras. In January of 1996 he was invited to return to Copan and help the University of Pennsylvania excavate the early acropolis and the tomb of the city's lineage founder. From 1992-1995 he had been studying art, iconography and epigraphy (hieroglyphic translation) under the late Dr. Linda Schele at the University of Texas at Austin. During that same time he worked across the state of Texas as a contract archaeologist.In 1994 he began working as a surveyor and a UT field school instructor in the jungles of Northwestern Belize. After finding numerous small villages, Dr. Barnhart discovered the ancient city of Ma'ax Na (Monkey House), a major center of the Classic Maya Period. He mapped over 600 structures at Ma'ax Na between 1995 and 1997 before moving his research focus to Chiapas, Mexico. Also while in Belize, Dr. Barnhart worked with the Belize Post Classic Project mapping the island of Caye Coco and excavating a series of burials on an island in Laguna de On. Dr. Barnhart received his Masters degree in May of 1996 and began teaching Anthropology classes at Southwest Texas State University the following September. He taught Archaeology and Anthropology classes at SWTS until 1998 when he was invited by the Mexican government to direct the Palenque Mapping Project. The Palenque Mapping Project was a three-year effort to survey and map the unknown sections of Palenque's ruins. Over 1100 new structures were documented, bringing the site total to almost 1500. The resultant map has been celebrated as one of the most detailed and accurate ever made of a Maya ruin. He received a Ph.D. from the University of Texas at Austin in 2001 with his dissertation entitled The Palenque Mapping Project: Settlement Patterns and Urbanism in An Ancient Maya City.Upon graduation, Dr. Barnhart and his colleagues established Maya Exploration Center through which to continue and share their research. As of 2020, he has led over 200 ancient sciences travel courses in 15 different countries. Also through MEC, he is the author of an annual wall calendar and an iPhone app which explains the ancient Maya calendar.In 2012, he produced a 24-lecture video series for the Teaching Company's Great Courses entitled “Lost Worlds of South America”. His second Great Course entitled “Maya to Aztec: Ancient Mesoamerica Revealed” was released in March of 2015. Then in 2018 his third Great Course was released, entitled “Ancient Civilizations of North America”. His most recent production with Great Courses is a 6-episode travel series called “Exploring the Mayan World”.Over the last two decades, he has appeared multiple times on the History Channel, the Discovery Channel, Discovery Channel 3D, Canada's Religion Television, Japanese NHK Public Television, and an award-winning documentary entitled “2012: The Beginning”. Dr. Barnhart is a Fellow of the Explorer's Club and leads travel courses for college professors on ancient astronomy, mathematics and sacred geometry. In 2020 he started his podcast – ArchaeoEd, which focuses on ancient cultures of the Americas.Become a supporter of this podcast: https://www.spreaker.com/podcast/next-level-soul-podcast-with-alex-ferrari--4858435/support.Take your spiritual journey to the next level with Next Level Soul TV — our dedicated streaming home for conscious storytelling and soulful transformation.Experience exclusive programs, original series, movies, tv shows, workshops, audiobooks, meditations, and a growing library of inspiring content created to elevate, heal, and awaken. Begin your membership or explore our free titles here: https://www.nextlevelsoul.tv

The Widow's Might - Mike Hudgins 8.9.2026 https://vccgn.org/s/2608Widow.pdf Continue reading →

V2 - Episódio reenviado devido Spotify! A Cafuné Profano nasceu nas ruas de Laguna de Abril, misturando diversos estilos em um som caótico e marcante. Após uma gravação pirata viralizar pelo país, a banda virou um fenômeno nacional, conquistando o público com músicas que uniam crítica social, humor e romantismo, alcançando rapidamente os maiores palcos e programas de TV. Tema do Episódio: Música, Aventura Ajude esse projeto Apoia-se: https://apoia.se/rpguaxa Se quiser fazer uma pequena contribuição aleatória, nosso PIX é rpguaxa@gmail.com Contatos: E-MAIL: rpguaxa@gmail.com BlueSky do RPGuaxa: https://bsky.app/profile/rpguaxa.bsky.social Instagram do Guaxa: https://instagram.com/rpguaxa BlueSky do Guaxa: https://bsky.app/profile/marceloguaxinim.bsky.social Instagram do Guaxa: https://instagram.com/marceloguaxinim Assine o Feed! http://deviante.com.br/podcasts/rpguaxa/feed/ Se não esta achando no seu agregador cole esse link lá que ele acha! Assine o Feed! Expediente: Produção, Narração e Edição Final: Marcelo Guaxinim. Edição: Nate Jogadores do Episódio: Jujuba, Herdy e Sah. Música: "Ancient Winds" Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/See omnystudio.com/listener for privacy information.

Edición del 10 de agosto Intervienen: Resumen de la actualidad con Diego Calvo Opinión con Ignacio Gómez Burzaco Opinión con Pedro Glez. Actualidad social con Eloy Cuadra Tertulia con Diana Mora, Sebastián Ledesma, Pedro Bravo de Laguna y Diego Calvo Análisis con Carlos Artiles Programa sin cortes

A 130 kilómetros de Huelva capital y en mitad del Parque Natural Sierra de Aracena y Picos de Aroche se encuentra la Laguna de Cañaveral de León. Se trata de una piscina natural única, declarada Bien de Interés Cultural (BIC) en 2009. Se ubica en la plaza principal del pueblo, delante del Ayuntamiento y con vistas a la sierra. Aquí no hay césped ni sombrillas. En este reportaje exploramos el pasado y el presente de esta singular laguna. Escuchar audio

Con Arturo Téllez, Paloma Gallego y Javier Ruiz. También cuenta con Martín Blecua, gaitero y Alberto Lasheras, guía Cartuja Nuestra Señora de las Fuentes. Como ultima invitada, Inmaculada Moreno, que es bióloga en Laguna de Sariñena.

Con Arturo Téllez, Paloma Gallego y Javier Ruiz. También cuenta con Martín Blecua, gaitero y Alberto Lasheras, guía Cartuja Nuestra Señora de las Fuentes. Como ultima invitada, Inmaculada Moreno, que es bióloga en Laguna de Sariñena.Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/mas-noticias--4412383/support.ESCUCHAR RADIO 

O deputado estadual Sérgio Guimarães voltou a cobrar mais transparência sobre as obras de recuperação da Ponte Anita Garibaldi, em Laguna, após ter um pedido de visita institucional ao local negado pela Agência Nacional de Transportes Terrestres (ANTT). Presidente da Comissão de Defesa Civil e Desastres Naturais da Assembleia Legislativa de Santa Catarina (Alesc), o parlamentar classificou a decisão como injustificável e afirmou que a negativa impede o acompanhamento de um problema que afeta diretamente milhares de catarinenses. Em entrevista à Rádio Cruz de Malta FM, Guimarães destacou que, embora a ponte esteja localizada em uma rodovia federal, considera seu dever fiscalizar a situação por se tratar de uma estrutura estratégica para o estado. "Está dentro do território catarinense e a gente tem vergonha na cara e quer transparência e acompanhar tudo o que está acontecendo na Ponte Anita Garibaldi. Está faltando transparência por parte da CCR ViaCosteira", afirmou.

Making Room for Jesus - Mike Hudgins 8.2.2026 https://vccgn.org/s/2608Room.pdf Continue reading →

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Caso Farías Laguna abre nuevas líneas de investigación Exigen responsabilidades por muertes de animales en LeónEl ajolote mexicano cruza fronteras con un nuevo refugio en EspañaMás información en nuestro podcast#grc

Back in October 2024, Poolside was an early AI star. Cofounded by former Github CTO Jason Warner, the startup had raised $500 million on a $3 billion valuation to build coding agents for governments and large companies. But over the next 18 months, Poolside largely disappeared from view, while OpenAI and Anthropic ballooned to nearly trillion-dollar valuations with a crop of Chinese labs building open source models nipping at their heels.  Now Poolside is back with a new model called Laguna that on public benchmarks beats its American and Chinese open source competition — with the very notable exception of Chinese lab Moonshot's latest AI model, Kimi K3.  "As an American company building for the West, we'll be the most capable open model in the West,” Warner, Poolside's co-CEO and cofounder, tells Forbes. “Globally, in this weight class of the 118 billion parameter model, we are the leader.” Warner claims that the startup spent those 18 months where it went quiet building the infrastructure for a “model building factory” that could pump out Laguna and continue with new and more powerful iterations every five weeks. "The classic notion of model building is an artisanal process…We've built an industrial model-building process,” he says. By Iain Martin, Forbes Staff Learn more about your ad choices. Visit megaphone.fm/adchoices

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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

A Cafuné Profano nasceu nas ruas de Laguna de Abril, misturando diversos estilos em um som caótico e marcante. Após uma gravação pirata viralizar pelo país, a banda virou um fenômeno nacional, conquistando o público com músicas que uniam crítica social, humor e romantismo, alcançando rapidamente os maiores palcos e programas de TV. Tema do Episódio: Música, Aventura Ajude esse projeto Apoia-se: https://apoia.se/rpguaxa Se quiser fazer uma pequena contribuição aleatória, nosso PIX é rpguaxa@gmail.com Contatos: E-MAIL: rpguaxa@gmail.com BlueSky do RPGuaxa: https://bsky.app/profile/rpguaxa.bsky.social Instagram do Guaxa: https://instagram.com/rpguaxa BlueSky do Guaxa: https://bsky.app/profile/marceloguaxinim.bsky.social Instagram do Guaxa: https://instagram.com/marceloguaxinim Assine o Feed! http://deviante.com.br/podcasts/rpguaxa/feed/ Se não esta achando no seu agregador cole esse link lá que ele acha! Assine o Feed! Expediente: Produção, Narração e Edição Final: Marcelo Guaxinim. 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For 20 years, the 24 Hours of Lemons has proven that cheap race cars, questionable engineering, brilliant themes, and terrible decisions make for some of the greatest motorsports in America.This week we're joined by Nick Pon, Associate Perpetrator of the 24 Hours of Lemons, for one of our favorite interviews ever.Nick takes us behind the curtain of the 20th Anniversary Laguna Seca race, revealing how nearly 360 teams were narrowed to just 45 invitations, why some legendary cars made the cut while others didn't, how the infamous oil-pan lottery actually worked, and what it really took to convince Laguna Seca to host the biggest celebration in Lemons history.We also dive into:

Reclaiming Your Destiny - Mike Hudgins 7.5.2026 https://vccgn.org/s/2607Destiny.pdf Continue reading →

The Road to LagunaIn this Willy's Gasser 441 Episode; Chris rubs his seats, Chrissy sits in a field, Tim eats oysters and while Mental does not break 150, but he also did not get kicked out (again). Really, the crew dives into the upcoming lottery announcement for the 20th anniversary celebration race at WeatherTech Raceway Laguna Seca—a once-in-a-generation 24 Hours of LeMons event with an absurdly competitive selection process and massive national interest.That's right! The countdown is on for one of the most anticipated grassroots racing events ever: the 20th anniversary celebration at WeatherTech Raceway Laguna Seca. So the team breaks down everything you need to know about the upcoming race lottery, what it means if you get selected, and what to do if you don't—because either way, you're probably still going to California. But this isn't just about racing. It's about the journey.From there, the episode turns into a full-blown cross-country motorsport pilgrimage guide, covering:- Classic racing museums worth detouring for (Peterson, IMS, Henry Ford)- Route 66 nostalgia for cross-country drivers -Copilot strategy, snacks, audiobooks, and keeping drivers alive.The conversation expands into a survival manual for hauling race cars cross-country, including:- Trailer safety, sway control, and pre-trip inspections- Wind zones in the Rockies, desert crossings, and and sketchy mountain passes- Fuel planning in long empty stretches (New Mexico, Arizona, West Texas)- GPS towing modes and avoiding trailer-unfriendly roads (PCH warnings)- Why I-40 might save your sanity vs. the chaos of I-70- What NOT to do with a race trailer on scenic highways- How to plan food, sleep, and sanity for multi-day haulsFinally, they zoom into Laguna Seca logistics and lifestyle planning:- Camping inside the track (highly recommended, sells out fast)- Nearby RV parks, hotels in Salinas/Seaside- Classic Monterey region attractions: Cannery Row, 17-Mile Drive, Carmel, Monterey Bay Aquarium, Route 66 nostalgia connections for inbound travelers- Why Laguna Seca is a bucket-list track (corkscrew, history, elevation drop, ocean views)- What to do if you don't get selected (spectate, Lucky Dog at Chuckwalla, HPDE options)- Multi-track “racing vacation stacking” (Laguna → Sonoma → Hooked on Driving events)- Deep-dive travel planning for racers towing across the U.S.It ends with classic Everyone Racers chaos: road trip survival humor, copilot strategy, snacks philosophy, and stories of trackside organization (including the legendary “Chrissy restoring order at Laguna” anecdote).Whether you're racing, spectating, or just dreaming about it—this is the roadmap to getting there.Hot Wheels GT-40 runs 13,549 scale miles at 111 mph (Steven Edelstein @ the drive) https://www.thedrive.com/news/weird-experiment-this-hot-wheels-car-drove-13459-scale-miles-in-five-daysCarvana Moving Into Defunct Dealerships. (Natalie Neff @ Autoweek) https://www.autoweek.com/news/a71614585/carvana-new-car-dealership-move/23 Ft Banana Car keeps getting hassled by the man! (Dan Mihalascu @ Autoblog) https://www.autoblog.com/news/americas-most-pulled-over-driver-is-a-man-in-a-giant-banana2006 Chevy Cobalt is only $7500 on Racing Junk https://www.racingjunk.com/super-touring/184806724/2006-cobalt-nasa-scca-luck-dog-.html?category_id=4&np_offset=29Get Panda Planner! Seriously, it's $30 bucks man. https://www.panda-planner.com/Panda Planner How To YouTube; https://www.youtube.com/watch?v=-G7zntrrMpMJoin our F1 Fantasy League https://fantasygp.com/leagues/ Use code 74259541 to join in the funThose cool light number panels from Amazon https://a.co/d/9wDuvek2026 Lemons Rally Schedule https://24hoursoflemons.com/lemons-rally/ ⁠https://www.youtube.com/channel/UCPrTs8wdzydOqbpWZ_y-xEA ⁠ - Our YouTube

Finishing Well - David Parker 6.28.2026 Continue reading →

Livia Brito se niega a pagarLaura Bozzo mexicanísimaLuis Miguel y su saludLibro de Laura ZapataMaluma en MéxicoShakira y Manuel García RulfoAbogados contra Emiliano y Mayela

Episode 808 - Dani Vee, Ingrid Laguna and Dan Thomas - Children's books, writing, feedback and the challenges of being in a creative relationship Dear lovely listeners, In episode 808 I chat to (newlyweds) Ingrid Laguna and Dan Thomas about their new releases My Brother Otto published by Text Publishing and Bedtime, No More Buts published by Scholastic. We chat about: ⭐️ the light and dark and why it's important for kids to experience both. ⭐️ how to write about real life experiences ⭐️ feedback and rejection We take a deep dive into what it's like being in a relationship with another creative and discuss:

Entrevista - Andrés Martínez - Controlador de Torre Aeropuerto de Laguna del Sauce y dirigente de ACTAU by En Perspectiva

If you know anything about Jesuit formation, you probably know that it takes a long time: two years for novitiate, three years for first studies, three more of regency, and then two or more years of theology studies and then—if a Jesuit has discern the priesthood—ordination. On average, a Jesuit is looking at ten to twelve years before becoming a priest! So, formation takes a long time. But then, once you're a priest, you're all set, right? Wrong! There's another stage of Jesuit formation called tertianship. This is a stage of renewal and recommitment, a period of time that happens years after ordination when a Jesuit returns to some of the foundational documents and experiences of Jesuit life. The Jesuit makes the Spiritual Exercises again, prays with the Constitutions and more. Tertianship takes a Jesuit out of their usual routines, often for an extended period of time, and prepares them for final vows. Tertianship has always been a little mysterious, for host Eric Clayton, at least. That's why he was so excited to talk to his friend, Fr. Andrew Laguna, who just returned from his tertianship in Salamanca, Spain. And that's the conversation you're about to hear today. Andrew breaks down what tertianship is, why it's important, what graces he experienced while there and, ultimately, why it matters for all of us, whether we're Jesuits or not. Whether you're interested in Jesuit life or formation or just want to hear from a wise Jesuit priest eager to share about how God is at work in his own vocation, you won't want to miss this conversation.

Desde hace algunos años se han hecho públicos muchos testimonios que hablan sobre las aterradoras apariciones de una misteriosa criatura en la Laguna de Zumpango, ubicada en el Estado de México. Quienes la han visto la describen como un ser de complexión humanoide, pero que tiene dos enormes alas que salen desde su espalda. Una especie de demonio que tiene un gran parecido a la famosa criatura vista en la película Jeepers Creepers, también conocida como El Demonio.Una persona que nos pidió mantener su identidad en el anonimato nos contactó para contarnos como fueron alguno de estos avistamientos de los que muchos en el Estado de México hablan.¿De que se tratara esta criatura? ¿Una gárgola? ¿Un demonio? ¿El mítico Mothman u Hombre polilla? En realidad no se sabe a ciencia cierta, pero es un hecho que la Laguna de Zumpango guarda una enorme cantidad de misterios, pues además de ser el sitio en el que se ha visto a este humanoide con alas, también es conocido por sus avistamientos de brujas en formas de bolas de fuego.¿Te atreverías a caminar por los alrededores de la Laguna de Zumpango ya por la noche?

Desde hace algunos años se han hecho públicos muchos testimonios que hablan sobre las aterradoras apariciones de una misteriosa criatura en la Laguna de Zumpango, ubicada en el Estado de México. Quienes la han visto la describen como un ser de complexión humanoide, pero que tiene dos enormes alas que salen desde su espalda. Una especie de demonio que tiene un gran parecido a la famosa criatura vista en la película Jeepers Creepers, también conocida como El Demonio.Una persona que nos pidió mantener su identidad en el anonimato nos contactó para contarnos como fueron alguno de estos avistamientos de los que muchos en el Estado de México hablan.¿De que se tratara esta criatura? ¿Una gárgola? ¿Un demonio? ¿El mítico Mothman u Hombre polilla? En realidad no se sabe a ciencia cierta, pero es un hecho que la Laguna de Zumpango guarda una enorme cantidad de misterios, pues además de ser el sitio en el que se ha visto a este humanoide con alas, también es conocido por sus avistamientos de brujas en formas de bolas de fuego.¿Te atreverías a caminar por los alrededores de la Laguna de Zumpango ya por la noche?

Tuesday Hour 3: Jason Kempf on the Iowa Cubs, Paul Laguna on World Cup & Lucas' Notebook pres. by Unjuiced

En el programa Herrera en COPE, el equipo se ha trasladado a la almazara Oleum Laguna en Villaconejos (Madrid) para conocer los secretos de uno de los aceites más galardonados de España. Su gerente, Pedro Laguna, ha explicado en una entrevista con Alberto Herrera las claves de un proyecto familiar que nació en 2017 y que ya se ha convertido en un referente de calidad y excelencia.Oleum Laguna es la almazara ecológica más joven de la Comunidad de Madrid, un proyecto de dos hermanos, Pedro y Pilar Laguna, cuya apuesta fundamental es la calidad. “Nuestra apuesta ha sido, y queremos que siga siendo, la calidad”, ha afirmado Pedro Laguna. Para ello, elaboran aceites de cosecha temprano premium con el objetivo de obtener productos de alta intensidad y con muchos matices en boca.Esta filosofía les ha valido más de 50 reconocimientos a nivel nacional e internacional, llegando a ser nombrados dos veces como mejor aceite de España y cuarto mejor del mundo. El carácter familiar ...

El periodista Alfonso Ferrer nos adelanta las primeras informaciones sobre sectas destructivas en Canarias.Junto a la Doctora en Historia, Cristo Gil, analizamos la perversión de la necrofilia.Carlos Pérez Simancas, guía y divulgador de la cultura canaria, y el artista Juan Mesa, nos adentran en la esencia y los rituales al sol realizados en la isla de la Gomera.Música final en directo gracias a Resonance (Javier Pérez Rodríguez).Escuchar audio

Programa especial desde el Teatro Leal de la Laguna (Tenerife) a la búsqueda de misterios.El físico, teórico y neurocientífico, Álex Gómez Marín, comparte con nosotros sus aventuras tras la investigación de la consciencia y de los OVNI's.El antropólogo Nando Hernández y el periodista José Gregorio González, nos descubren las plantas y lugares sagrados de los aborígenes canarios.Escuchar audio

Río San Buenaventura se desborda en XochimilcoProfepa inspeccionará obras en Laguna de Ohuira en Sinaloa Muere Margaret Kerry a los 97 añosMás información en nuestro Podcast#grc

Conway Jr Show Hour 2 (6.10) Conway kicks off the hour with a sweet story about checking into hotels and why you should always add your name to the reservation. For Conway, one hotel moment became unforgettable when Lakers legend James Worthy shared how much Tim Conway and The Carol Burnett Show meant to him. It was a reminder that great comedy does not just make people laugh — it stays with them. Then the crew gets into a wild Southern California school prank after nearly 200 doors were glued shut at Patrick Henry High School in San Diego right as finals week ramped up. That brings back memories of prank stores, itching powder, and Conway’s brother causing chaos on school desks. Later, the hour turns serious with dangerous surf in Laguna Beach, where a mother and her two children were swept into the ocean near Treasure Island Beach. The powerful swell created dangerous conditions across Orange County beaches, with one teen girl still missing. The hour wraps with a lighter look at foreign FIFA fans discovering American culture ahead of the World Cup — from Big Gulps and Taco Bell to the ultimate American obsession: ranch dressing. Because in the U.S., we put ranch on everything. Trending Keywords: Tim Conway, Carol Burnett Show, James Worthy, Lakers, hotel story, school prank, glued doors, Patrick Henry High School, San Diego, Laguna Beach, Treasure Island Beach, dangerous surf, FIFA fans, American culture, Big Gulp, Taco Bell, ranch dressing, Conway Show See omnystudio.com/listener for privacy information.

Thursday Hour 2: More on the Knicks, Paul Laguna on World Cup & Faceoff

In hour two, learning about our new favorite Mexican Baseball club: Algodoneros Del Unión Laguna. Crushing Jimmy for spamming our group chat with random “news”. Plus, Lee Sterling joins the show with his plays for the sports weekend.

KXFM has officially physically moved to our new location now being referred to by many as “The Orange Door Studio” on Oak Street.  Eric, Michelle and Admin Colleen employed the Power of the Pivot in exercising a creative solution to a technical challenge for their first time being on air live at the new station.  Eric and Michelle did a full hour show talking about the history of Laguna while live streaming knowing they didn't have the ability to record it for a new podcast.  For their second hour, they revisited a favorite show from the past that they found deeply impactful for the times we are living in this New Now.  (Thank you to Admin Colleen for rising to the challenge and making this podcast available with her editing wizzardry.)Please join us and listen to this timeless treasure filled with golden nuggets of wisdom from A Gift from the Sea ~ revisited.

Christina Schuller (now Sinclair), from Laguna Beach, steps Behind The Rope. One of the OG's of Laguna Beach. Season One! With Laguna Beach's 20th Anniversary having aired earlier this month, all things Laguna are top of the mind. With The Real World being one of the only predecessors to compare it to, Christina sheds light on what filming was like when “no one knew what they were doing”. That “no one” included producers. We discussed the “Game of Thrones” casting process and competition amongst Laguna Beach's High School Students to be one of chosen eight. Christina talks The Hills, The City and The Hills: New Beginnings. Christina opens up about what life was like when she was thrust into the public eye overnight and how that changed life forever. Of course we discuss some of her Laguna Beach co-stars Lauren Conrad, Kristin Cavallari, Stephen Colletti, Lo Bosworth, Talan Torriero, Trey Phillips and Morgan Olsen.  @christinasinclair @behindvelvetrope @davidyontef BONUS & AD FREE EPISODES Available at - www.patreon.com/behindthevelvetrope  BROUGHT TO YOU BY: QUINCE - quince.com/velvetrope (Get Free Shipping and 365 Day Returns to As You Indulge In Affordable Luxury)  ZENNI OPTICAL -  zenni.com/podcast (Use Code Podcast15 For 15% Off Your First Order Of The Most Affordable, Stylish Glasses and Sunglasses) ADVERTISING INQUIRIES - Please contact David@advertising-execs.com MERCH Available at - https://www.teepublic.com/stores/behind-the-velvet-rope?ref_id=13198 Learn more about your ad choices. Visit megaphone.fm/adchoices

Laguna Beach's Alex Murrel Johnson, otherwise know as Alex M, Steps Behind The Rope. With Laguna Beach's 20th Anniversary having aired earlier this month, all things Laguna are top of the mind. One of the granddaddy's of them all, or as we like to say, one of the best reality shows to ever exist. Alex explains what it was like to be a student at Laguna Beach High School when MTV came knocking on its door.  Alex discusses how she felt with her “bit*h” edit once she watched the show back as the season aired. Alex also discusses what it was like to gain instant fame overnight and how life changed once that fame, and money, started to roll in. Of course, we also discuss her Laguna Beach colleagues Lauren Conrad, Jason Wahler, Stephen Colletti and Kristin Cavallari. We discuss what our fav Laguna Beach students were like way back when, and the status of each of these relationships today. Alex discusses what life was like once her time on the show ended and she moved to LA, where she initially spent a lot of time hanging out in the clubs with other friends, including some of our favorite Hill's cast members, Audrina Patridge, Heidi Montag, Brody Jenner and Spencer Pratt. During her time in Los Angeles she also partied with Celebs like Paris Hilton, Aaron Carter and ex boyfriend Backstreet Boy Nick Carter. Last, but not least, we talk RHOC, Bravo and the current state of reality tv. Part II starts now.  @alexmurrel @behindvelvetrope @davidyontef BONUS & AD FREE EPISODES Available at - www.patreon.com/behindthevelvetrope  BROUGHT TO YOU BY: SUPER - super.com/credit (See How Super+ Can Help You Boost Your Credit Score) ADVERTISING INQUIRIES - Please contact David@advertising-execs.com MERCH Available at - https://www.teepublic.com/stores/behind-the-velvet-rope?ref_id=13198 Learn more about your ad choices. Visit megaphone.fm/adchoices

Laguna Beach's Alex Murrel Johnson, otherwise know as Alex M, Steps Behind The Rope. With Laguna Beach's 20th Anniversary having aired earlier this month, all things Laguna are top of the mind. One of the granddaddy's of them all, or as we like to say, one of the best reality shows to ever exist. Alex explains what it was like to be a student at Laguna Beach High School when MTV came knocking on its door.  Alex discusses how she felt with her “bit*h” edit once she watched the show back as the season aired. Alex also discusses what it was like to gain instant fame overnight and how life changed once that fame, and money, started to roll in. Of course, we also discuss her Laguna Beach colleagues Lauren Conrad, Jason Wahler, Stephen Colletti and Kristin Cavallari. We discuss what our fav Laguna Beach students were like way back when, and the status of each of these relationships today. Alex discusses what life was like once her time on the show ended and she moved to LA, where she initially spent a lot of time hanging out in the clubs with other friends, including some of our favorite Hill's cast members, Audrina Patridge, Heidi Montag, Brody Jenner and Spencer Pratt. During her time in Los Angeles she also partied with Celebs like Paris Hilton, Aaron Carter and ex boyfriend Backstreet Boy Nick Carter. Last, but not least, we talk RHOC, Bravo and the current state of reality tv. @alexmurrel @behindvelvetrope @davidyontef BONUS & AD FREE EPISODES Available at - www.patreon.com/behindthevelvetrope  BROUGHT TO YOU BY: ZENNI OPTICAL -  zenni.com/podcast (Use Code Podcast15 For 15% Off Your First Order Of The Most Affordable, Stylish Glasses and Sunglasses) FIRST DAY - firstday.com (Use Code Velvet For 57% Off & A Free Gift Of Vitamins To Fight Hidden Hunger For Kids, Teens & Adults) NOOM - noom.com (The Noom GLP-1 Microdose Program Starts At $79 and Is Delivered To Your Door In Seven Days) DAUGHTRY (Download Daughtry's New Single “Antidote”) ADVERTISING INQUIRIES - Please contact David@advertising-execs.com MERCH Available at - https://www.teepublic.com/stores/behind-the-velvet-rope?ref_id=13198 Learn more about your ad choices. Visit megaphone.fm/adchoices

Fresh off the Laguna Beach reunion, I'm answering your questions and giving you the behind-the-scenes of what this experience was actually like. We get into everything—from how it really felt walking onto that reunion stage after 20+ years, to where things stand with Lauren, what was cut, what happened off-camera, and why some moments didn't feel as natural as they looked. We also shift into grief, healing, and what it's like losing someone you love. I share my experience with my brother, how grief evolves, and the perspective that's helped me find acceptance and peace.Plus, I get into balancing all of this with real life: motherhood, work, and how I make everything fit without losing what matters most. Oh, and we even talk about Spencer Pratt running for Mayor!A word from my sponsors:Revolve: Revolve - Shop at https://REVOLVE.com/HONEST and use code HONEST for 15% off your first order. #REVOLVEpartnerSam Edelman: Visit us at https://samedelman.com to explore everything you need for spring and get 15% off with code honest15.Foria: Get 20% off your first order by visiting https://foriawellness.com/HONEST OR use code HONEST at checkout.Branch Basics: Get 15% off Branch Basics with the code HONEST at https://branchbasics.com/HONEST #branchbasicspodNutrafol: See thicker, stronger, faster-growing hair with less shedding in just 3-6 months with Nutrafol. For a limited time, Nutrafol is offering our listeners $10 off your first month's subscription and free shipping when you go to https://Nutrafol.com and enter the promo code HONESTUpward: Download Upward. The dating app where faith and values meet. https://https://upward.onelink.me/SOsL/qzs079soFor more Let's Be Honest, follow along at:@kristincavallari on Instagram@kristincavallari and @dearmedia on TikTokLet's Be Honest with Kristin Cavallari on YouTubeProduced by Dear Media.This episode may contain paid endorsements and advertisements for products and services. Individuals on the show may have a direct or indirect financial interest in products, or services referred to in this episode.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.